* fix(github-copilot): forward telemetry config to client
* Python: fix telemetry settings typing for github_copilot
`load_settings` does not coerce dict-typed fields, so GITHUB_COPILOT_TELEMETRY
and .env values reach the agent as plain strings. Declaring
`GitHubCopilotSettings.telemetry` as `dict[str, Any]` therefore misstated the
runtime contract and failed the test typing checks where a string is assigned.
Widen the annotation to `dict[str, Any] | str | None` and fix the union arm
resolution in `_check_override_type`: parameterized generics are not `type`
instances, so they were dropped from the allowed set and a valid dict override
was rejected at runtime. Arms without a runtime class, such as `Literal`, now
skip validation instead of narrowing it incorrectly.
Also drive the telemetry string tests through the documented environment
variable path rather than mutating `_settings` directly, and cover the
valid-JSON-but-not-an-object case.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 657d2953-4112-4a01-889b-c0c6863630c1
* Python: resolve settings override types through generic origins
Python 3.10 reports parameterized generics such as `dict[str, Any]` as
instances of `type`, so the union arm resolution kept the alias and
`isinstance` raised `TypeError: isinstance() argument 2 cannot be a
parameterized generic` on that interpreter.
Resolve every annotation through `get_origin` first via a shared
`_runtime_class` helper, which also removes the same latent failure for a
non-union parameterized generic field, and return `None` for annotations such
as `Literal[...]` that have no runtime class so validation is skipped rather
than narrowed incorrectly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 657d2953-4112-4a01-889b-c0c6863630c1
---------
Co-authored-by: Giles Odigwe <gilesodigwe@microsoft.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 657d2953-4112-4a01-889b-c0c6863630c1
* Python: defer turn-scoped after_run providers to the agent loop boundary
Each AgentLoopMiddleware iteration is a full agent run, so CompactionProvider.after_run fired per iteration and rewrote persisted history mid-task (#7236). Providers can now opt into turn scope with after_run_once_per_turn; iterations defer them via a contextvar, and the loop fires them once at the boundary. CompactionProvider opts in; HistoryProvider keeps its incremental per-run persistence.
* Python: key loop suppression to the looping agent and pass run options through
Two review follow-ups: the contextvar now carries the agent instance so a nested agent.run() inside a loop iteration is not suppressed as if it were an iteration, and the boundary SessionContext forwards the original run options to turn-scoped providers.
* fix(core): carry the loop-iteration stamp in run options, not a contextvar
The contextvar marker leaked in two ways. Held across a streamed yield
it bled into the caller's context, suppressing turn-scoped providers on
an unrelated same-agent run while the stream was paused, and a reset
from a different consuming task raised on the token. Keyed to the agent
instance, it also swallowed the boundary flush of a nested loop on the
same agent with its own session.
Stamp the runs the loop drives through their options instead. Run
options reach only the inner runs (they never enter the model request),
a nested or concurrent run starts with fresh options and keeps its own
turn, and there is no token to reset, so stream consumption is safe from
any task.
* Python: annotate custom option keys in the after_run provider test
* fix: nosec the loop-iteration options key (bandit B105 false positive)
* Python: fix: suppress the loop-token key lint with ruff: ignore
* Python: fix: silence the two pyright private-usage flags the repo's own idiom covers
---------
Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: Avoid unchanged AG-UI predictive state snapshots
Only emit the coalesced snapshot when predictive updates were actually pending or a deterministic state update was returned.
Assisted-by: Codex <codex@openai.com>
* Python: Exercise the predictive update path in snapshot tests
Use the handler streaming API to create pending state and narrow snapshot events by their concrete type.
Assisted-by: Codex <codex@openai.com>
* fix(a2a): reject empty invocations explicitly
Key decisions:
- Keep A2A continuation authority explicit; durable session task state only enriches diagnostics.
- Raise AgentInvalidRequestException with participant and available task context instead of inventing input.
- Leave AgentExecutor and Group Chat production contracts unchanged.
Files changed:
- packages/a2a/agent_framework_a2a/_agent.py
- packages/a2a/tests/test_a2a_agent.py
- packages/a2a/tests/test_a2a_group_chat.py
Notes for next iteration:
- No blockers. INPUT_REQUIRED pause/resume remains a separate task.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix(a2a): pause group chat for remote input
Key decisions:
- Translate A2A INPUT_REQUIRED task content into the existing Content user-input-request contract.
- Use the remote task ID as stable request correlation for streamed and finalized responses.
- Reuse AgentExecutor request handling so caller input resumes the same task without a workflow-specific A2A path.
Files changed:
- packages/a2a/agent_framework_a2a/_agent.py
- packages/a2a/tests/test_a2a_agent.py
- packages/a2a/tests/test_a2a_group_chat.py
Notes for next iteration:
- Checkpoint restoration of pending A2A input is now unblocked.
- The local issue file could not be moved because repository issue files are restricted by content exclusion policy.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix(a2a): restore pending input from checkpoints
Key decisions:
- Keep normalized INPUT_REQUIRED content durable by excluding transport-only protobuf raw representations.
- Restore through the existing AgentExecutor checkpoint and request-response path without a new schema or continuation API.
- Cover file-backed restoration in streaming and non-streaming Group Chat runs, including unrelated-response rejection and exact task resumption.
Files changed:
- packages/a2a/agent_framework_a2a/_agent.py
- packages/a2a/tests/test_a2a_group_chat.py
Notes for next iteration:
- The local issue file could not be moved because repository issue files are restricted by content exclusion policy.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* test(handoff): lock textless target context
Key decisions:
- Exercise the built Handoff workflow in streaming and non-streaming modes instead of bypassing routing, sessions, or termination.
- Keep the slice test-only because current production already carries the initial task to a textless handoff target without synthetic user input.
- Revisit the source to verify its handoff function call retains a matching result and user-turn termination sees only caller messages.
Files changed:
- packages/orchestrations/tests/test_handoff.py
Notes for next iteration:
- No production defect was reproduced.
- The local issue file could not be moved because repository issue files are restricted by content exclusion policy.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* test(handoff): use resolved IDs in event assertions
* fix(workflows): preserve A2A input request semantics
* fix(workflows): preserve input request correlation
* fix(a2a): deduplicate message-less input requests
* fix(workflows): preserve specialized input requests
* test(openai): use current web search model
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* feat(dotnet): agent-hooks interception contract as an experimental package
Add Microsoft.Agents.AI.AgentHooks, implementing the AGENT-HOOKS-0.1
control contract on the framework's native decorator seams, mirroring
the merged Python feature (#7515) in .NET idiom:
- One public factory (CreateAIAgentWithAgentHooks, per-run and
host-owned-session overloads) composes agent, chat and function
seams as one indivisible unit; the seam decorators are internal, so
partial installs are impossible by construction.
- All eight interception points: input/output at the agent seam,
pre/post_model_call below the function-invocation loop (every model
service call bracketed individually), pre/post_tool_call via the
function-invocation middleware seam, agent_startup/agent_shutdown
bracketing each run.
- Fail-closed enforcement throughout: transforms write back into the
native messages/arguments/results or throw; rich content is
preserved as AIContent objects; interceptor crashes surface as
host_error denies; enforcement-layer failures halt the run through
FunctionInvocationContext.Terminate (the loop's only loud escape).
- Streaming is fully buffered per spec buffered_output semantics: a
deny releases zero updates; transformed responses re-derive the
released updates so egress never diverges from verdicted content.
- Verdict-before-durability: end-of-run history and context-provider
writes defer behind the output verdict via gating provider wrappers
(flushed post-transform with verdicted-message substitution for
streams, dropped on deny); per-service-call persistence sits above
the chat seam and is covered by its own post_model_call verdict;
per-run history-provider overrides in run options are wrapped too;
nested guarded sub-agents persist inline at their own boundaries.
- Opt-in dependency: ResponsibleAI.AgentHooks 0.1.0-alpha.4 (bundles
native runtimes) referenced only by the new package; no existing
framework source is modified.
- 58 tests: deny-before-execution and transform write-back per seam,
rich-content preservation, streaming ordering with zero egress on
deny, error bracketing, concurrency isolation, host-owned sessions,
evaluate_only, approval-seam lift, persistence gating, misuse
fail-closed paths, and codec units.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(dotnet): close structural bypasses at the ChatClientAgent boundary
Address both reviewers' probe-confirmed findings; the runtime
enforcement held everywhere, every fix is at the structural boundary:
- Gate the implicit default ChatHistoryProvider: with no provider
configured, ChatClientAgent creates an InMemoryChatHistoryProvider
the factory never saw, so denied output became durable session
history and replayed to the model on the zero-config path (both
stream modes). The factory now materializes and gates the default,
setting the history-conflict flags to mimic implicit-default
semantics for service-managed-history agents.
- Wrap per-run provider overrides on BOTH dictionaries: base
AgentRunOptions.AdditionalProperties is merged into the chat options
with precedence, so a base-level override bypassed (and displaced)
the wrapped ChatOptions-level entry. Plain AgentRunOptions is
covered too, and the wrap is copy-on-write — the caller's options
and dictionaries are never mutated.
- Reject per-run ChatClientFactory on guarded agents (fail closed): it
would replace the guarded chat pipeline and the tool-wrapping stage
riding it, silently removing the chat and tool seams.
- Reject a supplied client already containing a
FunctionInvokingChatClient: it would execute tools below the chat
seam, before any post_model_call verdict and outside the tool seam.
- Run wire projections inside the guarded blocks at the chat and
function seams: a poisoned value whose serialization throws now
fails the run closed (function seam: host_error halt; chat seam:
gated persistence refused before the failure propagates).
- Suppress provider failure notifications once a run-level deny or
halt stands, so the denied turn's request messages never reach
provider code.
- Document the deferred-OpenTelemetry observer channel (request-side
spans capture pre-transform content under sensitive-data telemetry).
- Rename the factory to AsAIAgentWithAgentHooks per repo convention.
10 new boundary regression tests mined from the review probes
(default-provider durability in both stream modes with session-replay
assertions, both override dictionaries incl. the displacement shape,
plain-run-options override, copy-on-write, factory and supplied-FICC
rejections, poisoned-projection fail-closed); 68 total, all green.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(dotnet): redact denied-run failure notifications for both provider kinds
The deny/halt handling of provider failure notifications only covered
the chat-history wrapper; a context provider still received the denied
turn's request messages on its failure notification. Both gating
wrappers now REDACT instead of suppress: the notification is forwarded
with empty request messages and the original exception, preserving the
documented failure-cleanup contract (providers releasing per-run
resources on the failure signal keep working) while the denied turn's
request messages never reach provider code.
Regression tests assert both provider kinds receive the redacted
notification (zero request messages) on a denied run and full
notifications on ordinary, verdict-free failures. 70 tests total.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(dotnet): address Copilot review on the agent-hooks PR
- Run options: always clone chat-typed run options (the framework's
function-invocation middleware chains its per-run factory onto the
instance it receives, so forwarding the caller's instance leaked
that factory into it — reuse tripped the rejection, concurrent
reuse raced), and recognize the framework middleware's own factory
as legitimate: it wraps the guarded pipeline (tool rewriting), so
outer function-middleware composition now works, while its chained
factories are walked so a caller-supplied factory cannot ride in
unnoticed.
- Streaming: re-derived (transformed) updates preserve the response's
ContinuationToken (ToAgentResponseUpdates does not project it), so
transformed background streaming responses remain resumable; a
message-less response releases a metadata-only update carrying it.
- Codecs: transformed tool calls are validated for complete shape and
uniqueness before reconciliation (non-empty string id and name,
object-valued args, distinct ids) — malformed shapes fail closed
instead of becoming invalid native calls. Deliberately stricter
than the merged Python codec, which coerces added-call shapes.
- Role defaulting in message write-backs is confirmed exact Python
parity (user/assistant defaults per the merged codecs) and is now
locked by tests rather than changed.
- ADR 0035 records the seam order, persistence gating, fail-closed
behavior, alternatives and known limitations.
14 new tests (options reuse, outer function-middleware composition,
smuggled-factory rejection, continuation-token preservation, 8
malformed tool-call shapes, 2 role-default parity); 84 total, green.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* feat(dotnet): project the per-call tool set on pre_model_call emissions
Context providers can register additional tools during run preparation,
after agent_startup has been emitted, so tools_registered is inherently
a run-start snapshot and can be a partial view of the tools eventually
offered to the model.
- Emit the spec's optional pre_model_call tools field ({name,
description?}) from the per-call effective ChatOptions.Tools — the
completed set for each call, including provider-added tools.
- Document tools_registered as the run-start snapshot on the agent
seam (dynamic registrations surface per call and are bracketed by
the tool seam when invoked).
- Probe-confirm enforcement completeness for provider-added tools:
they flow through the guarded pipeline's tool-wrapping stage, emit
pre/post_tool_call, and a pre_tool_call deny blocks their
invocation exactly like constructor-registered tools.
Two new tests (bracketing + audit projections, deny-blocks); 86
total, green.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* refactor(dotnet): one artifact per file; rewrap foreign gating wrappers
Per review:
- Split the three multi-type files (AgentHooksGatingProviders.cs,
AgentHooksRunState.cs, AgentHooksWireCodecs.cs) into one type per
file, file name matching the type name, per repo convention. No
behavior changes; namespaces and access levels unchanged.
- Close a validation asymmetry at the provider gate: the per-run
override wrap skipped any gating wrapper, including one owned by a
DIFFERENT agent-hooks installation — which runs inline under this
run's state (its own gate is not covering here), so a denied run's
history could persist straight through it. Overrides are now
re-wrapped unless the wrapper belongs to this installation
(reference-equal configuration). The provider seam's inline
behavior for foreign/absent state is otherwise deliberate: inline
is the safe direction there (content of unguarded or differently
guarded runs is covered by its own verdicts or none), and throwing
would break the legitimate double-wrap flush flow.
One new regression test (foreign wrapper as per-run override on a
denied run persists nothing); 87 total, green.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* refactor(dotnet): accept params IEnumerable for agent-hooks interceptors
Per review: the constructor only iterates the interceptors, so widen
the parameter from params IInterceptor[] to the C# 13 params
IEnumerable<IInterceptor>. The sequence is enumerated exactly once
into the internal registration list (sequences may be
single-enumeration); per-item null validation and the factory's
at-least-one-interceptor check are unchanged, and an explicit null
sequence now throws ArgumentNullException. Params-form call sites are
source-compatible.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* build(dotnet): ship Microsoft.Agents.AI.AgentHooks as a preview package
Per maintainer review on the PR:
- Add the project to agent-framework-release.slnf and import the
shared packaging props so the package ships. Version follows the
repo default for unmarked packages (preview suffix), matching the
package's [Experimental] surface and alpha upstream dependency:
1.17.0-preview.<date>.1.
- Package metadata: sibling-style title, fuller description, tags;
shared icon and NUGET.md readme via the packaging props. Verified
dotnet pack locally: ResponsibleAI.AgentHooks 0.1.0-alpha.4 flows
as a normal dependency and the project references become 1.17.0
package dependencies.
- Update ADR 0035: shipping as preview per maintainer decision
replaces the build-only-pending-maturity stance.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* build(dotnet): version the agent-hooks package as alpha
Per maintainer review: the package's maturity marker follows the
ResponsibleAI.AgentHooks dependency it is built on (alpha), rather
than the repo's default preview suffix. Packs as
1.17.0-alpha.260804.1; ADR 0035 updated.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* refactor(dotnet): group agent-hooks internals into Core and Codecs folders
Per review: only the public surface (the factory extensions and
options) stays at the project root; the internal seam decorators, run
state and gating providers move to Core/, and the wire projection
codecs to Codecs/. Pure file moves — namespaces stay flat per the
core package's folder convention (ChatClient/, Memory/); no content
changes.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* docs(dotnet): clarify session scoping and name the sessionId argument
Per review:
- Name the AgentContextBuilder arguments at the run-state factory so
the GUID reads as what it is (the per-run agent-hooks session id).
- Document both branches of CreateRunState: session-scoped means the
host owns the emitter/builder and the session boundaries (one
session spanning runs, no agent_startup/agent_shutdown emitted by
the agent); the default is one session per run with a fresh
emitter, fresh sequence and isolated record trail, which is what
keeps concurrent runs' emissions from interleaving.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(dotnet): harden agent-hooks factory and input projection per review
- Input projection returns (payload, content, role) as one typed
result so the emission site never re-reads payload properties by
name: the both-fields-exist invariant holds by construction. (The
previous reads were fail-closed even hypothetically — JsonObject's
indexer yields null, and a null content is rejected by the SDK's
envelope validation — but reading back what we just produced was
needlessly fragile-looking.)
- Reject UseProvidedChatClientAsIs on the factory: it signals a fully
custom, do-not-touch client stack, which is incompatible with a
factory whose job is to decorate the supplied client and rely on
the agent's default pipeline above the chat seam. Honoring it would
silently change where (and whether) the seams sit.
- Log swallowed agent_shutdown emission failures (logger resolved the
same way the agent resolves its own: services, then the chat
client, then null) so incomplete session trails are trackable;
OutOfMemoryException stays unswallowed. The swallow remains
correct: the run's own outcome is already propagating and the
trail closure is best-effort by contract.
89th test: UseProvidedChatClientAsIs rejection.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* build(dotnet): attribute the Agent-Hooks protocol in the package identity
Per review:
- Title per suggestion: 'Microsoft Agent Framework - Responsible AI
Agent-Hooks Protocol Support'; description names the protocol
precisely (AGENT-HOOKS-0.1, maintained by the Responsible AI
project at github.com/responsibleai/agent-hooks) so the package
reads as protocol support, not a MAF-owned feature; tags aligned.
- Drop the [Experimental] attributes: per repo convention the
attribute gates unstable surface inside released packages
(Harness, core), while pre-release packages (Valkey and Mcp at
alpha, Mem0 and LocalCodeAct at preview) carry none — the version
suffix is the maturity signal.
- Drop the describing comment on the central package version entry.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* refactor(dotnet): split agent-hooks test fixtures into Support files
Per review: one type per file under Support/ (mock client, guards,
recording providers, helpers), matching the src-side convention; pure
mechanical split, flat namespace.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
---------
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* Python: Preserve structured instructions when merging chat options
`instructions` is declared as `str` on `ChatOptions`, but chat clients may widen it
to a provider-native structured form. Three merge paths combined it with an f-string,
which coerced any non-string value to its `repr`, turning structured metadata into
literal text before any client could see it:
- `merge_chat_options` (`_types.py`)
- `_merge_options` (`_agents.py`, agent defaults + per-run options)
- provider-contributed instructions in `_prepare_session_and_messages` (`_agents.py`)
The last of these is the reported case: once any context provider (for example
`SkillsProvider`) contributes instructions, structured instructions were replaced by
their `repr`, so the model received Python dict syntax as its system prompt and
Anthropic prompt caching silently stopped working.
Add a shared `_append_instructions` helper that concatenates strings as before and
otherwise extends element-wise, always appending so the leading portion stays
unchanged for providers that treat it as a stable, structure-sensitive prefix. A lone
mapping is treated as a single element rather than iterated into its keys.
On the Anthropic side, `_extract_structured_instructions` now normalizes bare strings
into text blocks, since appended instructions arrive alongside caller-supplied blocks.
Fixes#7700
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f428b149-a306-484a-b423-4e9de69f0b90
* Python: address review feedback on structured instructions fix
Parameterize the Anthropic regression test over both the with- and
without-SkillsProvider configurations so the structure-preserving behavior
is asserted in the baseline case too.
Normalize structured instructions in `_get_instructions_from_options` so
telemetry records the instruction text for provider-native block shapes,
extracting only `text` values to keep provider metadata out of spans.
Use `cast` for the structured `default_options` in both regression tests so
the test type checkers resolve the client options type correctly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f428b149-a306-484a-b423-4e9de69f0b90
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Copilot-Session: f428b149-a306-484a-b423-4e9de69f0b90
HandoffAgentExecutor clones each participant agent to attach handoff
tools, but the clone rebuilt the Agent without forwarding
additional_properties, so middleware and integrations observing
context.agent.additional_properties during handoff runs saw an empty
dict while the original agent retained its configuration.
Pass a deepcopy of the original agent's additional_properties into the
clone so handoff-executed agents keep their configured metadata and the
original agent stays untouched.
Fixes#7750
* .NET: Migrate 6 hosted-agent samples to source (ZIP) deploy
Extend the source (ZIP) deploy pattern established for Hosted-ChatClientAgent to Hosted-LocalTools, Hosted-Workflow-Simple, Hosted-TextRag, Hosted-Observability, Hosted-Files and Hosted-FoundryAgent. Each gains an azure.yaml (codeConfiguration/remote_build, ASPNETCORE_URLS, model env) and the canonical .agentignore, a self-contained csproj (single target, CPM opt-out, explicit published package versions, AgentFrameworkVersion), a Program.cs that drops the shared contributor scaffolding for DefaultAzureCredential, an updated .env.example and README, and drops the container-mode files (agent.yaml, agent.manifest.yaml, Dockerfile, Dockerfile.contributor). LocalTools, Workflow-Simple, TextRag, Observability and Files were verified deploying live via remote_build; Workflow-Simple returns a workflow runtime error at invoke that is unrelated to the deploy mode.
* .NET: Migrate Hosted-Invocations-EchoAgent and Hosted-LocalCodeAct to source (ZIP) deploy
EchoAgent (Invocations protocol) and LocalCodeAct migrated to the zip/code-deploy pattern (azure.yaml, .agentignore, self-contained csproj, README, container files removed). EchoAgent maps /readiness explicitly because the Invocations SDK does not auto-map it. Both verified live via remote_build on a Foundry project; LocalCodeAct's execute_code ran server-side (compute 21+21 -> 42).
* .NET: Migrate remaining hosted-agent samples to source (ZIP) deploy
Migrate Hosted-McpTools, Hosted-MemoryAgent, Hosted-AgentSkills, Hosted-AzureSearchRag, Hosted-Toolbox, Hosted-Toolbox-AuthPaths and Hosted-ToolboxMcpSkills to the zip/code-deploy pattern (azure.yaml with codeConfiguration + sample-specific env passthrough, canonical .agentignore, self-contained csproj, Program.cs dropping the shared contributor scaffolding for DefaultAzureCredential, updated .env.example and README, container files removed). Also restore the Hosted-Invocations-EchoAgent csproj filename the solution references. McpTools verified live via remote_build against the public Microsoft Learn MCP server; the memory/search/toolbox/skills samples build locally and deploy via remote_build but need their external resources (memory store, search index, toolbox connections, skills) provisioned to exercise end to end.
* .NET: Migrate Hosted-Workflow-Handoff to source (ZIP) deploy
Migrate the triage handoff workflow sample to the zip/code-deploy pattern (azure.yaml with codeConfiguration and Azure OpenAI env passthrough, canonical .agentignore, self-contained csproj using AgentFrameworkVersion for Foundry/Foundry.Hosting/Hosting, Program.cs dropping the shared contributor scaffolding for DefaultAzureCredential, updated .env.example and README, container files removed). Builds via remote_build; live needs an Azure OpenAI resource (AZURE_OPENAI_ENDPOINT/AZURE_OPENAI_DEPLOYMENT).
* .NET: Copy Hosted-AgentSkills skills/ into build output
The startup provisioning helper reads SKILL.md files from AppContext.BaseDirectory/skills, but the project did not copy the skills/ folder to the build/publish output, so at runtime the source directory did not exist and provisioning was silently skipped. Add a Content include (PreserveNewest), matching the resources/ pattern already used by Hosted-Files.
* .NET: Suppress OPENAI001 in Hosted-Workflow-Handoff for standalone ZIP build
The repo-wide Directory.Build.props suppresses OPENAI001, but that file does
not travel in the code/ZIP deploy package. The standalone dotnet publish the
Foundry code deploy runs then fails with error OPENAI001 on the experimental
GetResponsesClient().AsIChatClient() call. Add OPENAI001 to the project NoWarn
so the sample builds in the code-deploy pipeline, matching SimpleAgent.csproj.
* .NET: Document live-verified idiosyncrasies in Foundry hosted sample READMEs
Align every FoundryHostedAgents sample README with the documented azd flow and
add the idiosyncrasies found while live-testing each sample on a Foundry project:
- All samples: 'azd down' reports success but does not delete the hosted agent;
document the explicit REST DELETE needed to remove it.
- Hosted-Workflow-Handoff: it builds its own AzureOpenAIClient (data-plane), so
the agent identity needs the 'Cognitive Services OpenAI User' role on the
Azure OpenAI account. azd only grants 'Foundry User' on the project, so add a
step to grant the data-plane role and explain the triage-step failure without it.
- Hosted-Toolbox / Toolbox-AuthPaths / ToolboxMcpSkills: the toolbox must already
exist and the agent identity must be able to read it; toolboxes with OAuth-gated
tools return an oauth_consent_request and response.incomplete on first invoke.
* .NET: Address Foundry hosted sample review feedback
Make sample configuration reject blank azd substitutions and document every required environment value inside the scaffolded project flow.
Separate the hosted endpoint name from the Foundry managed prompt-agent name, fix standalone MemoryAgent diagnostics, and complete the contributor local package feed for Hosting, LocalCodeAct, and MCP.
Use azd for agent invocation and az rest for authenticated administration without exposing tokens. Add native MCP approval handling to the toolbox consent client and make its local path target the standard responses endpoint.
Validated all changed samples locally, the contributor flow in PowerShell and Bash, and the supported live scenarios on the TAO cace project.
* .NET: Fix advanced hosted sample project access
Document and validate the Foundry User grant required by hosted version identities that access project data plane APIs.
Add the Skills preview feature header and use a writable temporary directory for downloaded skills because source deployments mount the application directory read only.
Update AgentSkills, MemoryAgent, FoundryAgent, and ToolboxMcpSkills deployment guides with the post deploy identity grant. All four scenarios passed live on the TAO cace project.
* Simplify AG-UI Step04 human-in-the-loop sample to idiomatic pattern
The Step04 sample previously wrapped both the server and client agents in
custom ServerFunctionApproval*Agent middleware (~470 lines across two files)
to marshal a bespoke approval protocol over AG-UI. This is no longer needed:
MapAGUIServer natively emits the tool-approval interrupt when the model calls
an ApprovalRequiredAIFunction, and AGUIChatClient natively transports the
client's ToolApprovalResponseContent decision back to resume the run.
Changes:
- Server: map the ChatClientAgent directly with MapAGUIServer; remove the
ServerFunctionApprovalAgent wrapper, the JsonOptions plumbing, and the
ApprovalJsonContext registration.
- Client: use the AGUIChatClient-backed agent directly; the existing loop
already handles ToolApprovalRequestContent -> CreateResponse idiomatically.
- Delete ServerFunctionApprovalServerAgent.cs and
ServerFunctionApprovalClientAgent.cs.
Verified end-to-end (approval request -> approve -> tool executes -> final
response) against GitHub Models. Both projects build with 0 warnings.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: eeb2168d-2ecc-4f8d-9830-c287072eb7e1
* Update AG-UI Step04 README to describe native approval flow
The Step04 human-in-the-loop sample no longer uses the custom ServerFunctionApprovalServerAgent / ServerFunctionApprovalClientAgent wrappers. Update the README so it describes the idiomatic native flow: the server maps a plain agent with MapAGUIServer and relies on ApprovalRequiredAIFunction to raise the approval interrupt, and the client handles ToolApprovalRequestContent and replies with ToolApprovalResponseContent.
* Fix AG-UI Step04 README server port to match client default
The Step04 client defaults to http://localhost:5100 (and the server launchSettings also uses 5100), but the README told users to run the server on port 8888, so the client could not reach it. Align the Step04 server run command to 5100. Other steps intentionally keep 8888 because their clients default to that port.
* Update AG-UI .NET samples for latest MAF + AG-UI SDK and align with docs
- Bump AGUI.* packages 0.0.3 to 0.0.4 (Directory.Packages.props)
- Step01/02/03: drop AddHttpClient().AddLogging() server noise and simplify the
client run-started output to match the getting-started doc (no thread plumbing)
- Step04 (HITL): remove HTTP body logging and MEAI001 pragmas, give the approval
tool an explicit name, and align the resume decision message with the doc
- Step05 (state): replace the custom SharedStateAgent/StatefulAgent DataContent
pattern (dropped by released AGUI.Server) with declarative
AGUIStreamOptions.MapResultAsStateSnapshot plus a thin RecipeStateAgent that
reads RunAgentInput.State, and align the Recipe models with the docs
- Refresh README to the shipped API (MapAGUIServer, ApprovalRequiredAIFunction,
declarative state)
Verified: all 10 sample projects build; Step04 approval/resume and Step05 state
snapshot round-trip run end-to-end against GitHub Models.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: eeb2168d-2ecc-4f8d-9830-c287072eb7e1
* Name the Step02 backend tool search_restaurants to match the docs
Give the SearchRestaurants tool an explicit "search_restaurants" name so the
client displays an accurate tool name (not a compiler-mangled local-function
name) and stays aligned with the backend-tool-rendering doc.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: eeb2168d-2ecc-4f8d-9830-c287072eb7e1
* Add UTF-8 BOM to Step05 sample files to satisfy check-format
The check-format CI job enforces the repository's utf-8-bom charset rule via
dotnet format. The Step05 files added in this PR were saved without a BOM.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: eeb2168d-2ecc-4f8d-9830-c287072eb7e1
* Fix AG-UI sample conversation history
Let AgentSession own prior messages so clients send only each new turn, and give the frontend location tool a stable protocol name.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+RogerBarreto@users.noreply.github.com>
Copilot-Session: eeb2168d-2ecc-4f8d-9830-c287072eb7e1
* fix: prevent superlinear history growth by deduplicating messages in save_messages
* fix: address review feedback for history deduplication
* fix: Prevent superlinear history growth by deduplicating messages
* fix: add list[Message] type hints
* fix(sessions): resolve deduplication churn and collapsing of identical message
* fix(sessions): replace uuid/seen-set dedup with sequence aware filtering
* fix: use forward-scan sequence alignment in filter_new_messages
* fix(core): annotate new_msgs type to resolve pyright errors
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
AG-UI clients send plain JSON, but structured response types were only
accepted as already-built instances, and core's coercion stopped at the
outer object, letting raw dicts sit inside typed fields. Coercion now
walks declared annotations and returns the input untouched whenever it
cannot satisfy them.
* feat(core): first-class fatal signal (MiddlewareFailure) for function middleware
The function-invocation loop converts every exception raised by
function middleware into a tool-error result and keeps looping, so
middleware that needs fail-closed semantics (enforcement layers,
guardrails) had no loud escape: the agent-hooks feature simulated one
by mutating shared run state, raising MiddlewareTermination, and
re-raising the real failure two hops away at the run boundary.
Introduce MiddlewareFailure (a MiddlewareException sibling of
MiddlewareTermination) as the loop's explicit fail-closed escape:
- _auto_invoke_function re-raises it (both the direct and the
pipeline path) instead of absorbing it into a tool-error result;
ordinary exceptions keep the absorb-and-continue contract.
- A failing call fails the whole parallel batch: in-flight sibling
tool tasks are cancelled and awaited before the failure propagates.
- Every existing MiddlewareTermination absorb site (agent/chat
pipelines, _execute_single_function_call, harness loop, purview)
passes it through untouched by construction, and agent/chat
middleware exceptions already propagate, so one exception type
gives uniform fail-loud semantics across all three categories.
Migrate the agent-hooks feature to the new signal: delete the
_RunState.halted back-channel and its three run-boundary re-raise
checks, drop the halted arm of the termination special case in the
function middleware (the approval-request pass-through moves to the
single approval check on the normal path), and fail partial installs
loudly. Tool-seam host_error blocks keep surfacing as
InterceptionBlocked at the run boundary via the exception cause chain
(one deny surface at every seam, pinned by tests).
Spec 004 gains the middleware-failure invariants and matrix rows.
Closes#7522
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(core): harden tool-seam unwrap and pin review findings
Review round follow-ups for the MiddlewareFailure feature:
- Only agent-hooks' own tagged tool-seam halts (_ToolSeamBlockFailure)
authorize re-raising the chained InterceptionBlocked at the run
boundary; a third-party MiddlewareFailure with a crafted
InterceptionBlocked cause now propagates as raised instead of
laundering an attacker-shaped interception record into the feature's
deny surface (regression test added, verified by mutation).
- Document that middleware must not catch MiddlewareFailure (docstring
and spec 004): swallowing it converts a fail-closed abort back into
a running, possibly unguarded loop.
- Pin the trailing termination re-raise in the agent-hooks function
middleware: an inner short-circuit is bracketed and still propagates,
skipping outer middleware post-code (test fails with the re-raise
removed).
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(core): acyclic tool-seam unwrap chain; document cooperative batch cancellation
Address two automated-review findings on the MiddlewareFailure PR,
both confirmed empirically:
- _reraise_tool_seam_block created a two-object exception-chain cycle
(block.__cause__ -> wrapper -> block) by re-raising the chained
InterceptionBlocked `from` its transport wrapper. Detach the
wrapper's back-links and re-raise bare, recording the wrapper as
the block's __context__ — acyclic, both exceptions still visible in
tracebacks. Regression test walks the chain and pins finiteness
(verified to fail against the cyclic re-raise).
- Batch cancellation is cooperative: a synchronous tool body already
running in a worker thread (asyncio.to_thread) cannot be interrupted
by task cancellation and may complete its side effects after the
failure reached the caller; its result is discarded either way and
propagation is not delayed behind it. Narrow the stated contract
(MiddlewareFailure docstring, loop comment, spec 004) and pin it
with a blocking-sync-sibling regression test.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(core): settle dangling calls on service-managed conversations on abort
Address maintainer review on the MiddlewareFailure PR:
- A MiddlewareFailure escaping a tool batch on a service-managed
conversation left the hosted thread ending in unresolved
function_call items: _update_continuation_state persists
session.service_session_id when the model turn completes (before
tool execution), and probe-verified the next run sends only the new
user message against that conversation — OpenAI-style continuations
reject such a request, so a routine policy abort left the session
permanently stuck. Both loops now settle the thread before
propagating: one error function_result per dangling call, submitted
with tool_choice="none" in a single extra request whose response is
discarded; a settlement failure never masks the abort, and runs
without a service-managed conversation make no extra request.
Pinned by three regression tests (non-streaming, streaming, and the
no-conversation no-cost case); spec 004 and the MiddlewareFailure
docstring updated.
- Make the three tool-bracket escape tuples in the agent-hooks
function middleware identical (MiddlewareTermination,
MiddlewareFailure, CancelledError): a MiddlewareFailure raised
inside the post/error-bracket emit bodies is unreachable today, but
the uniform tuples remove the need to reason about why they would
differ, and preserve the exact exception (including the private
tool-seam tag) if the emitter ever surfaces one.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(core): advance settled continuation; settle approved-replay aborts
Address maintainer review on the MiddlewareFailure settlement path,
both probe-verified (branch rebased onto current main first):
- Advance the persisted continuation to the settlement response. For
response-ID continuations (OpenAI Responses store=True, where the
response id is the continuation handle) the settlement response is
the first endpoint whose chain includes the synthetic tool outputs;
leaving session.service_session_id on the pre-settlement response
made the settlement ineffective — the next run would continue from
the still-unresolved turn. The settlement response now runs through
_update_function_invocation_continuation_state (a no-op for stable
conversation-object ids). Pinned by a regression test that fails
with the advance removed.
- Cover the approval-resolution phase: a MiddlewareFailure raised
while an approved tool is replayed escapes loudly (probe-verified,
already the case) but executed before the loops' settlement seams,
leaving the original — already service-persisted — call unresolved.
_resolve_approval_responses now takes a settle_dangling_calls
callback invoked with the approved batch on abort; the settlement
helper became a layer method taking explicit calls
(approval-response wrappers unwrap to their underlying calls,
hosted-tool approvals are left to their provider protocol) and
carries its own best-effort containment. Pinned by deny-during-
replay regression tests in both response modes, mutation-verified.
Spec 004 invariants and matrix rows updated accordingly.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
---------
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
Registers the repository with Engineering System inventory via the
InventoryAsCode provider, mapping it to its Service Tree service and
routing compliance work items to the owning team.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 05291438-8e37-49d6-84b6-5ffb7814abb8
All four AddAIAgent overloads in AgentHostingServiceCollectionExtensions
created a ChatClientAgent without forwarding the IServiceProvider, so the
FunctionInvokingChatClient in the agent's pipeline had no service provider
and tools could not resolve their dependencies at invocation time.
Fixes#4453
Co-authored-by: Max Montes Soza <max-montes@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Guard the hosted storage error log before evaluating the agent name and update the SDK to the servicing release containing the net9 ILLink analyzer fix.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: e3a6cce8-e1e8-4cf2-9cf4-3c0c8f3ed6d8
* fix(workflows): preserve all trace contexts in FanInEdgeRunner aggregation
FanInEdgeRunner collected trace contexts and source span IDs using the
singular backward-compat properties (msg.trace_context / msg.source_span_id),
which return only the first element of the plural lists. When a message
arriving at a fan-in already carries multiple trace contexts (e.g. from
a prior fan-in aggregation), all but the first were silently dropped.
Iterate over the plural fields (trace_contexts / source_span_ids) and
extend the aggregated lists so every trace context and source span ID
from every source message is preserved. This keeps distributed tracing
links intact for nested fan-in topologies.
Added test_fan_in_preserves_multiple_trace_contexts_per_message that
sends a message with two trace contexts through a fan-in and asserts
all three contexts (2 + 1) reach the target executor.
* fix: address Copilot review comments on trace context aggregation
1. Pair trace_contexts and source_span_ids per-message (via zip) instead
of flattening independently. This prevents misalignment when a message
has mismatched counts — orphans are dropped per-message rather than
shifting all subsequent pairs out of alignment.
2. Remove TraceCapturingAggregator's override of Executor.execute()
(documented as "do not override"). Capture trace data from the
WorkflowContext passed to the handler instead.
---------
Co-authored-by: weed33834 <weed33834@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Resolve request-info type names only from exact caller-provided mappings or already-loaded module namespaces. Remove payload-selected imports and add focused regression coverage for both request and response type fields.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a53fe20b-c3f0-4583-badc-d5deac7c1049
* .NET: Add Foundry session and user identity pass-through
Let user agents pin hosted agent_session_id on AgentSession and
send x-ms-user-identity per call for Foundry hosted agents.
* .NET: Add live ITs for Foundry session and user identity
Cover service-managed and admin-pinned hosted sandboxes, sticky
hosted session id, and per-call x-ms-user-identity isolation with
separate AgentSessions sharing one sandbox. Echo container avoids
model quota for identity assertions.
* .NET: Reject Foundry hosted session switch when sticky
Persist sticky id in finally, clone run options before factory wrap,
validate whitespace pin on CreateHostedSessionAsync, and throw on
unexpected hosted session id change in the response. Docs: distinct
AgentSessions per user identity may share one sandbox.
* .NET: Clear nested user identity and preserve run options
Always assign UserIdentityScope including null so nested runs do not
inherit a parent identity. When upgrading plain AgentRunOptions, keep
background, format, and additional properties on the specialized clone.
* .NET: Clarify previous_response_id user binding in docs
Align WithUserIdentity guidance with Foundry Learn multiplex docs:
response chains are bound to the creating user even inside a shared
hosted sandbox.
* refactor(foundry): clarify hosted agent APIs
The ag-ui-protocol `InputContentUrlSource` carries the URL in `source.value`,
but `_extract_multimodal_source_fields` only read `source.url`/`source.uri`
for url-typed sources, so attachments sent in the spec shape were dropped
during the AG-UI to MAF conversion. The base64 branch already read
`source.value` correctly.
Read `source.value` first, keeping `url`/`uri` as fallbacks for the non-spec
shape. Adds tests for both.
Fixes#7653
* Add approval storage and improve truth checks
* Address PR comments
* Update spec
* Revert changes to agui since it is already handled in another pr
* Add missed change
* Bump Python package versions for 1.14.0 release
Bump the CHANGELOG-selected packages for the 1.14.0 release: minor versions for root/core, AG-UI, Foundry, OpenAI, and orchestrations due to additive public APIs; patch versions for declarative and GitHub Copilot fixes; and Pacific-date prerelease stamps only for changed alpha/beta packages. No beta cohort bump was applied. Core dependency floors follow the strict policy and remain unchanged because no dependent package requires a new 1.14 API. Release validation also identified and corrected missing AG-UI and Copilot Studio runtime dependencies and aligned GitHub Copilot metadata with its Python 3.11 SDK requirement. Lab is intentionally skipped because its changes are development-only, and the moved Azure Functions and Durable Task packages are documented but no longer versioned here.
* Raise AG-UI core dependency floor
* Harden functional workflow continuation authority
Use a versioned opaque single-use token on WorkflowRunResult, validate it before request correlation, consume it immediately before replayed user code, and rotate it on each pause. Carry the same explicit authority through streaming and non-streaming FunctionalWorkflowAgent responses.
Files changed: functional workflow/runtime result APIs, functional HITL regression tests, core agent guidance, and the functional HITL sample.
Next iteration: enforce pending-state overlap and token-authorized abandonment, then document and test checkpoint authorization boundaries.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Enforce one pending functional continuation
Reject fresh messages and checkpoint restores while an in-memory continuation is pending. Add token-authorized abandonment on FunctionalWorkflow and FunctionalWorkflowAgent, and clear retained replay state atomically when authority is consumed while preserving the active message for token rotation and checkpoints.
Files changed: functional workflow runtime and agent adapter, functional lifecycle regression tests, and core workflow guidance.
Next iteration: preserve and document authorized checkpoint continuation boundaries.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Preserve authorized functional checkpoint continuation
Treat checkpoint restore as a host- and storage-authorized path independent of process-local continuation tokens, and issue fresh authority whenever restored execution pauses again. Cover default and per-run storage, deterministic and custom request IDs, token rotation, and checkpoint-plus-response restore.
Files changed: functional workflow and checkpoint interface guidance, functional checkpoint lifecycle tests, the functional HITL sample, and core workflow guidance.
Next iteration: run the final repository-wide Python validation gates.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Validate Python continuation hardening
Run the complete Python workspace checks, aggregate coverage suite, repository hooks, and core package build from the final combined worktree. Keep the validation iteration code-neutral because all gates pass without corrective changes.
Files changed: none; this commit records the final validation gate.
Blockers: none. Next iteration: no remaining AFK tasks.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Handle functional checkpoint continuation failures
Publish retained continuation state only after checkpoint persistence succeeds, and cover reuse after a transient save failure.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
* Address functional continuation review findings
Add owner recovery for lost tokens, harden malformed token validation, preserve consistent failure surfaces, and keep agent pending state aligned with resumable workflow state.
Document process-local single-use continuation semantics and extend regression coverage across direct, streaming, checkpoint, and agent paths.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
* Handle functional continuation cancellation
Release the workflow run guard when cancellation interrupts resumed user code while keeping the single-use continuation token consumed.
Replace sample assertions with explicit runtime checks and add cancellation regression coverage.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
* Simplify functional workflow instance isolation
Remove continuation-token handling and align functional workflows with the graph workflow ownership model: one stateful instance per logical caller or session.
Add create_instance() for independent callers, document the ownership contract, and cover pending-state isolation between instances.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
* Scope functional workflow checkpoint storage
Do not inherit checkpoint storage when creating an independent workflow instance. Allow hosts to provide an explicitly caller-scoped storage adapter and document that shared checkpoint access requires host authorization and tenant isolation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
* Require building functional workflow instances
Make @workflow return a stateless FunctionalWorkflowDefinition and require build() before run() or as_agent(). This aligns functional workflows with the graph definition/build lifecycle and prevents module-level decorated definitions from retaining caller state.
Move checkpoint configuration to build(), export the definition type, migrate samples, and cover isolated built instances.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: a8f47743-1cdc-4924-8e1b-667d0d790b78
* Route local approvals through lifecycle owner
Key decisions:
- Add an internal typed approval lifecycle with pending, claimed, executing, and settled states.
- Keep authorization separate from execution; only LocalPendingToolTransitionOwner invokes approved local calls.
- Register server-owned occurrences before canonical ResumeDecision claims and retain one replayable result under the original call identity.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_approval_state.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_approval_result_event.py
- packages/ag-ui/tests/ag_ui/test_agent_wrapper_comprehensive.py
Verification:
- 952 AG-UI tests passed.
- Focused lifecycle/public tracer passed with warnings treated as errors.
- Ruff format/check and AG-UI Pyright passed.
- git diff --check passed.
Notes for next iteration:
- The function-calling-loop scenario mapping is inaccessible under the organization content-exclusion policy and could not be updated.
- The workspace Poe package fan-out is blocked by the pre-existing missing packages/durabletask/pyproject.toml; equivalent package-local checks were run.
* Make approval batches occurrence-safe
Key decisions:
- Give each local approval a scoped logical occurrence identity and share one occurrence across trusted thread aliases.
- Validate complete Resume Decision batches before applying claims, then account for accepted, rejected, and cancelled occurrences independently.
- Preserve sibling authority and original result identity across failures, mixed decisions, and reused raw call IDs.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_approval_state.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_agent_wrapper_comprehensive.py
Verification:
- 959 AG-UI tests passed with 90% lifecycle branch coverage.
- 30 focused lifecycle/public tracer tests passed with warnings treated as errors.
- Ruff format/check and AG-UI Pyright passed.
- git diff --check passed.
Notes for next iteration:
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
- Workspace typing fan-out remains blocked by the pre-existing missing packages/durabletask/pyproject.toml; package-local Pyright passed, while package-local MyPy retains three unrelated baseline errors.
* Make approval resume retries idempotent
Key decisions:
- Retain terminal decisions and outcomes by scoped occurrence so identical accepted and rejected retries reproject results without granting execution authority again.
- Reject conflicting names, arguments, decisions, wrong-scope lookups, and expired authority before an execution intent can reach the local transition owner.
- Keep protocol normalization in the runner while using server-owned lifecycle context to canonicalize retries and preserve existing AG-UI wire aliases.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_agent_wrapper_comprehensive.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
Verification:
- 965 AG-UI tests passed with 90% approval lifecycle coverage.
- 18 focused lifecycle, hostile-resume, wrong-thread, and endpoint retry tests passed with runtime and deprecation warnings treated as errors.
- Ruff format/check and AG-UI package-local Pyright passed.
- git diff --check passed.
Notes for next iteration:
- Terminal retention is process-local and unbounded until the later bounded-retention issue adds its explicit policy.
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
* Separate approval execution ownership
Key decisions:
- Carry explicit local, hosted, deferred in-run, or unavailable ownership on every approval occurrence and authorized intent.
- Keep lifecycle authorization separate from execution; local calls execute only through the local adapter while hosted and setup-injected decisions forward through owner-specific adapters.
- Leave declaration-only calls pending when no transition owner can act, and settle forwarded outcomes against the original occurrence without local fallback.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_approval_state.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
Verification:
- 967 AG-UI tests passed with 92% package coverage and 89% approval lifecycle coverage.
- 94 focused lifecycle, hosted, deferred-owner, hostile-resume, and approval tests passed.
- Ruff format/check and package-local Pyright passed.
- git diff --check passed.
Notes for next iteration:
- Executing-without-outcome recovery remains for the indeterminate execution-window issue.
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
- Workspace Poe fan-out remains blocked by the pre-existing missing packages/durabletask/pyproject.toml; equivalent package-local checks passed.
* Represent approval execution uncertainty
Key decisions:
- Distinguish reserved claims from execution windows that may have started an external side effect.
- Recover non-idempotent execution failures as indeterminate and reject identical retries without another invocation.
- Permit claim release only under an explicit safe policy and execution retry only with a predeclared idempotency key shared by local and forwarded owners.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
Verification:
- 972 AG-UI tests passed with 92% line coverage and 89% package branch coverage.
- 23 focused lifecycle, duplicate-resume, hosted-owner, and public settlement-window tests passed.
- Package-local Ruff and Pyright passed; git diff --check passed.
Notes for next iteration:
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
- Workspace Poe fan-out remains blocked by the pre-existing missing packages/durabletask/pyproject.toml; equivalent package-local checks passed.
* Reconcile approval snapshots with lifecycle state
Key decisions:
- Keep Approval State authoritative and emit typed snapshot reconciliation keyed by logical occurrence identity.
- Retire settled, rejected, cancelled, expired, indeterminate, and missing controls while preserving nonterminal authority.
- Reconcile stale snapshots before hydration or resume, and retain lifecycle deduplication when snapshot saves fail.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/agent_framework_ag_ui/_snapshot_session.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
Verification:
- 975 AG-UI tests passed with 92% package coverage and 89% approval lifecycle coverage.
- Package-local Ruff and Pyright passed; git diff --check passed.
Notes for next iteration:
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
- Workspace Poe fan-out remains blocked by the pre-existing missing packages/durabletask/pyproject.toml; equivalent package-local checks passed.
* Bound process-local approval lifecycle state
Key decisions:
- Protect pending, claimed, executing, and indeterminate occurrences from eviction while retaining terminal outcomes for a configurable 15-minute process-local deduplication window.
- Serialize complete approval batches by logical occurrence locks so aliases share atomic decisions and independent batches can progress concurrently.
- Fail capacity, claim, and settlement conflicts explicitly, and emit redacted structured lifecycle telemetry without tool names, arguments, or approval payloads.
- Remove legacy LRU eviction paths so active Approval State and middleware state are never silently discarded.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_approval_state.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_approval_state.py
Verification:
- 982 AG-UI tests passed with 92% package coverage and 91% approval lifecycle coverage.
- 34 focused lifecycle and storage tests passed with RuntimeWarning and DeprecationWarning treated as errors.
- Package-local Ruff and Pyright passed; git diff --check passed.
Notes for next iteration:
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
- Workspace Poe fan-out remains blocked by the pre-existing missing packages/durabletask/pyproject.toml; equivalent package-local checks passed.
* Complete approval lifecycle cutover
Key decisions:
- Make ApprovalLifecycle the sole owner of trusted aliases, occurrence metadata, authority transitions, and retained outcomes.
- Remove the parallel mutable pending-approval registry and route local, hosted, deferred, cancellation, replay, and snapshot reconciliation through lifecycle occurrences.
- Encapsulate middleware Approval State behind copy-isolated store methods while keeping AG-UI protocol normalization and event projection in the runner.
Files changed:
- packages/ag-ui/AGENTS.md
- packages/ag-ui/agent_framework_ag_ui/_agent.py
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/agent_framework_ag_ui/_approval_lifecycle.py
- packages/ag-ui/agent_framework_ag_ui/_approval_state.py
- packages/ag-ui/tests/ag_ui/test_agent_wrapper_comprehensive.py
- packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py
- packages/ag-ui/tests/ag_ui/test_approval_result_event.py
- packages/ag-ui/tests/ag_ui/test_approval_state.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
- packages/ag-ui/tests/ag_ui/test_run.py
Verification:
- 964 package-local AG-UI tests passed with 92% coverage and 90% approval lifecycle coverage.
- 85 warning-strict focused approval tests passed.
- Package-local Ruff and Pyright passed; git diff --check passed.
Notes for next iteration:
- The function-calling-loop scenario mapping remains inaccessible under the organization content-exclusion policy.
- Workspace Poe fan-out remains blocked by the pre-existing missing packages/durabletask/pyproject.toml; equivalent package-local checks passed.
* Align AG-UI approval resumes with protocol
* Align workflow approvals with AG-UI resumes
* Address AG-UI approval review findings
* fix AG-UI test typing checks
* fix AG-UI approval retention and cancellation retries
* Update agentserver to 2.1.0
* Update agentserver responses and invocations to x.1.0b1
* Pass platform context to state store provider
* Pass user id
* Correct requirements.txt
* Fix unit tests
* Fix unit tests
* Python: Fix Cosmos memory provider calling renamed add_cosmos toolkit API
The Agent Memory Toolkit renamed AsyncCosmosMemoryClient.add_cosmos to
upsert_memory with an identical signature. The provider declares
azure-cosmos-agent-memory>=0.2.0b3 with no upper bound, so a resolved
install can expose either name. after_run swallows write errors and only
logs a warning, so on a post-rename toolkit the agent turn still looks
successful while long-term memory silently stops receiving turns.
Resolve the write method once per after_run, preferring upsert_memory and
falling back to add_cosmos, so both ends of the declared range keep working.
Same treatment for the emulator test's direct seed call.
Fixes#7633
* Ponytail comment erased
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Clarify TODO comment regarding memory method rename
Updated TODO comment to include author and clarify context , to resolve linting error
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Python: scope under-specified approve-for-session permission decisions
PermissionDecisionApproveForSession carries an optional `approval` (tool
prompts) and an optional `domain` (URL prompts), so it can be constructed
with neither. A bare PermissionDecisionApproveForSession() serializes to
{"kind": "approve-for-session"}, which the Copilot CLI cannot interpret: it
dereferences the absent approval and crashes the CLI process with "Cannot
read properties of undefined (reading 'commandIdentifiers')", taking the
whole run down rather than failing a single tool call.
Wrap the resolved permission handler so such decisions are scoped using the
request that triggered them: shell prompts become an approval for that
prompt's command identifiers, MCP prompts an approval for that server and
tool, URL prompts an approval for that URL's domain, and so on.
The decision is only ever narrowed, never widened. When the prompt reports
can_offer_session_approval=False, or the request kind has no session-scoped
approval (such as a hook prompt), the decision is downgraded to a single-use
approval and a warning is logged. Decisions that already specify a scope are
forwarded unchanged, and handler exceptions still propagate so the SDK's
deny-on-error behavior is preserved.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1b45752e-b602-4117-8304-3c8a8b877e3e
* Fix test-suite type-checker errors for permission-decision normalizer
The permission-handler wrapper returned PermissionHandlerType (the sync-or-async
union), so awaiting its result in tests was rejected by the stricter CI type
checkers (pyrefly, ty, zuban). Give the wrapper a dedicated
AsyncPermissionHandlerType return type, and narrow the awaited result with an
isinstance assert before accessing its scope in the async-handler test.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1b45752e-b602-4117-8304-3c8a8b877e3e
* Add regression tests for extension permission approval normalization
Cover the two previously-untested branches of _derive_session_approval:
extension-management preserves the request operation, and
extension-permission-access preserves the extension name. Both assert the
serialized approval payload as well.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1b45752e-b602-4117-8304-3c8a8b877e3e
* Scope URL session approvals only for parser-unambiguous URLs
The URL branch derived the persisted domain with Python's urlparse, but the
Copilot CLI parses URLs with WHATWG semantics. The two disagree on crafted
authorities -- e.g. a backslash before the '@' in
'https://example.com<backslash>@evil.com' resolves to example.com under the CLI
but evil.com under urlparse -- so trusting urlparse could persist a session-wide
approval for an unrelated, attacker-chosen domain, widening authorization.
Add _derive_url_session_domain, which returns a domain only when the URL
contains none of the characters WHATWG and urlparse handle differently
(backslash, tab, newline, carriage return); any ambiguity (or a URL with no
host) narrows the decision to a single-use PermissionDecisionApproveOnce.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1b45752e-b602-4117-8304-3c8a8b877e3e
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1b45752e-b602-4117-8304-3c8a8b877e3e
* Python: fix(redis): honour a max_messages retention limit of zero
RedisHistoryProvider documents None as the sentinel for unlimited storage,
so max_messages=0 must retain nothing. It retained everything: trimming to
-max_messages emits LTRIM key 0 -1, which is Redis's "keep the whole list",
and the count > max_messages guard is true for any non-empty list, so the
trim ran on every save and did nothing.
Negative values were worse than a no-op. max_messages=-5 emitted
LTRIM key 5 -1, deleting the five oldest messages on every save while the
list still grew without bound.
Handle a limit of zero by deleting the key, which is what clear() in this
class already does, and reject negative values in __init__ alongside the
three ValueErrors it already raises for invalid configuration. None and
positive limits are unchanged.
* Python: never write the payload when Redis retention is disabled
Addresses the automated review on #7470. With max_messages=0 the previous
change still RPUSHed every message and deleted the key afterwards, so the
payload reached Redis - and any AOF or replica stream - before being removed,
and was briefly visible to other readers. Short-circuit instead: drop any
existing history and return before serializing, so nothing is written at all.
Also documents the new ValueError in the Raises: section, and asserts in the
test that the pipeline is never used.
* Python: leave stored history alone when Redis retention is disabled
max_messages=0 deleted the session key. _redis_key omits source_id, so
two providers with the default prefix share {key_prefix}:{session_id},
and the after-run pass persists in reverse provider order - a
zero-retention provider listed first would drop a co-located provider's
just-written history on every turn.
Return before serializing instead: no payload reaches Redis, an AOF or a
replica, and stored history is left as it is. Removing stored history is
what clear() is for.
---------
Co-authored-by: Chinmay V <203952148+chinmayv095@users.noreply.github.com>
* Python: add checkpointing support to AgentFrameworkWorkflow.run() in ag-ui
The ag-ui AgentFrameworkWorkflow.run() previously accepted only a
RunAgentInput payload and exposed no way to use the core workflow's
checkpointing/state-persistence, unlike the core agent-framework workflow
implementations. This left ag-ui workflows without resumable execution.
Add optional checkpoint_storage and checkpoint_id keyword arguments to
run(), threaded through run_workflow_stream() into the core Workflow.run().
This delegates to the existing core capability instead of reinventing it and
keeps the public surface consistent with Workflow.run():
- checkpoint_storage enables checkpoint creation at each superstep boundary.
- checkpoint_id resumes a run from a persisted checkpoint; incoming messages
are forwarded only as request-info responses (never as a new start-executor
message) to honor the core's message/checkpoint_id mutual exclusivity, and
responses + checkpoint_id performs a restore-then-send in one call.
Both can also be supplied via the input_data keys __ag_ui_checkpoint_storage
and __ag_ui_checkpoint_id so the FastAPI endpoint (which calls run(input_data)
positionally) can opt in without changing its call site; explicit keyword
arguments take precedence. Checkpoint resume bypasses the AG-UI thread snapshot
hydration early-returns so it always reaches the core restore path.
Backward compatible: run(input_data) keeps working unchanged, and the
non-checkpoint path still calls run_workflow_stream(input_data, workflow) with
its original two-argument convention. Adds focused tests covering checkpoint
creation, resume-from-checkpoint, input-data-keyed params, and the unchanged
default path.
Fixes#6632.
* Import Executor from the public agent_framework API in ag-ui workflow test
* Fix ag-ui checkpoint resume: preserve thread snapshot, coerce resume responses; fix CI lint/typing
A checkpoint-only resume no longer clobbers the stored AG-UI thread snapshot:
the snapshot builder is seeded with the prior stored history so the saved
snapshot keeps the earlier replayable transcript plus the newly produced output.
Resume responses are now coerced against the post-restore pending requests on a
checkpoint restore, so a JSON function_approval_response resumes through AG-UI
after a cold restore instead of failing with a response-type mismatch.
Also update the test-double workflow run() overrides to match the new keyword-only
parent signature and re-sort the workflow test imports so ruff and the typing
checkers pass.
* Coerce ag-ui resume responses without a second checkpoint restore
Reading pending request_info events for resume-response coercion previously
restored the checkpoint into the live workflow, which invoked every executor's
on_checkpoint_restore hook. workflow.run(checkpoint_id=...) then restored again,
running those hooks a second time. Custom restore hooks are not required to be
idempotent, so this could duplicate restoration work or break workflows that
expect exactly one restore per resume.
Load the persisted WorkflowCheckpoint directly from storage (runtime override
or the workflow's build-time context storage) and read its
pending_request_info_events instead. This exposes the same post-restore pending
set for the resume contract and response coercion without mutating workflow
state or running any restore hook, leaving workflow.run(checkpoint_id=...) as
the single restore per resume.
Add a regression test asserting on_checkpoint_restore runs exactly once on a
checkpointed ag-ui resume.
* Python: rework AG-UI workflow checkpointing onto public configuration surfaces
Checkpoint storage is now configured on AgentFrameworkWorkflow (or the
FastAPI endpoint) instead of being smuggled through input_data keys, and
a run resumes by supplying its checkpoint id in the AG-UI forwarded
props. With storage always in hand, resume-response coercion reads the
pending request set straight from the persisted checkpoint via the
public CheckpointStorage.load(), replacing the private runner-context
fallback, and the core run call forwards checkpoint arguments directly,
relying on core validation for conflicting parameters. Requesting a
resume without configured storage now fails with a clear error.
* Assign endpoint checkpoint storage in a single place
The raw-workflow branch assigned checkpoint_storage at construction and
the wiring block assigned it again. Construct the wrapper bare and let
the wiring block own the assignment; the existing-storage guard keeps
allowing a pre-wrapped runner without storage to adopt the endpoint's.
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
* Add the abilty for the caller to release and cancel background tasks
* Improve param validation
* Address PR comments
* Address PR comments.
* Address PR comments: cancel tasks before publishing the release
Set IsReleased and publish the ReleaseCompletion only after the in-flight
tasks have actually been cancelled, so a failure to cancel leaves the
session un-released instead of flagging it as released while its tasks are
still running.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix ClaudeAgent reusing one SDK client across distinct fresh sessions
RawClaudeAgent kept a single mutable ClaudeSDKClient on the agent instance
and reused it across distinct fresh AgentSession objects, because a fresh
session passes session_id=None and the old reuse check treated that as
"keep the current client". Two independent fresh sessions on one shared
agent instance therefore shared a single provider conversation, so the
second session continued the first session's conversation.
Treat a fresh (None) continuation id as always requiring a new client, so
an unbound session never inherits an existing provider conversation.
Legitimate continuity is preserved: once a session runs, its
service_session_id is written back, so later runs pass a real id and resume
correctly. Guard client selection/creation with an asyncio.Lock so
concurrent runs cannot race between the check and the client assignment.
Add regression tests asserting two fresh sessions produce two clients and
that an explicit continuation id still resumes the existing client.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 598a9fe1-28c5-4db1-88fd-e14acd9340af
* Python: Bind Claude SDK client ownership to each run
Replace the single mutable ClaudeSDKClient stored on the agent with a
per-run client. Because a ClaudeSDKClient represents exactly one provider
conversation, sharing one across distinct sessions collapsed them onto the
same conversation and, for concurrent runs, let a fresh session disconnect
a client another run was still streaming from.
_acquire_client now returns a per-run client (owned) that resumes the
framework session's provider conversation when one exists, and _get_stream
releases it in a finally once the run completes. An injected client is
reused verbatim and left to the caller. The streaming loop moves into
_stream_run so the client is a local per-run value rather than shared agent
state, which keeps distinct sessions isolated even under concurrency.
Continuity is preserved: a session's service_session_id is written back
after each run and forwarded as the resume id on subsequent runs. Replace
the client-lifecycle tests with per-run ownership and end-to-end isolation
tests (two fresh sessions get two separate clients, each disconnected).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 598a9fe1-28c5-4db1-88fd-e14acd9340af
* Python: Close remaining Claude session-isolation gaps
Address three shared-state gaps in the Claude adapter surfaced in review:
- Run-scope structured output: carry the run's structured_output through a
per-run state holder and a per-run finalizer instead of storing it on the
agent, so a concurrent run cannot overwrite another run's value before its
finalizer reads it.
- Bind an injected client to one session: an injected ClaudeSDKClient is a
single Claude conversation, so bind it to the first session that uses it and
raise AgentInvalidRequestException if a different session tries to reuse it.
A no-session run reuses the bound session so multi-turn continuity still
works; multi-session callers must omit client= or use one agent per session.
- Serialize the injected-client path with an asyncio.Lock so concurrent runs
cannot race its connect or interleave queries on the one shared client.
Owned per-run clients stay lock-free.
Update and extend the tests accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 598a9fe1-28c5-4db1-88fd-e14acd9340af
* Python: Bind injected Claude client on provider conversation identity
Compare an injected client's binding on the session's service_session_id
(the Claude conversation identity) rather than the framework-local
session_id, falling back to session_id only when the incoming session has
no provider id yet. A reconstructed session from
get_session(service_session_id=...) carries a fresh session_id but the same
provider conversation, so it now continues the bound conversation instead of
raising. Sessions targeting a different conversation are still rejected.
Add regression tests for reconstructed-same-conversation continuation and
different-conversation rejection.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 598a9fe1-28c5-4db1-88fd-e14acd9340af
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 598a9fe1-28c5-4db1-88fd-e14acd9340af
Gemini 3.x rejects a request whose functionCall parts lack a
thought_signature. The signature was carried as base64 protected_data on a
text_reasoning content and re-attached by adjacency, which requires the
carrier to immediately precede its call. An approval round trip replays the
call with no carrier at all, so the next turn failed with a 400.
Track signatures in a bounded per-client call_id map populated at parse time
from the resolved call_id, and backfill only when the emitted part has no
signature. Also stop clearing the held signature on contents that emit no
Part, so an approval response or an unsigned thought summary between the
carrier and its call no longer drops it.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: dd0909cd-c7c3-42cb-aef1-1e9a3e64d917
* Let the container choose who stores a hosted turn, and say so when it is stored twice
Turning storage off downstream was unconditional and silent. It is now a container choice, and a
deployment that ends up storing anyway is reported instead of quietly recording the conversation in
two places nothing reconciles.
FoundryResponsesOptions, passed through AddFoundryResponses, carries two settings.
AllowStoredOutputEnabled defaults to false, which is when hosting turns storage off for every run and
checks the result. Setting it to true leaves the agent's own configuration exactly as the container
built it, and nothing is checked, overridden, or refused. IncludeReasoningEncryptedContent applies
while storage is off, asking for the encrypted form of the reasoning tokens so reasoning survives
between turns, mirroring AsIChatClientWithStoredOutputDisabled.
Two checks replace the 400 that used to refuse a session carrying a conversation id. The readiness
probe runs each registered agent with its chat client swapped for one that calls nothing, so the
request the agent builds on its own is visible without leaving the container, and an agent asking for
its responses to be stored keeps the container out of rotation. Per request, a conversation id on the
session after the run means the agent's own service kept the turn, which fails with 501 and leaves
the session unsaved so later turns do not resume onto it. A misconfigured container is a server
problem, not a bad request, hence 5xx.
Only a confirmed "this asks to be stored" fails either check. An agent that is not a ChatClientAgent,
a request shape carrying no such setting, and a run that could not be completed all pass: this
package cannot tell what those would do.
* Rename the stored-session flag to say what it means
* Say plainly what server-side storage does to a hosted turn
* Read the store gate as an allow, and align the messages
The flag that decides whether the session may be saved reads as an allow at every use, while the
test it comes from keeps saying what is not allowed, so neither side has to be read inside out.
The wording now matches what the readiness probe says: server side storage must be off, because with
it on the agent's own service records a conversation and response nothing tracks while the hosted
agent records its own for the same request. The message the readiness probe raises no longer travels
through a shared constant, since each check says its own thing.
* Address the review comments left open on the merged PR
Five points raised on #7525 were marked resolved without a code change, and the code they pointed at
was still there.
A hosted workflow session is now recognised by its full type name, so a session of the same short
name from another namespace is not mistaken for one. The test double moves into the namespace it
stands in for, otherwise it would no longer exercise the check.
The per-run chat history provider is handed over on AgentRunOptions.AdditionalProperties, which
ChatClientAgent copies onto the chat options with precedence, rather than being written onto the
chat options here.
The test that pins down who supplies the history said the agent's own provider is used, while it
asserts the opposite, so it is renamed after what it checks.
Reading a response back in the hosted integration tests no longer swallows every failure: only
"not stored" and "not readable through this endpoint" are, so an expired token or a server fault
cannot be mistaken for an absent response and pass the test.
Also fills in the readiness message for the case where storing is explicitly allowed.
* Address the review on #7572
Four findings, all real, all in code this branch introduced.
A container that allows its own service to keep the conversation was still being handed the platform
history on every turn. That service replays the earlier turns itself, so the model was getting each
of them twice, which is the very thing this work exists to prevent. The history now goes in only
while nothing else holds it: the first turn of such a conversation still gets it, and the service
takes over from there.
A turn that fails for storing downstream was announcing itself as completed first and only then
failing, leaving the caller with two different answers for the same turn. The completed event is now
held back until the run is wound up and the session can be read, because the id of any conversation
the agent's service kept only lands there at the very end.
The readiness probe replaced the chat client but left the agent's chat history provider running, so
a provider backed by a database was reading and writing on every probe, and adding the probe's empty
turn to a real conversation. It is stood down for that run now.
The probe also treated any cancellation as the health check's own, so a timeout inside an agent could
fail readiness. Only a cancellation of the health check's token is left to propagate.
Fixing the completed event turned up a latent problem: the terminal event types are named the same in
two namespaces this file pulls in, and the short name binds to the ones the response stream never
produces, so vt is ResponseCompletedEvent was quietly always false. The three terminal types are
now named explicitly.
* Let the chat history provider carry the conversation
The handler used to read the hosting service's record of the conversation and prepend it to the
input of every run, then work out who should not get it: a resumed workflow by the name of its
session type, and a container whose own service already holds the conversation. Two exceptions, a
type name matched as a string, and a shape where the same turns could arrive from two directions.
An agent that reads its history through a provider is now given one, seeded with that record, for
the length of the run. The turns arrive the way the agent expects them rather than as fresh input,
so nothing is stored back as if it had just been said, and the provider is dropped when the run
ends. Only the new input is passed to the run now.
Everything else supplies its own history and is left alone: an agent built with a provider keeps
using it, an agent whose service keeps the conversation reads it from there, and an agent that is
not a ChatClientAgent, a hosted workflow for instance, carries the conversation in its own session
state and wants only the new input. The workflow session type name check is gone with it.
The session is saved on every turn again. It was being withheld when the agent's own service had
kept the turn, which is a decision about that service, not about the session; nothing this handler
adds for a turn reaches the session anyway.
* Fail a turn to skip its session, and name the store check after what it detects
The session was being withheld from the store on a condition about the agent's own service rather
than about the turn, and guarded by an emptiness check on a key that is never empty. A turn that is
being failed now says so, and only that skips the save. A turn that ends incomplete, waiting on
OAuth consent or interrupted by a shutdown, is not a failure: the caller comes back for it and needs
the state built up so far, the tool approval ids among it.
The session key is resolved once as a value that always exists, so both the load and the save use it
without asking again whether it is there.
CheckNotAllowedStoreUsage and notAllowedStoreUsageDetected now read as what they are: a check for an
agent storing when it should not, and the flag saying it was seen.
* Read a hosted response through the agent client, and only forgive a 404
Reading a response back tried the project-level client first and then the per-agent one, swallowing
403 as well as 404 to get past the first. The project-level client cannot see a hosted agent's
responses at all, so that attempt only ever produced the 403 the catch then had to forgive, and any
other 403, an authorization failure for instance, was read as "nothing is stored" and passed the
test.
Only the per-agent client is used now, and only a 404 counts as not stored. Verified against the
service: a well-formed id it has no response for answers 404 invalid_request_error "Response '...'
not found", the same id through the project-level client answers 403 session_not_accessible, and a
malformed id answers 400. Everything but the 404 now surfaces.
* Move the store setting next to the code that reads and writes it
The two halves of the stored output concern lived in a shared helper: one that installs the factory
turning storage off, and one that reads back what a request would have asked for. Each had exactly
one caller, so the helper only added a hop. They now sit in the converter that builds the request and
in the probe client that inspects it, and the helper keeps just the error the handler throws.
The test double standing in for a hosted workflow session is also gone. It was declared inside the
Workflows namespace because the handler used to recognise a resumed workflow by the full name of its
session type; that comparison no longer exists, so the double only needs to not be a ChatClientAgent.
* Say that the stored output setting could not be determined, which is the case being logged
* Update store isolation documentation
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 824a2e29-e79c-42aa-b28e-aa6c12ec3292
* Rename store isolation key provider
Rename the shared session isolation abstraction to reflect its use for both session and task stores.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 824a2e29-e79c-42aa-b28e-aa6c12ec3292
* Rename to AgentIsolationKeyProvider per review feedback
Drops the `Store` qualifier and keeps an `Agent` prefix so the type is not
confused with generic isolation-key abstractions from other libraries, while
leaving room for future non-store isolation (memory, retrieval).
- StoreIsolationKeyProvider -> AgentIsolationKeyProvider
- ClaimsIdentityStoreIsolationKeyProvider(+Options) -> ClaimsIdentityAgentIsolationKeyProvider(+Options)
- GetStoreIsolationKeyAsync -> GetIsolationKeyAsync
- UseClaimsBasedStoreIsolation -> UseClaimsBasedAgentIsolation
XML docs now state that the `Agent` prefix identifies the hosting API domain and
does not mean agent instances are isolated.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 824a2e29-e79c-42aa-b28e-aa6c12ec3292
* Update hosting spec for AgentIsolationKeyProvider rename
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 824a2e29-e79c-42aa-b28e-aa6c12ec3292
---------
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 824a2e29-e79c-42aa-b28e-aa6c12ec3292
* Allow storing executable functions when mixed with non-executable
* Address PR review feedback on executable function bypassing
- Guard enumerator acquisition so pending bypassed calls are restored when
the inner client throws synchronously, before the first MoveNextAsync.
- Always surface buffered streaming updates, even when stripping empties
them, so metadata such as ConversationId and ResponseId is not discarded.
- Document that the decorator must sit below ApprovalResponseBindingChatClient,
which drops approval responses that have no recorded request.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR comments
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Read hosted chat history through a provider instead of the request input
The handler used to fetch the platform conversation history and prepend it to the
input of every turn. For a ChatClientAgent that runs in parallel with its own chat
history provider, so the conversation had two sources at once. It also had a hidden
cost: platform items carry no chat-history source marker, so the agent's provider
stored them again as if this turn had written them, leaving a second copy of the
conversation inside the persisted session that then diverges from the platform.
Make the chat history provider the single source for a ChatClientAgent:
- Add FoundryChatHistoryProvider, which reads the conversation through
ResponseContext.GetHistoryAsync (it already resolves previous_response_id and the
conversation the request belongs to) and stores nothing, because the platform
persists the response items itself. An instance is created per request because it
holds that request's context, and it is passed as a run-scoped override so the host
does not have to mutate the agent.
- Register it only when the agent was created without a chat history provider. When
one was supplied at construction, that provider owns the conversation and the
platform history is not used at all.
- Stop adding the platform history to the input for a ChatClientAgent, since the
provider now delivers it.
A workflow hosted as an agent is not a ChatClientAgent and has no provider pipeline,
so it keeps receiving the platform history from the handler exactly as before.
* Add regression tests for the duplicated hosted chat history
Cover the three symptoms the previous handler produced, each verified to fail when
the handler is reverted to fetching the platform history into the turn input:
- the conversation the service already keeps was copied into the persisted agent
session by the default in-memory history provider;
- a custom history provider was asked to write that same conversation into its own
database, because platform items carry no chat-history source marker and so look
like content this turn produced;
- an agent with its own provider received both that provider's history and the
platform's in a single request.
Also state precisely, in the provider's remarks, why nothing is written back: for a
stored request the response orchestrator hands the finished response to its responses
provider, which persists the input and output items that a later turn then reads back
through GetHistoryAsync; for a non-stored request nothing is persisted and nothing is
readable, so the request is self-contained either way.
* Keep unstored turns in the session so mixed conversations stay whole
A conversation can mix turns the service stores with turns it does not. History is
resolved from previous_response_id or the conversation regardless of the current
request's store flag, so an unstored turn still reads the stored ones back, but the
service records nothing for it and a later turn would never see it again.
Reading the platform history through FoundryChatHistoryProvider alone lost those
turns: from the second turn onwards the handler treats the session as a resume and
stops feeding history in, and the provider kept nothing of its own, so an unstored
turn simply vanished from the conversation. A regression test drives three turns of
one conversation, the first stored and the rest not, and without this change the
model receives only [second question, ok, third question]: the stored opening turn
is gone.
Give the provider both halves instead of choosing one:
- reading returns what the service serves, followed by the turns kept in the session,
which are by definition later than anything the service recorded;
- writing keeps a turn only when the service was not asked to store it, so a stored
turn is never duplicated and an unstored one is never lost.
The turns are held in the agent session under the provider's own state key, so they
travel with the session the host already persists.
* Refuse a stored turn once a conversation holds unstored ones
A conversation can move between stored and unstored turns, and the unstored ones live
only in the agent session. Going back to a stored turn after that would have the
service record it on top of turns the service never saw, so anyone reading the
conversation back from the service would find an answer with no question. Refuse it
before the model is called instead of writing that gap.
Cover the whole shape with a walkthrough of nine turns over one conversation and three
provider instances, each with its own session:
- an instance that never took an unstored turn starts from the turn the service last
saved, and does not see another instance's unstored turns;
- an instance that did keeps reading the saved turns and adds its own on top;
- asking such an instance for a stored turn is refused, twice, while unstored turns
keep working;
- a turn stored from one instance does not appear for another, because it sits on a
different branch of the conversation and so is not among the turns leading to what
that other instance last saved.
* Say plainly that kept turns belong to the session
The turns the service was not asked to store are written into the agent session's state
bag under this provider's own state key, and a new provider is built for every request,
so nothing is held on the provider object itself. The walkthrough named its three
threads after provider instances, which read as if the object carried the memory.
Name them after the sessions they are, and add a test that pins the behaviour down: a
turn kept through one provider object is read back by a different one given the same
session, and is absent for one given another session.
* Show which half of the conversation each provider decides
The session decides what is kept, but the provider still decides two things: which
service-side conversation is read, because it holds the request's response context, and
whether the turn is kept at all, because it holds the request's store flag.
Add two tests that separate those from the session:
- two providers reading one session, each built for a request of a different
conversation, return the same kept turn behind different served turns;
- two providers writing to one session, one for a stored request and one for an
unstored one, leave only the unstored turn behind.
* Say why a hosted workflow keeps taking history from the handler
The comment stated that a workflow hosted as an agent has no provider pipeline
without saying what that means. It derives from AIAgent directly, so it never calls
a ChatHistoryProvider and does not read the run options' additional properties: the
provider could not reach it even if it were registered.
* Ask the session store whether a turn is a resume
The handler decided that a turn was resuming an existing conversation by looking for
state on the session. That reading broke once the handler itself started writing to the
session before the check: it records the caller's identity there, so a session created
moments earlier already carried state and the very first turn of a conversation looked
like a resume. Its history was then never fetched, and the agent answered knowing
nothing of a conversation the service was already holding. It only showed up when
hosted, because running locally there is no identity to record.
Let the store answer the question instead. GetSessionAsync now returns null when nothing
is stored rather than quietly handing back a new session, so a non-null result means a
prior turn established this session and nothing else has to be inferred. Callers that
just want a usable session can use the new GetOrCreateSessionAsync, which is written in
terms of GetSessionAsync so a store overriding one gets the other for free.
Both store implementations and their tests follow the plain-lookup contract: a miss
creates nothing, deserializes nothing, and touches no directory.
* Drop the experimental marker from an internal type
FoundryChatHistoryProvider is internal, so the attribute reached no caller: the marker
exists to warn people consuming the public surface. It also does not follow from the base
type, which does not carry one, and most internal types in this package have none either.
Removing it leaves two usings behind, so they go as well.
* Stand down the agent's second-manager guard for the host's own provider
An agent refuses a second history manager once the model reports a conversation id of its
own, which happens as soon as the container lets the model keep the conversation. The
guard is meant for an application that configured a provider by hand and would otherwise
end up with two of them. Here the host is the one supplying the provider, deliberately and
for every turn, so the guard was rejecting the arrangement it is hosting: the first turn
failed while streaming, and every later one failed before reaching the model at all.
Turn the three conflict settings off on the agent the host is serving, and let the
provider decide what reaches the model. A test drives two turns of one conversation
against a model that reports a conversation id and asserts both complete.
* Pass a caller's request not to store on to the chat client
A request asking the hosting service not to store the response was honoured there and nowhere else, so the service behind the agent's own chat client kept recording the conversation and reporting an id for it. A caller opting out of storage still ended up with a stored conversation, and the container went on continuing it.
Only that direction travels. Carrying store=true across would either force storage on a container whose author turned it off on purpose or change nothing, since storing is already the default.
* Hand the conversation to the agent's own provider instead of a host one
The host no longer supplies a chat history provider of its own. It writes the turns the service holds into the provider the agent already created for itself, and only when that is the stock in-memory one, so an agent given a provider keeps sole control of its storage and the model receives the conversation once.
A conversation the caller stops asking the service to store moves into the session state and stays there. The session's conversation id no longer names anything the service records and cannot be cleared, so the session is cloned without it on that single turn. Asking for a stored turn afterwards is refused: the service would record a turn whose predecessors it does not hold.
An agent that does not read history through a provider, a hosted workflow for example, is still given its prior turns as input, now marked as chat history so no provider along the way stores them as new.
* Run the agent's own request factory instead of replacing it
ChatClientAgent chains a request's raw representation factory with the agent's by taking the agent's only when the request's returns null. The factory added for an unstored turn always answers, so anything the container configured on the agent's ChatOptions was silently dropped for that turn.
The agent's factory is now invoked first and its result is what carries the setting. A result that is not a CreateResponseOptions belongs to some other chat client, which has no notion of storing a response, so it is handed back untouched.
* Cover a stored conversation that stops being stored and asks again
The refusal was only tested on a conversation the service never stored. Reaching it from a stored one goes through the turn that rebuilds the session without its conversation id, so the mark saying the conversation left the service has to survive that rebuild to be found on the next turn.
* Leave the conversation to the AgentServer storage provider alone
The AgentServer SDK records a hosted turn through its own storage provider, around the handler, and serves the conversation back through ResponseContext.GetHistoryAsync. Anything the container stores of its own is a second conversation that storage provider never sees and no one reconciles.
The handler now takes that history as the single source and hands it to the agent as input alongside this turn's messages. The agent's own provider is replaced for the run by one holding its messages in a field, so a run that calls tools still has what its earlier calls produced while nothing survives the request. The service behind the agent's chat client is asked not to store on every turn, whatever the caller asked of the hosting service.
A session that still carries a conversation id means that service is recording a second conversation regardless, so the turn is refused with a 400 rather than run against something nobody can reconcile.
* Narrow the history skip to a resumed workflow
Withholding the conversation from every agent that is not a ChatClientAgent assumed they all carry it in their own session. A hand-written one that keeps nothing would answer with no history from its second turn on, so the check is now on the session type a workflow runs with, which is what actually accumulates the turns.
The conversation and previous response id tests went with it: the session key falls back to the partition of a freshly minted response id, which never has a session saved for it, so a loaded session already implies one of the two was sent.
Also asks a Chat Completions client not to store, since the setting carries the same name on both OpenAI request shapes.
* Add a live test that a hosted turn is not stored twice
The AgentServer SDK's storage provider records every hosted turn around the handler, and
that record is the conversation the caller reads. The agent's own run inside the container
talks to its own service, and when that service is asked to keep the turn it writes a
second copy of the same exchange, on a trail of its own that nobody reads and nobody
reconciles. The caller's conversation looks clean, so the second copy goes unnoticed.
The new downstream-store scenario runs an ordinary Foundry ChatClientAgent, like the first
hosted agent sample, wrapped so that after the run it appends DOWNSTREAM_ID=<id> to the
reply, carrying whatever its own run left behind. The tests then go looking for that id on
the service: finding it means a second copy exists.
Verified live against a Foundry project. On main both tests fail, reporting a readable id
such as resp_0940e276..., and here the container reports DOWNSTREAM_ID=none and both pass.
* Let the session carry the conversation in the downstream store test
The run options were setting the conversation on every call, which the session already does.
The single turn test now binds the session to the conversation up front, and the multi turn
test starts from the agent's own default session and reads back what the hosted agent kept
for the caller off ChatClientAgentSession once the first turn returns.
Re-verified live: still fails on main, reporting a readable id such as resp_0c07a5e4..., and
still passes here.
* Ensure usage is merged for all looping components
* Add max tool approval loop fixes
* Fix net472 build break in usage aggregation tests
DateTimeOffset.UnixEpoch is not available on .NET Framework 4.7.2, so the
WithAggregatedUsage copy tests failed to compile for that target framework.
Use an explicit DateTimeOffset instead; the specific instant is irrelevant,
the value only needs to be non-default so the copy assertion is meaningful.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR comments
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Gemini thought-summary parts (part.thought=True) were dropped in _parse_parts, so reasoning never reached ChatResponse.contents. Emit them as text_reasoning content instead, matching OpenAIResponsesClient. Round-trip is safe: _convert_message_contents never re-emits reasoning text as a Part.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: b8aa906e-1408-40c1-9a45-6deb40dc36f8
* Python: fix CopilotStudioAgent LineTooLong on large activities
Bump microsoft-agents-copilotstudio-client to >=1.2.0,<2 and forward a configurable read_bufsize (default 1 MiB) to the underlying aiohttp ClientSession via ConnectionSettings.client_session_settings. Copilot Studio streams each activity as a single SSE data line, so activities larger than aiohttp's 512 KB per-line limit previously raised aiohttp.http_exceptions.LineTooLong. Adds a client_session_settings parameter to CopilotStudioAgent and unit tests covering the default, override, and partial-settings cases.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2766dc09-ab5f-4adc-8627-98361d7ccef0
* Python: apply read_bufsize default to supplied CopilotStudio settings
Address review feedback on the LineTooLong fix: when a user supplies their own ConnectionSettings but no client, inject the read_bufsize default so activities larger than aiohttp's 512 KB per-line limit still stream. Document configuring read_bufsize on the explicit pre-built-client path in the package and sample READMEs and the explicit-settings sample. Add unit tests covering the supplied-settings path.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2766dc09-ab5f-4adc-8627-98361d7ccef0
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2766dc09-ab5f-4adc-8627-98361d7ccef0
* Python: Fix reasoning content parsing in OpenAIChatCompletionClient
Fix two issues with reasoning content handling in the Chat Completions
client:
1. (#6979) reasoning_details plaintext buried as encrypted data:
The client dumped the entire reasoning_details array into
Content.protected_data without setting Content.text, causing AG-UI
to emit ReasoningEncryptedValueEvent instead of visible
ReasoningMessageContentEvent for plaintext reasoning providers
(e.g. OpenRouter). Now extracts readable text from reasoning_details
entries into Content.text while preserving protected_data for
round-trip fidelity.
2. (#6978) Mistral list content causes crash:
Mistral reasoning models return content as a list of typed chunks
([{"type": "thinking", ...}, {"type": "text", ...}]) instead of a
plain string. _parse_text_from_openai assumed content was always a
string, causing a Pydantic ValidationError downstream. Now detects
list content and parses thinking chunks as Content.from_text_reasoning
and text chunks as Content.from_text.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix pyright strict-mode type errors and handle content-as-string shape
- Use cast() for proper type narrowing in _extract_reasoning_text and
_parse_chunked_content to satisfy pyright strict mode
- Handle {"content": "..."} string shape in _extract_reasoning_text
(addresses review comment about missing format coverage)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix mypy errors: cast list content to Any in tests
model_construct bypasses Pydantic runtime validation but mypy still
checks declared types. Use cast(Any, ...) for the list content args.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review comments: summary field, reasoning field, and round-trip
- Add 'summary' field extraction in _extract_reasoning_text for
reasoning.summary entries from OpenRouter
- Handle message.reasoning and message.reasoning_content top-level
fields (plaintext reasoning without reasoning_details) in both
streaming and non-streaming paths
- reasoning_details takes priority when both fields are present
- Preserve original Mistral chunk list in additional_properties
('_source_content_list') so _prepare_message_for_openai can
reconstruct the structured list content for multi-turn reasoning
- Add 5 new tests covering all new behaviors
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix ruff used-dummy-variable: rename _skip_structured_siblings
Remove leading underscore from _skip_structured_siblings variable since
it is accessed (not a dummy variable). Ruff's used-dummy-variable rule
flags variables with leading underscores that are read.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix missing newline at end of test file
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: type-agnostic chunk round-trip and reasoning field echo-back
- Honor the _source_content_list marker regardless of the first emitted
content's type by handling it before the type match, so a chunk list
beginning with a text chunk still round-trips as one structured message
(addresses github-actions review comment on results[0]).
- Tag every chunked-content item with a shared _structured_content_group
id and skip only exact group siblings during serialization, instead of
suppressing all later text/reasoning content.
- Record provenance of top-level reasoning/reasoning_content fields in
_reasoning_source_field and echo the value back under the same key on
the next request, which providers such as vLLM require (addresses
Kimahriman review comment). Replaces the prior behavior that replayed
surfaced reasoning as visible answer text.
- Factor the duplicated reasoning parsing into _parse_reasoning_content.
- Add tests for provenance capture, reasoning/reasoning_content round-trip,
reasoning-only messages, text-first chunk round-trip, and unrelated
sibling preservation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2f3c0308-51bf-4b66-8b53-87a8546743f5
* Replace provider-specific reasoning logic with configurable parse/prepare hooks
Following review feedback (#7028), keep OpenAIChatCompletionClient free of
provider-specific quirks for 'almost OpenAI-compatible' endpoints. Instead of
branching in core for OpenRouter/vLLM/Mistral, expose two optional callables so
callers adapt the client themselves:
- response_parser (OpenAIChatResponseContentsParser): post-processes the Content
list parsed from each response choice/streaming delta, to surface non-standard
fields (e.g. reasoning/reasoning_content/reasoning_details) for display.
- message_preparer (OpenAIChatMessagePreparer): post-processes the outgoing request
message dicts built from each framework Message, to echo provider-specific fields
back on later turns (e.g. vLLM reasoning) for multi-turn continuity.
Both default to None (no-op; byte-identical stock OpenAI behavior). This reverts the
provider-specific reasoning/chunked-content parsing and round-trip markers previously
added to core; Mistral chunked content is now handled by agent-framework-mistral.
- Add the two callables to RawOpenAIChatCompletionClient / OpenAIChatCompletionClient
constructors and invoke them at the parse and prepare seams.
- Export the type aliases from the package and the core lazy openai namespace (+ .pyi).
- Replace the removed-behavior tests with tests for the two hooks.
- Document the hooks in packages/openai/AGENTS.md.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2f3c0308-51bf-4b66-8b53-87a8546743f5
* Skip non-string content in default text parsing
Structured list content (e.g. Mistral reasoning models returning content as a
list of chunks) was wrapped verbatim into a text Content, producing a malformed
Content whose text is a list that crashes downstream (issue #6978). Default text
parsing now skips non-string content so a configured response_parser receives a
clean slate to expand it. Applies to both streaming and non-streaming paths.
Add tests for the skip and for a response_parser expanding chunked content.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2f3c0308-51bf-4b66-8b53-87a8546743f5
* Address review: hook signature, per-role preparer, robust round-trip
- response_parser now receives the already-selected ChatCompletionMessage /
ChoiceDelta instead of Choice | ChunkChoice, so callers no longer duplicate the
streaming dispatch (removes the Any/hasattr pattern from tests). The client owns
the dispatch; parsers read provider fields directly.
- message_preparer now runs once per Message for every role: the build logic moved
to _build_openai_messages and the hook is applied at a single exit point in
_prepare_message_for_openai, so system/developer messages no longer bypass it.
- Round-trip example/test now correlates surfaced reasoning via an
additional_properties marker on message.contents with bounded, order-aware,
one-to-one dict removal, instead of fragile request-string matching. Adds a test
proving an answer whose text equals the reasoning text is no longer dropped.
- Update packages/openai/AGENTS.md for the new parser signature and guidance.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2f3c0308-51bf-4b66-8b53-87a8546743f5
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 2f3c0308-51bf-4b66-8b53-87a8546743f5
* feat(python): add agent-hooks middleware as experimental core feature
Implement the AGENT-HOOKS-0.1 interception contract as a first-class
experimental feature in agent_framework core.
- Single public factory agent_hooks_middleware() returning a private
agent/chat/function middleware trio (one object per middleware
category); partial or stacked installs fail closed with loud errors.
- All eight interception points: input/output at the agent seam,
pre/post_model_call at the chat seam, pre/post_tool_call at the
function seam, agent_startup/agent_shutdown bracketing each run.
- Fail-closed enforcement throughout: transforms write back into the
native contexts (messages, arguments, results) or raise; content is
preserved as Content objects; MiddlewareTermination short-circuits
are guarded at every seam; enforcement-layer failures halt the run;
interceptor crashes surface as host_error denies.
- Streaming is fully buffered per spec buffered_output semantics: no
update egresses before the post_model_call/output verdicts; a deny
at pull time releases zero updates; run state stays active across
lazy pulls with cleanup on every exit path.
- Session scoping: per-run by default (startup/shutdown bracket each
run) or host-owned via emitter/builder parameters for one session
spanning multiple runs.
- agent-hooks-sdk is an opt-in agent-hooks extra (not in all),
lazy-imported per the _mcp.py pattern; core imports cleanly without
it and the factory raises a clear ModuleNotFoundError.
- ExperimentalFeature.AGENT_HOOKS + @experimental decorator, lazy root
export, typing surface, PACKAGE_STATUS.md entry.
- 55 tests built on real Agent/mock-client flows covering deny-before-
execution, transform write-back, rich-content preservation, complete
streaming ordering, error cleanup, concurrency isolation, nested
agents, and importability without the optional SDK.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* style(python): unquote ResponseStream annotation per pyupgrade
The pre-commit pyupgrade hook rewrites the quoted forward reference;
ResponseStream is imported at runtime in this module, so the quotes
were unnecessary.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* refactor(python): address agent-hooks review feedback
Reworks the agent-hooks feature per PR review:
- Verdicts now precede durability: a run-scoped persistence gate
(_sessions.py) defers per-service-call history persistence and
after-run provider work until the covering post_model_call/output
verdict permits; denied content never persists, transforms persist
post-write-back. Unhooked runs are unchanged (verified against an
instrumented baseline).
- ResponseStream.buffered_and_gated: a buffered-gate combinator that
applies the run's pending stream hooks before the gate, then seals
the stream, so no middleware can rewrite egress after the output
verdict. Replaces the hand-rolled replay iterator.
- MiddlewareBundle (public, _middleware.py): the factory returns an
indivisible bundle categorize_middleware splits, making partial
installs impossible by construction; members are validated at
construction. Bare (non-sequence) middleware at agent construction
is now normalized instead of silently dropped, and unrecognized
middleware logs a warning instead of vanishing.
- Factory split and rename: create_agent_hooks_middleware (per-run
sessions) and create_agent_hooks_middleware_from_emitter
(host-owned); the sentinel parameter-diffing is gone.
- Wire conversions live in per-point codec classes owning to_wire and
write_back. Fixes in that code: tool-call name transforms apply or
raise; non-object args transforms raise; argument write-back merges
only changed keys (original values, including bytes, preserved by
identity); message-list write-back matches by identity, not index.
- function_approval_request objects on the normal return path pass
through un-emitted, preserving the human approval pause.
- Hosted (service-executed) tool calls surface in the post_model_call
content projection; the tool-seam limitation is documented.
- Import probe covers the full SDK surface and re-raises as
missing-extra only for the agent_hooks module; module logger added;
_json_safe replaced by make_json_safe (which gained bytes support);
tools_registered uses normalize_tools; dependency-pyright analyzes
the module again via the test dependency-group.
- Tests: 75 in the feature suite (persistence gating, stream-hook
sealing, approval passthrough, codec units, bundle validation,
bare-bundle installs), full core suite green.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* refactor(python): second review round for agent-hooks
Addresses the second review round on the agent-hooks feature:
- Nested-run persistence ownership: RawAgent.run stamps a run identity
over the run's dynamic extent (including streaming pulls and result
hooks); the persistence gate binds to its owning run via an
offer/adopt handshake keyed to the agent instance and accepts only
its owner's persists — nested runs persist inline regardless of how
they were started (tool calls, middleware, custom run loops). The
tool-seam suspension remains for custom-loop sub-agents invoked as
tools; the one residual case (custom loop nested in a custom loop
off the tool path) is fail-closed and documented. Fixes a latent
pre-existing re-deferral: flush() now drains with the gate context
suspended, so a nested hooked run's permitted after-run persistence
no longer re-defers into an enclosing gate.
- as_tool stream_callback consumes the released (verdicted) stream;
observers cannot see denied or pre-transform content. Both
directions are regression-tested.
- categorize_middleware gained supported_categories: a bundle member
landing in a category a call site cannot install raises; bare
middleware warns like _add_middleware. Wired at the chat-client
sites and the provider seam.
- ResponseStream.buffered_and_gated owns the re-derivation rule via a
rederive callable (gates cannot choose released updates) and is
marked experimental.
- Wire codecs compare with bool-aware equality (Python == equates
1 == True, which made bool/number transforms look untouched and get
dropped) and _ToolResultCodec.write_back owns the untouched-wire
rule via the before value.
- middleware parameters accept a bare middleware or bundle everywhere
the runtime does (constructors, run overloads, as_agent, telemetry
and harness layers, foundry); the bare-source rule has a single
owner in categorize_middleware; bare middleware assigned to the
attribute now executes (documented behavior change).
- MiddlewareBundle is experimental and validates members; approval
passthrough, typing-check fixes (ty ignores mypy-coded ignore
comments), logging, and documentation updates per review.
Test count: 85 feature tests plus 12 new this round across sessions,
middleware, agents; full core suite green; typing checked under
mypy, pyrefly, ty, zuban, and pyright.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* docs(python): drop previous-behavior notes from middleware docstrings
Per review: docstrings describe current behavior only. The
bare-middleware behavior change stays recorded in the PR description
and commit history.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(python): gate ownership survives retrying middleware
A retry or fallback middleware issuing a second call_next() gave the
new attempt a fresh run identity that the persistence gate's
first-bind-wins ownership rejected, so the retried attempt's history
persisted inline before the output verdict — a denied response became
durable again. The gate now accumulates every identity adopted
through its own offer ticket: all attempts' persistence stays behind
the one final verdict (deny drops all of it, allow flushes all of
it). Accumulation over rebind-replace is deliberate: rebinding would
flip an earlier attempt's still-running background work from deferred
to inline, which is the fail-open direction. A foreign agent still
cannot bind: tickets are minted only by the covered pipeline's final
handler and adoption is instance-keyed.
Also consolidates the bare-middleware-source rule into a single
_as_middleware_list owner used by every interpretation site (the
harness merge, BaseAgent.__init__, categorize_middleware, both
client-kwargs merges, get_response, SessionContext.extend_middleware),
including the str/bytes exclusion the stray copies missed. The
constructor now stores a copy of the caller's sequence; assign to the
middleware attribute for post-construction changes.
Retry regression tests cover denied and allowed retried runs in both
stream modes and fail with first-bind-wins restored.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(python): streaming seam runs pipeline descent inside the gate
The streaming agent seam ran call_next() outside the persistence
gate (only _consume entered it later), so a retry middleware that
drained a successful attempt with get_final_response() and discarded
it persisted that attempt's exchange before any verdict existed; a
later deny dropped only the retry attempt's deferred work. The
descent is now wrapped in the gate exactly like the non-streaming
seam: attempt identities adopted during descent are accepted owners,
so in-pipeline draining defers, deny drops every attempt, and a
middleware that raises after draining strands the pending persists
unexecuted. The bind_owner docstring now states the actual soundness
invariant covering both bind sites: every bind comes from a run
inside the covered pipeline.
New tests cover drained-and-discarded attempts (deny and allow, both
stream modes) and a sub-agent tool inside a drained attempt; the
streaming deny variant fails with the gate wrap reverted.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* fix(python): flush deferred persistence on streaming no-result termination
With the pipeline descent now running inside the persistence gate, a
middleware that drains a successful attempt and then terminates
without a result left that attempt's deferred persistence stranded:
the streaming no-result termination path raised before any flush, so
history of exchanges that really happened and passed their own
verdicts quietly vanished (streaming only; non-streaming already
flushes before its re-raise). The path now flushes before re-raising
the termination, with a state.halted guard first so an enforcement
failure during the drained attempt still strands pending fail-closed
and surfaces the halt, mirroring the non-streaming ordering exactly.
The regression test covers both seams; the streaming variant fails
without the fix.
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
---------
Signed-off-by: MohammadHaroonAbuomar <40180927+MohammadHaroonAbuomar@users.noreply.github.com>
* .NET: Bound the tool-approval auto-approval loop (#7472)
`ToolApprovalAgent` re-invoked the inner agent from two unbounded `while (true)`
loops whenever every surfaced approval request was auto-approved. Each pass is a
fresh `InnerAgent.RunAsync` / `RunStreamingAsync` call, so a per-request cap such
as `FunctionInvokingChatClient.MaximumIterationsPerRequest` restarts every time
and cannot bound the chain. Under `AllToolsAutoApprovalRule` a model that keeps
requesting an auto-approved tool therefore drives billable model calls
indefinitely; the reporter measured 100M+ tokens over three days.
Adds `ToolApprovalAgentOptions.MaxAutoApprovalIterations` (default
`ToolApprovalAgent.DefaultMaxAutoApprovalIterations`, 10) and bounds both loops.
Naming, default and `Throw.IfLessThan` validation follow the existing
`LoopAgent.DefaultMaxIterations` / `LoopAgentOptions.MaxIterations` convention in
this assembly.
On reaching the cap the agent takes one final inner turn without auto-approving
again, so a remaining approval request is surfaced to the caller to decide.
Returning early instead would hand back an empty response, because
`ProcessAndQueueOutboundApprovalRequestsAsync` strips every approval request once
they are all auto-approved -- the case the loop exists to avoid. This mirrors the
Python behaviour, which logs and issues one final request with tools disabled
once its iteration budget is spent (`_tools.py`).
Python is not affected: it caps at `DEFAULT_MAX_ITERATIONS` (40) and persists
`attempt_count` in the budget state across approval resumes, so a resumed run
continues the count rather than restarting it.
Tests: the runaway is reproduced on both the streaming and non-streaming paths
with an inner agent that never stops requesting an auto-approved tool. Inner
invocations equal the cap plus the final turn, and scale with the configured cap,
so the assertions fail if the bound is removed.
No sample changes: with the loop bounded, Agent_Step01, Agent_Step06,
Agent_Step07 and Hosted-AgentSkills are safe as written.
* .NET: Add Arrange/Act/Assert comments to the cap constructor test
Matches the test convention documented in dotnet/AGENTS.md and used by the
surrounding tests in this file.
* Increase default max auto approval iterations to 40
* Apply suggestion from @westey-m
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* Update comments in ToolApprovalAgent.cs
---------
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
The proxy target validation in ValidateProxyTarget already ensures
requests stay on the configured backend. Add an inline suppression
comment following the repo's established pattern.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 4363ab44-4d9e-41a0-97d3-4ab0b973f0b2
* .NET: Add tenant-scoped task store isolation for A2A hosting
Wrap ITaskStore with IsolationKeyScopedTaskStore when a
SessionIsolationKeyProvider is registered, mirroring the existing
session store isolation pattern. This ensures task operations are
scoped per tenant in multi-user deployments.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: adc30d6c-ce66-40bb-933e-9801c2156cda
* fix formatting issue
---------
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: adc30d6c-ce66-40bb-933e-9801c2156cda
* Python: bound tool result compaction summaries
Keep ToolResultCompactionStrategy from re-inserting oversized tool result payloads through the synthetic summary message by bounding the generated digest text.
Add regression coverage proving a large tool result is not embedded verbatim, keeps a bounded prefix, and marks truncation.
* Python: keep excluded tool results out of compaction digests
Build ToolResultCompactionStrategy digest content from messages still included in the group so a summary cannot restore payloads that an earlier compaction already excluded.
Use the strategy cap constant in the large-payload regression and add coverage for already-excluded tool results.
Validation: uv run pytest packages/core/tests/core/test_compaction.py -q -k 'tool_result_compaction'; uv run ruff check packages/core/agent_framework/_compaction.py packages/core/tests/core/test_compaction.py; uv run ruff format --check packages/core/agent_framework/_compaction.py packages/core/tests/core/test_compaction.py; uv run poe test -P core; uv run poe build -P core; env HOME=/tmp/sds-home XDG_CACHE_HOME=/tmp/sds-cache uv run poe test -A.
* Python: align compaction digest review cleanup
Align ToolResultCompactionStrategy's included-message filter with the module's existing EXCLUDED_KEY boolean semantics.
Make the large-payload regression size scale from _SUMMARY_MAX_CHARS so it continues to exercise truncation if the digest cap changes.
Validation: uv run pytest packages/core/tests/core/test_compaction.py -q -k 'tool_result_compaction'; uv run ruff check packages/core/agent_framework/_compaction.py packages/core/tests/core/test_compaction.py; uv run ruff format --check packages/core/agent_framework/_compaction.py packages/core/tests/core/test_compaction.py; uv run poe test -P core; uv run poe build -P core.
* Python: collapse tool result digest scan
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
LocalEvaluator.evaluate initialized item_passed to True and only ever
cleared it inside the loop over check results. With no checks configured
the loop never runs, so an item with zero scores was recorded as passed:
result_counts reported one pass, all_passed was True, and
raise_for_status() did not raise.
Initialize item_passed from bool(check_results) so an item with no
evaluated checks fails closed. This matches the .NET contract in this
repository, where AgentEvaluationResults.ItemPassed ends with
'return result.Metrics.Count > 0' and is pinned by
LocalEvaluator_WithZeroChecks_ItemsHaveZeroMetricsAndFailAsync.
Add a focused regression covering the counts, all_passed, the empty
score list, and raise_for_status(). Update the LocalEvaluator class and
evaluate() docstrings, which previously described the pass rule without
the zero-check case.
Fixes#7397
* Add skill to replace hardcoded foundry project endpoint and model
* Include more samples and fix migration samples part 1
* Fix migration samples
* Replace Foundry hosted agent validation skill
* Fix hosted agent file sample
* Fix agent result format
* Reorganize jobs
* Update discovery heuristic for apps
* Split agents into even more jobs
* Add toolbox endpoint
* Add more pre configured resources
* Fix using deployed agent sample
* Add sample status
* Add playbook
* Exclude hidden folder in sample discovery
* Install autogen dependencies
* Grant azure search RBAC role
* Increase timeout for magentic
* Build search resouce id deterministically
* Remove grant in the workflow
* Move azure cli login closer to when the sample actually runs
* Refactor playbook
* Fix using deployed agent sample
* Actually save the playbooks
* Fix action syntax error
* Fix magentic sample
* Address copilot comments
* Fix link inspection
* Address comments
* Correct README
* Fix playbook path
* Remove trailing space
* Python: Give the AG-UI Thread Snapshot lifecycle a single owner module
Both the agent and workflow runners independently implemented the thread
snapshot lifecycle: hydration replay, the load-once stored read, resume
message seeding, the stored/request/deferred-default state overlay, and
the save whose storage failures must never surface on an already-streamed
run. The two copies had already drifted in small ways (one hydrate helper
re-checked a store the caller had verified; the two cancelled-resume-id
helpers differed on missing-id handling).
Introduce ThreadSnapshotSession in _snapshot_session.py as the one owner
of that lifecycle, opened once per run and inert when no store or scope
is configured so callers stop branching on configuration. Rewire both
runners onto it, consolidate _cancelled_resume_interrupt_ids in
_run_common (defensive variant) and _event_messages_to_snapshot_dicts in
the new module, and delete the superseded per-runner copies. The session
interface is covered by dedicated tests; existing suites pin runner
behavior. Public exports are unchanged.
* Python: Narrow AG-UI event types in snapshot session tests
The hydration test accessed run_id, snapshot, and messages on values
typed as BaseEvent, which fails the tests/samples type checkers. Narrow
each event with isinstance assertions before reading its fields.
* Python: Add hosted agent sample for the agent harness
* Disable file providers and fix call_server usage in hosted harness sample
Addresses PR review: disable the harness file-memory and file-access
providers so the headless sample doesn't expose file tools or write
outside storage/, and correct the app.py docstring to match
call_server.py (which takes no prompt argument).
* Python: update hosted harness sample for current APIs
---------
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
The _orchestration/_helpers module had no production callers; its only
importer was its own test file. It also carried a stale fork of the live
metadata sanitization in _agent_run.py: the dead copy truncated oversized
values, behavior the live copy deliberately replaced with drop-plus-warning
because truncation can produce invalid JSON.
Move _tooling.py and _predictive_state.py to the package root and remove
the now-empty _orchestration subpackage. Public exports are unchanged.
* feat(python): add Mistral chat client
Implements native Mistral support (#7366) with streaming, tool calling,
and structured output. Talks to the REST API directly over httpx: the
mistralai SDK's pinned OpenTelemetry deps conflict with the workspace.
* refactor(python): simplify Mistral client per review
Drop the streamed tool-call accumulator and multi-choice parsing in
favor of the framework's built-in fragment merging, mark n unsupported,
omit unset strict from json_schema, and leave CI secret wiring to
maintainers.
* test(python): drop n forwarding assertion
n is typed as unsupported on MistralChatOptions; the option-mapping test
still passed n, failing pyrefly/ty/zuban/mypy in CI.
* refactor(python): drop n from MistralChatOptions
n is not part of the base ChatOptions, so removing the key rejects it
without an explicit None override.
* feat(python): mark Mistral feature usage
Both clients flip the shared FeatureIndex.MISTRAL bit before each
request, matching the feature-usage telemetry other providers emit.
* fix(python): key streamed tool calls by index
Mistral omits the tool call id on continuation fragments, and the
framework only coalesces empty-id fragments into the immediately
preceding call, so interleaved parallel calls merged into the wrong
call with corrupted arguments. Accumulate fragments per (choice,
index) and emit each call only once complete.
* fix(python): restore Mistral SDK client injection
Dropping the mistralai dependency turned the embedding client's
client= parameter into a breaking change for injected SDK clients.
Add http_client= for httpx.AsyncClient and keep client= working:
httpx goes to the REST path, a duck-typed mistralai.Mistral goes
through the legacy SDK path with a DeprecationWarning until the
next major release.
* chore(python): tidy Mistral sample header
* docs: add ADR-0032 proposing durable/Azure Functions repo extraction
Proposes extracting the Durable Task and Azure Functions hosting integrations into a dedicated repository (microsoft/agent-framework-durable-extension), keeping a backward-compatible shim and the [all] extra so the move is invisible to consumers. Status: proposed, for stakeholder signoff ahead of the code-removal PR.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd
* Fix GitHub user handles
* docs: generalize publish-lag example in ADR-0032
Replace the WorkflowHitlContext-specific illustration with a generic description of the publish-lag mechanism. The named symbol is currently exported by the extension and present in core's shim, so using it as an 'unpublished' example read as internally inconsistent.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd
* Add note about issue transfers
* Updates to ADR based on offline discussion
---------
Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd
CodeQL / Analyze (csharp) (push) Has been cancelled
CodeQL / Analyze (python) (push) Has been cancelled
dotnet-build-and-test / paths-filter (push) Has been cancelled
dotnet-build-and-test / dotnet-build-and-test-check (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Debug, windows-latest, net9.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net8.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-foundry-hosted-it (push) Has been cancelled
dotnet-build-and-test / dotnet-test-functions (push) Has been cancelled
dotnet-build-and-test / Integration Test Report (push) Has been cancelled
* Bump Python package versions for 1.13.0 release
Bump all 37 Python package projects because the CHANGELOG-driven release includes cross-package feature-usage telemetry, with core and root advancing to 1.13.0, OpenAI to 1.12.0, patch bumps for other stable packages, and 260730 stamps for alpha and beta packages. No optional beta cohort bump was applied; every prerelease package changed. Raise core floors conservatively across co-released packages.
Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541
* Align co-released Python package dependencies
Update the four hosting adapter pins to the co-released agent-framework-hosting alpha and raise the Azure Functions Durable Task floor to the co-released beta.
Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541
* Minimize Python release lockfile updates
Regenerate uv.lock with the pre-commit hook pinned uv version so the release changes only workspace package versions while preserving platform markers and agentlightning 0.3.0.
Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541
---------
Copilot-Session: e234a28b-c2fd-4ff4-a51d-3d8917936541
* Python: Support archive-type MCP skills in MCPSkillsSource
Add `archive`-type skill support to `MCPSkillsSource` so an MCP server can
advertise packaged skills (ZIP / TAR / gzip-compressed TAR) that are
downloaded, safely unpacked to a local directory, and served like file-based
skills, while keeping the guarantee that MCP-delivered scripts are never
executed.
- Dispatch `skill://index.json` entries by `type`: `skill-md` (existing,
fetched on demand) and `archive` (new). Unknown types are skipped.
- `_ArchiveEntryLoader` downloads, extracts, and prunes archive skills and
delegates discovery to an internal `FileSkillsSource` created with no
script extensions and no runner, so bundled scripts surface as read-only
resources only.
- Hardened stdlib extraction: path-traversal (zip-slip) guard, non-regular
TAR member skipping, and file-count / uncompressed-size / download-size
limits.
- Configure via `archive_*` constructor kwargs (no options object, per Python
conventions); use `CachingSkillsSource` for refresh rather than a source
level refresh interval.
- Fix `FileSkillsSource` to treat `None` extensions as "use defaults" and an
empty tuple as "discover none" (an empty tuple previously fell back to
defaults).
Port of .NET PR #6631.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 9e358a4e-538f-46be-8c58-128b6182352d
* Propagate non-not-found archive download errors in MCPSkillsSource
Only swallow "resource not found" MCP errors when downloading an archive
resource; re-raise every other error (auth failure, INTERNAL_ERROR,
connection drop, timeout) so a transient transport failure is not silently
turned into a missing skill. This matches the existing failure model used by
`_try_read_index` and `MCPSkill.get_resource`, and avoids a failed
`CachingSkillsSource` refresh overwriting a previously cached list with a
partial result.
Add tests asserting archive-download INTERNAL_ERROR and ConnectionError
propagate out of `get_skills`.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 9e358a4e-538f-46be-8c58-128b6182352d
* Python: Expose archive skill options on FoundryToolbox and demo in sample
- FoundryToolbox.as_skills_provider() now forwards the MCPSkillsSource archive
options (archive_skills_directory, archive_resource_extensions,
archive_resource_search_depth, archive_max_file_count, archive_max_size_bytes,
archive_max_uncompressed_size_bytes). Only explicitly-set options are
forwarded so unset ones keep the MCPSkillsSource defaults. This lets a hosted
toolbox agent redirect archive extraction to a writable directory (the default
is under the cwd, which may be read-only in a container).
- Add unit tests covering default (no options forwarded) and override forwarding.
- Update the 12_foundry_toolbox_mcp_skills sample to demonstrate all three
progressive-disclosure stages with an archive skill: escalation-policy now
ships a references/refund-matrix.md resource and is uploaded as a ZIP archive;
main.py disables load_skill and read_skill_resource approval and points
archive extraction at a temp directory. README, toolbox.yaml, and ignore files
updated accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 4f14f83d-1868-45c1-be1a-12f49a58ac36
* Python: Fix ty type error in toolbox archive-option test
Cast provider._source to _FoundryToolboxSkillsSource before accessing the
private _archive_options, so the ty checker (which runs over tests) resolves
the concrete type instead of the SkillsSource base. Replaces the mypy-style
type: ignore that ty did not honor.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 4f14f83d-1868-45c1-be1a-12f49a58ac36
* Rework archive-type skill support in MCPSkillsSource to unpack archives
entirely in memory instead of extracting them to a local directory, and
apply reviewer feedback.
* Python: Raise on archive member path-traversal (zip-slip)
Treat a `..` path-traversal member in an archive skill as a hostile archive
and reject the whole skill, matching how the file-count and uncompressed-size
limits reject a malformed archive (previously the member was silently skipped
while the rest of the skill still loaded).
- `_normalize_archive_member_name` now raises `ValueError` on a `..` escape;
benign degenerate entries (empty, `.`, `/`) still return None (skipped) and
absolute paths are still neutralized to relative. The raise propagates to
`_ArchiveEntryLoader._build_skill`, which already skips the skill on error.
- Update tests: traversal cases now assert a raise, and add an end-to-end test
that a zip-slip archive drops the whole skill.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 9e358a4e-538f-46be-8c58-128b6182352d
* Python: Revert archive skill demo in toolbox MCP skills sample
Restore the 12_foundry_toolbox_mcp_skills sample to its pre-PR, skill-md-only
form (matching the .NET Agent_Step26_FoundryToolboxMcpSkills sample, which uses
skill-md and no ZIP archive):
- Revert main.py, toolbox.yaml, README.md, .azdignore, .dockerignore, and
escalation-policy/SKILL.md to the single-file SKILL.md version.
- Remove the archive demo files added by this PR (.gitignore and
escalation-policy/references/refund-matrix.md).
- Soften two README notes so they no longer claim archive skills are
unsupported/silently dropped (this PR adds archive support); instead frame
single-file SKILL.md as a focus choice and point to the archive_* options.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 9e358a4e-538f-46be-8c58-128b6182352d
* Python: Clarify archive framing in mcp_based_skill sample README
The mcp_based_skill sample is a generic MCP consumer that discovers whatever
the server advertises; it does not itself demonstrate archive skills. Reword
the archive note so it reads as an MCPSkillsSource capability rather than a
sample feature, and fix the stale "unpacked to a local directory" claim to
"unpacked in memory" (matching the in-memory extraction implementation).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 9e358a4e-538f-46be-8c58-128b6182352d
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 9e358a4e-538f-46be-8c58-128b6182352d
Copilot-Session: 4f14f83d-1868-45c1-be1a-12f49a58ac36
* Add regression tests and sample guidance for stable agent IDs in checkpointed workflows
* Updated tests to address PR comments
* Improve test for checkpoint state.
Use the exact /review command without mentioning an unrelated GitHub user account.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Python: Apply header_provider headers to ambient MCP requests
MCPStreamableHTTPTool.header_provider was only invoked from call_tool(),
so the initialize handshake, load_tools/load_prompts discovery, and
background pings all went out with no headers. MCP servers that require
auth on initialize (e.g. Azure AI Search knowledge-base MCP endpoints)
therefore returned 401 before any tool call could run.
Add an ambient fallback in the _inject_headers httpx request hook: when
neither the per-call ContextVar nor the active-call snapshot is set, the
hook invokes header_provider({}) so every ambient request is
authenticated. Providers that require per-call kwargs raise on the empty
dict; that is caught, logged, and the request proceeds unauthenticated,
preserving prior behavior. Calling the provider on demand also keeps
dynamic token refresh working for post-connect requests.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: cf0c1dbf-99bc-4f3f-bcf4-7791ce7dbe6a
* Python: address review - distinguish unset vs empty headers, warn once
Review feedback on the ambient header_provider fallback:
- Distinguish 'unset' (no active call) from 'set but empty' (call_tool
produced no headers). Use _mcp_call_headers.get(None) and the None-ness
of the snapshot instead of a truthiness check, so a provider that
legitimately returns {} during a real call is no longer re-invoked by
the ambient fallback mid-call.
- A kwargs-dependent provider raises on every ambient request (initialize,
discovery, recurring pings). Warn once per tool instance with a
traceback via _ambient_header_warning_emitted and drop subsequent
occurrences to DEBUG to avoid log spam.
Add regression tests for both behaviors.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: cf0c1dbf-99bc-4f3f-bcf4-7791ce7dbe6a
* Python: narrow ambient header_provider catch to KeyError
Only the missing-per-call-kwargs case (KeyError, e.g. the
mcp_api_key_auth.py sample indexing kwargs['mcp_api_key']) is tolerated
during ambient requests. Any other exception - a token-refresh failure
or a provider bug - now propagates instead of being silently converted
into unauthenticated traffic, matching the call_tool path which does not
catch header_provider exceptions.
Add a regression test asserting a non-KeyError provider failure
surfaces from the request hook.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: cf0c1dbf-99bc-4f3f-bcf4-7791ce7dbe6a
* Python: address review - raise instead of assert, simplify ambient logging
- Reword the ambient-fallback comment to describe the kwargs-dependent
provider pattern generically instead of naming a sample file, which
would go stale if the sample is renamed (also in a test docstring).
- Replace the type-narrowing assert with a RuntimeError carrying a
concise message for the unreachable no-provider state.
- Drop the warn-once/_ambient_header_warning_emitted machinery; the
KeyError ambient case is expected and benign, so log a single DEBUG
line and proceed without headers.
Update the corresponding test to assert behavior (request proceeds
without an Authorization header and no WARNING is emitted) instead of
log-count.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: cf0c1dbf-99bc-4f3f-bcf4-7791ce7dbe6a
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Copilot-Session: cf0c1dbf-99bc-4f3f-bcf4-7791ce7dbe6a
from_dict resolved the expected type identifier from the payload itself
(_get_type_identifier(value) prefers value["type"]), so the mismatch
guard could never fire: any supplied 'type' matched itself, and a payload
like {"type": "function_tool", ...} silently deserialized into a Message,
getting its type rewritten on the next to_dict. The docstring has always
promised a ValueError on mismatch.
Resolve the identifier from the class instead, matching what to_dict
emits, so a mismatched or foreign 'type' now raises as documented.
Payloads without a 'type' field and dependency-injection lookups are
unchanged: in every previously valid case the class-resolved identifier
is the same string the payload carried.
Fixes#7255
Signed-off-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Preserve model emission order in AG-UI messages snapshot
* Address moonbox3's review: cover the remaining snapshot gaps
- Preopened message ids (tool-only path) now open a text segment when
the first text arrives, so their content can't drop out of the snapshot.
- A tool result closes the current tool-call segment, so
call A -> result A -> call B snapshots as two pairs in stream order.
- emitted_call_ids only marks calls actually emitted, keeping stale
segment ids eligible for the leftover fallback.
- The leftover path carries its tool results too instead of dropping them.
* Python: narrow leftover tool-call ids so pyright accepts the update
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: Add GitHub Copilot BYOK sample
Demonstrates routing GitHubCopilotAgent requests through a custom OpenAI-compatible
endpoint via ProviderConfig instead of the default GitHub Copilot backend.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Python: Address BYOK sample review feedback
- Make the provider type configurable via BYOK_PROVIDER_TYPE (default "openai") instead
of hardcoding "openai" — a partial autofix commit had already updated the docstring to
document this env var but left the code hardcoded, which this finishes.
- Stop calling the endpoint "OpenAI-compatible" everywhere; Anthropic isn't OpenAI-wire-
compatible, so reword to "your own endpoint" and list the actual supported providers
(mirrors the equivalent .NET sample fix).
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Python: Fix FoundryAgent inheriting OPENAI_CHAT_MODEL for agent-reference requests (#7272)
* Python: Fix FoundryAgent inheriting OPENAI_CHAT_MODEL for agent-reference requests
* fix(foundry): update test typing annotations to pass mypy, pyrefly, and ty
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Bound summarization input before provider call
SummarizationStrategy now selects complete message groups that fit a configurable summary input token budget before calling the summary client. Only messages actually sent to the summarizer are annotated and excluded, leaving oversized later groups for a later compaction pass instead of shipping the whole transcript unbounded.
Validation: uv run pytest packages/core/tests/core/test_compaction.py -k bounds_summary_input -m "not integration" failed before the implementation and passed after it; uv run pytest packages/core/tests/core/test_compaction.py -m "not integration" passed; uv run poe test -P core passed; uv run poe install completed; uv run poe check -P core passed.
* Handle oversized leading summary groups
Skip individually over-budget leading groups when selecting summarization input so a large early transcript item does not prevent later compactable groups from being summarized.
Validation: uv run pytest packages/core/tests/core/test_compaction.py -k skips_oversized_first_group -q; uv run pytest packages/core/tests/core/test_compaction.py -q; uv run poe check -P core.
* Escalate repeated summary failures
Track consecutive SummarizationStrategy failures and emit a single error once the strategy has failed three times without a successful summary. Reset the escalation state after a successful summary so only persistent failures become loud.
Validation: uv run pytest packages/core/tests/core/test_compaction.py -k 'repeated_summary_failures or resets_failure_escalation' -q; uv run pytest packages/core/tests/core/test_compaction.py -q; uv run poe check -P core.
* Refine summary input selection
Avoid rebuilding and re-tokenizing the full selected summary transcript on every candidate group while preserving complete-group selection and oversized leading group skipping.
Tighten the scripted summarizer test helper to expected Exception failures instead of BaseException.
Verification: uv run pytest packages/core/tests/core/test_compaction.py -q; uv run poe syntax -P core.
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Fix Copilot Actions token environment
Expose workflow tokens through GITHUB_TOKEN so Copilot CLI uses native Actions authentication, while preserving user-token integration test support.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Gate Copilot integration tests explicitly
Use GitHub Actions authentication only when both GITHUB_ACTIONS and GITHUB_TOKEN are present, and require an explicit local opt-in that relies on stored Copilot login.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
---------
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
OpenAI validates the Chat Completions message 'name' against
^[^\s<|\/>]+$, so an agent display name containing a space (or
< | \ / >) failed every request with a 400. Sanitize at the three
assignment sites, mirroring SanitizeAuthorName in the .NET client
(dotnet/extensions): remove characters outside [a-zA-Z0-9_], omit the
name when nothing remains, truncate to 64 characters.
Fixes#7126
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Python: fix Anthropic streaming double-counting token usage
* Python: address review on the Anthropic usage increment helper
- accumulate the emitted totals in a plain dict instead of string-cast
TypedDict views, so static checkers see real types throughout
- compute the increment through _types.add_usage_details with negated
emitted totals instead of a hand-rolled subtraction loop; keys absent
from a snapshot stay untouched, matching the partial-delta semantics
* feat(observability): add support for OpenAI cache write tokens in usage details
* feat(openai): add cache write tokens handling in usage details
* Fix test
* Use Actions token for DevFlow Copilot auth
Grant the review job Copilot request permission and remove the user token fallback so organization-billed GitHub Actions authentication is exercised directly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Enable DevFlow PR review comparisons
Pass the dedicated DevFlow repository token for A/B artifact branches while keeping the built-in Actions token as the only Copilot credential.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Allow team-triggered DevFlow reviews
Accept an exact @devflow /review PR comment only from organization members, verify the commenter against the developer team with the GitHub App, and react after authorization.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Use Actions token for issue triage Copilot auth
Grant the triage job Copilot request permission and remove the user PAT so issue reproduction exercises organization-billed GitHub Actions authentication.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Use tracked DevFlow CI model configuration
Point PR review and issue triage runs at the dashboard's tracked GPT-5.6 Sol and Claude Opus 5 model configuration.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Use Actions tokens for Copilot test workflows
Remove Copilot PAT secrets from integration and sample validation workflows, grant Copilot request permission at the required caller and job boundaries, and preserve the environment variable expected by the tests.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
---------
Copilot-Session: d9fa4e9c-c32d-42fb-8ee4-4772473e6479
* Python: Defer provider-injected approvals to in-run execution
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1
* Python: Remove vacuous AG-UI approval test
Drop the forged-approval test that was stripped by pending-approval validation; the real pause-approve-resume regression remains the authoritative provider-injected coverage.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1
* fix(foundry-hosting): root hosted checkpoints under durable home directory
* fix: add None guard for _checkpoint_storage_path in test
* Disable Foundry image test
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
* Allow workflow checkpoint full replayability
Seed the initial run input through the start executor's internal self-edge and record an entry checkpoint (iteration 0) before any executor runs, plus a response-entry checkpoint when responses are delivered, so a run is fully replayable from its checkpoints. Simplify the runner to only checkpoint after each superstep. Drop stale events in apply_checkpoint on restore, and deprecate the unused RunnerContext.reset_for_new_run.
* Fix type
* Add max iteration detailed doc string
* Refine comments
* Python: Fix OpenAIChatCompletionClient passing raw JSON-Schema dict response_format through unwrapped
Raw schema dicts (e.g. {"type": "object", ...}) were forwarded to the
Chat Completions API verbatim, which OpenAI rejects with a 400. The
Responses client already auto-wraps the same input. Mirror its raw-schema
detection (primitive types / schema keywords), wrap into the
{"type": "json_schema", "json_schema": {...}} envelope with
additionalProperties: false injection and title -> name promotion, and
leave already-valid response_format dicts untouched.
Fixes#7197
(cherry picked from commit dce5c3b06328fbde45eb2a9a25638af5b1ec85e3)
* Python: Add live integration coverage for raw JSON-Schema response_format dicts
Adds a response_format_raw_json_schema param to test_integration_options in
both the Chat Completions and Responses client test suites, proving the same
bare schema dict (title set, additionalProperties omitted) round-trips through
both live APIs and yields parsed structured output.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Python: Fix response format dict typing
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Add zip/code-deploy POC for Hosted-ChatClientAgent (.NET)
Migrate the sample to Foundry source (ZIP) deployment as the default: add azure.yaml with codeConfiguration (remote_build, dotnet_10) and the tool-generated .agentignore, make the csproj self-contained (single target, CPM off, published PackageReferences), and simplify Program.cs to the pristine end-user hosting path. Container files are kept for now; contributor and remaining samples handled in follow-ups.
* .NET: Auto-bind Foundry hosted port for zip/code deploy; migrate Hosted-ChatClientAgent to source (ZIP)
Foundry.Hosting: AddFoundryResponses now binds Kestrel to FoundryEnvironment.Port (the PORT env var, default 8088) for a plain WebApplication.CreateBuilder (Tier 3) host, mirroring AgentHostBuilder. This lets a source/ZIP-deployed .NET agent pass the readiness probe with no Dockerfile. It respects an explicit ASPNETCORE_URLS override and is idempotent. Adds FoundryListenPortTests plus a serialized env-var collection.
Hosted-ChatClientAgent: migrate to source (ZIP) deploy as the default. Add azure.yaml with codeConfiguration (remote_build, dotnet_10) and the tool-generated .agentignore, make the csproj self-contained (single target, CPM off) with a local Directory.Packages.props, embed the local-dev per-agent route so the Using-Samples REPL can reach the local server, and rewrite the README around the azd flow. Documents AZURE_TOKEN_CREDENTIALS=dev for local runs.
Using-Samples/SimpleAgent: fix the per-agent endpoint scheme rewrite so the local HTTP dev port is preserved (the policy now lives on the per-agent ProjectOpenAIClientOptions that actually serves the request).
* .NET: Bind Foundry hosted port unconditionally; drop container files and the local-only agent route
Zip/code deploy runs the sample as a plain ASP.NET app, so the Foundry readiness
port was never bound and every invoke returned HTTP 424 session_not_ready. The
first attempt skipped the binding when ASPNETCORE_URLS was already set, but the
.NET base image always sets it to port 80, so the skip always tripped. Kestrel
ListenAnyIP overrides ASPNETCORE_URLS, so the binding is now unconditional and
PORT stays the only knob.
Sample cleanup for zip deploy:
* Remove Dockerfile, Dockerfile.contributor, agent.manifest.yaml and agent.yaml.
Source deploy needs none of them.
* Remove LocalDevEndpoint.cs and the invented per-agent local route. The local
server already serves the standard POST /responses route, so the client can
reach it directly.
* Trim .env.example: the port and environment variables are no longer needed.
* Exclude .checkpoints/ from the upload so local session state does not ship.
SimpleAgent now asks at startup whether to chat with the local server or the
deployed agent, the same choice azd ai agent invoke exposes through --local.
Local uses an OpenAI responses client pointed at http://localhost:8088; Foundry
uses the per-agent endpoint.
Add scripts/New-ContributorStage.ps1, which stages a sample to a temp folder with
the local Agent Framework source packed into a feed inside the upload, so
contributors can deploy framework changes through the same azd flow end users run.
* Pin hosted agent listen port in azure.yaml
* Use the documented env map in azure.yaml
* Make the contributor flow an extra step inside the end-user flow
* Keep contributor scaffolding out of the sample project file
* Document the full deploy walkthrough and add a bash contributor script
* Trim troubleshooting detail from the sample README
* Pass the model deployment name to the hosted container
* Add --local and --remote flags to the SimpleAgent REPL
* Use central package management in the hosted sample
* Clarify where the contributor step fits in the deploy walkthrough
* Restore the HTTP scheme rewrite for local AIProjectClient runs
* Keep the sample package versions in the project file
* Drop the sample Directory.Packages.props
* Add a container deploy variant of the hosted chat client agent sample
* Treat a blank model deployment variable as unset
* Let azd prompt for the Foundry project and expand the contributor section
* Document the stale conversation 404 in the hosted agent samples
* Remove using directives already covered by global usings
* Bind the Foundry listen port only inside a hosted container
* Resolve the Foundry listen port from IConfiguration
* docs: ADR-0027 feature-usage bitmask in the User-Agent
Add an ADR, design spec, and per-language bit registry for a lightweight
feature-usage signal: a 64-bit mask, emitted as a `(feat=vN.<hex>)` User-Agent
comment, stamped per request on first-party (Azure/Foundry) clients only.
- docs/decisions/0027-feature-usage-bitmask-user-agent.md — ADR (options-first,
with Limitations, Open Questions, and v1->v2 migration)
- docs/specs/002-feature-usage-telemetry.md — design spec + implementation plan
- docs/specs/feature-usage-bit-registry.md — per-language bit tables + governance
Granularity is per package with core broken out per feature (each orchestration
pattern and built-in context/history provider). Registries are per language
(decoder selects by the language already in the UA). OpenTelemetry emission is
deferred (privacy). Docs only; no code changes.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: fix dead links to removed registry JSON in ADR-0027
The registry JSON was consolidated into feature-usage-bit-registry.md; point
the ADR's two remaining links at the markdown instead of the deleted file
(fixes markdown-link-check 404s).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: address review — drop JSON-parity wording, clarify per-language decode
- ADR option J: the parity test compares the enum against the per-language table
in the registry doc, not a (now-removed) JSON file.
- Spec .NET mapping: the wire format is shared, but the mask is decoded
per-language (select the table via the UA product token) — fixes the
"decoded numbers mean the same thing in both SDKs" wording that conflicted
with the per-language, non-synchronized bit indexes.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: add dedicated mask-only opt-out env var (AGENT_FRAMEWORK_FEATURE_MASK_DISABLED)
Re-introduce a dedicated opt-out that disables only the feature mask while keeping
the base agent-framework-<lang>/{version} User-Agent, alongside the existing
AGENT_FRAMEWORK_USER_AGENT_DISABLED (whole UA). Updates the spec accumulator gate,
API surface, opt-out table and examples; the registry opt-out section; and the
ADR (decision outcome, consequences, open questions -> decided).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: add prior-art comparison (AWS botocore m/, Stainless, Azure, etc.)
Add a Prior art section to ADR-0027 surveying how comparable SDKs encode
identity/usage in the User-Agent or sidecar headers, with citations:
- AWS botocore `m/` feature-code list — the direct analog (per-request,
usage-based feature flags in the UA); contrasts short-code set vs our hex
bitmask.
- OpenAI/Anthropic Stainless `X-Stainless-*` headers (static identity).
- Azure azure-core UserAgentPolicy + AZURE_TELEMETRY_DISABLED.
- Google x-goog-api-client; LangSmith version token + tracing opt-in.
Also add an Open Question on honoring the cross-tool DO_NOT_TRACK convention.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: fold in botocore lessons; record accumulation-scope decision
botocore's m/ feature list scopes features to a per-request contextvars set that
resets between calls — clean per-call attribution, but it assumes every feature
lives inside a service request. That holds for an SDK natively bound to its own
services; it does not for us, where many features (agent/workflow/provider
construction, session setup) are not bound to any request.
- ADR: add Accumulation scope options — P (process-global monotonic, chosen) vs
Q (botocore per-request set, rejected) with the request-binding rationale;
reference P in the decision; reframe the "no per-call attribution" limitation
as a deliberate scope choice.
- ADR Prior art: bitmask gives bounded token size for free (vs botocore's
1024-byte cap + truncation); mechanism is private, wire format is the contract;
fix a duplicated phrase.
- Spec: note the mask is process-global, monotonic, never reset (intentional,
lock/Interlocked.Or-safe), the token is safe-by-construction (no sanitization),
and the helpers are private API.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: update feature mask ADR
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: refresh feature usage telemetry design
Rebase the proposal on current main, renumber it to ADR-0033/SPEC-004, and reconcile the registry and implementation notes with current Python and .NET surfaces.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: expand feature usage mask to 128 bits
Repartition the v1 registries with additional skill categories, define the bit-allocation tenet, and document the two-lane .NET accumulator and 128-bit decoder contract.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: tighten feature telemetry activation and scoping
Require approved pipeline and actual-origin classification, preserve OpenAI transport defaults, use activation-based marking, and move index ownership into packages with parity and no-overlap validation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: preserve SDK transport defaults for telemetry
Record the transport-preservation requirement at the ADR decision level.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: split declarative agent and workflow usage
Allocate separate adjacent v1 indexes for declarative agents and declarative workflows in Python and .NET, shifting later unreleased rows.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: accept feature usage telemetry ADR
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: record feature telemetry ADR participants
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: expand feature telemetry ADR consultation
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
* docs: clarify feature telemetry semantics
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
_WORD_PATTERN matched only ASCII (`[a-z0-9]...`), so a message written in
CJK, Cyrillic or any other non-Latin script produced an empty keyword set.
_select_topics returns early on an empty keyword set, so non-English users
never had memory topic files loaded automatically.
Make the pattern Unicode-aware (`[^\W_][\w-]+`, a letter/digit start plus
word chars/hyphen), which is the exact Unicode generalization of the old
pattern: English tokenization is unchanged and CJK/Cyrillic text now yields
keywords.
Mirrors OpenAIResponsesHostingLiveTests with the hosted agent backed by an
Anthropic chat client, confirming the app-owned hosting helper surface
(OpenAIResponses + AgentSessionStore) is provider-agnostic end to end.
Skipped unless ANTHROPIC_API_KEY is configured.
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* .NET: Add GitHub Copilot BYOK sample
Demonstrates routing GitHubCopilotAgent requests through a custom OpenAI-compatible
endpoint via SessionConfig.Provider instead of the default GitHub Copilot backend.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Update dotnet/samples/02-agents/AgentProviders/github-copilot/Agent_With_GitHubCopilot_BYOK/README.md
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* Update dotnet/samples/02-agents/AgentProviders/github-copilot/Agent_With_GitHubCopilot_BYOK/README.md
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* .NET: Address remaining BYOK sample review feedback
- Make the provider type configurable via BYOK_PROVIDER_TYPE (default "openai") instead
of hardcoding "openai", since the sample already documents Azure/Anthropic support.
- Stop calling the endpoint "OpenAI-compatible" everywhere; Anthropic isn't OpenAI-wire-
compatible, so reword to "your own endpoint" and list the actual supported providers.
- Move the "About BYOK" explainer to the top of the README so the term is introduced
before it's used, and finish applying the WireApi/ModelId comment suggestions.
- Reword the AgentProviders/README.md entry to match (not OpenAI-specific).
* .NET: Fix UTF-8 BOM on BYOK sample Program.cs
The repo's .editorconfig requires utf-8-bom for .cs files; check-format was
failing because the new file was written without one.
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* Fix Gemini harness tool declarations
Forward Agent Framework FunctionTool JSON Schemas to the Gemini SDK parameters_json_schema field and enable Developer API server-side tool invocation reporting when native Gemini tools are mixed with function declarations.
Preserves Vertex AI behavior and existing function-calling tool_choice config.
Validation:
- uv run --directory python poe check -P gemini
- uv run --directory python poe build -P gemini
- uv run --directory python poe test -A -m 'not integration'
- uv run --directory python pytest packages/gemini/tests/test_gemini_client.py -q -m integration (8 skipped: credential-gated)
* Python: Use typing_extensions TypedDict in Gemini tests
Use typing_extensions.TypedDict for the Gemini JSON Schema test helper so Pydantic can build the model on Python 3.11.
This keeps the CI fix scoped to the failing test compatibility issue without changing Gemini client behavior.
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
InputWaiter_WaitForInputAsync_CompletesWhenTimeoutExpiresAsync races a
300ms SemaphoreSlim timeout against a 5s Task.Delay guard and asserts
which one won by object identity. On the loaded net472/windows-latest
leg, thread pool starvation can delay the 300ms continuation past the
5s guard, so Task.Delay wins and the assertion fails.
It failed in 7 of the last 18 failed dotnet-build-and-test runs, always
on net472/windows-latest and always in merge_group, blocking PRs that
do not touch the workflows code.
Quarantine it following the existing convention used for #5845, and
track the real fix in #7360.
Copilot-Session: 0be6f810-51de-4f49-b9c7-8d1c7efa2c43
The A2A hosting layer now forwards the caller-supplied
SendMessageConfiguration from RequestContext.Configuration into
AgentRunOptions.AdditionalProperties under the key
'a2a.configuration'. This covers all three handler paths:
non-streaming, streaming, and task continuation.
The server-configured AgentRunMode remains authoritative for
AllowBackgroundResponses — the caller's ReturnImmediately is
forwarded but does not override the server decision.
Closesmicrosoft/agent-framework#5869
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 5ee820de-3e34-493b-a19c-1db6bc04871d
* Python: Add TodoProvider and AgentModeProvider context provider samples
Add two Python samples under samples/02-agents/context_providers/ mirroring the
.NET samples from #7262:
- todo_provider.py: scripted walkthrough of TodoProvider that plans multi-step
work and prints the evolving todo list after each turn.
- agent_mode_provider.py: interactive loop using AgentModeProvider with a /mode
slash command, demonstrating built-in plan/execute and custom modes.
Also index both samples in the context_providers README.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 8725831c-086b-475f-90e6-cdba41d59c33
* Python: Address review comments on AgentModeProvider sample
- Replace the AGENT_MODE_USE_CUSTOM env var with an in-file USE_CUSTOM_MODES
constant for choosing between built-in and custom modes.
- Use plain input() in the interactive loop instead of asyncio.to_thread.
- Update the README prerequisites to reference the in-file toggle.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 8725831c-086b-475f-90e6-cdba41d59c33
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 8725831c-086b-475f-90e6-cdba41d59c33
* fix(python): handle callable class middleware safely in _determine_middleware_type (#6697)
* test(python): type-annotate test middleware lists to pass test-typing checks
Promote Microsoft.Agents.AI.GitHub.Copilot from release candidate to
released by replacing IsReleaseCandidate=true with IsReleased=true, so the
package builds with the stable central version (no -rc suffix). Also clears
the package-validation baseline and disables package validation for this
first stable release, since the package has never shipped a stable NuGet to
validate against (mirrors the Microsoft.Agents.AI.Harness graduation in
#7119). Non-breaking: the package exposes no [Experimental] APIs to un-mark.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f523064c-60b4-4d18-bf95-c16c5fda9126
* Fix sub-workflow checkpoint restore to preserve sub-workflow state
Add Runner.capture_checkpoint_object/restore_from_checkpoint_object (quiescent-only nested checkpoint) and embed a sub_workflow_checkpoint in WorkflowExecutor.on_checkpoint_save/on_checkpoint_restore so a resumed parent restores each sub-workflow's mid-progress state instead of only replaying pending request-info events. Keeps a backward-compat fallback when sub_workflow_checkpoint is absent.
* Move checkpoint-object construction into the runner context
Add RunnerContext.create_checkpoint_object alongside create_checkpoint (create_checkpoint now delegates to it and persists), so Runner.capture_checkpoint_object builds the snapshot via the context instead of a one-off get_messages peek primitive. In-flight messages are captured non-destructively (per-source lists copied). The checkpoint-less capturing contexts (azurefunctions, durabletask) raise NotImplementedError to match create_checkpoint.
* Remove per-execution bookkeeping from WorkflowExecutor
The sub-workflow is a single shared instance, so per-execution ExecutionContext/request routing never provided real isolation. Delegate request/response tracking to the sub-workflow itself: can_handle accepts targeted propagated responses, _handle_response validates against the sub-workflow's pending requests and forwards responses immediately, and on_checkpoint_save embeds only the sub-workflow checkpoint (on_checkpoint_restore keeps a legacy reader for older checkpoints). Also emit the fresh-message/checkpoint-while-pending warning from FunctionalWorkflow.run to match Workflow.run.
* Drop redundant decode in WorkflowExecutor.on_checkpoint_restore
The storage backend already materializes the full checkpoint on load (FileCheckpointStorage decodes recursively; InMemoryCheckpointStorage deep-copies), so the embedded sub_workflow_checkpoint (and legacy execution_contexts) arrive already decoded - like every other executor's on_checkpoint_restore state. Remove the no-op decode_checkpoint_value calls and the now-unused import.
* Clean up
* Do not allow checkpoint storage in sub workflow
* Address comments
* Fix syntax check
* Add warning
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: Fix duplicate function call on approval round-trip (#7267)
`_replace_approval_contents_with_results` deduped restored function calls
against only the message currently being scanned. On an approval round-trip
the hosting layer replays the stored `function_call` item and its
`mcp_approval_request` item as two separate assistant messages, so the
per-message check never fired and the approval request restored a second
copy of the call.
Only one copy received the function result; the orphaned copy was left
unanswered, which the Responses API rejects with
"No tool output found for function call call_<id>".
Collect existing call ids across all messages instead, and add a restored
call to that set so two approval requests for the same call cannot both
expand.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Refactor approval placeholder result handling
Refactor approval handling logic to improve clarity and maintainability.
* Refactor test to support reused call IDs after completion
Updated the test to allow reused call IDs after completion, ensuring that a completed call does not suppress later approval requests with the same ID. Adjusted assertions to reflect the new behavior.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Promote the GitHub Copilot package from release candidate (1.0.0rc4) to released (1.0.0): bump the version, switch the classifier to Production/Stable, update PACKAGE_STATUS.md, and drop the --pre install flag from the package and sample READMEs. Add a github-copilot-1.0.0 CHANGELOG section covering the promotion and the input-attachment forwarding shipped in this release. No core/root bump: this is a standalone package promotion and the core[all] extra references the package without a version pin.
Copilot-Session: f523064c-60b4-4d18-bf95-c16c5fda9126
* Python: Forward GitHub Copilot input attachments as inline blobs
The Python GitHubCopilotAgent built the prompt from message text only, so
DataContent (images/documents) passed on input was silently dropped. The .NET
provider already forwards these as attachments.
Map input data content to the Copilot SDK's inline BlobAttachment (base64,
no temp files) in both the streaming and non-streaming send paths. Data content
without a media type is dropped with a warning instead of silently.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
* Python: Handle non-base64 data URIs and fix attachment docstring
Address PR review feedback:
- Guard _get_data_bytes_as_str against ContentError so a non-base64 data:
URI (which _validate_uri still classifies as type="data") is skipped with a
warning instead of failing the entire Copilot request.
- Correct the docstring: remote URIs and non-base64 data URIs are neither
attached nor added to the prompt (the prompt is built from text content only).
- Add tests for the non-base64 data URI path.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
* Python: Fix flaky attachment test under telemetry
The end-to-end non-base64 data URI test failed in CI because GitHubCopilotAgent's
telemetry layer serializes message content (observability._to_otel_part ->
_get_data_bytes_as_str), which raises ContentError on a non-base64 data: URI
before the attachment code runs. That is an unrelated core-observability
limitation, not attachment behavior.
Use RawGitHubCopilotAgent (no telemetry layer) for that test so it isolates the
provider's send path. The direct helper test still covers the ContentError guard.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
* Python: Support async credentials in `FoundryToolbox`
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Refactor: Use AzureCredentialTypes for credential type annotations in Toolbox classes
* Remove auth_flow method from _ToolboxAuth class
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Switch to using new community toolkit VectorData packages
* Fix formatting.
* Update dotnet/Directory.Packages.props
Co-authored-by: Adam Sitnik <adam.sitnik@gmail.com>
* Fix build error.
* Upgrade MEAI
* Upgrade additional dependencies
* Address rename after package upgrade.
* Revert some packages versions due to version mismatches
---------
Co-authored-by: Adam Sitnik <adam.sitnik@gmail.com>
Prepare the focused alpha release for the progressive A2A adapters from #7258. No other package versions or dependency bounds change.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 003e02dd-dba0-40a5-9ebf-083901aefb57
Bump root and core to 1.12.1, OpenAI to 1.11.0 for new public prompt-cache options, Foundry to 1.10.3, and Gemini and Foundry Hosting to beta 260722 based on CHANGELOG entries. Promote AG-UI from 1.0.0rc9 to stable 1.0.0. No beta cohort bump was applied, and core floors remain unchanged under the strict affected-dependency policy because the connectors do not require a new core API.
* Python: Fix reasoning-paired client tool replay
* Python: Handle middleware-terminated reasoning tool loops
* Python: Replay encrypted reasoning function groups
Key decisions:
- Request encrypted reasoning on client-managed Responses calls while preserving caller include values.
- Store encrypted payloads in Content.protected_data and reconstruct one provider reasoning item per reasoning id.
- Replay active and completed function call/result groups; retain continuation-owned history behavior and the existing orphan-safe MCP path.
Files changed:
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Extend encrypted reasoning preservation to streaming and framework serialization boundaries.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Preserve encrypted reasoning through streaming
Key decisions:
- Capture encrypted reasoning from terminal streamed output items in Content.protected_data.
- Preserve summary and private reasoning as distinct framework contents while reconstructing one provider reasoning item per id.
- Prove replay after Message JSON and workflow checkpoint round trips, including encrypted-only and completed function groups.
Files changed:
- python/packages/core/agent_framework/_types.py
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Extend lossless stateless reasoning replay to hosted MCP call/output groups.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Replay hosted MCP reasoning groups
Key decisions:
- Preserve hosted MCP call/output groups in client-managed history instead of deleting them when reasoning cannot be reconstructed.
- Keep call/result coalescing and orphan-result exclusion intact, while retaining continuation-owned duplicate avoidance.
- Cover completed, active, and multi-call reasoning groups plus the public outgoing request boundary.
Files changed:
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Preserve middleware-terminated and parallel function groups atomically.
- Add preflight rejection for non-replayable reasoning groups in the dedicated validation slice.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Preserve terminated parallel reasoning groups
Key decisions:
- Return ordinary function results when middleware terminates a loop, removing the provider-specific durable marker.
- Preserve every parallel call and available sibling result as one encrypted reasoning group in stateless replay.
- Prove successful and policy-blocked batches through the public two-agent Foundry workflow and outgoing HTTP boundary.
Files changed:
- python/packages/core/agent_framework/_tools.py
- python/packages/core/tests/core/test_function_invocation_logic.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
- python/packages/foundry/tests/foundry/test_foundry_agent.py
Next iteration:
- Add preflight rejection for non-replayable and partially compacted reasoning groups.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Reject unsafe stateless reasoning replay
Key decisions:
- Validate client-managed reasoning groups after compaction and report every affected reasoning and call identifier before transport.
- Permit service-owned continuation and fully excluded atomic groups while rejecting partial compaction projections.
- Surface encrypted-reasoning capability failures without lossy retries.
Files changed:
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Run the resource-specific Foundry proof and finish PR #7233; that live proof remains intentionally local and requires the configured developer resource.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Preserve reasoning metadata in Foundry hosting
* Python: Avoid duplicating reasoning text metadata
* Python: Gate encrypted reasoning for Foundry agents
* Python: Type stateless reasoning integration test
* Python: Narrow Foundry mock call arguments
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Support prompt cache breakpoints for GPT-5.6 models in OpenAI clients
Add request-level prompt_cache_options to OpenAIChatOptions and
OpenAIChatCompletionOptions, and forward a per-part prompt_cache_breakpoint
from Content.additional_properties onto the content blocks each API supports.
Text parts that carry a breakpoint keep typed list content, since the
plain-string form cannot hold one; without a breakpoint the existing string
forms are unchanged.
* Clarify system-message content-shape comment
* Address review: SDK prompt cache types, private helper, add sample
Replace the custom PromptCacheOptions TypedDict with the openai SDK's own
types for each API, which raises the openai floor to 2.45.0 where those
types were introduced. Make the breakpoint helper private to the two chat
clients. Add a prompt caching sample with a README entry, and unquote the
helper's Content annotation so the pyupgrade hook passes.
* Guard the prompt cache options import for older openai versions
The SDK's PromptCacheOptions types only exist in openai 2.45.0 and
later, so each client falls back to a local mirror when the import
fails and the dependency floor stays at 2.25.0. A TYPE_CHECKING-only
import is not enough because the options classes are introspected with
get_type_hints() at runtime. Verified against openai 2.25.0: the
package imports, the fallback resolves, and part-level breakpoints
still work; sending the option itself requires 2.45.0, which the field
docstrings now note.
* Make the old-openai fallback for PromptCacheOptions deliberately empty
Assigning None instead, as suggested in review, trips pyright's
reportInvalidTypeForm on the field annotation (the symbol becomes
type | None after the try/except). An empty TypedDict gives the same
effect for users on older openai versions: any content they put in
prompt_cache_options is flagged by their type checker, since the
option cannot be sent on those versions anyway, while
get_type_hints() on the options classes keeps working at runtime.
* Guard prompt_cache_options at runtime instead of via an empty fallback type
The empty-TypedDict fallback flagged valid `prompt_cache_options` usage under
pyright on every openai version — including this PR's own
`client_prompt_caching.py` sample (`poe check -S`) — because pyright resolves the
try/except symbol to the fallback shape regardless of the installed openai, while
mypy/ty resolve the failed import to `Any` and never warn. So a type-only "warn on
old openai" signal is not achievable cleanly across type checkers.
Restore the faithful fallback (mirrors the SDK's `mode`/`ttl` shape) so the option
type-checks identically on every supported openai version, and add a runtime guard:
setting `prompt_cache_options` on openai < 2.45 now raises a clear
ChatClientInvalidRequestException instead of forwarding an unusable option to the
SDK. This keeps the option non-silent for all users regardless of type checker,
without forcing an openai upgrade. Adds tests covering the guard for both clients.
* Gate system/developer breakpoint shape on a real mapping value
The system/developer branch switched to list-form content whenever
prompt_cache_breakpoint was set to any non-None value, but the option is
only attached when the value is a mapping. A malformed value (e.g. a
string) therefore changed the message shape without adding a breakpoint.
Decide the shape from the built part instead, matching the user-role path.
* Added example demonstrating creating an AIAgent using the Microsoft.AI.Extensions implementation of IChatClient using Dapr as the inference backend provider - in this example, using Ollama
Signed-off-by: Whit Waldo <whit.waldo@innovian.net>
* Update dotnet/samples/GettingStarted/AgentProviders/Agent_With_Dapr/README.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Added copyright statement at top of file
Signed-off-by: Whit Waldo <whit.waldo@innovian.net>
* Update dotnet/agent-framework-dotnet.slnx
That's odd the IDE added it a second time.
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* Address review nits: configurable Dapr gRPC endpoint and document VersionOverride
Make the Dapr sidecar gRPC endpoint configurable via the DAPR_GRPC_ENDPOINT environment variable
(defaulting to http://localhost:3501) and document it in the README. Add a comment explaining why the
Microsoft.Extensions.* VersionOverride entries are needed and when they can be removed.
---------
Signed-off-by: Whit Waldo <whit.waldo@innovian.net>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+RogerBarreto@users.noreply.github.com>
* .NET: Add OpenAI Responses protocol helpers and optional execution state (ADR-0032)
* Fix netstandard2.0/net472 build; harden helpers and workflow checkpoint key per review
* .NET: Migrate hosting Responses samples to Azure.AI.Projects and fix workflow resume
Migrate HostingResponsesAgent and HostingResponsesWorkflow samples from
Azure.AI.OpenAI to Azure.AI.Projects (AIProjectClient.AsAIAgent), using the
FOUNDRY_PROJECT_ENDPOINT/FOUNDRY_MODEL convention.
Fix HostedWorkflowState.RunOrResumeAsync: on subsequent turns, restore the
session's latest checkpoint and run the workflow forward with the new turn's
input (mirroring the Python hosting host's restore-then-run semantics) instead
of resuming a halted run with no input, which waited on input indefinitely.
Add round-trip resume tests and update ADR-0032/spec-003 wording.
* .NET: Fix HostedWorkflowState resume hang on unserviced external requests
On resume, HostedWorkflowState.RunOrResumeAsync drained the workflow with the
blocking WatchStreamAsync overload, so a workflow that halts at an unserviced
RequestInfoEvent (human-in-the-loop / approval) blocked forever — asymmetric
with the first-turn RunAsync path, which returns at the same halt. Break the
drain when a superstep completes with HasPendingRequests, restoring symmetry
with turn 1. Add a HITL approval-gate workflow and a resume-does-not-block test.
* .NET: Warn when a HostedWorkflowState resume makes no progress
Add an optional ILoggerFactory to HostedWorkflowState and log a warning when a
resumed turn produces no events, mirroring the Python host's zero-event restore
warning (a stale checkpoint or an input that does not match the workflow's
expected type leaves session state unprogressed). Add a non-chat string workflow
helper, a capturing logger, and a red/green test.
* .NET: Resume HostedWorkflowState from durable checkpoint on cursor miss
Add CheckpointManager.GetLatestCheckpointAsync(sessionId) and have
HostedWorkflowState fall back to it when its in-memory head cursor misses, so a
durable CheckpointManager resumes a session across a process restart or a new
holder instead of restarting from the workflow's start executor. Mirrors the
Python host's per-turn get_latest read-through. Add a counting workflow that
proves resume-vs-fresh via accumulated state, plus a red/green test, and update
ADR-0032/spec-003 and the XML remarks.
* .NET: Serialize HostedWorkflowState turns through a workflow lock
A single workflow instance backs the holder and workflow instances do not
support concurrent runs (the runner throws "already owned by another runner"),
so concurrent turns could fault or race the head cursor. Serialize all turns
through one SemaphoreSlim (mirroring the Python host's workflow lock) and make
HostedWorkflowState IDisposable to own it. Add a gated workflow and a
deterministic concurrency red/green test.
* .NET: Cover non-chat resume and multi-turn checkpoint advance
Add tests for HostedWorkflowState resuming a non-chat-protocol workflow (no
TurnToken) and for a third turn continuing to advance the head checkpoint,
closing the coverage gaps the parity review flagged.
* .NET: Add streaming workflow resume path and stream the workflow sample
Add HostedWorkflowState.RunOrResumeStreamingAsync, which yields the turn's
WorkflowEvents as they occur (fresh run or checkpoint resume) under the same
serialization lock and records the head checkpoint after the stream drains,
keeping the blocking and streaming workflow paths in lockstep with the Python
host. Honor stream:true in the HostingResponsesWorkflow sample by projecting
AgentResponseUpdateEvent updates over the Responses SSE wire. Add a streaming
resume test and update the README/spec.
* .NET: Cover Responses input adaptation to a typed workflow start executor
Demonstrate that HostedWorkflowState's generic RunOrResumeAsync<TInput> is the
input-adaptation seam (parity with Python's ResponsesChannel run hook): the app
adapts the Responses input into the workflow start executor's own type at the
call site. Add a typed-brief workflow and a test, and note the seam in spec-003.
* .NET: Drain workflow resume non-blocking to prevent hang and truncation
The resume drain used a SuperStepCompletedEvent{HasPendingRequests} proxy over
the blocking public WatchStreamAsync. That proxy (a) truncated a resumed turn
when a superstep both emitted a request and queued downstream work, and (b)
could fail to fire at all — re-introducing the indefinite hang — when a resume
input drove no superstep (e.g. a rejected non-chat input).
Make StreamingRun.WatchStreamAsync(bool blockOnPendingRequest, CancellationToken)
public and drain both the blocking and streaming resume paths with
blockOnPendingRequest:false, exactly matching the first-turn RunAsync semantics
(Run.RunToNextHaltAsync). Add guard tests: resume with a rejected input does not
hang, and a resume superstep with a request plus downstream work is not
truncated (verified red against the old proxy).
* .NET: Return file-store checkpoint index in commit order
CheckpointManager.GetLatestCheckpointAsync takes the last entry of a store's
index as the head checkpoint. FileSystemJsonCheckpointStore backed its index
with a HashSet, whose enumeration order is not contractual: after a rollback
frees and reuses a slot, enumeration can diverge from commit order, so the
durable read-through could resume a stale checkpoint. Mirror the HashSet with an
insertion-ordered list and enumerate it from RetrieveIndexAsync so 'latest' is
reliable. Add a CheckpointManager.GetLatestCheckpointAsync contract test over the
file store.
Note: the HashSet disorder is only reachable via the internal rollback path, so
the test locks the ordering contract rather than reproducing the rare disorder.
* .NET: Advance cursor when a streaming resume is abandoned
RunOrResumeStreamingAsync recorded the head checkpoint only after the stream was
fully enumerated. If an SSE consumer disconnected mid-turn after supersteps had
committed, the in-memory cursor kept the previous turn's head; because the next
turn is then a cursor hit, durable read-through could not self-heal, so it
resumed pre-disconnect state. Record the run's last committed checkpoint in a
finally so an abandoned stream still advances the cursor. Add a red/green test.
* .NET: Stream only the final agent's updates in the workflow sample
ExtractUpdates streamed every agent's updates, so the sequential Writer->Reviewer
sample streamed the intermediate draft and the final answer over SSE, differing
from the non-streaming response (final message only). Filter the streamed updates
to the final agent so streaming and non-streaming produce the same response.
Live-verified against Foundry: one output item streamed instead of two.
* .NET: Isolate the holder lock in the concurrency test
The concurrency test asserted the second same-session turn did not enter the
workflow, which also passes via the engine's concurrent-run ownership guard
(which faults) rather than the holder lock (which waits). Assert instead that the
second turn is not completed while the first holds the lock: a fault would
complete the task, so a pending task isolates the holder lock from the engine
guard. Verified red with the lock removed.
* Fix IDE1006 naming in tests; address review feedback and add hosting/live tests
* Document commit-order contract for ICheckpointStore.RetrieveIndexAsync
* Restructure hosting samples under af-hosting with client/server split matching Python parity
* Clarify hosting sample README wording and drop Python comparisons
* Make AgentSessionStore.DeleteSessionAsync abstract and rename session id parameter to sessionStoreId
* Rename OpenAIResponses id helpers and parse the request once for id extraction
* Reclaim per-session locks in HostedAgentState and demonstrate session locking in the agent sample
* Internalize per-session locking in HostedAgentState (automatic, on by default) and remove mirroring-Python wording from code and spec
* Remove HostedAgentState; app-owned routes use AgentSessionStore directly
HostedAgentState only bundled an AIAgent with an AgentSessionStore and, after
the per-session lock was removed, its GetOrCreateSessionAsync/SaveSessionAsync/
DeleteSessionAsync were pass-throughs that just bound the agent argument.
Create-on-miss already lives in the store (unlike Python, whose get/set-only
SessionStore justifies its AgentState holder), so the type earned its place
only via the lock.
Each AgentSessionStore.GetSessionAsync now returns an independent session
instance per call, so concurrent gets fork the same stored state (e.g.
branching from previous_response_id or managing several conversation ids)
without sharing an instance. The store does no cross-call locking; serializing
concurrent runs against the same id is the application's concern.
- Delete HostedAgentState and its unit tests.
- Rewire the local_responses sample and the OpenAI hosting unit/integration
tests to call AgentSessionStore (GetSessionAsync/SaveSessionAsync) directly.
- Update ADR-0032, spec-003, and the af-hosting sample READMEs.
* Isolate hosted session snapshots and distinguish conversation vs response continuation
Mirrors the Python hosted-session isolation work: a hosted session read must be
an independent copy, and the app-owned route must persist under the right
continuation key depending on how the caller continued the thread.
- AgentSessionStore.GetSessionAsync: document the isolation invariant (each
call returns an independent AgentSession so concurrent branches from one
previous_response_id do not observe each other's mutations or alter stored
state); fix the stale "or null if not found" wording (in-box stores return a
fresh created session on miss). The in-box stores already satisfy this via a
serialize/deserialize snapshot round-trip.
- local_responses sample + hosting unit-test route: choose the save key by
channel. A stable conversation id is a mutable head (write back under the
same id; app owns single-writer coordination). A previous_response_id
continuation or first turn is an immutable snapshot (save under the new
response id so branches from the same prior response stay independent).
- Add regression tests: independent get returns a distinct instance
(InMemoryAgentSessionStore); previous_response_id supports independent
branches ([1,2,2,3,3]); conversation id advances the mutable head ([1,2]).
- Update the sample README and ADR-0032 wording.
* Add workflow-factory support to HostedWorkflowState for concurrent sessions
HostedWorkflowState backed every session with one shared Workflow instance and
serialized all turns through a lock, so independent sessions could not run
concurrently. Add a workflow-factory constructor and remove the run lock.
- New constructor HostedWorkflowState(Func<CancellationToken, ValueTask<Workflow>>
workflowFactory, ..., bool cacheWorkflow = false):
- cacheWorkflow: false (default) builds a fresh instance per run, so independent
sessions run in parallel. A resume rehydrates a fresh instance from the
session's checkpoint in the shared store.
- cacheWorkflow: true builds the workflow once, lazily on first use, and reuses
it (a deferred, cached target that, like a shared instance, cannot run
concurrent turns).
- Remove the internal SemaphoreSlim run lock and IDisposable; the instance
constructor is unchanged in behaviour (one shared instance still cannot run
concurrent turns). Turns are no longer serialized by the holder; a single
writer per session is the application's responsibility.
- Switch the local_responses_workflow sample to the factory constructor with an
explicit cacheWorkflow: false, and document the option.
- Add tests: parallel independent sessions (factory), fresh-instance resume,
cached factory builds once and reuses, uncached factory builds per run.
- Update ADR-0032, spec-003, and the sample README.
* Clarify in ADR-0032 how .NET covers AgentState factory and async-setup via DI
* Rebuild cached workflow after a faulted build and add checkpoint index dedup tests
* Python: preserve Gemini 3 thought_signature across function-call replays
Gemini 3 requires the opaque thought_signature attached to each functionCall
part to be echoed back on every replay of that call, or the request is rejected
with 400 INVALID_ARGUMENT. The signature previously survived only via
raw_representation, so any layer that reconstructs a FunctionCallContent (e.g.
harness tool approval) dropped it and broke the next step of the tool loop.
Capture the signature into additional_properties on parse and replay it when
building the Gemini Part, independent of raw_representation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
* Store Gemini thought_signature as base64 for JSON-safe persistence
Content.additional_properties is serialized via json.dumps(message.to_dict())
by history providers (e.g. RedisHistoryProvider), which fails on raw bytes.
Store the thought_signature as a base64 string on parse and decode it back to
bytes when building the Gemini Part. Also narrow call_id/name in the round-trip
test to satisfy the type checkers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
* Harden Gemini thought_signature decode against corrupted history
Guard the untyped additional_properties value with an isinstance(str) check and
decode with validate=True, degrading gracefully (warn + drop the signature) on
malformed data instead of raising binascii.Error mid tool loop. Matches the
defensive base64 handling already used for data URIs in this file.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
* Carry Gemini thought_signature on reasoning content via protected_data
Represent the signature as a text_reasoning content's protected_data (base64)
immediately preceding the function call, instead of a bespoke additional_properties
key. This uses the framework's first-class opaque-signature field (as Anthropic
does), survives streaming accumulation, and stays intact when the harness
reconstructs the function call. Replay correlates the signature by adjacency.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: make FoundryToolbox.as_skills_provider() disable_caching effective
as_skills_provider() forwarded disable_caching to SkillsProvider, which
ignores it for a caller-supplied SkillsSource, so it was a no-op and the
toolbox re-read skill://index.json on every agent run.
Compose caching in as_skills_provider() instead: wrap the context-independent
_FoundryToolboxSkillsSource in DeduplicatingSkillsSource(CachingSkillsSource(...)).
Add a cache_refresh_interval param, fix the docstring, and add tests covering
cached, disabled, and refresh-interval behavior.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Clarify caller-invariant skill-set wording in as_skills_provider docs
Emphasize that the toolbox advertises the same skill set to every caller (the
per-request call-id governs execution/authorization, not which skills are
listed) rather than leaning on 'ignores SkillsSourceContext'.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Make MCP skills reconnect-safe via session_provider
Cached MCPSkill objects captured the MCP ClientSession at construction, so
after a FoundryToolbox reconnect (which replaces its session) load_skill and
read_skill_resource would fail against the closed session. This regressed once
as_skills_provider() started caching discovery by default.
Add an optional session_provider callable to MCPSkillsSource and MCPSkill
(exactly one of client or session_provider). When supplied, the session is
resolved on every fetch, mirroring how MCPTool resolves self.session live at
call time. _FoundryToolboxSkillsSource now passes a provider that returns the
toolbox's current session, so cached skills always use the live session.
The fixed client= path is unchanged and backward-compatible. Update core tests,
foundry_hosting tests, and core AGENTS.md.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Fix ty error: type captured session_provider as Callable in test
ty could not call the provider narrowed from \object\ (Top callable). Type the
captured value as Callable[[], object] and drop the redundant callable() assert.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Simplify _resolve_mcp_session_provider per review
Address review feedback: replace the dense (client is None) == (session_provider
is None) guard with explicit branches, and drop the cast by binding the narrowed
client to a typed local. Keeps strict 'exactly one' semantics (raises on both and
on neither), matching the codebase convention (e.g. security.py mcp_tool/url).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Add PR #7135 entries to the 1.12.0 changelog
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Drop redundant @pytest.mark.asyncio from MCP skills tests
asyncio_mode is 'auto', so the marker is unnecessary. Remove it from the whole
file for consistency with the async-by-default convention. Per review feedback.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Bump Python package versions for 1.12.0 release
Bump packages represented in the 1.12.0 changelog, promote Foundry Hosting, Azure Content Understanding, Gemini, Mistral, Monty, and Tools to beta, and apply the requested beta cohort date stamp. Root and core move to 1.12.0, released and RC packages use their selected increments, alpha packages including Hosting MCP use the 260721 stamp, and core floors are raised only for proven consumers.
Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf
* fix version in readme
* Add Responses conversation ID changes to release notes
Include the breaking Hosting Responses conversation ID helper changes from #7234 in the Python 1.12.0 changelog.
Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf
* Python: Fix PropertySchema.to_json_schema() not recursing into nested schemas
Nested array 'items' and object 'properties' kept the declarative 'kind'
key and empty 'enum' placeholders, producing JSON Schema OpenAI rejects
('schema must have a type key'). Recursively apply the same conversion the
top-level properties loop performs, including the serialized named-list
properties shape and nested required arrays.
Fixes#7198
(cherry picked from commit c156ffd05924fb5a1884625f2fc3d9bdc3e152b1)
* Python: Validate nested properties list before mutating to avoid partial conversion
Review feedback: the list-shaped properties branch popped name/required from
each element and returned on the first unexpected one, leaving earlier
elements half-converted. Validate the whole list first so an unexpected
shape leaves the node fully untouched.
* Python: Type nested-properties normalization for strict Pyright and drop unreachable dict branch
ObjectProperty always stores nested properties as a named list, so the
elif-dict branch in _normalize_nested_schemas was unreachable; remove it
and flatten the list conversion behind an early return. Cast the narrowed
items/props values so strict Pyright no longer reports unknown types.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Python: Emit additionalProperties: false on nested object nodes in PropertySchema.to_json_schema()
OpenAI strict structured outputs require additionalProperties: false on
every object node, but the chat clients only inject it at the schema
root, so declarative schemas with nested objects (e.g. array items)
failed with a schema-validation 400. Route the top-level properties loop
through _normalize_schema_node so all object nodes get the key, and add
a live OpenAI integration test covering the nested array-of-objects
response_format shape.
Verified live against the Responses API: the previous emission fails
with "In context=('properties', 'issues', 'items'),
'additionalProperties' is required to be supplied and to be false";
the new emission returns valid structured output.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* .NET: Bind tool-approval responses to surfaced approval requests
Harden the tool-approval flow so an approved tool call always matches the
request the framework surfaced for approval.
Add ApprovalResponseBindingChatClient as the outermost decorator above
FunctionInvokingChatClient. It records each model-originated
ToolApprovalRequestContent in the session state and, on the next request,
binds every ToolApprovalResponseContent to its recorded request: the
response tool call is rebound to the recorded call, matched entries are
consumed for one-time use, and only approvals tied to a framework-issued
request take effect.
Apply the same binding in the ToolApprovalAgent harness by tracking the
requests it surfaces and binding collected responses to them during a
queue cycle.
Add ChatClientAgentOptions.DisableApprovalResponseBinding (default off) and
a UseApprovalResponseBinding builder extension for custom chat client stacks.
Includes unit tests for the decorator and the harness.
* .NET: Bind approval responses once per turn and avoid re-enumeration
Address review feedback on the approval-response binding decorator:
consume a matched request from the per-turn lookup so a duplicate response
with the same request id in one turn is honored only once, and return the
materialized message list instead of the original enumerable so a single-use
sequence is not enumerated twice. Rename the local pending list to
pendingRequests for clarity. Adds a duplicate-response regression test.
* .NET: Snapshot recorded approval requests and consume duplicates in the harness
Address review feedback on ToolApprovalAgent:
store a snapshot of each surfaced/pending approval request (cloned tool call
with a copied arguments dictionary) so a later mutation of the caller-visible
instance cannot change the recorded call used to bind the response, and consume
a surfaced request on match so a duplicate response with the same request id in
one pass is honored only once. Apply both symmetrically in the harness and the
ApprovalResponseBindingChatClient decorator. Adds regression tests for the
snapshot and duplicate-response cases.
* .NET: Address review feedback on approval-response binding
- Harness: store surfaced approval requests in a dictionary and consume matches directly, drop the extra hashset and the redundant record-time dedup; replace clear-on-resolution with a debug assert.
- Harness pipeline: add UseApprovalResponseBinding() as the outermost decorator in HarnessAgent (it uses UseProvidedChatClientAsIs) behind a new DisableApprovalResponseBinding option, with tests.
- Decorator: avoid message/content allocations when nothing changes, keep the original content when a response already matches the recorded call, clear pending each inbound turn, and shorten helpers.
* .NET: Compare tool calls by fields instead of serializing
Replace the JSON-serialization comparison in the approval-response binding
decorator with a direct field comparison. Fast-path FunctionCallContent by
comparing CallId, Name, and arguments field by field; any other tool call
shape rebinds. The comparison only skips an allocation (the call is always
rebound to the recorded request otherwise), so a miss just triggers a safe
rebuild. Adds a test that a matching response is forwarded unchanged.
* .NET: Bind approval responses against requests present in history
Fix a merge-queue regression where AG-UI mixed server/client tool invocation
stopped executing the server tool. The binding decorator validated approval
responses only against its own recorded pending state, so a matched approval
request/response pair replayed from conversation history was treated as
unbound and dropped, and the auto-approved server tool never ran.
Treat known requests as the recorded pending state plus any approval requests
already present in the current messages, and stop dropping approval requests
(a request in history is the pairing authority). A response with no known
request anywhere is still dropped, so a forged approval cannot execute.
Also address review feedback: return the mutable contents buffer from a
helper instead of a null-forgiving operator, and use clearer naming
(PrepareMutableContentsBuffer / mutableContentsBuffer). Adds regression tests
for a request in history and a response bound to a history request with empty
pending state.
* Python: fix header_provider headers not reaching streamable HTTP requests
MCPStreamableHTTPTool.call_tool stores header_provider output in a
ContextVar, but the streamable HTTP transport sends requests from tasks
spawned at connect time, whose contexts never observe values set later.
The request hook therefore always read an empty dict on real connections
and the per-call headers (e.g. Authorization) were silently dropped.
Keep the ContextVar for in-context reads and add an instance-level
snapshot of the active call's headers that the request hook falls back
to across tasks.
* Python: serialize header_provider tool calls to prevent cross-call header mixing
Parallel tool invocations run concurrently per function-invocation batch,
so two call_tool invocations on the same MCPStreamableHTTPTool could
overwrite each other's active-header snapshot while requests were still
in flight, attaching the wrong per-call credentials. Hold a per-instance
lock for the duration of a header-bearing call, add a regression test
that fails without the lock, and normalize captured header casing in the
transport-task test.
* .NET: Populate AgentResponse metadata in CopilotStudioAgent
Map CreatedAt, FinishReason, RawRepresentation and AdditionalProperties onto
AgentResponse and AgentResponseUpdate, and map the activity timestamp and
properties onto ChatMessage, so Copilot Studio agents expose the same metadata
surface as other AIAgent implementations. Streaming sets the finish reason on
the terminal update while still emitting already-received content if the source
faults. Add unit tests covering the metadata mapping.
* fix: add Async suffix to async test methods (IDE1006)
* Python: forward GitHubCopilotOptions verbatim to create_session
Refactor the GitHub Copilot agent to forward the full options dict to the
Copilot SDK's create_session/resume_session instead of hand-mapping a fixed
subset. GitHubCopilotOptions stays as the curated, typed surface, but any
other create_session parameter (reasoning_effort, context_tier,
enable_citations, ...) is now passed through verbatim. Unknown keys surface
as TypeError from the SDK instead of being silently dropped.
De-duplicates the near-identical _create_session/_resume_session bodies into
a shared _build_session_kwargs helper.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f9d016d5-4d8c-43f0-a8fb-f3cf3d1ad7eb
* Python: address review feedback on GHCP options passthrough
- Strip agent-internal/client-level keys (on_pre_tool_use, on_function_approval,
timeout, cli_path, log_level, base_directory) from the forwarded kwargs so they
cannot leak into create_session/resume_session and raise TypeError.
- Source caller tools from the merged options layer so tools supplied via
default_options are honored instead of silently dropped.
- Honor a caller-supplied native 'hooks' dict in _build_session_hooks (composing
with the on_pre_tool_use shortcut) instead of unconditionally overwriting it.
- Validate mock create_session/resume_session calls against the real SDK
signatures in tests so invalid kwargs surface as TypeError, and add regression
tests for the passthrough contract.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f9d016d5-4d8c-43f0-a8fb-f3cf3d1ad7eb
* Python: avoid redundant re-read of model in _build_session_kwargs
model is popped from default_options into settings at init, so a per-run
model already lands in the merged kwargs. Keep that value when present and
only fall back to the resolved setting otherwise, instead of re-reading opts.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f9d016d5-4d8c-43f0-a8fb-f3cf3d1ad7eb
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add agent-framework-azure-cosmos-memory context provider (draft)
Introduces CosmosMemoryContextProvider, a ContextProvider that wraps the azure-cosmos-agent-memory toolkit to give agents long-term, Cosmos DB-backed memory (fact/procedural recall + user summaries). Includes package scaffolding, unit tests (mocked client), live Azure integration tests (marked), samples, README, and AGENTS.md.
Draft: uv.lock is intentionally left unchanged. This package depends on azure-cosmos-agent-memory (requires Python >=3.11), which is unsatisfiable against the workspace's current >=3.10 floor, so adding it to the shared lock requires a workspace decision (raise floor to 3.11 or exclude from workspace). Test coverage to be expanded.
* ci: exclude azure-cosmos-memory from uv workspace resolution
The package depends on azure-cosmos-agent-memory which requires Python
>=3.11 and a prompty pre-release (>=2.0.0a9). Both are unsatisfiable
against the workspace's >=3.10 floor and pre-release policy, causing
uv sync to fail in every Python CI job. Exclude the package from the
shared workspace so it is resolved and tested as a standalone package.
* ci: fix code-quality failures for azure-cosmos-memory
- Strip trailing whitespace from package files (pre-commit trailing-whitespace hook)
- Exclude the package README from markdown-code-lint: the package is excluded
from the uv workspace, so its README snippets import a module that is not
installed in the workspace env and Pyright cannot resolve it
* Exclude azure-cosmos-memory README from markdown-code-lint task
* Address PR review comments on cosmos-memory context provider
- Wire credential into Cosmos and AI Foundry clients; let toolkit own
DefaultAzureCredential when none supplied (remove dead import).
- Honor auto_extract=False by zeroing extraction/summary cadence thresholds.
- Skip whitespace-only conversation turns and store stripped content.
- Show confidence 0.0 and coerce confidence to float in _format_memories.
- Register both 'integration' and 'azure' pytest markers accurately.
- Fix duplicated install block in README.
- Update and extend unit tests for new credential wiring and fixes.
* Include azure-cosmos-memory in the uv workspace
Follow the github_copilot pattern for a package with a Python 3.11-only
dependency: lower requires-python to >=3.10 and gate azure-cosmos-agent-memory
behind a python_version >= '3.11' marker. Add a direct, gated prompty
pre-release dependency so the workspace's if-necessary-or-explicit prerelease
policy permits the toolkit's transitive prompty requirement. Guard the test
modules with pytest.importorskip so the 3.10 CI leg skips cleanly. Remove the
workspace exclude and the markdown-code-lint exclude, and regenerate uv.lock.
* Address review feedback on cosmos-memory provider
Rename provider parameters to match Agent Framework conventions:
foundry_endpoint (was ai_foundry_endpoint) and embedding_model/chat_model
(were *_deployment_name). Move DEFAULT_* to module-level constants, type
memory_types as a Literal, use DEFAULT_CONTEXT_PROMPT as the default value,
and add ProcessorConfig/CosmosMemorySettings TypedDicts. Resolve connection
settings via agent_framework load_settings with required-field validation,
replacing the manual getenv/raise blocks. Scope user_id/thread_id to the
provider state and drop the unpreventable first-turn warning.
Rewrite the samples around Agent (not raw SessionContext), provider-scoped
state, and session-id threading; use PEP 723 inline dependencies instead of a
samples dependency group; use a plain input() loop; remove the dead custom
processor stub. Update README/AGENTS for the renamed parameters and env vars.
Add a samples ruff per-file-ignores entry now that the package is linted in CI.
* Add emulator-backed vector search integration test
Bump azure-cosmos-agent-memory to >=0.2.0b2 (adds the embeddings/chat client
injection seam) and add tests/test_emulator.py: an integration (not azure)
suite that exercises real Cosmos vector search with a quantizedFlat index
against a local Cosmos DB emulator, using deterministic in-memory fakes for
embeddings and chat so no Azure AI Foundry account or LLM is required.
To run on a stock emulator the fixture strips the toolkit's full-text index
(the provider only does pure vector search) and requests provisioned autoscale
throughput instead of serverless. The suite skips cleanly when no emulator is
reachable.
* Fix CI typing and package checks for azure-cosmos-memory
The package recently joined the uv workspace, so its source and tests are now covered by the Test Typing Checks and Package Checks gates for the first time.
tests: rename stale constructor kwargs to the current provider API (foundry_endpoint/embedding_model/chat_model); use a typed _STUB_AGENT for the unused agent param so pyright/pyrefly/ty/zuban all accept it; make processor_config values ints; assert non-None memory_client in the emulator tests.
source: relax reportUnknown*/reportOptional* for this package only (the toolkit ships no py.typed; mirrors the hosting-telegram precedent); decouple the conditional toolkit import from the annotation type; use settings.get(); fix memory_types list invariance; drop a redundant None guard; read role via getattr.
* Apply pyupgrade: single-arg AsyncGenerator in test_integration
* Make Cosmos memory extraction drain transparently on provider exit
The provider now drains in-flight background memory extraction in __aexit__, so applications no longer need to call flush() in their own control flow; the client's close() would otherwise cancel pending extraction tasks. flush() is hardened against clients that expose no usable background-task registry.
sample: interactive_chat reads input via asyncio.to_thread so the event loop stays free and background extraction runs during the session; removes the manual flush now that the provider drains on exit.
tests: add explicit transparent-extraction integration tests (emulator: after_run schedules extraction and __aexit__ drains it; live Azure: a fact is extracted and recalled in a later session with no manual flush). Emulator tests reuse a single fixed database to avoid exhausting the emulator's partition budget across runs.
* Add custom extraction-prompt seam and sample to cosmos-memory provider
Adds a prompts_dir option to CosmosMemoryContextProvider that points the Agent Memory Toolkit pipeline at a caller-supplied directory of Prompty templates, so callers can override extract_memories.prompty to control what the extraction LLM produces. The toolkit exposes no public prompts-directory seam, so the provider contains the one internal touch (swapping the pipeline's template loader after the store connects); applies to both provider-built and supplied clients.
sample: interactive_chat_custom_extraction.py - the interactive chat wired with a custom coding-assistant extraction rubric. It derives a complete prompts directory at runtime (copies the bundled templates and augments extract_memories.prompty) so it stays schema-compatible with the installed toolkit.
tests: unit tests assert the provider redirects the pipeline loader only when prompts_dir is set; an emulator integration test proves end to end that a unique marker in a custom extract_memories.prompty reaches the extraction LLM call.
* docs: document prompts_dir custom-extraction seam in cosmos-memory README
Replaces the stale, non-functional CustomMemoryProcessor snippet with the working prompts_dir approach, lists the new interactive_chat_custom_extraction.py sample, and corrects the interactive-sample feature list.
* Address review: rename _new_session, drop defensive toolkit import guard
Sample (comment): rename _new_thread to _new_session in both interactive samples (a new session is the new thread).
Provider (comment): replace the _memory_toolkit_available flag + __init__ ImportError guard with a plain guarded import that re-raises a clear ImportError, matching the github_copilot package's pattern for its 3.11-only SDK. Kept requires-python >=3.10 (bumping this one workspace member to 3.11 would force the entire uv workspace lock floor to 3.11). Tests now run importorskip before importing the package, mirroring github_copilot.
* Pass cadence via cadence_thresholds instead of mutating os.environ
* Mark package alpha and drop private naming in samples
* Require Python 3.11 and inject user summary as untrusted context
* CI: exclude azure-cosmos-memory from uv sync on Python 3.10
* Re-trigger CI (flaky external link check)
* Require chat/embedding models instead of silent defaults
* Fix pyright: narrow resolved chat/embedding models to str
---------
Co-authored-by: Theo van Kraay <thvankra@microsoft.com>
TokenBudgetComposedStrategy estimates tokens by feeding a JSON-serialized
message to the tokenizer, but _serialize_message() used ensure_ascii=True.
That escapes non-ASCII text into \uXXXX sequences, so CJK and other
non-Latin content is token-counted as the escape sequences rather than the
characters the model actually sees, inflating the estimate (~1.6x for mixed
Japanese, more for pure CJK) and skewing compaction/token-budget decisions.
Serialize with ensure_ascii=False, matching the ensure_ascii=False already
used elsewhere in this module. Only affects token estimation; the serialized
string is never stored or transmitted.
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Python: preserve explicit null arguments in auto function calling
FunctionTool.invoke dumped validated arguments with model_dump(exclude_none=True),
which strips any argument the model set to null. A required nullable parameter
(e.g. unit: Literal["C","F"] | None) that the model deliberately sets to null was
therefore dropped, and the function failed to invoke on the missing argument.
Use exclude_unset instead: keep the arguments the model actually provided (null
included) and omit only the ones it left out, so the function's own defaults still
apply. Because the input model is generated from the function signature, its field
defaults match the signature defaults, so omitted optionals are unchanged.
Fixes#5934
Signed-off-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
* Python: extend null-arg fix to the auto function-calling path
The earlier change fixed FunctionTool.invoke, but _auto_invoke_function
(the path a model-emitted function_call actually takes) still ran
model_dump(exclude_none=True), so an explicit null for a required
nullable argument was still dropped and the call failed with a missing
argument. Switch it to exclude_unset to match invoke, and add a
regression test that drives _auto_invoke_function with an explicit null.
---------
Signed-off-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Refactor MessageMerger to preserve message order
Refactored MessageMerger to delegate update grouping and merging to M.E.AI, preserving the correct order and structure of assistant messages, especially for reasoning content without message IDs. Removed per-message bucketing and CreatedAt-based sorting. Added tests to verify message order and correct merging of reasoning and text updates.
* Set CreatedAt from merged responses preservation of original message timestamps during merging.
* Set merged message CreatedAt to current UTC time
Removed logic for tracking unique creation times and now always assign DateTimeOffset.UtcNow to the merged response's CreatedAt property. This simplifies timestamp handling during message merging.
* Refactor MessageMerger id-less folding logic
Refactored MessageMerger to fold identifierless reasoning segments into the following id'd message at the flattened-message level, ensuring correct merging across response buckets (fixes#6329). Updated ComputeMerged to merge id-less messages with the next message of the same role. Removed redundant per-bucket folding logic. Added unit tests to verify correct folding behavior and role matching.
* Remove unused property
Removed the unused Role property from MessageMergeState for code cleanliness.
* Refactor MessageMerger to iterate backward for merging
Changed MessageMerger to iterate messages in reverse order, ensuring all consecutive messages without IDs preceding a message with an ID are merged correctly. Updated merging logic, index handling, and comments to reflect this new approach.
* Update code comment to better reflect its behavior.
---------
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* feat(durabletask): surface HITL respond-URL addressing to workflow executors
Let a workflow notify a human reviewer (e.g. email an approval link) from inside the
graph, without the caller threading the instanceId/requestId by hand.
- durabletask: the orchestrator injects host_context {instance_id, workflow_name,
request_path_prefix} into each activity input; CapturingRunnerContext surfaces it as
host_metadata. No new core API.
- azurefunctions: WorkflowHitlContext.from_context(ctx) builds the canonical
respond/status URLs (returns None in-process so callers degrade gracefully).
Re-exported through the agent_framework.azure lazy namespace.
- Nested sub-workflows: the address context (root instance + workflow name +
accumulated {executor}~{ordinal}~ prefix) propagates down call_sub_orchestrator via a
new SUBWORKFLOW_ADDRESS_KEY marker, so an executor at any depth builds a URL that
targets the addressable top-level instance with a qualified request id. The per-child
ordinal matches the read-side enumerate() index used by the status/respond endpoints.
The marker is stripped from untrusted input alongside SUBWORKFLOW_INPUT_KEY
(confused-deputy / info-leak guard).
- Samples 12 and 13 reworked into the retry-safe two-step notify pattern: the emitter
generates an explicit request id and a downstream NotifyExecutor builds the URL and
notifies, so failed upstream retries never produce a dead link.
Tests: unit coverage for the metadata round-trip, address/ordinal agreement (fan-out at
depth and nested prefix accumulation), marker stripping, and URL building; integration
tests assert the helper-built URL equals the server respondUrl and resumes the run, for
both the flat (12) and nested (13) samples.
* refactor(durabletask): read back request_info id instead of generating one in samples
Add WorkflowHitlContext.pending_request_id(ctx), an async helper that returns the
id request_info just generated (read from the runner context's pending request-info
events). This works on any host via the core RunnerContext protocol method, so it
needs no core change.
Samples 12 and 13 now call request_info() and read the id back to forward to the
NotifyExecutor, instead of minting a uuid by hand and passing request_id=. The
read-back happens in the same activity execution that generated the id, so the
pending request event and the notify message still commit together with the same id
(retry-safe; failed upstream retries notify no one).
* docs(azurefunctions): document request_info id read-back and notify safety
Tighten pending_request_id docstring to require calling it immediately after request_info, and explain why that is safe on the durable host (each executor runs in its own activity with its own runner context, so the pending set only holds this executor's requests and the newest is the one just emitted). Document the two-step notify pattern in the 12 and 13 sample READMEs, including the downstream-notifier retry safety and the nested address-prefix propagation.
* fix(python): resolve ty typing error and address PR review comments
- test_subworkflow_orchestration: replace the mypy-only type:ignore[arg-type] with a cast so the ty checker passes too (the other four checkers already honored the ignore).
- samples 12/13 README: guard the notify snippet against None before build_respond_url to match the documented graceful-degradation behavior.
- integration tests 12/13: reword comments that implied request_info now generates an explicit uuid4; it generates the id internally by default.
* fix(python): honor configurable Functions route prefix and address HITL PR review
- Resolve the route prefix from host.json (extensions.http.routePrefix, default api) in a new azurefunctions _routes module, used by both the server endpoints and WorkflowHitlContext, so a custom or empty routePrefix no longer 404s respond/status URLs (was hardcoded /api/ in four places).
- Extract respond/status URL construction into one shared builder called from _app.py and _hitl_context.py, removing the sync-by-test duplication.
- Broaden loopback detection (localhost, 127.0.0.0/8, 0.0.0.0, ::1, [::1]) via a _is_loopback helper so local links use http.
- Pin the host_context key names as shared constants in durabletask so producer and azurefunctions consumer cannot drift.
- Reword a stale base_url comment to reference WEBSITE_HOSTNAME.
- Add unit tests for the route module and loopback handling.
* refactor(azurefunctions): derive server-side HITL URLs from the request URL
The run and status endpoints now derive the base URL and route prefix from the incoming request URL (the value the host actually routed) via split_request_url, so the caller-visible respond/status URLs no longer depend on reading host.json on the server. The in-workflow helper keeps reading host.json since it has no request context. Replaces strip_route_prefix and updates its tests.
* test(durabletask): enforce sub-workflow ordinal and read-index agreement
Extract the read-side subworkflows grouping into a shared _index_subworkflows helper (used by the orchestrator) and add test_readside_index_matches_dispatch_ordinal, which round-trips a fan-out through that helper and asserts subworkflows[executor][ordinal] resolves to the child the dispatch stamped that ordinal onto. Turns the previously comment-only write-ordinal / read-index invariant into a shared, CI-enforced one.
* fix(azurefunctions): suppress bandit B104 on loopback host set
* samples: add Neo4j Shopping Assistant (standalone, published AgentMemory 1.0.1)
The .NET port of the official Neo4j Agent Memory "retail assistant" example
(neo4j-labs/agent-memory examples/microsoft_agent_retail_assistant, referenced
from the Learn integration page), which is currently Python-only.
Wires Neo4jMemoryContextProvider (AIContextProvider), MemoryToolFactory
memory tools, and a ProductCatalog of retail tools over a Neo4j :Product
graph, via the published AgentMemory + AgentMemory.AgentFramework 1.0.1
NuGet packages.
Lives at the repo root rather than under dotnet/samples/: that tree is
.NET 10 + Central Package Management + Microsoft.Agents.AI ~1.13 with
source ProjectReferences, while AgentMemory currently targets net9.0 +
Microsoft.Agents.AI 1.9.0. A repo-native version needs AgentMemory bumped
to track the newer Agents.AI/Extensions.AI line first. Cross-linked from
dotnet/samples/02-agents/AgentWithMemory/README.md as a "See also" entry,
same pattern already used for the cross-folder Custom Memory Implementation
link.
Verified: dotnet build succeeds (0 warnings, 0 errors) against the published
packages, proving the AgentMemory public surface is package-consumable.
Matches sibling AgentWithMemory samples' conventions (BOM + copyright file
header on .cs files, README sections: Features Demonstrated / Prerequisites
/ Environment Variables / Run the Sample / Expected Output).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* samples: move Neo4j shopping assistant into AgentWithMemory as Step06
Relocates the standalone shopping-assistant sample from the repo root into
dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory,
following that folder's naming/README/solution conventions. Renames its identity
from "Neo4j" to "AgentMemory" (the library it actually demonstrates) since this
is a community .NET port, not an officially recognized Neo4j integration - Neo4j
is still referenced where it's a genuine technical detail (the graph backing
store, env vars, Cypher). Adds empty Directory.Build.props/targets markers so it
stays isolated from the repo's net10.0/CPM build, and registers it (skipped, like
the Mem0 sample) in the CI sample-verification list since it needs a live Neo4j
instance.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* Update dotnet/samples/02-agents/AgentWithMemory/README.md
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* Update dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory.csproj
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* cleanup :)
* DefaultAzureCredential warning
* fixes - simplification userId
* minor doc fix
* NU1015 fix
* PR review fixes-improvements
* Bump AgentMemory to 1.2.0, let the context provider surface memory tools
WithMemoryOwnerScoping(sp) (1.1.0) already removed the need to manually
wrap agent.RunAsync in ownerContext.BeginOwnerScope(userId). This picks
up 1.2.0's ExposeMemoryToolsFromContextProvider option, so
Neo4jMemoryContextProvider now appends the memory tools to AIContext.Tools
itself on every model call — no more separate MemoryToolFactory wiring,
AIContextProviders = [memoryProvider] is enough.
Addresses westey-m's PR review suggestion.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* improvements according to pr review comments
* Fix CI: use plural TargetFrameworks to actually restrict this sample to net10.0
Directory.Build.props sets a repo-wide TargetFrameworks (plural) list
before this project's own properties are evaluated, and the SDK decides
multi-targeting from that plural property at Sdk.props time. The prior
singular TargetFramework=net10.0 override didn't take effect early
enough, so restore still ran against net9.0/net8.0/netstandard2.0/net472
too - frameworks the published AgentMemory 1.2.0 packages don't support
(NU1202), plus surfaced an OpenTelemetry.Api advisory as an error
(NU1902) since TreatWarningsAsErrors is on repo-wide.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* Fix CI: pin OpenTelemetry.Api to unblock NU1902 audit failure
The sample opts out of central package management, so it was pulling in
OpenTelemetry.Api 1.12.0 transitively (via Microsoft.Agents.AI), which has
a known moderate-severity vulnerability (GHSA-g94r-2vxg-569j). The repo
treats NuGet audit warnings as errors, so restore failed outright and took
down every dotnet-build matrix leg plus check-format.
Pinned OpenTelemetry.Api to 1.15.3, matching Directory.Packages.props.
With restore succeeding, previously-masked analyzer/format issues surfaced
and are fixed too: RCS1118 (const local for immutable Cypher queries),
CA1859 (List<IRecord> param instead of IReadOnlyList<IRecord>), and IDE1006
naming violations (s_seed field prefix, PascalCase Cypher/Shopper consts).
Verified locally with the same mcr.microsoft.com/dotnet/sdk:10.0 image CI
uses: dotnet build --warnaserror and dotnet format --verify-no-changes both
pass clean, and a full solution build completed ~24 min with zero errors.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
Co-authored-by: westey <164392973+westey-m@users.noreply.github.com>
* .NET: [Feature]: .NET Improve ChatClientAgentSession constructor
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* test: make deserialize test actually reproduce issue #7109
VerifyDeserializeWithWhenWritingNullOptions passed against both the old
and the fixed constructor, so it did not guard against the regression.
The bug only reproduces when required constructor parameters are respected
(the issue uses RespectRequiredConstructorParametersDefault=true). With
WhenWritingNull a null conversationId is omitted from the JSON, and STJ then
throws 'missing required properties including: conversationId' because the
constructor parameter had no default value.
Adding RespectRequiredConstructorParameters = true to the test options makes
the test red against the parameter-without-default constructor and green with
the default-valued constructor parameters, so it now protects the fix.
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+RogerBarreto@users.noreply.github.com>
* CI: resolve PR author in community team check
pull_request_target events expose the author on payload.pull_request, not payload.issue. Read that field first and fall back to pulls.get so limit-community-prs no longer calls issues.get and fails with 401.
* Docs: clarify issueNumber accepts PR numbers
* Harden manual integration test workflow
Require two write-capable approvals for the exact PR head SHA, pin all targets to immutable commits, and narrow secret and OIDC access.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: b8c7d85f-7576-4fc7-a7b5-c77833344088
* Require integration workflow credentials
Declare credentials consumed by the reusable integration workflows as required while retaining the explicitly optional Foundry models key.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: b8c7d85f-7576-4fc7-a7b5-c77833344088
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: fix per-run additional_beta_flags leaking into Anthropic request kwargs
_prepare_options copied every key from the caller-supplied options dict
into run_options except "instructions" and "response_format". A
per-run additional_beta_flags value is correctly folded into the betas
set by _prepare_betas, but the raw key was never excluded, so it
survived into run_options and was forwarded straight through to
AsyncMessages.create(), which rejects it with TypeError: got an
unexpected keyword argument 'additional_beta_flags'. Add it to the
exclusion set alongside the other framework-level keys.
Fixes#5764
* Exclude additional_beta_flags from filtered_kwargs too
Copilot's review on the original fix pointed out the exclusion only
covered the options-dict copy, not kwargs passed directly to
_prepare_options — so additional_beta_flags supplied as a raw kwarg
would still leak through and reproduce the same TypeError. Add the
same exclusion to filtered_kwargs for consistency, with a regression
test covering the kwarg path.
---------
Co-authored-by: Chris Brown <albatrossflyon1@gmail.com>
* Python: feat: cross-session origin attribution on context messages
Add an optional origin_session_id parameter to SessionContext.extend_messages
that propagates into the existing _attribution payload on
Message.additional_properties. Downstream context observers can use it to
detect when a provider injects content stored under a different session than
the requesting one.
Populate the field from the harness memory consolidation pipeline
(_harness/_memory.py) when injected topics include contributions from
sessions other than the current one. Add a self-contained sample observer
under samples/02-agents/context_providers/cross_session_observer.py
demonstrating how to subscribe to the signal.
Backward-compatible: omitting the parameter preserves the existing
attribution shape exactly. Tests added in test_sessions.py and
test_harness_memory.py cover the new parameter, the harness cross-session
case, and the same-session case.
Motivated by Dai et al., Stateful Agent Backdoor (arXiv:2605.06158, May
2026), which specifically surveys MAF in section 6.1 / Table 10.
See #5914 for design discussion.
Surfaced during independent audit conducted by @finnoybu (Ken Tannenbaum, AEGIS Initiative); [MEDIUM, python/packages/core].
* Address cross-session attribution review feedback
* Address follow-up review feedback
* Address follow-up review comments
* Address origin attribution review feedback
---------
Co-authored-by: finnoybu <21694570+finnoybu@users.noreply.github.com>
* Python: Make foundry toolbox MCP skills sample self-contained
Rework sample 12 (foundry_toolbox_mcp_skills) so users can build it from
zero with azd, mirroring samples 04 and 09:
- Bundle two single-file SKILL.md skills (support-style, escalation-policy)
and a skills-only toolbox.yaml (with one connectionless code_interpreter
tool, required by `azd ai toolbox create`).
- Rewrite the README as an azd-native, from-zero guide (create skills ->
create toolbox -> set TOOLBOX_ENDPOINT -> run) and fix the stale
MCPSkillsSource API description to match main.py.
- Switch config from TOOLBOX_NAME to the versioned TOOLBOX_ENDPOINT
(.env.example, agent.yaml, agent.manifest.yaml); add .azdignore.
Also enable the sample to run unattended behind ResponsesHostServer:
- Forward disable_load_skill_approval / disable_read_skill_resource_approval
/ disable_run_skill_script_approval from FoundryToolbox.as_skills_provider()
to the underlying SkillsProvider, so load_skill needs no approval round-trip
(the Responses host runs without an AgentSession, which the default approval
flow requires). main.py now uses as_skills_provider(disable_load_skill_approval=True).
- Add unit tests covering the default and overridden approval behaviour.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 4f14f83d-1868-45c1-be1a-12f49a58ac36
* Python: Address PR review on toolbox MCP skills sample
- Remove the unused parameters section from agent.manifest.yaml (TOOLBOX_ENDPOINT
is supplied via environment_variables, matching sample 04).
- README: state the sample is self-contained directly instead of contrasting
with the C# sample.
- README: describe skill discovery behaviour without naming the internal
MCPSkillsSource class.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 4f14f83d-1868-45c1-be1a-12f49a58ac36
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix AG-UI workflow handoff replay results
Decisions:
- Reconcile finalized function results only for call IDs exposed in the current run, and skip results already emitted or never exposed.
- Treat message-derived function results as workflow responses only when their IDs match pending interrupts.
Files:
- Updated _workflow_run.py reconciliation and resume filtering.
- Added runner and public two-turn handoff acceptance coverage.
- Expanded finalized-response call-ID, privacy, and deduplication tests.
Verification:
- uv run poe test -P ag-ui
- uv run poe syntax -P ag-ui -C
- uv run poe pyright -P ag-ui
- uv run poe test-typing -P ag-ui
Notes:
- No blockers. The handoff sample remains unchanged; local PRD and issue files are not included.
* Prevent duplicate AG-UI workflow tool results
* Preserve finalized AG-UI tool results
* Use AG-UI text emission controls
* Python: Fix Magentic manager duplicating conversation history
_complete() reused one persistent AgentSession, so the default history provider
re-injected prior turns on top of the full prompt the manager already rebuilds
each call — duplicating task/facts/plan and compounding every round. Use a fresh
session per call; keep self._session only for
checkpointing. GroupChatOrchestrator is unaffected. Add a regression test and
update the session-propagation test.
* Python: Clean up Magentic manager per Copilot review (drop dead _session)
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: quiet A2AExecutor logging for unmapped content types
Tool-use responses include function_call/function_result content that the A2A executor does not surface, causing a WARNING per tool call. Log these at DEBUG and skip instead, matching the outbound content-conversion convention used across the Python chat clients.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ee74cc55-44df-4fcf-b38f-1f79f2600dfc
* Address PR review: assert debug log args and drop redundant cast
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ee74cc55-44df-4fcf-b38f-1f79f2600dfc
* fix: preserve function-call name when merging streaming deltas
`Content._add_function_call_content` built the merged name with
`getattr(self, "name", getattr(other, "name", None))`. Because
`Content.__init__` always sets `self.name` (defaulting to `None`), the
attribute is never missing, so the `getattr` default is never consulted
and `other.name` is ignored. When two function_call contents are merged
and only the second carries the name -- e.g. a streaming delta where the
function name arrives after the first chunk -- the name was silently
dropped.
Use the same "either side" pattern already used for the sibling
`exception` field on the next line: `getattr(self, "name", None) or
getattr(other, "name", None)`. Extend the existing merge test to cover
the late-name and both-None cases.
* test: construct nameless function-call deltas via Content(...) directly
Per review (pyright `reportArgumentType`): `Content.from_function_call`
annotates `name: str`, so passing `name=None` to model a streaming delta
with no name yet tripped the typing gate. Build those nameless deltas
with the `Content("function_call", ...)` constructor instead (its `name`
param is `str | None`) — the factory just wraps that same constructor, so
the runtime objects and the merge assertions are unchanged.
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: bridge AG-UI request state into sessions
Decisions:
- Project resolved AG-UI Shared State into the per-run AgentSession without typed restoration.
- Preserve existing local/service session identifiers and keep AG-UI state out of provider metadata.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
Verification:
- uv run poe check -P ag-ui
- uv run poe test -P ag-ui (912 passed)
Notes:
- Scoped cross-run Session Continuation State remains for the next dependent issue.
* Python: persist scoped AG-UI session continuity
Decisions:
- Store private Session Continuation State atomically in scoped thread snapshots and restore it through the core AgentSession contract.
- Exclude Shared State keys, all HistoryProvider buckets, and tool approval state; request overlays evict colliding private values.
- Finalize interrupted response streams before snapshotting so provider after_run mutations are included.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_agent_run.py
- packages/ag-ui/agent_framework_ag_ui/_snapshots.py
- packages/ag-ui/tests/ag_ui/test_endpoint.py
- packages/ag-ui/tests/ag_ui/test_snapshots.py
- packages/ag-ui/AGENTS.md
Verification:
- uv run poe test -P ag-ui (921 passed)
- uv run poe check -P ag-ui
- uv run poe typing -P ag-ui
Notes:
- Lifecycle, isolation, and broader storage guidance remain for the next dependent issue.
* Python: document AG-UI session continuity lifecycle
Decisions:
- Keep scoped thread snapshots as the single reset and continuity boundary, with missing request Shared State preserving private continuation.
- Document trusted typed-restoration storage, State Authorities, custom-store round trips, and one-active-run last-writer-wins consistency.
- Verify failure, hydration privacy, scope/thread isolation, and reset mechanics through public endpoint and store seams.
Files changed:
- packages/ag-ui/README.md
- packages/ag-ui/tests/ag_ui/test_endpoint.py
- packages/ag-ui/tests/ag_ui/test_snapshots.py
Verification:
- uv run pytest -q <focused lifecycle tests> (6 passed)
- uv run poe test -P ag-ui
- uv run poe syntax -P ag-ui -C
- uv run poe typing -P ag-ui
- uv run poe check -P ag-ui
- uv run poe markdown-code-lint
Notes:
- No runtime capability probe, secondary state store, locking, or configuration flag was added.
- No blockers remain for this lifecycle and guidance slice.
* Python: harden AG-UI session continuity
* Python: isolate AG-UI request state
* Fix: Ollama parallel tool calls collide on same call_id
* fix(ollama): use uuid4 for tool call IDs and support colons in tool names
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* docs: add env example files for durabletask samples
* docs: clarify env example values and comments
* docs: set default Redis URL in streaming sample env example
* fix: clear service_session_id in _agent_wrapper when propagate_session=True
When propagate_session=True, the child agent inherits the parent's
service_session_id. After the parent's first LLM call, MAF auto-populates
this from the Responses API conversation_id. The child sends it as
previous_response_id which the server rejects because the parent's
tool_call is still pending (400 error).
This fix saves and clears service_session_id before calling the child
agent and restores it in a finally block, preserving session.state
sharing while isolating the server-side conversation pointer.
Fixes#5874
* refactor: use child session copy instead of in-place mutation
Address Copilot review comments:
- Create a child AgentSession with shared state dict but isolated
service_session_id, avoiding race conditions under concurrent
asyncio.gather tool invocations.
- Update tests to verify child gets a separate session object and
that child-set service_session_id does not leak to parent.
* fix: update test_chat_agent_as_tool_propagate_session_true for child session isolation
The existing test asserted captured_session is parent_session, but since
we now create a separate child AgentSession (to avoid racing under concurrent
asyncio.gather), the child is a different object. Updated assertions to verify:
- child is NOT the parent object (isolation)
- child shares the same session_id and state dict (by reference)
- child's service_session_id is None (isolated)
* fix: add type narrowing asserts for captured_session
Add 'assert captured_session is not None' before attribute access to
satisfy mypy/pyright type checking on Optional values.
* Python: Fix test typing checks
---------
Co-authored-by: Benke Qu <bequ@microsoft.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
* Python: emit participant tool calls in AG-UI workflows
Decisions:
- Pass function call, function result, and approval request content from streaming agent updates regardless of role.
- Preserve the assistant-role gate for text and reuse the shared AG-UI content emitters without dual custom-event emission.
Files changed:
- packages/ag-ui/agent_framework_ag_ui/_workflow_run.py
- packages/ag-ui/tests/ag_ui/test_workflow_run.py
Verification:
- uv run poe test -P ag-ui
- uv run poe pyright -P ag-ui
- uv run poe test-typing -P ag-ui
- uv run poe syntax -P ag-ui -C
Notes:
- Existing workflow golden scenarios do not exercise participant tool calls, so no snapshot changed.
- No blockers.
* Python: guard participant tool call duplication
Decisions:
- Assert the workflow stream emits one TOOL_CALL_START when a streamed call is also present in final conversation history.
- Keep production flow unchanged because latest-assistant final-response conversion prevents duplication.
Files changed:
- packages/ag-ui/tests/ag_ui/test_workflow_run.py
Verification:
- uv run pytest packages/ag-ui/tests/ag_ui/test_workflow_run.py -k 'participant_tool_call or repeat_tool_call' -q
- uv run poe test -P ag-ui
- uv run poe pyright -P ag-ui
- uv run poe test-typing -P ag-ui
- uv run poe syntax -P ag-ui -C
- git diff --check
Notes:
- No blockers; no call-id guard was required.
* Python: scope workflow tool content bypass to resumable tool calls
- Exclude approval request content from the role bypass. Workflow approvals
resume through request_info pending state, so an approval interrupt emitted
from streamed content would have no pending request to resume against.
- Admit mcp_server_tool_call and mcp_server_tool_result so provider-hosted MCP
tool calls from workflow participants emit standard tool call events.
- Add unit tests for MCP passthrough, approval exclusion, and mixed
text-plus-tool content in non-assistant updates.
* Bump Python package versions for 1.11.0 release
Bump the CHANGELOG-selected packages for the 1.11.0 release: core and the root package move to 1.11.0 for the new stable APIs, Foundry and OpenAI receive patch bumps, changed prerelease packages receive the 260709 stamp or next RC counter, and Monty joins the bump set for corrected published dependency metadata. No beta cohort bump was applied. Raise core floors conservatively on every package publishing this cycle and correct dependency floors exposed by lower-bound validation.
Copilot-Session: ee33d338-c1fc-4182-9106-0345ccf26b8e
* Fix Gemini streaming type suppression
Move the targeted Pyright suppression to the SDK contents argument, where the google-genai invariant content-list alias produces the compatibility diagnostic, and remove the now-unnecessary member suppression.
Copilot-Session: ee33d338-c1fc-4182-9106-0345ccf26b8e
* Raise Monty core dependency floor
Align Monty with the conservative release policy by requiring agent-framework-core 1.11.0 or later for the package version published in this cycle.
Copilot-Session: ee33d338-c1fc-4182-9106-0345ccf26b8e
The RESOURCE_INSTRUCTIONS example told the model to use
eferences/FAQ instead of
eferences/FAQ.md, contradicting the
actual exact-match resource lookup (which lists and matches names
including the extension). This caused read_skill_resource to fail with
'Resource not found'. Align the example with the .NET original.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: fix removed ChatAgent references in _clients.py docstrings
* docs: make _clients.py tool-support examples copy/paste-safe
Import Agent in each tool-support protocol docstring example so
copy/pasting no longer raises NameError, and define the shell
executor (LocalShellTool) in the SupportsShellTool example.
Addresses Copilot review feedback on #6924.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: wrap SupportsShellTool example in async function
`async with LocalShellTool()` is a SyntaxError at module level, so the
copy/pasted snippet must live inside an async function to be valid.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Sumesh Bharathi Ramasamy <sumesh@iconicair.io>
Co-authored-by: Cursor <cursoragent@cursor.com>
* Bind policy-enforcement approvals to a single tool invocation
PolicyEnforcementFunctionMiddleware retained approved call_ids in a set
that was never cleared, so a reused call_id could re-authorize a later
or different tool call without a fresh approval. It also accepted an
approved response as long as the invocation metadata carried a pending
call_id, without checking the response id or embedded function_call.
Bind each approval to the exact invocation shown for review: call_id,
function name, arguments, the security label (integrity/confidentiality),
and the session. Validate that the approval response itself names the
pending request (its id and embedded function_call), and consume the
approval on first use. A reused call_id, a different function, changed
arguments, an escalated label, a different session, or a mismatched
approved response now all require a fresh approval. Adds regression tests
covering each of those cases plus legitimate re-approval.
* Require approval response identifiers to be present and match
Make the policy-enforcement approval-response check reject a response
that omits its id or embedded function_call.call_id: both must now be
present and equal to the pending call_id, closing a None-identifier
bypass. Adds a regression test.
* Disclose all policy violations in a single approval request
PolicyEnforcementFunctionMiddleware computed the approval decision once
and reused it across the integrity and confidentiality checks, so a call
that violated both policies produced an approval request describing only
the untrusted-context violation and then silently waved the undisclosed
confidentiality violation on replay.
Detect every applicable violation up front and surface them together in a
single approval request, so a granted approval waves only what it
disclosed. The binding (call_id, function, arguments, security label,
session) and consume-once behavior are unchanged. Adds a regression test
covering a combined untrusted-context and confidentiality violation.
* Bind policy approval to the disclosed violation set and fingerprint
A pending policy approval was bound to the call body, security label, and
session but not to the violations it disclosed. Because the violation set
depends on the tool's policy metadata (max_allowed_confidentiality,
accepts_untrusted), a replay could compute a different or larger set after
that metadata changed and execute it under the old approval even though the
user never reviewed that risk.
Record the canonical disclosed violation fingerprint (type plus reason) in
the pending record and require the replay to trip the same set, otherwise
re-request approval disclosing the new set. Also require the approval
response's approved flag to be a strict boolean True so a truthy non-boolean
value is not treated as approval. Adds regression tests for a new violation
appearing on replay, a same-type violation whose disclosed risk worsened,
and a non-boolean approved flag.
* Add Python hosting protocol helper surface
Introduce AgentFrameworkState and SessionStore for app-owned hosting routes, add Responses run conversion/rendering helpers, and update the local Responses sample to use native FastAPI routing with streaming support.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix CI failures, session continuity, and streaming model reporting
- Fix constrained TargetT TypeVar in AgentFrameworkState: split __init__
into per-shape overloads (instance/sync factory/async factory/awaitable)
since a bound TypeVar combined with one big Callable/Awaitable union
parameter was unsolvable across pyright/pyrefly/ty/zuban.
- Fix _FakeAgent test fixtures to structurally satisfy SupportsAgentRun
(matching attribute types and overloaded run()), which the above surfaced.
- Add SessionStore.put() to alias an additional session id to an
already-resolved session, and use it in the local_responses sample to fix
a real session-continuity bug: previous_response_id rotates every turn,
so without aliasing the newly minted response id, turn 3+ of a
conversation silently lost all prior history. Verified against a live
Foundry model across a 3-turn conversation.
- Fix responses_stream_events_from_run to report the real model instead of
the "agent" fallback: AgentResponse.from_updates never carries a raw
representation forward, so capture model from the individual streamed
updates' raw representations instead. Verified live.
- Add response_model=None to the sample's FastAPI route (it could not boot
at all: FastAPI tried to build a Pydantic response model from the
JSONResponse | StreamingResponse return annotation).
- Map responses_to_run's ValueError to HTTP 400 instead of a 500.
- Add HTTP round-trip integration tests (packages/hosting-responses) that
exercise the same FastAPI + AgentFrameworkState + Responses helper wiring
as the sample via httpx.ASGITransport, including a regression test for
the session-continuity fix.
- Add Workflow-target test coverage, SessionStore.put/reset_session tests,
and TypeError-path coverage to packages/hosting/tests/hosting/test_state.py.
- Extend call_server.py / call_server_af.py to a third conversation turn so
they actually exercise the continuity chain (previous scripts stopped at
turn 2, which would never have revealed the bug above).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify session-continuity aliasing: fold put() into get()
Per feedback: the growth of SessionStore was not the problem -- it's
intentional, since OpenAI's previous_response_id is designed to let a
caller continue (fork) from any earlier response, not just the latest
one, so every response id has to stay independently resolvable. That
part stays as-is.
What was too complex was the call site: routes had to manually fetch a
session and then conditionally alias it with a separate put() call.
Folded that into a single get(session_id, alias=...) call instead:
- SessionStore.get() gains an optional `alias` keyword that registers an
additional id for the same session in the same call (no-op if alias is
None or equal to session_id). Removed the separate put() method.
- AgentFrameworkState.get_session() passes `alias` through.
- local_responses sample and the HTTP round-trip integration tests now
do `await state.get_session(lookup_id, alias=response_id)` instead of
pulling the store out and orchestrating get()/put() by hand.
- Documented that this in-memory SessionStore intentionally never evicts
(by design, to support forking), and that a storage-backed replacement
(Redis, a database, ...) is responsible for its own TTL/eviction
policy.
Verified against a live Foundry model across a 3-turn previous_response_id
chain after the simplification.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Refine hosting state helpers
Split the shared state surface into AgentState and WorkflowState, keep SessionStore and CheckpointStore as plain storage, and make state helpers responsible for get-or-create behavior. Update the Responses sample and HTTP round-trip tests to store the post-run session explicitly under the minted response id, and support WorkflowBuilder/orchestration-style builders via structural build() support.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix hosting state test protocol fakes
Widen fake agents' get_session service_session_id parameter to match the SupportsAgentRun protocol under the Python 3.11 test typing checkers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify Responses stream helper naming
Rename responses_stream_events_from_run to responses_stream_from_run across exports, tests, docs, and the local Responses sample to align with the generic <protocol>_stream_from_run helper convention.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add state-level storage setters
Add AgentState.set_session and WorkflowState.set_checkpoint_storage so app code can pair get-or-create helpers with explicit post-run storage without reaching into the underlying stores. Update Responses docs, tests, and sample to use state.set_session.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify WorkflowState checkpoint handling
Remove CheckpointStore from WorkflowState so workflow checkpointing uses the existing CheckpointStorage abstraction directly. Keep WorkflowState focused on resolving workflow targets, including builders, and update hosting docs/tests accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Rename Responses streaming run helper
Rename responses_stream_from_run to responses_from_streaming_run across the hosting-responses exports, tests, docs, and local Responses sample.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Align Python hosting spec with protocol helpers
Rewrite SPEC-002 to match the accepted helper-first hosting ADR and the implementation PR: AgentState, WorkflowState, SessionStore, Responses helpers, app-owned security/state responsibilities, and the minimal FastAPI Responses shape.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove old Python hosting channel implementation
Remove the unreleased AgentFrameworkHost/channel implementation, the old hosting-telegram package, and old host/channel samples. Keep agent-framework-hosting focused on AgentState, WorkflowState, and SessionStore, and keep hosting-responses focused on helper-first Responses conversion. Update SPEC-002 to match the accepted helper-first ADR and the implementation surface.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Restore helper-first workflow sample
Rebuild the local Responses workflow sample on the protocol-helper surface, add production-readiness cautions to the local hosting samples, and align file-backed workflow checkpoint/cursor storage under one sample storage root.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address hosting helper review feedback
Handle streaming failures as terminal Responses SSE events, guard concurrent target/session initialization, and scope workflow sample checkpoint storage per continuation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify Responses sample continuation behavior
Document unknown conversation_id behavior in the agent sample and make the workflow sample explicitly reject conversation_id while continuing to use responses_session_id for previous_response_id.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify Responses sample option policy
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add AG-UI SSE keepalive endpoint option
Key decisions: add keepalive_seconds as endpoint-owned FastAPI registration configuration with default 15, accept None as the explicit off switch, validate that non-None values are greater than zero during route registration, and keep agent/workflow runner constructors unchanged. Declare sse-starlette>=3.4.5,<4 as a direct AG-UI dependency without changing the existing StreamingResponse path in this slice.
Files changed: packages/ag-ui/agent_framework_ag_ui/_endpoint.py adds validation and the public endpoint parameter; packages/ag-ui/tests/ag_ui/test_endpoint.py covers default, supported runner shapes, endpoint ownership, and invalid intervals; packages/ag-ui/pyproject.toml and uv.lock add the direct sse-starlette dependency metadata.
Verification: uv run pytest focused keepalive endpoint tests -q; uv run poe test -P ag-ui; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe validate-dependency-bounds-test -P ag-ui; git diff --check; git diff --cached --check. Also ran validate-dependency-bounds-project --mode both --package ag-ui --dependency sse-starlette; it completed but broadened the lower bound, so the issue-required >=3.4.5,<4 contract was restored and re-locked.
Notes: uv run poe typing -P ag-ui and uv run poe check -P ag-ui currently fail in mypy before checking project files because .venv/lib/python3.13/site-packages/numpy/__init__.pyi uses type-statement syntax while the test mypy profile targets Python 3.11. Local issue file was moved to issues/done/ but not staged.
* Python: Emit AG-UI SSE keepalive comments
Key decisions: switch only enabled AG-UI FastAPI endpoint keepalive responses to EventSourceResponse, keep encoded AG-UI SSE frames as bytes on that path to avoid double encoding, and emit the fixed static SSE comment ': keepalive' while preserving existing SSE headers.
Files changed: packages/ag-ui/agent_framework_ag_ui/_endpoint.py adds the EventSourceResponse enabled path and static comment factory; packages/ag-ui/tests/ag_ui/test_endpoint.py adds an endpoint test for a long output-silent gap, keepalive comments, headers, valid data frames, and no data: data: double encoding.
Verification: uv run pytest packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_keepalive_enabled_emits_static_comment_during_silent_gap -q; focused endpoint pytest selection; uv run poe test -P ag-ui; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; git diff --check; git diff --cached --check.
Notes: uv run poe check -P ag-ui still fails in the test-typing mypy phase before project files are checked because .venv/lib/python3.13/site-packages/numpy/__init__.pyi uses type-statement syntax while the mypy test profile targets Python 3.11. Local PRD/Ralph/context artifacts were not staged.
* Python: Preserve disabled AG-UI SSE keepalive behavior
Key decisions: cover keepalive_seconds=None at the FastAPI endpoint seam and assert it preserves the legacy StreamingResponse SSE shape without emitting transport keepalive comments.
Files changed: packages/ag-ui/tests/ag_ui/test_endpoint.py adds disabled keepalive endpoint coverage for headers, valid AG-UI data frames, no keepalive comments, and no data: data: double encoding.
Verification: uv run pytest packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_keepalive_disabled_preserves_streaming_response_shape packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_keepalive_enabled_emits_static_comment_during_silent_gap -q; uv run poe test -P ag-ui; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test-typing -P ag-ui --checker pyright; git diff --check.
Notes: no production code changes were needed because the endpoint already branches to the existing StreamingResponse path when keepalive_seconds=None. Local PRD/Ralph/context artifacts were not staged.
* Python: Document AG-UI SSE keepalive behavior
Key decisions: document keepalive_seconds at the FastAPI endpoint seam as a default-enabled transport keepalive with None as the off switch, and record that SSE keepalive emits comments without changing AG-UI events or adding protocol heartbeat events.
Files changed: packages/ag-ui/agent_framework_ag_ui/_endpoint.py expands the public endpoint docstring; packages/ag-ui/AGENTS.md records endpoint-owned keepalive guidance; packages/ag-ui/tests/ag_ui/test_endpoint.py adds a public docstring regression.
Verification: uv run pytest packages/ag-ui/tests/ag_ui/test_endpoint.py::test_add_endpoint_docstring_describes_keepalive_transport_behavior -q failed before the doc update; focused keepalive endpoint tests passed; uv run poe test -P ag-ui; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test-typing -P ag-ui --checker pyright; uv run python scripts/check_md_code_blocks.py packages/ag-ui/AGENTS.md; git diff --check.
Notes: no standalone docs page was added. Local issue bookkeeping was moved to issues/done but not staged; local PRD and Ralph/context artifacts remain unstaged.
* Python: Tighten AG-UI FastAPI dependency bound
* Python: Defer AG-UI keepalive transport imports
* Python: Fix Bedrock non-ASCII escaping in JSON content blocks
The Bedrock Converse `json` content block was serialized with
`json.dumps(json_value)`, whose default `ensure_ascii=True` escapes
CJK/emoji/accented characters to `\uXXXX` and surfaces garbled text.
Add `ensure_ascii=False` to match the sibling OpenAI client and the
16+ other call sites across the repo. Includes a regression test.
Closes#6627
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Fix Bedrock test trailing whitespace
---------
Co-authored-by: kimnamu <kimnamu@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
The Foundry service rejects requests that include tool declarations when an
agent is specified (HTTP 400 invalid_payload, "Not allowed when agent is
specified."). RawFoundryAgentChatClient._prepare_options stripped tools,
tool_choice, and parallel_tool_calls only on the non-preview path, so when
allow_preview=True (where the agent identity is bound on the OpenAI client via
get_openai_client(agent_name=...)) the tool fields were still sent and the call
failed.
This client always targets a pre-provisioned agent, so it must never send tool
declarations. Drop the tool fields unconditionally and log a single warning
when the caller supplied tools, noting they are used only for client-side
function dispatch. The non-agent FoundryChatClient (model-based) is unaffected.
Fixes#5130.
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* fix: DevUI list[Message] entry for declarative ToolAgent (#6533)
When a declarative ToolAgent is created with default settings the
entry JoinExecutor declares `input_types = [dict | str | list[Message]
| ActionTrigger | ...]`. DevUI called `select_primary_input_type`
which returned bare `Message` instead of `list[Message]`, then passed
a single Message to the executor that expects a list — causing a
"cannot handle message of type Message" runtime error.
Changes:
- Add `_is_list_message_type` helper (GenericAlias cannot be used with
isinstance; get_origin/get_args required).
- Add `_find_chat_message_type` that recursively searches union members
and returns `list[Message]` in preference to bare `Message`.
- `select_primary_input_type`: first-pass uses `_find_chat_message_type`
so the declarative entry type is correctly returned as `list[Message]`.
- `generate_input_schema`: returns `{"type":"string"}` for `list[Message]`
so DevUI renders a plain text box.
- Add `_looks_like_message_dict` heuristic (role present, type=="message",
or exactly {"input":...}) to distinguish serialised Message payloads
from structured workflow inputs without false positives.
- `parse_input_for_type`: handle `list[Message]` target — wrap plain
strings/Message objects, convert lists of dicts item-by-item, pass
structured workflow inputs through unchanged.
- Add 12 regression tests (57 total pass).
* fix: resolve pyright unknown-type errors in parse_input_for_type
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Python: Add refresh_interval (TTL) to CachingSkillsSource
Port .NET's CachingAgentSkillsSourceOptions.RefreshInterval to the Python
skills cache. Previously CachingSkillsSource cached a source's skill list
indefinitely (only clearing on a failed fetch), so callers had no built-in
way to periodically re-discover skills whose backing source changes at
runtime (notably MCPSkillsSource over the network).
CachingSkillsSource now accepts an optional refresh_interval (timedelta):
a cached list older than the interval is treated as stale and re-fetched on
the next call. When None (default) the cache never expires, so existing
behavior is unchanged. Freshness is measured with a monotonic clock via a
monkeypatchable _monotonic() helper. SkillsProvider.__init__ and from_paths
expose a cache_refresh_interval kwarg threaded into the built-in cache.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Address review feedback on CachingSkillsSource refresh_interval
- from_paths: do not forward cache_refresh_interval when disable_caching=True,
matching the docstring and avoiding a TypeError for legacy subclass __init__
signatures.
- Correct docstring/AGENTS.md wording: a failed fetch does not update the cache
(initial failure leaves it empty; a refresh failure keeps the prior list),
rather than "resetting"/"leaving empty" in all cases.
- Fix test typing: narrow provider._source via isinstance before accessing
inner_source/_refresh_interval so ty/zuban/mypy/pyright all resolve them.
- Add regression tests for disable_caching + interval and legacy-subclass paths.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Do not forward cache_refresh_interval from from_paths into __init__
The refresh interval is already baked into the composed CachingSkillsSource
that from_paths builds, and __init__ leaves a caller-supplied source
un-wrapped, so forwarding cache_refresh_interval into cls(...) was a no-op
for caching behavior while breaking legacy subclasses whose __init__ predates
the kwarg (with caching enabled or disabled). Remove the forwarding entirely.
Strengthen the regression test to cover the real break: a legacy subclass
calling from_paths(paths, cache_refresh_interval=...) with caching enabled
must not raise and the composed source still carries the interval.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Drop _monotonic wrapper; call time.monotonic() directly
Address review feedback: remove the _monotonic() helper that existed only to
aid testing. CachingSkillsSource now calls time.monotonic() inline, and the
refresh-interval tests monkeypatch time.monotonic directly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Restore main's AGENTS.md sections lost in merge resolution
The merge used 'checkout --ours' for AGENTS.md, which took the whole file
from this branch and inadvertently reverted main's non-conflicting additions
(the __init__.pyi tree entry and the 'Root Public API' section). Restore
main's version and re-apply only the intended SkillsSource decorators change
(refresh_interval docs + reworded cache-failure semantics).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add AG-UI approval state store
Key decisions: introduce a bounded process-local server-side Approval State store for AG-UI agent approvals; scope pending approval validation by AG-UI thread id plus the endpoint's configured server-side scope when present; fail closed when approval-like resume decisions arrive without matching server-owned pending Approval State, covering replayed and wrong-scope attempts without requiring Thread Snapshot persistence.
Files changed: packages/ag-ui/agent_framework_ag_ui/_approval_state.py adds the approval-only in-memory store and scoped thread-key helper; _agent.py owns the default store; _endpoint.py forwards the configured scope to approval handling independently of snapshot persistence; _agent_run.py keys pending approvals by scoped thread id and rejects approval resumes with missing state; tests/ag_ui/test_endpoint.py covers successful default resumes, replay failure, and wrong-scope failure without a snapshot store.
Verification: uv run pytest focused approval resume tests -q; uv run poe test -P ag-ui; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; git diff --check; git diff --cached --check. uv run poe typing -P ag-ui and uv run poe check -P ag-ui currently stop in mypy before package checking on .venv/lib/python3.13/site-packages/numpy/__init__.pyi using Python 3.12 type-statement syntax under the configured Python 3.11 target.
Notes: local issue/PRD planning artifacts were not staged. Follow-up slices still own already-approved sibling release, queued/auto-approved approval state preservation, replayable tool result persistence, synthetic-skip tightening, and final security/invariant coverage.
* Python: Release AG-UI approved siblings on resume
Key decisions: preserve core already-approved approval request groups inside AG-UI server-side Approval State for the visible approval interrupt; restore those siblings as server-generated approval responses only after the visible canonical resume passes server-owned validation; keep cancelled visible approvals fail-closed without executing or fabricating sibling results.
Files changed: packages/ag-ui/agent_framework_ag_ui/_agent_run.py stores hidden already-approved sibling approval requests with pending approval entries and rehydrates them during resume; packages/ag-ui/tests/ag_ui/test_endpoint.py adds mixed approval-batch endpoint coverage for approved, rejected, and cancelled visible approvals.
Verification: uv run pytest focused mixed approval sibling tests -q; uv run pytest neighboring approval endpoint tests -q; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test -P ag-ui; git diff --check; git diff --cached --check. uv run poe typing -P ag-ui and uv run poe check -P ag-ui pass pyright/pyrefly/ty/zuban for this change but still stop in mypy before package checking on .venv/lib/python3.13/site-packages/numpy/__init__.pyi using Python 3.12 type-statement syntax under the configured Python 3.11 target.
Notes: local .ralph, PRD, and CONTEXT.md planning artifacts were not staged. Follow-up slices still own queued/auto-approved approval state preservation, replayable tool result persistence, synthetic-skip tightening, and final security/invariant coverage.
* Python: Preserve AG-UI queued approval state
Key decisions: persist only the core tool-approval state bag inside the AG-UI server-side Approval State Store, keyed by the scoped AG-UI approval thread id; restore that approval-only state into each per-run AgentSession before approval resolution; pop server-collected auto-approved responses into validated server-generated approval messages so they execute exactly like resumed approvals without trusting client state.
Files changed: packages/ag-ui/agent_framework_ag_ui/_approval_state.py stores bounded tool approval state alongside pending approval entries; _agent.py passes the shared store into agent runs; _agent_run.py restores/saves tool approval state and drains collected auto-approved responses through existing pending-approval validation; packages/ag-ui/tests/ag_ui/test_endpoint.py covers queued approval surfacing and auto-approved response execution through SSE behavior.
Verification: uv run pytest focused queued/auto approval endpoint tests -q; uv run pytest neighboring approval endpoint tests -q; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test -P ag-ui; git diff --check; git diff --cached --check. uv run poe check -P ag-ui still stops in mypy before package checking on .venv/lib/python3.13/site-packages/numpy/__init__.pyi using Python 3.12 type-statement syntax under the configured Python 3.11 target; syntax, pyright, pyrefly, ty, and zuban portions completed successfully before that failure.
Notes: local .ralph, PRD, and CONTEXT.md planning artifacts were not staged. Follow-up slices still own replayable tool result persistence, synthetic-skip tightening, and final security/invariant coverage.
* Python: Persist AG-UI approved tool results
Key decisions: fold approval-resolved function_result messages into AG-UI Thread Snapshot history under their original tool call ids; strip server-generated canonical function_approvals resume controls from replayable snapshots; keep live TOOL_CALL_RESULT emission unchanged while preserving next-turn provider history validity.
Files changed: packages/ag-ui/agent_framework_ag_ui/_agent_run.py adds snapshot merge helpers for approval-resolved tool results; packages/ag-ui/tests/ag_ui/test_endpoint.py covers mixed approval batch resume, hydration, and next-turn replay through observable endpoint behavior.
Verification: uv run pytest focused replayable approval endpoint test -q; uv run pytest neighboring approval replay tests and test_approval_result_event.py -q; uv run poe syntax -P ag-ui -F; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test -P ag-ui; git diff --check; git diff --cached --check. uv run poe check -P ag-ui still stops in mypy before package checking on .venv/lib/python3.13/site-packages/numpy/__init__.pyi using Python 3.12 type-statement syntax under the configured Python 3.11 target; syntax, pyright, pyrefly, ty, and zuban portions completed successfully before that failure.
Notes: local .ralph, PRD, and CONTEXT.md planning artifacts were not staged. Follow-up slices still own synthetic-skip tightening and final security/invariant coverage.
* Python: Limit AG-UI synthetic skipped results
Key decisions: treat server-owned Approval State, current approval resume decisions, and existing replayable tool results as non-abandoned tool calls for AG-UI sanitizer repair; keep the defensive skipped-result fallback for genuinely abandoned tool calls; reject client-injected tool results as insufficient to satisfy pending server-owned Approval State.
Files changed: packages/ag-ui/agent_framework_ag_ui/_message_adapters.py adds protected tool-call context to synthetic skip injection; packages/ag-ui/agent_framework_ag_ui/_agent_run.py derives protected ids from pending approvals and stored approval-only state; packages/ag-ui/tests/ag_ui/test_message_adapters.py and test_endpoint.py cover protected pending calls, resume decisions, abandoned-call repair, and forged tool-result behavior.
Verification: uv run pytest focused sanitizer red/green tests -q; uv run pytest focused pending-approval endpoint tests -q; uv run pytest package sanitizer plus neighboring approval endpoint tests -q; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test -P ag-ui; git diff --check; git diff --cached --check. uv run poe typing -P ag-ui passes pyright/pyrefly/ty/zuban but still stops in mypy before package checking on .venv/lib/python3.13/site-packages/numpy/__init__.pyi using Python 3.12 type-statement syntax under the configured Python 3.11 target.
Notes: local .ralph, PRD, and CONTEXT.md planning artifacts were not staged. Follow-up slice still owns final AG-UI approval repair security and exact-once invariant coverage.
* Python: Verify AG-UI approval invariants
Key decisions: cover final AG-UI approval repair invariants at the FastAPI endpoint seam; treat wrong-thread resumes, client-supplied approval message spoofing, and client-injected approval state as non-executing fail-closed paths; assert exact-once replayable tool results for completed approval batches; document that Approval State is process-local and production authentication, authorization, and deployment/storage durability remain application responsibilities.
Files changed: packages/ag-ui/tests/ag_ui/test_endpoint.py adds endpoint-observable security and exact-once coverage; packages/ag-ui/README.md documents Approval State production responsibilities.
Verification: uv run pytest packages/ag-ui/tests/ag_ui/test_endpoint.py -q -k 'approval_resume_wrong_thread or approval_function_name_mismatch_message or approval_argument_mismatch_message or approval_client_fields_do_not_mutate or approval_resume_persists_replayable_tool_results'; uv run poe syntax -P ag-ui -F; uv run poe syntax -P ag-ui -C; uv run poe pyright -P ag-ui; uv run poe test -P ag-ui; uv run poe test-typing -P ag-ui --checker pyright; git diff --check; git diff --cached --check.
Notes: local .ralph, PRD, and CONTEXT.md planning artifacts were not staged. This completes the final AG-UI approval repair security and invariant coverage slice.
* Python: Clear AG-UI queued approvals on cancel
* Python: Address AG-UI approval review feedback
* Fix: Skip web_search_options for Azure OpenAI Chat Completions API
Azure OpenAI Chat Completions API does not support the web_search_options
parameter. Sending it results in a 400 error: 'Unknown parameter:
web_search_options'.
This fix:
- Stores the use_azure_client flag during initialization
- In _prepare_tools_for_openai, skips web search tools when the client
is Azure-based, logging a warning that guides users to the Responses
API (OpenAIChatClient) for web search support on Azure
Closes#3629
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix: raise ValueError instead of silently ignoring web search on Azure
Address review feedback: silent logger.warning was too easy to miss.
Raising ValueError ensures callers know immediately that web search is
incompatible with Azure Chat Completions and directs them to the
Responses API alternative.
- Changed logger.warning to ValueError in _prepare_tools_for_openai
- Added test_prepare_tools_with_web_search_on_azure_raises
- Added test_prepare_tools_with_web_search_on_openai_allowed
* Fix Azure web search test regex
---------
Co-authored-by: Autumn <Autumn@Autumns-MacBook-Air.local>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Python: Remove experimental marker from Skills API
Promote the Skills feature from experimental to stable, mirroring
.NET PR #6861. Removes the @experimental(SKILLS) decorators from the
skills APIs and the SKILLS ExperimentalFeature enum member, updates
tests and samples accordingly. MCP skills (MCP_SKILLS) remain
experimental, matching the .NET change.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add experimental-stage assertions for MCP skills types
Guard MCPSkill, MCPSkillResource, and MCPSkillsSource against accidental
promotion by asserting their docstring warning block and
__feature_stage__/__feature_id__ metadata remain experimental (MCP_SKILLS).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove redundant stable-stage test for Skills API
Drop TestSkillsStableStage: asserting the absence of experimental
markers on a released API is not meaningful, and the feature-stage
decorator machinery is already covered by test_feature_stage.py.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: add ATR validation FunctionMiddleware sample (execution-boundary validation, #5366)
Adds python/samples/02-agents/middleware/atr_validation_middleware.py: a
FunctionMiddleware that validates tool arguments at the execution boundary and
raises MiddlewareTermination before call_next() when they match an attack
pattern, so the tool never runs. This is the deterministic, single-enforcement-
point pattern named in #5366 and answers its open follow-up about a recommended
validation-at-execution-boundary sample.
The check is a small self-contained deny-list mirroring Agent Threat Rules (ATR)
intent (prompt injection, exfiltration, credential access in tool args); a
docstring notes how to swap in the full open ruleset via pyatr. No external
dependency, so the sample stays import-clean.
Updates the middleware README Files table.
Signed-off-by: Adam Lin <adam@agentthreatrule.org>
* Python: Samples: run the real ATR engine in atr_validation_middleware
Address review on #6528:
- Load and run the real ATR ruleset via pyatr (ATREngine + AgentEvent
tool_call event) instead of re-implementing a regex deny-list; the
built-in deny-list is now only a fallback when pyatr is not installed.
- Add re.DOTALL (and a whole-text scan) to the fallback patterns so
multiline injection payloads are not missed.
- Move load_dotenv() into main() so importing the module has no side
effects.
- Route the middleware block/allow messages through a module logger
instead of print().
- Include the matched ATR rule id in the log and in the
MiddlewareTermination message for auditability.
- Update the middleware README entry to match.
* fix(samples): make ATR validation middleware pass ty/pyrefly typing CI
Resolve the three type-checker errors flagged on the samples typing jobs
(ty + pyrefly, reportMissingImports/reportAttributeAccessIssue via pyright):
- pyatr is an optional, unstubbed runtime dependency that is not installed
in the typing CI env; mark its imports with `# type: ignore` so the
unresolved-import error is suppressed while keeping the graceful
ImportError -> deny-list fallback intact.
- Replace the function-attribute engine cache
(`_detect_with_atr._engine`), which ty/pyrefly reject, with a clean
`functools.lru_cache`-backed `_load_atr_engine()` loader.
- Type the argument-scanning helpers to accept the real
`FunctionInvocationContext.arguments` type (`BaseModel | Mapping[str, Any]`)
and normalise a pydantic model via `model_dump()` before scanning, fixing
the invalid-argument-type error.
ty / pyrefly / pyright (samples config) / ruff check + format all clean on
the file; runtime block/allow behaviour verified for both dict and BaseModel
arguments.
* Python: Samples: simplify ATR middleware to plain pyatr import
Address review feedback (@eavanvalkenburg): now that the sample runs the
real pyatr engine, drop the optional-import scaffolding.
- Add a dependency header declaring pyatr (pip install pyatr).
- Switch to a plain top-level `import pyatr` and remove the
try/except ImportError fallback path.
- Remove the regex deny-list (_FALLBACK_PATTERNS, _detect_with_fallback);
keep 2-3 representative pattern shapes inline as a reference comment so
readers still see the kind of rules ATR encodes. Detection is now a
single straight-line engine call.
- Keep the prior typing fixes: `# type: ignore` on the pyatr import
(unstubbed, absent in the typing CI env), the functools.lru_cache
engine loader, and the BaseModel | Mapping[str, Any] signatures.
* fix: use PEP 723 inline script metadata for sample dependencies
---------
Signed-off-by: Adam Lin <adam@agentthreatrule.org>
Co-authored-by: eeee2345 <eeee2345@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Revise Python hosting channels ADR
Refocus the accepted-but-unreleased Python hosting channels ADR on protocol-specific Agent Framework conversion helpers and an optional execution-state host instead of a channel route-contribution framework.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Align hosting ADR with split state helpers
Update the protocol-helper ADR to reflect AgentState and WorkflowState, plain SessionStore and CheckpointStore behavior, explicit post-run session storage, workflow checkpoint storage, and direct WorkflowBuilder/orchestration-builder support.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Generalize protocol helper taxonomy
Add protocol-neutral helper families for run conversion, result rendering, streaming, session-id extraction, and command/action parsing. Classify protocol-specific helpers based on quick scans across Activity/Bot Framework, Discord, A2A, and MCP.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify stream helper naming
Use the single <protocol>_stream_from_run(...) helper naming convention in the hosting protocol-helper ADR.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use state-level storage helpers in hosting ADR
Update ADR examples so app code calls AgentState.set_session and WorkflowState.set_checkpoint_storage instead of reaching into underlying stores directly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address hosting ADR review comments
Clarify fail-closed Foundry isolation helpers, fix workflow checkpoint resume examples, describe durable checkpoint cursor storage, add caller-owned session authorization comments, and switch the Django sketch to an async view.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify workflow checkpoint state in hosting ADR
Keep WorkflowState focused on resolving workflow targets, use existing CheckpointStorage directly, describe app-owned checkpoint cursor storage, and mark appendix code as minimum-shape sketches rather than runtime-ready samples.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Rename stream helper convention
Use <protocol>_from_streaming_run(...) as the protocol-helper naming convention for rendering streaming run output.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* added notes on state and continuity
* updates based on review
* added consulted
* updates based on review
* remove pyright for illustrative code
* Add streaming to Responses ADR sketch
Extend the FastAPI appendix sketch with the streaming branch and note that the Django sketch omits streaming to avoid duplicating the same state/finalization pattern.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* added note on extending the server
* added note on responsible for
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Lazy load root agent_framework exports
Move the root public API to lazy runtime exports backed by a typed stub, keep Runner deprecation handling in the owning workflow runner module, and document the maintenance pattern.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Tighten harness factory typing
Add a private harness stub so create_harness_agent has a fully known public signature without depending on agent-framework-tools at runtime.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address lazy root export review comments
Harden the circular import guard and add root export smoke tests covering representative lazy imports, star imports, and root stub export synchronization.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Mypy intentionally targets Python 3.10 for test typing, but NumPy 2.5 stubs include Python 3.12 type statement syntax. Skip following NumPy stubs so dependency maintenance can validate the repository tests without parsing NumPy internals.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: use client_kwargs instead of invalid options kwarg in workflow sample
Workflow.run() does not accept an options parameter. The store=False
kwarg was silently ignored. Use client_kwargs to correctly forward it
to the underlying chat client.
Fixes#6293
* fix: use backend-neutral wording in client_kwargs comment
---------
Co-authored-by: Benke Qu <bequ@microsoft.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Bug
---
`RawAnthropicClient._prepare_options` forwards `response_format` as the
**deprecated** beta parameter `output_format={"type": "json_schema", "schema":
{...}}` plus the beta flag `structured-outputs-2025-11-13`. When the same
request also includes `tools`, Claude emits concatenated / malformed JSON —
e.g. three copies of the schema's empty default like
`{"matches":[]}{"matches":[]}{"matches":[]}` — instead of populating the
schema. Anthropic's GA shape — `output_config={"format": {"type":
"json_schema", "schema": {...}}}` — works correctly with tools.
Verified empirically on `agent-framework-anthropic` against
`claude-sonnet-4-6` for a structured-output workload that combined
`response_format` with a tool (`run_shell`); the deprecated path produced
the malformed concatenated output, the GA path did not.
Changes
-------
- Move `response_format` into `run_options["output_config"]["format"]` and
stop adding the `structured-outputs-2025-11-13` beta flag (the GA path
doesn't need it).
- Merge the format into any caller-supplied `output_config` so e.g.
`output_config["effort"]` (adaptive-thinking effort level) survives the
transformation.
- Drop the now-unused `STRUCTURED_OUTPUTS_BETA_FLAG` constant (private to
this module — no external callers).
- `_prepare_response_format` keeps the same `{"type": "json_schema",
"schema": ...}` return shape; the docstring is updated to point at the
GA target.
Test plan
---------
- `uv run pytest packages/anthropic/tests` → 130 passed.
- New tests:
- `test_prepare_options_uses_output_config_for_response_format` — the
GA `output_config.format` shape is emitted, the deprecated
`output_format` key is not, and the `structured-outputs-2025-11-13`
beta flag is not added.
- `test_prepare_options_preserves_caller_supplied_output_config_effort`
— a caller-supplied `output_config["effort"]` survives the merge.
- `test_prepare_options_no_response_format_omits_output_config` — no
`output_config` is added implicitly when `response_format` is absent.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Python: Improve error message when TypeVar is used in handler registration
Fixes#4547. Adds early detection of unresolved TypeVar instances in:
- @handler decorator (both explicit and introspected type paths)
- @executor decorator (both explicit and introspected type paths)
- WorkflowContext type argument validation (direct and union members)
When a TypeVar is detected, a clear ValueError is raised with actionable
guidance to use concrete types via @handler(input=ConcreteType, output=ConcreteType).
* Address PR review: runtime-safe TypeVar detection and unit tests
- Add shared is_typevar() helper in _typing_utils.py that safely detects
TypeVar from both typing and typing_extensions modules
- Replace all isinstance(x, TypeVar) calls with is_typevar() in
_executor.py, _function_executor.py, and _workflow_context.py
- Add 18 unit tests covering TypeVar validation for @handler, @executor,
and WorkflowContext[T] (explicit params, introspection, union members)
* Fix pyright error: add type annotation to _TYPEVAR_TYPES
Pyright's reportUnknownVariableType flagged the inferred type as
partially unknown. Adding an explicit `tuple[type, ...]` annotation
resolves the strict-mode check.
* Suppress pyright reportUnknownVariableType for _TYPEVAR_TYPES
Pyright cannot infer the runtime type of TypeVar constructors, so the
tuple elements resolve to type[Unknown]. A type annotation alone does
not satisfy strict mode — add an inline suppression for this specific
diagnostic since the unknown types are intentional (runtime TypeVar
class detection).
* Reject nested TypeVars in workflow annotations
---------
Co-authored-by: Kranthi Kumar Manchikanti <kmanchikanti@microsoft.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
Pass knowledge_source_params with include_reference_source_data=True for
each resolved knowledge source on the KnowledgeBaseRetrievalRequest, so
ref.source_data is populated when the source has source_data_fields
configured. Uses SearchIndexKnowledgeSourceParams (azure-search-documents
12.0.0) and resolves real source names for both created and existing
knowledge bases (avoids the prior 'None-source' name).
Fixes#5095
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
GitHubCopilotAgent never forwarded the Copilot SDK's skill_directories
(and disabled_skills) parameters to create_session/resume_session, so
native Copilot CLI skills could not be configured through the agent.
Add both as fields on GitHubCopilotOptions and forward them (with
runtime-override and empty-list-clears-defaults semantics matching
instruction_directories) in _create_session and _resume_session.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Replace internal AG-UI implementation with external ag-ui packages
Remove the in-tree Microsoft.Agents.AI.AGUI sources and consume the external
AG-UI .NET SDK packages (AGUI.Abstractions, AGUI.Formatting, AGUI.Protobuf,
AGUI.Client, AGUI.Server) at 0.1.0-preview instead.
- Microsoft.Agents.AI.Hosting.AGUI.AspNetCore keeps its own ASP.NET glue
(MapAGUI / AddAGUI / SSE result) layered over the framework-agnostic
AGUI.Server primitives (ToChatRequestContext / AsAGUIEventStreamAsync).
- Migrate call sites to the options-based AGUIChatClient constructor and recover
the originating AG-UI input via ChatOptions.TryGetRunAgentInput.
- Multi-turn continuation flows through parentRunId + threadId on
RawRepresentationFactory; shared state flows through RunAgentInput.State and is
surfaced as StateSnapshotEvent raw representations.
- Update samples, hosting/unit/integration tests, and central package versions.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add migration README for removed Microsoft.Agents.AI.AGUI package
Keep the package folder in place with a README explaining that the in-tree AG-UI protocol abstractions moved to the external AGUI.* NuGet packages, with a mapping of old namespaces to the new packages and a migration guide.
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* feat(durabletask): add workflow naming helpers (multi-workflow phase 0)
Foundation for hosting multiple workflows (and later sub-workflows) on one
durable task host. Adds a host-agnostic naming module that derives the stable
durable names a hosted workflow registers under.
- New `_workflows/naming.py`:
- `workflow_orchestrator_name(name)` -> `dafx-{name}` (orchestration name,
aligned byte-for-byte with .NET `WorkflowNamingHelper`).
- `workflow_name_from_orchestrator(name)` -> reverse, `None` when not prefixed.
- `validate_workflow_name(name)` -> rejects empty / malformed / auto-generated
`WorkflowBuilder-<uuid>` names (validate-and-reject rather than silently
sanitize, since the name becomes a durable identity and an HTTP route segment).
- `is_auto_generated_workflow_name(name)`, `DURABLE_NAME_PREFIX`.
- Export the helpers from the package public API.
- Mark `WORKFLOW_ORCHESTRATOR_NAME` deprecated in favor of per-workflow names
(kept functional; the single-workflow path still uses it until phase 1).
- 39 unit tests covering round-trips and validation.
Design: docs/design/durabletask-multiworkflow-and-subworkflows.md
* feat(durabletask): host multiple workflows per worker with scoped names (phase 1)
Enables hosting more than one MAF workflow on a single standalone Durable Task
worker, and aligns both hosts on workflow-scoped durable names so two co-hosted
workflows that reuse an executor id cannot collide.
Naming (shared, host-agnostic):
- orchestration: dafx-{workflowName} (matches .NET; the name DT tooling surfaces)
- non-agent activity / agent entity: dafx-{workflowName}-{executorId} (scoped)
- New naming helpers workflow_scoped_executor_id / workflow_executor_activity_name.
Standalone worker (agent-framework-durabletask):
- configure_workflow is now additive: stores workflows keyed by Workflow.name,
rejects duplicate / auto-generated (WorkflowBuilder-<uuid>) / invalid names,
registers one orchestrator per workflow plus its scoped activities/entities.
- The shared orchestrator dispatches scoped names derived from workflow.name.
- New registered_workflow_names property.
Client (DurableWorkflowClient):
- Optional default workflow_name on the client; start/run/stream accept a per-call
workflow_name and target dafx-{name}.
- Opt-in ownership validation on status/HITL methods: when a workflow name is
resolvable, an instance whose orchestration name does not match is treated as
not-found (status -> None, pending -> [], send_hitl_response / await -> raise),
mirroring the Azure Functions route-scoping check.
Azure Functions host (agent-framework-azurefunctions):
- Registration now uses the same scoped names so the shared orchestrator's
dispatch matches (single workflow per app for now; flat workflow/* routes kept).
- Workflow name is validated up front; workflow agents register under the scoped
entity id; _is_workflow_orchestration scopes to dafx-{workflow.name}.
Samples + tests:
- Durable Task and Azure Functions workflow samples now name their workflow.
- Unit tests cover multi-workflow registration, name validation, client targeting,
and ownership; integration tests target the named workflows.
WORKFLOW_ORCHESTRATOR_NAME remains exported (deprecated). This is a hard switch:
in-flight single-workflow instances created before upgrade (under the old
workflow_orchestrator name) will not resume.
Design: docs/design/durabletask-multiworkflow-and-subworkflows.md
* feat(azurefunctions): host multiple workflows per app with per-workflow routes (phase 2)
Completes multi-workflow hosting on the Azure Functions host, building on the
shared scoped-naming foundation from the worker phase.
AgentFunctionApp:
- New `workflows=` parameter accepting a list (keyed by each `Workflow.name`) or a
name->Workflow mapping; the existing `workflow=` is a single-workflow alias.
Both may be combined. Duplicate names and mapping-key/name mismatches are rejected.
- Each workflow registers its own `dafx-{name}` orchestration, workflow-scoped
activities/entities, and per-workflow HTTP routes:
`workflow/{name}/run`, `workflow/{name}/status/{instanceId}`,
`workflow/{name}/respond/{instanceId}/{requestId}`. Routes are always
per-workflow (even for a single workflow) so callers don't change URLs as an app
grows from one workflow to many.
- Route ownership check is per-workflow (`_is_owned_orchestration(status, name)`):
a leaked instance id for another orchestration -- or another workflow -- is
treated as not-found, extending the route-scoping defense.
- `get_agent(context, name, workflow_name=...)` resolves a workflow agent under its
scoped id; bare `agents=` registration keeps the standalone surface. New
`workflows` introspection property; `.workflow` now returns the sole workflow
(or None when several are hosted).
- Removed the now-unused flat-URL helper `_build_status_url` (handlers inline
per-workflow URLs).
Samples + tests:
- Azure Functions workflow samples (09-12) name their workflow; integration tests
target the per-workflow routes.
- Unit tests cover multi-workflow registration, duplicate/mapping/auto-name
rejection, and per-workflow ownership.
Note: sample README / demo.http route docs are updated in the docs phase.
Design: docs/design/durabletask-multiworkflow-and-subworkflows.md
* feat(durabletask): sub-workflows via durable child orchestrations (phase 3)
Run WorkflowExecutor nodes as durable child orchestrations on both hosts.
- Protocol: add call_sub_orchestrator to WorkflowOrchestrationContext, implemented by the durabletask and Azure Functions adapters.
- Registration: planner classifies WorkflowExecutor as subworkflow_executors; collect_hosted_workflows walks nested workflows (parent first, deduped by name). Both hosts recursively register every nested workflow's orchestration/agents/activities once; only top-level workflows get HTTP routes. Names validated up front before any registration side effects.
- Orchestrator: dispatch WorkflowExecutor nodes via call_sub_orchestrator(dafx-{innerName}) with deterministic child instance ids ({instanceId}::{executorId}::{counter}), a trusted-input marker carrying nesting depth (bounded at 25), and outputs routed as messages (default) or parent outputs (allow_direct_output).
- Tests: registration/collect, orchestrator prepare/process/unwrap, recursive registration on both hosts. Sample: 11_subworkflow.
* feat(durabletask): sub-workflow HITL via qualified request ids (phase 4)
Surface a nested sub-workflow's human-in-the-loop request behind the top-level instance (B2 single addressing surface).
- Orchestrator records dispatched sub-workflow child instance ids in its custom status (subworkflows map) before suspending in task_all, so the read side can reach a child's pending request while the parent is paused.
- Read side (durabletask client get_pending_hitl_requests; AF status route) recurses into nested child statuses, qualifying each nested request id as {executorId}::{requestId} (accumulated for deeper nesting).
- Write side (durabletask client send_hitl_response; AF respond route) splits a qualified id on '::', resolves the owning child orchestration via the parent's subworkflows map, and raises the event on the leaf child with the bare request id. Unknown/inactive sub-workflow -> error/404.
- Shared SUBWORKFLOW_REQUEST_SEPARATOR ('::') in naming so both hosts and the client agree. respondUrl/respond always targets the top-level instance.
- Tests: TestSubworkflowHitl (durabletask client, 7), TestAgentFunctionAppSubworkflowHitl (AF, 7). Sample: 12_subworkflow_hitl (HITL pause inside an embedded sub-workflow).
* docs(durabletask): ADR + sample route docs for multi-workflow and sub-workflows (phase 5)
- Add ADR-0030 capturing the multi-workflow and sub-workflow hosting decisions (naming, scoped inner names, per-workflow routes, child-orchestration sub-workflows, hard-switch migration, B2 sub-workflow HITL, scoped agent addressing) with considered alternatives; mark the design doc as implemented and link the ADR.
- Update Azure Functions workflow samples (09-12) README/demo.http to the per-workflow route shape (workflow/{name}/run|status|respond) introduced in phase 2.
- Extend the durabletask sample catalog with the workflow hosting patterns (08-12), including the new 11_subworkflow and 12_subworkflow_hitl samples.
* fix(durabletask): harden sub-workflow hosting + add sub-workflow integration tests
Post-review hardening of the multi-workflow / sub-workflow durable hosting:
- Trust boundary: strip the reserved sub-workflow envelope key from untrusted
client input at both host boundaries (DurableWorkflowClient.start_workflow and
the AF start route) so a forged envelope cannot reach the trusted pickle path.
- Nested HITL addressing: qualify nested pending requests by (executorId, ordinal)
using a '~' separator (was '::', which collided with core's auto::N functional
request ids); the parent status subworkflows map is now a per-executor list so
multiple children dispatched in one superstep stay independently addressable.
- Reject two different workflow instances that share a name (the same instance
reused by sibling nodes is still deduped); validate executor ids (separator-free,
length-bounded) when hosting durably.
- Remove the arbitrary sub-workflow nesting depth cap: a WorkflowExecutor wraps a
concrete Workflow so the nesting tree is finite at build time, and the durable
instance-id length limit is the natural ceiling (matches .NET, which has none).
Tests/samples:
- New durabletask integration tests for sub-workflow composition (11) and nested
sub-workflow HITL (12); new no-agent AF sub-workflow HITL sample (13) + test.
- Exempt no-agent samples from the model-credential gate in both integration
conftests so the nested-HITL plumbing is covered deterministically.
- Update durabletask sample 12 docs to the new qualified-id format.
Validated: 484 unit tests; durabletask integration 08/09/11/12 and AF 12/13 pass
against the live emulators; pyright 0 errors; ruff clean.
* fix(durabletask): address PR review feedback on naming, typing, and docs
- Unquote df.DurableOrchestrationClient annotations so pyupgrade passes.
- Narrow the split_subworkflow_request_id result before unpacking in a naming test so the strict type checkers pass.
- Correct the durabletask sample catalog to the {executor}~{ordinal}~{requestId} qualified id format.
- Reword the Azure Functions sub-workflow sample intro so it does not imply a difference from a same-numbered sample.
- Drop internal shorthand (B2, phase labels) from code comments.
* fix(durabletask): reject case-insensitive workflow name collisions
The route ownership guard compares the durable orchestration name with casefold(), but registration kept raw names as distinct keys. Hosting 'Orders' and 'orders' therefore succeeded while either workflow's status/respond route could operate on the other's instances. Reject case-insensitive name collisions at registration (within a composition via collect_hosted_workflows, and across registration calls via the case-folded _registered_orchestrations map and the top-level guard in both hosts) so the case-folded ownership boundary stays real. Single names of any case remain valid; only collisions are rejected.
* docs(durabletask): remove multiworkflow/subworkflow ADR and design docs
Drop the ADR and design exploration documents and the dangling docstring reference to them.
* refactor(durabletask): simplify workflow client status parsing and drop deprecated orchestrator-name symbols
Extract a shared _parse_custom_status helper in DurableWorkflowClient to remove duplicated custom-status JSON parsing across three call sites.
Drop the now-unused single-workflow compatibility shims WORKFLOW_ORCHESTRATOR_NAME and WorkflowRegistrationPlan.orchestrator_name, replaced by per-workflow workflow_orchestrator_name(name).
* fix(core): drop WORKFLOW_ORCHESTRATOR_NAME from agent_framework.azure re-exports
The constant was removed from agent-framework-durabletask, but the core azure lazy-loading namespace still re-exported it, breaking pyright in packages/core. Remove it from both the runtime _IMPORTS map and the .pyi stub.
* fix(durabletask): atomic multi-workflow registration and bubble sub-workflow events
Make configure_workflow / AgentFunctionApp registration atomic: check every cross-call name collision before mutating any state, so a colliding nested sub-workflow no longer leaves a host partially configured (with the top-level name stuck in the registry). Applied to both the standalone worker and the Functions app.
Bubble sub-workflow intermediate events: a workflow run as a child orchestration now returns a SUBWORKFLOW_RESULT_KEY envelope carrying its outputs plus event timeline, and the parent re-tags the child's intermediate events with the WorkflowExecutor node id and republishes them, matching the in-process WorkflowExecutor contract. Top-level runs still return a bare outputs list.
Adds cross-registration atomicity tests on both hosts and unit tests for the result envelope and event bubbling. Resolves review threads on _worker.py, orchestrator.py, and test coverage.
* fix(azurefunctions): widen workflow orchestrator wrapper return type
The shared run_workflow_orchestrator now returns list | dict (the sub-workflow result envelope), so the azurefunctions _workflow.py wrapper that delegates to it must widen its Generator return annotation to match. Caught by the package-level pyright in CI (Package Checks), which type-checks the whole package, not just the files changed in the previous commit.
* fix: bump GitHub.Copilot.SDK to 1.0.5 to resolve strong-naming mismatch
SDK 1.0.5 introduced strong-naming (PublicKeyToken=cc7b13ffcd2ddd51).
The adapter was compiled against the unsigned SDK (PublicKeyToken=null),
causing CS0012 for any consumer referencing both packages.
Fixes#6948
* fix: update tests and extension for SDK 1.0.5 namespace changes
- Add 'using GitHub.Copilot;' to CopilotClientExtensions.cs
- Change extension namespace to Microsoft.Agents.AI.GitHub.Copilot
- Update test files for new SDK types and removed APIs
- Add #pragma to suppress GHCP001 experimental warnings in tests
- All 45 tests pass across net8.0, net9.0, net10.0
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
* style: run dotnet format to fix linting issues
Remove unnecessary using directives (IDE0005) and fix file encoding (CHARSET).
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
* fix: revert CopilotClientExtensions namespace to GitHub.Copilot
Per reviewer feedback, extension methods should live in the namespace
of the type they extend (CopilotClient). This follows .NET team guidance.
The original namespace was GitHub.Copilot.SDK which was renamed to
GitHub.Copilot in SDK 1.0.5.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
* refactor: narrow tools parameter from AITool to AIFunctionDeclaration
Since SessionConfig.Tools only accepts AIFunctionDeclaration, change the
constructor and extension method parameters to accept IList<AIFunctionDeclaration>
instead of the more general IList<AITool>. This makes the API honest about what
it actually uses and avoids silently discarding non-AIFunctionDeclaration tools.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
* fix: sync Directory.Packages.props with upstream main
Take upstream's package versions (including MessagePack 3.1.7 pin
that fixes NU1902/NU1903 vulnerability warnings) while keeping
GitHub.Copilot.SDK at 1.0.5 which is the purpose of this PR.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Tamir Dresher <tamirdresher@users.noreply.github.com>
Co-authored-by: Copilot <Copilot@users.noreply.github.com>
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
* Python: Add SkillsSourceContext to SkillsSource.get_skills
Thread an invocation context (agent + optional session) through the skill
source pipeline so sources and decorators can make context-aware decisions.
- Add frozen, experimental SkillsSourceContext(agent, session).
- Change SkillsSource.get_skills and all sources/decorators to accept and
forward the context.
- Make FilteringSkillsSource predicate context-aware: (skill, context) -> bool.
- Add optional cache_isolation_key_selector to CachingSkillsSource for
per-key cache isolation (None keeps the shared-bucket behavior).
- Build the context in SkillsProvider from before_run agent/session.
- Update foundry_hosting toolbox source, exports, tests, and docs.
Python port of .NET PR #6797.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Clarify skills source docstring examples
Address PR review: docstring examples referenced `context` without
constructing it. Add a `SkillsSourceContext` construction line (with a
placeholder agent) to each source example and a note that the provider
normally supplies it. Use `source_context` in the FilteringSkillsSource
example to avoid clashing with the predicate's `context` parameter.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix CI type errors and skill_filtering sample predicate
Address CI failures from the SkillsSourceContext change:
- Update the skill_filtering sample to the 2-arg predicate signature
(skill, context); the old 1-arg lambda would fail at runtime.
- Replace ad-hoc _StubAgent test stubs with the shared MockAgent /
MockAgentSession from conftest so all type checkers (incl. ty) accept
the SupportsAgentRun-typed agent. Add a small _NamedMockAgent subclass
for tests needing distinct agent names, and drop now-unnecessary
attr-defined ignores.
- Use cast(SupportsAgentRun, ...) in foundry_hosting tests, which have no
shared mock infrastructure.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Make SkillsProvider caching safe-by-default; clarify context docstrings
Address PR review comments:
- Do not auto-wrap a caller-supplied SkillsSource in the provider's default
CachingSkillsSource. A shared, unkeyed cache around a context-aware source
replays the first invocation's skills for later SkillsSourceContexts,
leaking skills across agents/tenants. Default caching now applies only to
the built-in, context-independent file/in-memory leaf sources
(Deduplicating(Caching(leaf))), matching the .NET provider. Callers who
want caching on a custom pipeline compose CachingSkillsSource (optionally
with a cache_isolation_key_selector) themselves. disable_caching now only
affects the built-in leaves. Adds a leak-prevention test.
- Reword the misleading "Unused by this source" context docstrings on the
File/InMemory/MCP sources: the param is part of the get_skills contract;
these sources just return the same skills regardless of context.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Allow disabling approval for SkillsProvider tools
Add disable_load_skill_approval, disable_read_skill_resource_approval, and disable_run_skill_script_approval keyword arguments to SkillsProvider.__init__ and SkillsProvider.from_paths. When set, the corresponding tool is registered with approval_mode=never_require so it runs without approval for trusted-skill scenarios. Approval remains required by default.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Preserve from_paths compatibility for SkillsProvider subclasses
Forward the disable_*_approval kwargs from SkillsProvider.from_paths only when explicitly enabled, so subclasses that override __init__ with the previous signature keep working when the flags are left at their defaults. Add a regression test covering a legacy-signature subclass.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Process messages to an executor serially within a superstep
Add a per-executor asyncio.Lock in Executor.execute so each executor processes its messages one at a time within a superstep, while preserving concurrency across distinct executors. Includes a regression test.
* Create per-executor lock lazily under the running loop
asyncio.Lock created in Executor.__init__ would bind to the first event loop it was awaited under, so reusing an executor/workflow across loops (e.g. successive asyncio.run calls) raised 'bound to a different event loop'. Create the lock lazily via _get_execution_lock(), re-creating it when the running loop changes. Adds a loop-scoped lock test.
* Re-create runner context event queue lazily under the running loop
Like the per-executor lock, the runner context's asyncio.Queue bound to the first event loop it was awaited under, so reusing a workflow across loops (e.g. successive asyncio.run calls) raised 'bound to a different event loop'. Re-create the queue lazily via _get_event_queue() when the running loop changes. Adds an integration test reusing a workflow across event loops.
* Use lazy-None init for the event queue, matching the executor lock
Initialize _event_queue to None and create it on first use in _get_event_queue, mirroring the per-executor lock. Avoids constructing a queue in __init__/reset_for_new_run that is immediately discarded once the running loop is known.
* Improve comments
* Fix formatting
* .NET: Fix flaky OpenTelemetryAgentTests via thread-safe activity collector
The Ctor_NullOrWhitespaceSourceName test subscribed a process-global TracerProvider to the shared default source Experimental.Microsoft.Agents.AI and exported into a plain List<Activity>. That source is also used by CompactionTelemetry, and xUnit runs the Compaction test classes in parallel, so a compaction span could be appended from another thread mid-assertion, throwing 'Collection was modified'.
Add a thread-safe ConcurrentActivityList collector (locked Add plus snapshot enumeration) for all InMemoryExporter collectors in the file, and scope the shared-source test to its own invoke_agent TraceId after ForceFlush so parallel compaction spans cannot affect the count or source-name checks.
* .NET: Assert ForceFlush result in OpenTelemetryAgentTests default-source test
Assert the boolean returned by TracerProvider.ForceFlush(timeout) so a flush timeout surfaces as a clear test failure instead of silently snapshotting incomplete activities.
* Validate Foundry toolbox name is a single path segment before building the proxy URL
Reject toolbox name/identifier inputs that carry path separators or relative-path segments (including their percent-encoded forms) before they are interpolated into the toolbox MCP proxy request URL, so a caller-influenced marker cannot alter the request target. Validation runs both at per-request marker resolution and at the shared open choke point, and is covered by red-to-green unit tests.
* Reject residual percent-encoding in toolbox name validation
After the bounded percent-decode loop, also reject a name that still contains a percent sign, so encoding nested deeper than the decode cap cannot survive validation. Dispose the service via await using in the rejection test. Adds a deeply-encoded coverage case.
* Validate toolbox name by request-target effect instead of a character list
Replace the character/decoding checks with an effect-based check: build the proxy URL and confirm the name resolves to a single, intact path segment between 'toolboxes' and 'mcp' with the scheme, authority, path shape, and fragment unchanged, and that the segment round-trips back to the name. This forgives characters that stay inside the segment (for example ':' , '@' , parentheses) while still rejecting names that would move the request target, including '?' and '#' and their percent-encoded forms. Adds coverage for the delimiter cases and for the newly-allowed names.
When a create-response request references a conversation id that does not
exist, validate its existence up front and return a clean not-found error
mapped to HTTP 404, consistent with the Conversations API, instead of failing
mid-execution and surfacing a generic server error.
Centralize the responses validation error codes and their HTTP status mapping
in a single ResponseErrorCodes type so handlers translate a code to a 404 or
400 without ad-hoc string comparisons. Add unit and HTTP integration tests.
* fix: require explicit TokenCredential in AddFoundryToolboxes
The AddFoundryToolboxes extension methods now require callers to
pass a TokenCredential explicitly rather than relying on an
internally-created default credential. This makes the credential
choice intentional and avoids non-deterministic credential probing
in production environments.
Breaking change (experimental API):
- AddFoundryToolboxes(IServiceCollection, params string[]) becomes
AddFoundryToolboxes(IServiceCollection, TokenCredential, params string[])
- AddFoundryToolboxes(IServiceCollection, Action?, params string[]) becomes
AddFoundryToolboxes(IServiceCollection, TokenCredential, Action?, params string[])
- Azure.Identity package dependency removed from Foundry.Hosting library.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: simplify redundant generic type argument (IDE0001)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: avoid duplicate FoundryToolboxService registration
Inject the AddFoundryToolboxes credential directly into the
FoundryToolboxService factory and fail early if the service was
already registered. This avoids registering TokenCredential in the
host DI container while preserving a single toolbox service instance
for both request handling and hosted startup.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: resolve CA1873 in GitHubCopilotAgent by using LoggerMessage source generator
Replace the direct logger.LogWarning() call (which eagerly evaluates
string.Join()) with a [LoggerMessage]-generated extension method in
GitHubCopilotAgentLogMessages.cs.
Fixes build error:
GitHubCopilotAgent.cs(580,13): error CA1873: Evaluation of this argument
may be expensive and unnecessary if logging is disabled
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fixing more dotnet samples
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Ben Thomas <25218250+alliscode@users.noreply.github.com>
* .NET: Bump Azure.AI.Projects to 2.1.0-alpha.20260629.1
Bumps Azure.AI.Projects beta.3 to alpha.20260629.1 and aligns transitive deps (System.ClientModel 1.14.0, Azure.Core 1.59.0, Msal 4.84.2). Adapts to renamed AgentSessionFiles APIs (Upload/GetAll/Delete, scoped GetAgentSessionFiles, SizeInBytes), AgentToolboxes (CreateVersion/Delete), and strongly typed toolbox tools (WebSearchToolboxTool, MCPToolboxTool). Adds azure-sdk public dev feed for prerelease restore.
* Use positional arg for AgentSessionFiles.DeleteAsync cleanup
* Move to Azure.AI.Projects 2.1.0-beta.4 (released beta)
Swaps the alpha daily build for the published 2.1.0-beta.4. Drops the azure-sdk public dev feed since beta.4 and its deps are on nuget.org. Beta.4 requires Azure.Core 1.60.0, which cascades the 10.0.8 servicing packages (Microsoft.Bcl.AsyncInterfaces, System.Diagnostics.DiagnosticSource, System.Text.Json, System.Threading.Channels, Microsoft.Extensions.DependencyInjection.Abstractions, Microsoft.Extensions.Logging.Abstractions) to 10.0.9.
* Reconcile Azure.Core 1.60.0 bump with merged main
Reverts the over-eager System.Threading.Channels 10.0.9 bump back to 10.0.8 (it was not part of the Azure.Core 1.60.0 cascade and caused a net472 MSB3277 conflict against the 10.0.8 that Microsoft.Extensions.AI pulls). Drops the now-obsolete Azure.Core VersionOverride=1.59.0 in HostedWorkflowHandoff (added on main to satisfy AgentServer while the central pin was lower); the central pin is now 1.60.0 which already satisfies the >=1.59.0 floor, and the override was downgrading this project below sibling projects (CS1705).
* Fix Hyperlight workspace link staging
Reject symlinks, Windows junctions, and reparse points during Hyperlight input staging, and harden output collection/cleanup against the same link types.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address Hyperlight staging review
Anchor workspace enumeration to the resolved root and avoid following links while classifying output cleanup entries.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Improve Hyperlight path resolve errors
Handle RuntimeError from path resolution alongside OSError when validating Hyperlight sandbox paths and report the source-root validation context in the error message.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Mark Hyperlight real sandbox tests as integration
Ensure Windows unit CI excludes real Hyperlight sandbox tests by applying the integration marker consistently.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clean up Hyperlight integration sandboxes
Close real sandbox fixtures and provider-owned registries in Hyperlight integration tests so they do not rely on process teardown.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Make Foundry Hosting resilient to missing user identity in local runs
AgentFrameworkResponseHandler threw InvalidOperationException (surfaced as a
500 on every request) when the isolation-key provider returned null, which
always happens locally because the platform x-agent-user-id header is absent.
Running a hosted image outside Foundry therefore failed out of the box.
The handler now branches on FoundryEnvironment.IsHosted: hosted stays strict
(null identity is still a hard error), but non-hosted (local docker run /
dotnet run) tolerates a null identity - per-user isolation is simply not
triggered, the request proceeds with userId null (no partition), and no
hosted context is stamped or validated.
Because local runs no longer need a fallback, the sample-side
DevTemporaryLocalUserIdProvider and AddDevTemporaryLocalContributorSetup are
removed from Hosted_Shared_Contributor_Setup and all sample Program.cs files.
To simulate distinct users locally, send an x-agent-user-id request header;
the default provider reads it exactly as it reads the platform-injected value.
The Memory sample smoke script now drives alice/bob against one container via
that header. AGENT_NAME defaults added to Hosted-ChatClientAgent and
Hosted-MemoryAgent so a hosted deploy (where AGENT_* is a reserved env var)
does not crash at startup.
Updates the two affected unit tests to assert the local-success path and
amends ADR 0031.
* Address review: correct isolation-guarantee and Memory-sample local docs
- AgentFrameworkResponseHandler: note the null/local case is unscoped/shared,
not fully partitioned per user.
- HostedSessionIsolationKeyProvider XML docs: phrase the non-null UserId rule as
a constraint on the returned-context case, since null is now allowed locally.
- Hosted-MemoryAgent: the PerUser() memory scope requires a resolved user, so a
local run needs an x-agent-user-id header; corrected the Program.cs comment
and README (removed the inaccurate "shared bucket locally" claim).
- Test: assert absence of any u-* per-user directory via a wildcard search
rather than checking for a literal "u-" directory.
* Python: [BREAKING] Extract caching from SkillsProvider into CachingSkillsSource decorator
Adds a composable CachingSkillsSource(DelegatingSkillsSource) decorator that caches the inner source's skills list, and rewires SkillsProvider to wrap its resolved source in it by default (skipped when disable_caching=True). Removes the provider's baked-in caching (_cached_context field and _get_or_create_context). Mirrors .NET #6768.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add ty ignore for dynamic _test_context attribute in skills test helper
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Stop skill discovery at skill boundaries
File-based skill discovery kept descending after finding a SKILL.md, which treated content nested beneath a skill boundary as an independent skill root. Return immediately after recording a directory that contains SKILL.md so everything below it stays part of that skill, and add a regression test with a nested SKILL.md.
Fixes#6682
* Python: Attach nested skill content to the parent skill
Removing the SKILL.md subdirectory skip in resource and script scanning so that content beneath a skill boundary is attached to that skill, and update the discovery docstring and the nested-skill test to match. Complements the discovery early-return so a nested SKILL.md is never treated as an independent skill root.
* .NET: Consolidate skill-source caching and make skill sources disposable
Move all caching into the generic CachingAgentSkillsSource decorator and
remove the duplicate inline cache from AgentMcpSkillsSource, so a single
cache layer governs skill fetching. Add RefreshInterval-based expiry to
CachingAgentSkillsSourceOptions.
Make AgentSkillsSource (and its decorators) IDisposable so pipelines can
release owned resources, and give AgentSkillsProvider an ownsSource flag
controlling whether it disposes the source it wraps. Provider convenience
constructors and the builder set ownsSource: true.
Serialize ArchiveEntryLoader's reconcile/extract/read of the shared on-disk
directory with a per-instance lock to prevent concurrent corruption.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Fix IDE0032 by using an auto-property in test source
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Make cancellation cache test deterministic
Ensure the first caller owns the fetch before the second caller queues, so
the cancellation-restart assertion is no longer race-prone.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Throw ObjectDisposedException from CachingAgentSkillsSource after disposal
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Document AgentSkillsProviderBuilder source ownership and single-build contract
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Update API compatibility suppressions for AgentSkillsProvider ctor change
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Add test asserting archive skill updates are observed after reconcile
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Allow custom argument marshaling for skill scripts
Add an optional argument_marshaler hook so callers can plug in their own argument conversion logic for inline skill scripts. Supplied at the InlineSkillScript, InlineSkill, and ClassSkill levels; when omitted, behavior is unchanged. This supports backends (e.g. vLLM) that send tool-call arguments in a non-conforming shape such as a JSON string.
Port of .NET PR #6498. Closes#6543.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback on skill argument marshaling
- Widen InlineSkillScript.run args to accept a raw str (the one place a marshaler-converted value is valid), and drop the now-unneeded type: ignore markers in tests.
- Constrain the SkillScriptArgumentMarshaler output type to dict | None so the type enforces the inline-script contract instead of a docstring note.
- Add a clear TypeError when a str reaches an inline script with no marshaler configured.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Rename SkillScriptArgumentMarshaler to SkillScriptArgumentParser
In Python 'marshalling' specifically connotes the stdlib marshal module, so the term is misleading here. Rename the type alias, the argument_parser parameter/attribute, docstrings, exports, and tests accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fold argument_parser docstring into Args section
The skill constructors are fully keyword-only, so name/description/function are already documented under Args. Singling out argument_parser into its own Keyword Args section was inconsistent; merge it into Args for InlineSkillScript, InlineSkill, and ClassSkill.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
The auto-injection of InMemoryHistoryProvider was gated on there being no
context providers at all, so registering any non-history provider (e.g.
SkillsProvider, FileAccessProvider, or a RAG memory provider) suppressed local
history. On stateless clients this dropped prior messages across turns — most
visibly the tool-approval resume turn lost the prior assistant function_call,
causing a 400 "Expected toolResult blocks" error.
Gate the injection on the absence of a loading HistoryProvider instead, matching
the pattern already used in _workflows/_agent.py. Add regression tests covering
a non-history provider, an existing loading provider, and a persist-only
provider.
Fixes#5672
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix GeminiChatClient dropping image/file content
GeminiChatClient._convert_message_contents only handled text and function_call content, so data/uri (image, PDF, audio) parts were silently dropped and never reached Gemini. Convert data URIs to inline_data Parts and external URIs to file_data Parts, warning on genuinely unconvertible content. Adds tests for the multimodal conversion paths.
Fixes#6688
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: strip data-URI mime params and handle non-inferable URIs
Strip parameters (e.g. charset) from a data URI media type before passing it to Gemini, and wrap types.Part.from_uri so a URI with no media_type and no guessable extension is passed through as file_data without a mime type instead of raising ValueError. Adds tests for both paths.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: reuse shared data-URI helpers
Reuse _get_data_bytes and detect_media_type_from_base64 from agent_framework instead of reimplementing base64 extraction/decoding and data-URI header parsing in the Gemini client. This also removes the manual header parsing that previously needed charset-parameter stripping. Updates tests accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Hosting packages (hosting, hosting-responses, hosting-telegram) were excluded
from the 1.10.0 release but their entries remained in the CHANGELOG.
Also removes the core hosting channel entry since it's unreachable without
the hosting packages.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add per-agent and per-user session storage isolation for Foundry Hosting
Partitions hosted session and checkpoint files as {root}/a-{agentName}/u-{userId}/c-{contextId}.json so a container that serves multiple agents and multiple users cannot leak state across tenants. The user layer collapses to a-{agent}/c-{conv}.json when no x-agent-user-id is present (raw local). Adds a reject-style path-traversal guard (CWE-22) for the untrusted user id plus a resolve-and-assert-under-root containment check, and keeps the strict-resume 403 identity check as a second defense layer.
AgentSessionStore.GetSessionAsync/SaveSessionAsync take a required (nullable) userId so a caller can never silently persist a session unscoped; the handler resolves the user id before loading the session and threads it to both. Tool approvals ride in the session checkpoint (ToolApprovalIdMap to AgentSessionStateBag), so the partitioned path covers them and no separate approval store is needed. Renames the sample HOSTED_USER_ISOLATION_KEY env var to HOSTED_USER_ID and DevTemporaryLocalSessionIsolationKeyProvider to DevTemporaryLocalUserIdProvider. Documents the design in ADR 0031. Adds handler-driven multi-agent/multi-user file-system tests and store-level traversal/isolation tests.
* Fail fast with a clear 501 when hosted container is served responses protocol 1.0.0
A 2.0.0-only hosted image served container protocol 1.0.0 (no x-agent-foundry-call-id
header) previously threw and surfaced an opaque 500 on every request. It now returns a
clear 501 "unsupported_container_protocol_version" naming the required protocol.
* HostedProtocolCompatibility gate keyed on FoundryEnvironment.IsHosted plus
PlatformContext.CallId (the 2.0.0 exclusive marker); invoked before isolation resolution
* HostedProtocolCompatibilityTests unit coverage; AgentFrameworkResponseHandlerTests note
clarifies the non-hosted path
* UnsupportedProtocolHostedAgentTests integration test deploys a dedicated
it-unsupported-protocol agent as 1.0.0 and asserts the 501 (validated live on cace)
* TestContainer recognizes the unsupported-protocol scenario
* it-bootstrap-agents.ps1 placeholder default raised to responses 2.0.0 and adds the
it-unsupported-protocol agent; HostedAgentFixture protocol version is overridable
* Address PR review: whitespace protocol gate and InMemory store agent keying
* HostedProtocolCompatibility treats a whitespace-only x-agent-foundry-call-id as
absent (IsNullOrWhiteSpace) so a proxy injecting whitespace cannot bypass the gate;
unit test covers empty, spaces and tab
* InMemoryAgentSessionStore keys sessions by agent.Name (omitting the agent segment
when Name is unset), mirroring FileSystemAgentSessionStore, so session continuity
survives a recreated or transient agent rather than keying on the per-instance agent.Id
* Add AgentSkillsSourceContext to AgentSkillsSource.GetSkillsAsync
Pass agent/session context through the skills retrieval pipeline so
sources, filters, and caching can make context-aware decisions.
- AgentSkillsSourceContext (Agent, Session) is built by AgentSkillsProvider
from the InvokingContext and flows through all sources and decorators.
- FilteringAgentSkillsSource predicate now receives an AgentSkillFilterContext
bundling the skill and the source context.
- CachingAgentSkillsSource supports per-key isolation via
CachingAgentSkillsSourceOptions.CacheIsolationKeySelector; a null selector
preserves the shared-cache behavior.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Make AgentSkillsSourceContext constructor public and harden cache key
- Make the AgentSkillsSourceContext constructor public so external callers
can invoke AgentSkillsSource.GetSkillsAsync directly; drop the
Mcp.UnitTests InternalsVisibleTo entry it required.
- Use a dedicated sentinel cache key for the shared bucket so an isolation
selector returning an empty string gets its own bucket.
- Document cache-key cardinality guidance and baseline the experimental
API breaking changes in CompatibilitySuppressions.xml.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Drop AgentSkillFilterContext in favor of a two-argument filter predicate
Replace the AgentSkillFilterContext bundle with a
Func<AgentSkill, AgentSkillsSourceContext, bool> predicate in
FilteringAgentSkillsSource and AgentSkillsProviderBuilder.UseFilter, and
update the tests accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: align GitHub Copilot approval to SDK on_pre_tool_use hook
Replace the bespoke on_function_approval enforcement in the GitHub Copilot provider with the Copilot SDK's native on_pre_tool_use hook. When no caller hook is supplied, a default hook returns 'ask' for approval_mode='always_require' tools (routed to on_permission_request) and defers others; a caller-supplied on_pre_tool_use takes precedence and logs a warning for any unenforced approval tool.
Fixes#6746
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix type-checker errors and restore load_dotenv in sample
Use a complete PreToolUseHookInput in on_pre_tool_use hook tests so pyright/pyrefly/ty/zuban no longer report missing required TypedDict keys. Restore load_dotenv() in the function-approval sample for consistency with the other GitHub Copilot samples (PR review feedback).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Deprecate on_function_approval instead of removing it
Per PR review feedback, keep the on_function_approval callback working (still enforced in the tool handler for approval_mode='always_require' tools) but emit a DeprecationWarning at construction, so existing users get a signal rather than a silent behavior change. The default on_pre_tool_use ask-hook is not installed when on_function_approval is set, avoiding double-gating. Precedence: user on_pre_tool_use > on_function_approval > default ask-hook. Adds tests for the deprecated path and documents it in the package README.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Make on_function_approval and on_pre_tool_use mutually exclusive
Per automated review feedback, instead of a precedence ordering between the deprecated on_function_approval callback and the new on_pre_tool_use hook (which silently double-gated when both were set), raise ValueError if both are supplied - at construction (both in default_options) or per run (per-run on_pre_tool_use with a construction-time on_function_approval). This matches the repo convention for deprecated-vs-new params (see _workflows/_workflow.py) and removes the flag-threading. Updates tests and the package README.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Replace dotnet/nuget/icon.png with the new Microsoft Foundry Agent Framework color logo (resized to 128x128, the NuGet-recommended icon size). Source: docs/assets/PNG/Microsoft Foundry Agent Framework - Color.png.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Replace the direct logger.LogWarning() call (which eagerly evaluates
string.Join()) with a [LoggerMessage]-generated extension method in
GitHubCopilotAgentLogMessages.cs.
Fixes build error:
GitHubCopilotAgent.cs(580,13): error CA1873: Evaluation of this argument
may be expensive and unnecessary if logging is disabled
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Replace the direct logger.LogWarning() call (which eagerly evaluates
string.Join()) with a [LoggerMessage]-generated extension method in
GitHubCopilotAgentLogMessages.cs.
Fixes build error:
GitHubCopilotAgent.cs(580,13): error CA1873: Evaluation of this argument
may be expensive and unnecessary if logging is disabled
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Bump Azure.AI.AgentServer to 2.0.0 protocol and migrate Foundry.Hosting
Bumps Core .25->.26, Invocations .4->.5, Responses .5->.6 and adopts the 2.0.0 container protocol.
Breaking change: IsolationContext (UserIsolationKey + ChatIsolationKey) is replaced by PlatformContext (UserIdKey from x-agent-user-id, CallId from x-agent-foundry-call-id). The per-chat key is gone; HostedSessionContext is now user-only and the per-request CallId is forwarded outbound to Foundry first-party services (toolbox/MCP).
Also fixes a real call-id egress bug: AsyncLocal writes inside the streaming response iterator are reverted across yield boundaries, so the call id was dropped before the toolbox/MCP egress ran. The handler now re-applies HostedCallContext.CallId before each egress point.
Adds HostedConversationKey to map a request to a stable MAF AgentSession via conversation_id, else the partition key embedded in previous_response_id, else the minted response id. This keeps store=false previous_response_id chains and conversation_id forks on a single hosted MAF session without using the container session id.
Sample manifests bump the responses protocol to 2.0.0 (invocations stays 1.0.0). Integration tests split store/session semantics into HostedResponsesStoreConfigTests with its own scenario, read stored responses through the per-agent endpoint client, and inject the model deployment into the container.
* Pin Azure.Core 1.59.0 for Hosted-Workflow-Handoff sample
AgentServer 1.0.0-beta.26 (pulled transitively via Foundry.Hosting) requires Azure.Core 1.59.0. This sample disables transitive pinning and references Azure.Core directly, so override just this project to the SDK-required version without moving the solution-wide central pin.
* Add guard test for request-scoped call-id cleanup
Asserts HostedCallContext.CallId does not leak into the caller's execution context after CreateAsync's stream completes, while confirming the agent run still observed the call id. Documents the request-scoped contract and guards against stale-header leakage across requests handled on the same thread.
* Refresh hosting READMEs for AgentServer 2.0 migration
Updates stale docs to match the shipped code: the MemoryAgent README now describes the x-agent-user-id user-identity header (chat isolation key removed) feeding HostedSessionContext.UserId; the IntegrationTests README corrects the scenario count (six to eleven), adds the missing memory scenario row, and stops claiming all scenarios are skipped now that several are validated and active.
* Add ADR 0030 superseding 0026 for AgentServer 2.0 platform context
Documents the migration from ResponseContext.Isolation (UserIsolationKey/ChatIsolationKey) to ResponseContext.PlatformContext (UserIdKey/CallId): user-only HostedSessionContext, the request-scoped HostedCallContext call-id forwarded on egress, HostedConversationKey session keying, and removal of the PerChat/PerUserAndChat memory scopes. Marks ADR 0026 as superseded.
* Add breaking-change v2.0-only disclaimer to package metadata
Augments the package Description and adds PackageReleaseNotes stating this release targets the Foundry Responses container protocol v2.0 only, is not compatible with v1, and directs consumers to a previous release for the v1 protocol definition.
* Address review comments: dead chat-key surface and weak test assertions
Fixes the automated review findings: the MemoryAgent/AgentSkills .env.example now say one variable (only HOSTED_USER_ISOLATION_KEY remains); the MemoryAgent smoke script drops the unused ChatKey parameter and its call-site arguments; HostedConversationKey null test now exercises a real null (and whitespace); and the reuse-one-session test asserts an exact SessionCount of 1 instead of <= 1.
* Python: Add include_detailed_errors option for skill script execution
Port the .NET fix from #6680. SkillsProvider previously swallowed
exceptions from skill script execution and resource reading, returning a
generic error string so the model could not self-correct.
- Add an include_detailed_errors option to SkillsProvider.__init__ and
from_paths. When True, script-execution failures return an error string
with the exception message appended; when False (default), the exception
is logged and re-raised, delegating to the function-invocation pipeline's
own include_detailed_errors policy.
- _read_skill_resource now logs and re-raises instead of returning a
generic error string. Resources take no model arguments, so a swallowed
generic error is not actionable by the model.
- Update and add tests covering the new propagation and detailed-error
behavior.
Fixes#6681
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Re-raise skill script/resource errors instead of adding a provider option
Address PR review: returning a plain error string from the skill provider
bypassed the shared tool-error contract (no exception metadata, not counted
toward consecutive-error limits), risking infinite retries.
Instead of porting the .NET provider-level IncludeDetailedErrors option,
_run_skill_script and _read_skill_resource now always log and re-raise on
failure. This delegates error handling to the function-invocation pipeline,
whose existing include_detailed_errors policy is the Python equivalent of
.NET's FunctionInvokingChatClient.IncludeDetailedErrors and correctly
preserves exception metadata and consecutive-error counting.
Validation failures (empty/unknown skill, script, or resource names) still
return user-facing error strings. Tests updated accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: [BREAKING] Make all SkillsProvider tools require approval by default
All tools exposed by SkillsProvider (load_skill, read_skill_resource,
run_skill_script) now require approval by default. Previously only
run_skill_script could be gated, and only when require_script_approval=True.
- Register all three tools with approval_mode="always_require"
- Add read_only_tools_auto_approval_rule and all_tools_auto_approval_rule
static rules plus tool-name constants (mirrors FileAccessProvider)
- Remove the require_script_approval option from __init__ and from_paths
- Add skills_auto_approval sample; update script_approval sample/docs
Closes#6728
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: batch skill approval responses and tidy sample
- Collect a response for every approval request and send them in a single
agent.run so the approval loop always makes progress (no infinite loop when
a request lacks a function_call); reject non-function requests instead of
skipping them. Applied to both the skills_auto_approval and script_approval
samples.
- Extract ToolApprovalMiddleware into a local variable in skills_auto_approval
for readability.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: add approval handling to remaining skills samples
The secure-by-default change makes all SkillsProvider tools require approval,
which left the other skills samples emitting approval requests instead of the
documented answers. Add ToolApprovalMiddleware with the all-tools auto-approval
rule (and a session, which the middleware requires) so these samples run
unattended as before:
- code_defined_skill, file_based_skill, class_based_skill, mixed_skills,
skill_filtering, mcp_based_skill
- providers/foundry/foundry_chat_client_with_toolbox_skills
The dedicated script_approval (manual) and skills_auto_approval (selective)
samples continue to demonstrate interactive approval handling.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: simplify "host approval" wording to "approval"
Apply maintainer suggestions dropping "host" from the skill-approval
docstrings, and align the matching SkillsProvider docstring/AGENTS.md note for
consistency.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Update agent-framework-azure-ai-search to work across the stable/GA azure-search-documents SDK (12.0.0, api-version 2026-04-01) and the preview SDK (12.1.0b1, api-version 2026-05-01-preview) for both semantic and agentic modes.
- Bump the dependency to azure-search-documents>=12.0.0,<13 and the package to 1.0.0b260618.
- Add an api_version parameter (threaded into SearchClient, SearchIndexClient, and KnowledgeBaseRetrievalClient) plus STABLE_API_VERSION/PREVIEW_API_VERSION constants, re-exported from agent_framework.azure.
- Auto-detect preview-only agentic features (output mode, low/medium reasoning effort) via _preview_features_active(), which requires both the preview SDK and a preview api-version; defaults (extractive + minimal) work on both channels and preview-only options raise an actionable error otherwise.
- Make knowledge-base imports SDK-version resilient and fix the 12.x surface (k -> k_nearest_neighbors, defensive additional_properties).
- Update tests (pass on both SDKs), docs, samples, CHANGELOG, and uv.lock.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add samples for the harness blog part 2
* Address PR comments
* Fix blog links.
* Address PR comments
* Fix bug where mode was incorrectly defaulted when reading the mode before the first run.
* Add reference to new sample readme
Ollama's `format` param only accepts '', 'json', or a JSON-schema dict, so
passing a Pydantic model class (the form OpenAIChatClient/FoundryChatClient and
create_harness_agent plan mode use) raised a ValidationError while building the
request. Convert a model class to its JSON schema when mapping response_format
-> format, keeping the original class for typed response parsing.
* Python: add GitHub MCP security label sample
* modified samples to create devui auth token, support debugging with security, and change context label only using the labels of unhidden result from tools
* FIDES: secure MCP labeling, _meta IFC parsing, and docs updates
* FIDES: secure MCP labeling, _meta IFC parsing, and docs updates
* modified docs
* fixed PR comments, simplified github_mcp example
* commented github_mcp example
* remove the parse_github_mcp_labels and fix the user_identity label propogation
* fix: use standard GitHub MCP endpoint with X-MCP-Features: ifc_labels instead of /insiders
- Switch MCP_URL from /mcp/insiders to /mcp/ in github_mcp_example.py
- Add MCP_HEADERS constant with X-MCP-Features: ifc_labels to opt-in to
server-side IFC label emission in _meta payloads
- Fix SecureMCPToolProxy to pass headers via httpx.AsyncClient so they are
included on session.initialize(), not just on tool calls (was causing 401
to silently surface as anyio cancel-scope CancelledError)
- Update README, FIDES_DEVELOPER_GUIDE, FIDES_IMPLEMENTATION_SUMMARY, and
0024-prompt-injection-defense.md to remove all /insiders references
* address PR comments
* Simplify GitHub MCP security sample to DevUI-only; document SecureAgentConfig quarantine client global behavior
* minor PR comments
* fixing failed checks
* fixing failed checks
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Python: bump package versions for 1.10.0 release
- Released cohort (core, openai, foundry, root): 1.9.0/1.8.2 -> 1.10.0
- agent-framework-ag-ui: rc5 -> rc6 (tool history replay fix)
- Beta/alpha packages with changes: anthropic, azurefunctions, bedrock,
durabletask, hyperlight, purview, foundry-hosting, gemini, hosting,
hosting-responses, hosting-telegram, tools bumped to new date stamp (260625)
- Inter-package dependency bounds updated for changed packages
- CHANGELOG.md updated with [1.10.0] section and compare links
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: update stale hosting dependency pins in hosting-responses and hosting-telegram
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* CI: cap xdist workers at 4 for Azure OpenAI and Functions integration jobs
The Azure OpenAI and Functions+Durable Task integration jobs ran with
`-n logical` (~20 workers on the hosted runner), oversubscribing the box and
collapsing the whole pytest session (all workers reporting `node down: Not
properly terminated`) in the merge queue. Pin these two jobs to `-n 4` in
python-merge-tests.yml and python-integration-tests.yml to remove the
oversubscription while keeping full coverage.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* test: temporarily skip flaky Python integration tests crashing the merge queue
Revert the `-n 4` xdist experiment (it did not prevent the runner crash) and
instead skip the integration tests that collapse the pytest-xdist runner in the
merge queue (all workers report `node down: Not properly terminated`):
- Azure OpenAI: flip the per-file `skip_if_azure_openai_integration_tests_disabled`
guard to an unconditional skip (integration tests only; unit tests still run).
- Azure Functions / Durable Task: skip the four specific failing tests
(test_weather_agent, test_parallel_workflow_end_to_end, test_weather_agent_with_tool,
test_conditional_branching).
Tracked for re-enablement in #6777.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* test: skip flaky test_math_agent_with_tool (durabletask integration)
Same empty-AgentResponse flakiness as test_weather_agent_with_tool in the same
file (AssertionError: assert 0 > 0 / empty .text). Skip it in the merge queue.
Tracked in #6777.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Enforce ApprovalRequiredAIFunction in GitHub Copilot provider
The GitHub Copilot SDK owns the tool-calling loop and invokes registered
custom functions directly, so the standard FunctionInvokingChatClient
approval round-trip never runs for this provider. As a result a tool wrapped
in ApprovalRequiredAIFunction (only a marker) could execute without any
Agent Framework approval.
Add an agent-level onFunctionApproval callback and wrap approval-required
tools in an ApprovalGatedAIFunction that enforces approval before invoking
the underlying function. Secure-by-default: with no callback, or when the
callback denies or throws, execution is denied. The gate forwards tool
metadata (including the Copilot skip_permission flag) so it stays
transparent to the SDK. This mirrors the Python provider's behavior.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Propagate cancellation from GitHub Copilot approval callback
Let OperationCanceledException propagate from the approval callback instead
of swallowing it into a denial, so cooperative cancellation is honored.
Other callback failures still deny by default. Added a unit test.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Enforce ApprovalRequiredAIFunction via Copilot SDK OnPreToolUse hook
Replace the custom approval enforcement (ApprovalGatedAIFunction wrapper +
onFunctionApproval callback) with the GitHub Copilot SDK's native OnPreToolUse
hook, which the SDK already provides for pre-execution gating.
When a tool wrapped in ApprovalRequiredAIFunction is registered and the caller
hasn't supplied their own OnPreToolUse hook, the agent installs a default hook
that returns "ask" for those tools (routing the decision to OnPermissionRequest)
and defers (null) for all other tools, preserving today's behavior for
non-approval tools. If the caller supplies their own OnPreToolUse hook, it takes
precedence and they own approval handling; the agent logs a warning naming any
approval-required tool that will not be auto-gated, and the behavior is
documented. Adds an optional ILoggerFactory parameter for the warning.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Address PR review feedback on GitHub Copilot approval hook
- Build the approval-required tool-name HashSet directly instead of via an
intermediate List.
- Remove the redundant MEAI001 NoWarn suppression (tests already suppress it via
.editorconfig and the source project builds clean without it).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Include the optional description attribute on <resource> and <script>
elements within <available_resources> and <available_scripts> blocks,
aligning .NET with the Python implementation. The description is emitted
only when non-null/non-empty and is XML-escaped.
Co-authored-by: Marco Minerva <marco.minerva@gmail.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Move the cache-once-then-replay logic out of AgentSkillsProvider into a
new CachingAgentSkillsSource decorator following the DelegatingAgentSkillsSource
pattern used by DeduplicatingAgentSkillsSource and FilteringAgentSkillsSource.
- Add internal CachingAgentSkillsSource (lock-free, thread-safe; clears on failure)
- AgentSkillsProviderBuilder applies caching after aggregation, before filter/dedup
- Add builder DisableCaching() opt-out method
- Convenience constructors wrap with CachingAgentSkillsSource before dedup
- Remove DisableCaching from AgentSkillsProviderOptions
- Add CachingAgentSkillsSourceTests
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fixing some samples and sample verification.
* Workaround for continuation token moved to sample.
* Address PR review comments: reset _stdinEof on reuse, null-guard modelId, format
- WorkflowRunner: reset _stdinEof=false at start of ExecuteAsync so reused
instances don't exit immediately on the next external request
- 04_memory: throw clear InvalidOperationException when DefaultModelId is null
rather than silently sending null to the Foundry Responses API
- dotnet format: no code changes, formatting only
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Improving memory sample by not creating an agent just to get a chat client.
---------
Co-authored-by: Ben Thomas <25218250+alliscode@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Prefer allocated HTTPS endpoints when resolving Aspire DevUI backends and fall back to HTTP for existing services. Update the DevUI Aspire sample so WriterAgent exercises HTTPS redirection.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Switch the remaining MAF-specific [Experimental(OPENAI001)] usages in Microsoft.Agents.AI.Foundry.Hosting to MAAI001 (AgentsAIExperiments). None of these public types surface an OpenAI experimental type, so OPENAI001 was a copy-paste inconsistency; MAAI001 is the correct id for MAF hosting/agent abstractions.
Fixes#6742
* .NET: Foundry hosted-agent toolbox OAuth consent support
Add per-user OAuth (MCP CONSENT_REQUIRED) support for Foundry hosted agents.
* Defer hard toolbox startup failures so a per-user OAuth-gated toolbox no
longer bricks the container at startup (new Degraded status, retried per
request). The container stays routable and surfaces consent on the first
user request.
* Emit the platform-canonical oauth_consent_request output item (instead of
mcp_approval_request) for toolbox OAuth consent, matching the Python
implementation and how the Foundry platform heads render consent.
* Parse the toolbox CONSENT_REQUIRED (-32006) error and surface the consent
link; resume by re-sending the prompt with no reply item needed.
* Add the Hosted-Toolbox-AuthPaths OAuth consent REPL client sample that
detects oauth_consent_request, prints the consent link, and re-sends.
* Add tests for the consent parser, startup deferral, and oauth_consent_request
emission.
Fixes#6562
* .NET: Address review feedback on toolbox OAuth consent
* Make RecomputeStatus the single source that refreshes ConsentRequiredToolboxNames
from the pending-consent set, so a per-request marker that records consent via
GetToolboxToolsAsync no longer leaves ConsentRequiredToolboxNames stale (which
made ResolvePendingConsentsAsync skip surfacing it).
* Surface lazy / per-request marker consent in the same request: after resolving
markers the handler now emits oauth_consent_request + incomplete when a marker
hit CONSENT_REQUIRED, instead of silently running without that toolbox.
* Add FoundryToolboxService.GetPendingConsents() snapshot accessor.
* Fix stale ToolboxConsentParser doc comment (mcp_approval_request -> oauth_consent_request).
* .NET: Harden toolbox consent paths from code review
* Thread-safety: GetPendingConsents() now returns an immutable snapshot rebuilt
in RecomputeStatus under the lock, instead of enumerating the live
_pendingConsents dictionary off-lock (which could throw under concurrent requests).
* Resource leak: OpenToolboxAsync builds the endpoint Uri before allocating the
HttpClient and now disposes the HttpClient when McpClient.CreateAsync throws
(the unreachable/deferred case retried per request), not only when ListToolsAsync fails.
* StrictMode now gates on the pre-registered ToolboxNames set rather than the
opened-toolbox cache, so a registered-but-deferred toolbox is no longer rejected
as unknown.
* Sample REPL: the legacy approval-args consent fallback only reads the explicit
consent_url key, so a normal function-tool approval carrying a URL argument is
not misread as an OAuth consent request.
* .NET: Scope per-request toolbox marker consent to the request
Addresses review feedback that a marker-originated toolbox could leak into global
scope after consent. GetToolboxToolsAsync now returns a request-scoped
ToolboxResolution (tools or consent requirements) instead of recording marker
consent in the container-global _pendingConsents and appending resolved tools to
the service-wide Tools list.
* Marker consent is surfaced as oauth_consent_request for the requesting turn only
and collected in the handler's marker loop; it no longer injects tools into, or
raises a consent prompt on, a later request that did not reference the marker.
* Marker resolution no longer flips the container StartupStatus to ConsentRequired
(per-request markers must not affect readiness, per the StartupStatus contract).
* Remove the now-unused GetPendingConsents()/snapshot path; _pendingConsents is once
again exclusively the pre-registered/startup consent set.
* .NET: Add consent request-scoping UTs and an OAuth consent integration test
Unit tests (Microsoft.Agents.AI.Foundry.Hosting.UnitTests):
* New FoundryToolboxMarkerScopingTests proves per-request marker resolution is
request-scoped: a marker consent is returned to the caller without mutating
ConsentRequiredToolboxNames, StartupStatus, or the service-wide Tools cache, and
marker-resolved tools are returned to the caller rather than injected globally
(so a request with no marker sees neither the tools nor the consent).
* Adds a test-only ToolboxOpener seam on FoundryToolboxService so the consent/tools
resolution can be exercised without a live MCP proxy. Makes ToolboxOpenResult and
CachedToolbox internal (CachedToolbox.Client nullable, guarded at dispose).
Integration test (Foundry.Hosting.IntegrationTests):
* New toolbox-oauth-consent scenario wired into the TestContainer (pre-registers a
Foundry toolbox via AddFoundryToolboxes from IT_TOOLBOX_NAME), a
ToolboxOAuthConsentHostedAgentFixture, and a ToolboxOAuthConsentHostedAgentTests
that invokes the deployed agent and asserts the consumer captures an
oauth_consent_request consent link (container stays routable, no 424). Skipped by
default per the IT convention; documents the consent-gated toolbox prerequisite.
* Adds the scenario to it-bootstrap-agents.ps1 and the README scenario table.
* Refactor runner/workflow responsibilities, add concurrency guards, and fix checkpoint ancestry bug
Move runner-state ownership out of Workflow into Runner for clearer responsibilities. Add a weakref-based concurrent-run guard in Workflow and fix the stream-drop race in run_until_convergence. Fix the checkpoint ancestry bug by tracking the previous checkpoint id as runner instance state so parent pointers persist across resumed runs. Move Runner to a deprecated lazy __getattr__ export (backward-compatible with DeprecationWarning) and export CheckpointID.
* Scope runtime checkpoint storage to its owning run
Close the stream-drop race where a dropped run's deferred async-generator finalizer could leave a runtime checkpoint storage override set (inherited by a new run) or clear a successor run's storage. run() now defensively clears any stale override before starting, and _run_core only clears the override if this run still owns it (mirroring the _active_run ownership guard). Adds regression tests for both the inheritance and clobber cases.
* Collapse runtime-storage ownership into the active-run weakref
_runtime_storage_owner always held the same weakref as _active_run, so the two ownership conditions were equivalent. Derive ownership from a single owns_run = (_active_run is my_active_run) captured before the active-run clear, and remove the redundant field. No behavior change.
* Nest runtime-storage clear under the owns_run guard
Both the active-run release and the runtime-storage clear are gated on owns_run, so fold the storage clear inside the if owns_run block. No behavior change.
* Reset resume flag in a finally so it can't leak across runs
_resumed_from_checkpoint was only cleared on the success path of run_until_convergence, so a failure during a resumed run (e.g. executor failure) left it True. The next fresh run then skipped the superstep-0 checkpoint and parented later checkpoints to the stale resume point. Move the reset into a finally. Add a regression test that fails a resumed run via an executor error and asserts the next fresh run creates the superstep-0 checkpoint.
* Fix tests and formatting
* Fix formatting
* Address comments
* Update type ignore statements
* Make all AgentSkillsProvider tools require approval by default
- Wrap all tools (load_skill, read_skill_resource, run_skill_script) with
ApprovalRequiredAIFunction unconditionally
- Add ReadOnlyToolsAutoApprovalRule and AllToolsAutoApprovalRule static
properties following the FileAccessProvider pattern
- Remove ScriptApproval from AgentSkillsProviderOptions and
UseScriptApproval from AgentSkillsProviderBuilder
- Add Agent_Step07_SkillsAutoApproval sample
Closes#6727
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add UseToolApproval to hosted AgentSkills scenarios
Wire AllToolsAutoApprovalRule into the integration test container and
the Hosted-AgentSkills sample so skill tools execute without blocking
on approval when no interactive approval handler is configured.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add API compatibility suppressions for removed ScriptApproval members
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Embed resource and script instruction text directly in the default
prompt template instead of using placeholder substitution. Custom
templates now only need the {skills} placeholder.
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add Telegram channel for agent-framework-hosting
- Add agent-framework-hosting-telegram package with TelegramChannel
supporting polling and webhook transports, streaming edits with
Telegram Bot API rate limiting, per-chat serial workers, and
multi-modal inbound/outbound (text, photo, document, voice)
- Add local_telegram sample demonstrating multi-channel hosting with
a TelegramChannel alongside ResponsesChannel, using per-chat
FileHistoryProvider and a run_hook for Telegram persona temperature
- Fix test layout: move tests to tests/hosting_telegram/ (no __init__.py)
- Remove old [tool.mypy] section and mypy poe task; source type-checking
is handled by pyright via shared_tasks
- Update uv.lock, pyproject.toml workspace sources, and PACKAGE_STATUS.md
Fixes#6588
Refs #6265
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Address Telegram channel CI failures and review feedback
- Fix webhook secret validation to use constant-time compare_digest
- Harden webhook update parsing: require integer chat IDs and guard slash-only commands
- Fix streaming edge cases in TelegramChannel:
- prevent edit worker deadlocks when text exceeds 4096 chars
- prevent deadlock when placeholder send fails (message_id stays None)
- enforce edit throttling with minimum interval sleep
- honor send_typing_action=False in streaming mode
- always forward final multimodal output (e.g. images), while avoiding duplicate text sends
- Expand Telegram tests for slash-only command handling, non-int chat IDs, and streaming behavior (long text, final images, typing toggle)
- Fix sample/docs feedback:
- rename sample package to agent-framework-hosting-sample-local-telegram
- switch sample uv.sources from feature branch to main
- align docs/tool names with lookup_weather
- fix broken links and server run instructions in README/call_server.py
- align local_telegram app docstrings with reasoning hook behavior and strip model in responses_hook
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix TelegramChannel streaming to iterate contents for multimodal support
- Remove stale PR reference from module docstring
- Add Google-style docstring to TelegramChannel.__init__ documenting all keyword args
- Fix _stream_to_chat to iterate update.contents instead of using
getattr(update, 'text', None); text chunks are extracted from Content
items with type='text', non-text content in updates is correctly
ignored (images etc. are forwarded via the final response)
- Update _FakeStreamUpdate test helper to use contents list matching the
real AgentResponseUpdate API; add from_text/from_image class methods
- Update _FakeResponseStream to accept _FakeStreamUpdate objects directly
- Add test verifying multimodal stream updates don't corrupt text accumulator
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Split local_telegram into simple Telegram-only and new multi-channel sample
local_telegram is now a focused Telegram-only sample:
- Removes ResponsesChannel and all responses_hook code
- Removes call_server.py (no HTTP endpoint to call)
- Uses a deterministic lookup_weather tool (hash-based, not random)
- Single run_hook that strips model and raises reasoning effort
- Drops agent-framework-hosting-responses dependency
New local_multi_channel sample shows running both channels at once:
- ResponsesChannel + TelegramChannel sharing a FileHistoryProvider
- Cross-channel session resumption via previous_response_id
- call_server.py moved here (the Responses endpoint lives here now)
- Demonstrates the multi-channel coordination story
Update README table to list both samples with clear descriptions.
Also delete personal_assistant/.venv which was not tracked but caused
pyright to crawl the entire installed venv (thousands of files),
making sample pyright checks hang indefinitely.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fallback when Telegram final edit fails
- only mark final edit as sent after a confirmed 2xx edit response
- fall back to sendMessage when final edit returns a non-success status
- add regression test covering failed final edit fallback behavior
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix optional await_args typing in telegram test
- assert await_args is not None before reading kwargs in streaming fallback test
- resolves test-typing failures across mypy/pyright/ty/zuban for hosting-telegram
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: [Breaking] Refactor FileSkillsSource for depth-based discovery and predicate filters
Refactors FileSkillsSource to make script and resource discovery more flexible.
## Changes
- **Drops** resource_directories / script_directories options (preconfigured
directory whitelists).
- **Adds** search_depth option (>= 1, default 2): controls how deep the
recursive scan goes within each skill directory.
- **Adds** script_filter / resource_filter predicate options that receive a
FileSkillFilterContext (skill_name + relative_file_path), allowing
whitelist/blacklist filtering by file path.
- **Adds** FileSkillFilterContext class exported from agent_framework.
## Notes
- The Skills API is marked @experimental -- the option removals are intentional
breaking changes within the experimental surface.
- Security checks (path containment, symlink detection) are preserved and
continue to use the skill root directory as the trusted boundary.
- Ports the same refactoring from .NET PR #6109 while following Python
conventions (instance methods, Callable type hints, __slots__).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR feedback: clarify depth constants and skip nested skill directories
- Add clarifying comments distinguishing MAX_SEARCH_DEPTH (SKILL.md
discovery) from DEFAULT_SEARCH_DEPTH (per-skill resource/script scanning).
- Stop recursing into subdirectories that contain their own SKILL.md,
preventing child skill files from being attached to the parent skill.
- Add test verifying nested skill boundary is respected.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove __slots__ from FileSkillFilterContext and add type-ignore comments
- Remove __slots__ from FileSkillFilterContext per reviewer feedback —
the optimization is negligible and inconsistent with sibling classes.
- Add type: ignore[attr-defined] / ty: ignore[unresolved-attribute]
comments to test lines accessing private _resources/_scripts attributes,
matching the convention established on main.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify filter predicates: remove FileSkillFilterContext, use Callable[[str, str], bool]
Address reviewer feedback:
- Remove FileSkillFilterContext class — a dedicated class for two strings
is overkill in Python. Filters now receive (skill_name, relative_file_path)
directly as positional args.
- Update docstrings to describe behavior instead of referencing private
instance attributes.
- Remove FileSkillFilterContext from exports and __all__.
- Update all test lambdas and remove TestFileSkillFilterContext class.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use DEFAULT_SEARCH_DEPTH as default argument directly
Instead of accepting int | None and resolving None to the default
internally, use DEFAULT_SEARCH_DEPTH as the parameter default value
on both FileSkillsSource.__init__() and SkillsProvider.from_paths().
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Migrate 02-agents/Agents samples to AIProjectClient (Foundry)
Replace AzureOpenAIClient with AIProjectClient as the AI provider in all
02-agents/Agents samples, aligning with the Foundry-first approach.
Changes:
- 19 Program.cs files migrated to use AIProjectClient.AsAIAgent()
- 19 .csproj files updated (Azure.AI.OpenAI -> Microsoft.Agents.AI.Foundry)
- Environment variables: AZURE_OPENAI_* -> FOUNDRY_PROJECT_ENDPOINT/FOUNDRY_MODEL
- Updated description comments to reflect Foundry backend
- Provider-specific samples in AgentsWithFoundry/ intentionally unchanged
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Migrate 02-agents/AgentSkills, AgentWithMemory, AgentWithRAG, AgentOpenTelemetry to AIProjectClient
Replace AzureOpenAIClient with AIProjectClient as the AI provider.
Environment variables: AZURE_OPENAI_* -> FOUNDRY_PROJECT_ENDPOINT/FOUNDRY_MODEL.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Migrate 03-workflows samples to AIProjectClient (Foundry)
Replace AzureOpenAIClient with AIProjectClient as the AI provider in
all 03-workflows samples that use an AI model.
Environment variables: AZURE_OPENAI_* -> FOUNDRY_PROJECT_ENDPOINT/FOUNDRY_MODEL.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix PR 6557 build breaks and align Foundry client usage
- Add explicit Azure.Identity package references to migrated sample projects
that use DefaultAzureCredential
- Fix AgentWithRAG_Step05_Neo4jGraphRAG to use AIProjectClient.AsAIAgent()
with ChatOptions.ModelId instead of AIProjectClient.AsIChatClient()
- Keep migrated samples on AIProjectClient pattern (no FoundryAgent/AzureOpenAIClient)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR 6557 Foundry review follow-ups
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix post-rebase sample build and format regressions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Updates to fix issues from switching to Responses.
* Fixing more tests and deleting checkpoint directories created for samples.
* Fixing formatting
* Restore DefaultAzureCredential warnings in agents samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Fix hosted agent crash after tool call by rooting session store under $HOME
FileSystemAgentSessionStore.CreateDefault rooted the hosted session store at the
filesystem root "/.checkpoints", which is read-only inside a Foundry hosted
container. After a local tool call the response handler persists the session, so
the write to "/.checkpoints" threw IOException and tore down the container, which
the platform surfaced as "mount: /app: mount failed: No such file or directory.".
Root the hosted store at $HOME (default /home/session), the only writable and
durable location per the container image spec. Persistence failures stay fatal but
are now wrapped in a clear, actionable IOException instead of the opaque raw error.
Add unit tests covering hosted and local path resolution plus the clear error, and
enable the ToolCalling Foundry Hosted Agents integration tests (verified live).
Fixes#6231
* .NET: Harden hosted session store against a filesystem-root HOME
Address review feedback on #6714: a misconfigured HOME pointing at a filesystem
root (e.g. "/") resolved back to "/.checkpoints" and would reintroduce the original
read-only-root crash. CreateDefault now falls back to the default session-data
directory (/home/session) when HOME is missing, blank, a filesystem root, or an
unnormalizable path. Adds a unit test locking in the "never the filesystem root"
behavior for a hosted HOME of "/".
Related #6231
* Python: surface Gemini cached and thinking token counts in usage details
* Python: surface Bedrock cache token counts in usage details
* Python: surface Gemini cached and thinking token counts in usage details
* Python: surface Bedrock cache token counts in usage details
* Return None from Bedrock _parse_usage when no token counts are present
Matches the UsageDetails | None return annotation and the Gemini
connector's behavior, so a usage payload with no recognized keys no
longer propagates an empty mapping. Adds a regression test.
Skill content now always emits <available_resources> and <available_scripts>
blocks, using self-closing elements when empty, so models receive an
authoritative list per category and do not hallucinate resource/script names.
FileSkill now also emits its resources block.
Closes#6348
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET Foundry: add CreateMcpTool projectConnectionId overload
Adds FoundryAITool.CreateMcpTool(serverLabel, serverUri, projectConnectionId, ...)
so hosted MCP tools can authenticate through a Foundry project connection, matching
the Python FoundryChatClient.get_mcp_tool(..., project_connection_id=...) factory.
The connection id is applied via the McpTool.ProjectConnectionId extension that ships
in Azure.AI.Projects.Agents (patches project_connection_id), already referenced by the
Foundry package. Includes unit tests and sample/README guidance plus the existing
FromResponseTool workaround.
* Fold projectConnectionId into existing CreateMcpTool overload
Replaces the separate project-connection overload with an optional
projectConnectionId parameter on the existing serverUri CreateMcpTool, so all
settings (authorizationToken, headers, allowedTools, ...) stay available and there
is no positional overload ambiguity. Adds tests for the default (no connection)
path and for preserving other settings. Sample/README now show only the supported
overload.
* NET: Support archive-type skills in AgentMcpSkillsSource
Add archive-type skill discovery to the MCP skills source. Index entries
are dispatched to per-type loaders (skill-md and archive) via a new
IMcpSkillEntryLoader strategy. The archive loader downloads, safely
unpacks, and serves packaged skills through an internal file skills
source, while ensuring MCP-delivered scripts are never executed.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix CS0121 ambiguity in UseSource null test
Cast null! to AgentSkillsSource to disambiguate from the new
Func<ILoggerFactory?, AgentSkillsSource> overload.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: fix misleading comment and catch UnauthorizedAccessException in Dispose
- Remove hardcoded '50' from test comment; it now says 'default cap'
without citing a specific number that can drift from the constant.
- Catch UnauthorizedAccessException alongside IOException in test
Dispose for robust cleanup.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Decouple shared refresh from per-caller cancellation
Use CancellationToken.None for the shared refresh so one caller's
cancellation does not abort work for all concurrent waiters. Waiters
use WaitAsync(cancellationToken) to cancel independently. The refresh
owner checks its own token after publishing the result.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix file encoding: add UTF-8 BOM to archive tests
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix file encoding: add UTF-8 BOM to ArchiveFormat.cs
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify pruning doc: covers non-actionable entries too
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add branch-coverage tests and drop [Experimental] attribute
- Add 5 unit tests covering FilterValidEntries/download condition branches
(missing name, invalid name chars, missing url, unsupported format, text-only blob)
- Remove [Experimental] attribute from AgentMcpSkillsSourceOptions (alpha package suffices)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: propagate skill script/resource exceptions instead of swallowing them
Stop catching and returning generic error strings in RunSkillScriptAsync and
ReadSkillResourceAsync. Exceptions are now logged and rethrown so that
FunctionInvokingChatClient can decide whether to surface details to the model
via its existing IncludeDetailedErrors option (default: safe generic message).
Fixesmicrosoft/agent-framework#6304
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add IncludeDetailedErrors option for skill script execution
Add an IncludeDetailedErrors option to AgentSkillsProviderOptions. When enabled,
RunSkillScriptAsync appends the exception message to the error returned to the
model so it can self-correct (e.g. retry with different arguments). When
disabled (default), the exception is logged and rethrown, letting
FunctionInvokingChatClient apply its own IncludeDetailedErrors policy.
ReadSkillResourceAsync now logs and rethrows as well, since resources take no
arguments and a generic swallowed error is not actionable by the model.
Fixesmicrosoft/agent-framework#6304
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add prompt-injection caution to IncludeDetailedErrors doc
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix SearchDirectoriesForSkills to stop recursing after finding SKILL.md
When a directory contains SKILL.md, subdirectories are part of that skill
and should not be treated as independent skill roots. Add a return after
adding the directory to results to prevent incorrect recursion.
Also adds a regression test verifying nested SKILL.md files are not
discovered as separate skills.
Fixesmicrosoft/agent-framework#6683
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix test: use matching directory name so nested SKILL.md would pass validation
The child skill's frontmatter name must match its directory name,
otherwise it gets rejected by validation regardless of the recursion fix.
This ensures the test actually validates the stop-recursing behavior.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Purview: prefer token principal for user identity
Align Purview middleware identity resolution so user-token principals are preferred before supplied message identities, while app-token flows continue to use validated fallback user IDs. Also fix the content activities user route and add regression coverage for identity precedence and route construction.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Fix user ID resolution logic in ScopedContentProcessor and add unit test for empty token user ID
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add sample for per-run refreshable MCP authentication headers
Adds a Foundry RAPI sample that attaches per-run, refreshable authentication headers to MCP requests using existing primitives: a DelegatingHandler on the MCP transport's HttpClient plus an AsyncLocal run scope. The same agent runs under two contexts, each minting a fresh token, proving the header is per run rather than bound at agent or connection creation time.
The handler attaches the bearer only over HTTPS to the MCP server's own origin, logs the non-secret label only, disables cookies, and checks certificate revocation. The README covers security considerations and production notes.
Fixes#1631
* Address PR review: harden redirect handling, nest-safe scope, README env vars
Disable AllowAutoRedirect on the shared handler so a redirect cannot carry the bearer past the origin check. Save and restore the prior run scope instead of clearing to null so the helper is safe under nesting. Note the Foundry env vars in the samples folder README row and update the sample README security notes.
* Require approvals for file-access and expose auto approval funcs for it
* Scope file-access auto-approval rules to local tools; fix base-Agent sample
Address PR #6599 review feedback:
- read_only/all_tools auto-approval rules now reject any call carrying a
server_label so they stay scoped to FileAccessProvider's local tools and
never auto-approve a same-named hosted tool.
- Expand the FileAccessProvider docstring to explain the runtime effect of
approval_mode="always_require" and point to ToolApprovalMiddleware /
create_harness_agent.
- Fix the base-Agent file_access_data_processing sample, which would otherwise
stop executing file tools under the new always_require defaults, by adding
ToolApprovalMiddleware with all_tools_auto_approval_rule.
- Add tests covering hosted (server_label) calls and update docs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clean up comments
* Update sample after merge
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Explicitly emit available_resources and available_scripts in skill content
AgentInlineSkillContentBuilder now always emits <available_resources> and
<available_scripts> elements, using self-closing tags when a skill has no
resources or scripts. This signals to the model exactly what is callable so it
does not hallucinate non-existent resource or script names. Script parameter
schemas are wrapped in a nested <parameters_schema> element.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Emit available_resources block for file-backed skills
Align AgentFileSkill with inline/class skills by surfacing discovered
resources in the loaded skill content. AgentFileSkill.GetContentAsync now
appends an <available_resources> block (before <available_scripts>) listing
resource names so the model has an authoritative list and does not
hallucinate resource names. Extracted a reusable BuildAvailableResourcesBlock
helper in AgentInlineSkillContentBuilder.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Change A2A default session store to NoopAgentSessionStore
Align the A2A hosting layer default session store with the AG-UI
sibling by using NoopAgentSessionStore, making persistence an explicit
opt-in choice.
Update samples to document how to register a persistent session store
for multi-turn conversations.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify test name to specify session store default
Rename test to FallsBackToNoopSessionStoreDefaultAsync to avoid
implying all stores default to noop (task store still uses InMemory).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
OpenTelemetryAgent auto-wired OpenTelemetryChatClient above FICC, producing
OTel(FICC(leaf)). FICC resolved its ActivitySource at construction time as null,
so execute_tool spans were never emitted for tool-calling agents.
This repositions OTel below FICC, producing FICC(OTel(leaf)), via a deferred
NoOp slot pre-placed as the innermost decorator in WithDefaultAgentMiddleware
and activated once at the agent level.
- Add internal DeferredOpenTelemetryChatClient: inert DelegatingChatClient whose
Activate(sourceName) swaps its target to inner.AsBuilder().UseOpenTelemetry().Build().
- WithDefaultAgentMiddleware always registers the slot innermost so it lands below FICC.
- OpenTelemetryAgent activates the slot once in its constructor and forwards run
options straight through, removing the per-run ChatClientFactory outer wrap.
- Add and update unit tests, including a proof that execute_tool spans are emitted
on the agent source and parented under invoke_agent.
* Add samples for harness blog post part 1
* Add readme for python samples
* Update python instructions to match dotnet instructions
* Address PR comments
* Add link to blog posts
* Fix blog post naming.
* Add more blog post links
* Project ToolExecution events as FunctionCallContent/FunctionResultContent
GitHubCopilotAgent's event-dispatch switch previously had no case for
ToolExecutionStartEvent or ToolExecutionCompleteEvent. Both fell through
to the default case and were wrapped as opaque AIContent with
RawRepresentation, preventing downstream consumers and models from
recognizing tool call results.
Add explicit cases that project:
- ToolExecutionStartEvent → FunctionCallContent (role: Assistant)
- ToolExecutionCompleteEvent → FunctionResultContent (role: Tool)
This mirrors the Python fix already shipped in #4734/#4814/#4828.
Fixes#5897
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix(#5897): Address review feedback for ParseArguments robustness
- Handle non-generic IDictionary variants (Hashtable, etc.) that don't
match IDictionary<string, object?> due to generic invariance
- Return null for empty/whitespace string arguments instead of wrapping
them in a spurious { value = "" } dictionary, aligning with
ParseFunctionArgumentsObject convention elsewhere in the repo
- Add test coverage for Dictionary, Hashtable, and JsonElement argument
types
- Add edge-case test for Success=true with null Result
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix non-generic IDictionary key handling in ParseArguments (#5897)
Use direct (string) cast for dictionary keys instead of ToString()
coercion, matching the established pattern in ObjectExtensions and
PortableValueExtensions. This validates keys are actually strings
rather than silently accepting and coercing non-string keys.
Add test verifying non-string dictionary keys throw InvalidCastException.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix missing 'using System' in ToolExecutionEventProjectionTests
Add the missing 'using System' directive needed for InvalidCastException
reference at line 375 of ToolExecutionEventProjectionTests.cs.
Fixes#5897
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use source-generated JsonTypeInfo for AOT-safe argument deserialization
Replace reflection-based JsonSerializer.Deserialize<T>() calls with the
JsonTypeInfo overload that uses source-generated metadata, eliminating
IL2026/IL3050 trimming and AOT warnings without suppressions.
Changes:
- Register Dictionary<string, object?> in GitHubCopilotJsonUtilities JsonContext
- Add JsonSerializerOptions constructor parameter (defaults to
GitHubCopilotJsonUtilities.DefaultOptions)
- Use GetTypeInfo()-based Deserialize overload in ParseArguments
- Remove [UnconditionalSuppressMessage] attributes
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix dotnet format: add 'this.' qualification to instance method call
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Adapt to GitHub.Copilot.SDK 1.0.0 API after merge with main
- Update ToolExecutionEventProjectionTests: Arguments is now JsonElement?
(not object?), remove tests for string/Dictionary/Hashtable arguments
- Remove AutoStart option (removed in 1.0.0)
- Simplify ParseArguments to handle JsonElement primarily
- Add tests for empty object and nested JSON arguments
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix(hosting): emit url_citation annotation events from streamed AI Search responses
OutputConverter.ConvertUpdatesToEventsAsync accumulated text content deltas but
silently dropped CitationAnnotation metadata from TextContent.Annotations. As a
result, hosted agents that use CreateAzureAISearchTool emitted citation markers in
text (e.g. 【5:0†source】) but produced empty annotations arrays and no
response.output_text.annotation.added SSE events.
The fix accumulates UrlCitationBody SDK annotations across all TextContent updates
for a message and emits them via TextContentBuilder.EmitAnnotationAdded after
EmitTextDone (as required by the SDK lifecycle) and before EmitDone. Non-citation
and region-less annotations are silently skipped, matching the existing OpenAI
ChatCompletions path in AgentResponseExtensions.
Adds 7 unit tests (N-01–N-07) covering: basic emission, ordering constraints,
multiple annotations, multi-update accumulation, and skip conditions.
Fixes#6641
* test: convert annotation test comments to XmlDoc and group in region
* fix: remove redundant long casts on annotation region indices
* test: assert done events carry url_citation annotation metadata
---------
Co-authored-by: Roger Barreto <19890735+RogerBarreto@users.noreply.github.com>
2026-06-22 10:10:02 +00:00
2647 changed files with 173547 additions and 101442 deletions
- Each pull request that modifies code should add just one bulleted entry to the `CHANGELOG.md` file containing a change title (usually the PR title) and a link to the PR itself.
- New PRs should be added to the top of the `CHANGELOG.md` file under a "## [Unreleased]" heading.
- If the PR is the first since the last release, the existing "## [Unreleased]" heading should be replaced with a "## v[X.Y.Z]" heading and the PRs since the last release should be added to the new "## [Unreleased]" heading.
- The style of new `CHANGELOG.md` entries should match the style of the other entries in the file.
- If the PR introduces a breaking change, the changelog entry should be prefixed with "[BREAKING]".
This automated update keeps Python dependency metadata coherent across the uv workspace. Python dependencies can be declared in multiple `pyproject.toml` files, but the workspace has one shared `python/uv.lock`, so dependency maintenance should update and validate them together instead of through per-manifest Dependabot PRs.
const prBody = [
"### Motivation & Context",
"",
"This automated update keeps Python dependency metadata coherent across the uv workspace. Python dependencies can be declared in multiple `pyproject.toml` files, but the workspace has one shared `python/uv.lock`, so dependency maintenance should update and validate them together instead of through per-manifest Dependabot PRs.",
"",
"### Description & Review Guide",
"",
"- **What are the major changes?** Refresh Python dev dependency pins, update package dependency ranges when the bounds tooling succeeds, and refresh `python/uv.lock`.",
"- **What is the impact of these changes?** Keeps the Python workspace dependency set current while producing at most one dependency PR for the week. If dependency range validation fails, this PR contains only the dev dependency updates that still pass final validation, and separate issues track failed range candidates.",
"- **What do you want reviewers to focus on?** Review the generated dependency metadata changes and any dependency-range updates for package-specific compatibility concerns.",
'<!-- NOTE FOR AUTOMATED/AI REVIEWERS: the "What do you want reviewers to focus on?"',
" item above is intended for human reviewers only. Automated/AI reviewers should",
" ignore it and review the entire change rather than narrowing scope to it. -->",
"",
"",
"### Related Issue",
"",
"No linked issue; this PR is generated by scheduled Python dependency maintenance.",
"",
"### Contribution Checklist",
"",
"- [x] The code builds clean without any errors or warnings",
"- [x] All unit tests pass, and I have added new tests where possible",
"- [x] The PR follows the [Contribution Guidelines](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)",
"- [ ] This PR is linked to an issue and there is no other open PR for this issue (see Related Issue above).",
'- [x] **This is not a breaking change.** If it _is_ a breaking change, add the `breaking change` label (or add "[BREAKING]" to the title prefix, before or after any language prefix) — a workflow keeps the label and title prefix in sync automatically.',
].join("\n")
### Description & Review Guide
const prBodyFence = "```"
const command = [
"PR_BODY_FILE=\"$(mktemp)\"",
`cat > "$PR_BODY_FILE" <<'EOF'`,
prBody,
"EOF",
"gh pr create --repo microsoft/agent-framework --base main \\",
` --head ${owner}:${branch} \\`,
` --title "${prTitle}" \\`,
" --body-file \"$PR_BODY_FILE\"",
].join("\n")
- **What are the major changes?** Refresh Python dev dependency pins, update package dependency ranges when the bounds tooling succeeds, and refresh `python/uv.lock`.
- **What is the impact of these changes?** Keeps the Python workspace dependency set current while producing at most one dependency PR for the week. If dependency range validation fails, this PR contains only the dev dependency updates that still pass final validation, and separate issues track failed range candidates.
- **What do you want reviewers to focus on?** Review the generated dependency metadata changes and any dependency-range updates for package-specific compatibility concerns.
<!-- NOTE FOR AUTOMATED/AI REVIEWERS: the "What do you want reviewers to focus on?"
item above is intended for human reviewers only. Automated/AI reviewers should
ignore it and review the entire change rather than narrowing scope to it. -->
const issueBody = [
"The Python dependency maintenance workflow generated and validated dependency updates, then pushed them to the automation branch.",
"",
`- Branch: \`${branch}\``,
`- Commit: \`${branchSha}\``,
`- Compare: ${compareUrl}`,
`- Workflow run: ${runUrl}`,
"",
"GitHub Actions is not permitted to create pull requests in this repository, so a maintainer needs to create the PR manually.",
No linked issue; this PR is generated by scheduled Python dependency maintenance.
### Contribution Checklist
- [x] The code builds clean without any errors or warnings
- [x] All unit tests pass, and I have added new tests where possible
- [x] The PR follows the [Contribution Guidelines](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)
- [ ] This PR is linked to an issue and there is no other open PR for this issue (see Related Issue above).
- [x] **This is not a breaking change.** If it _is_ a breaking change, add the `breaking change` label (or add "[BREAKING]" to the title prefix, before or after any language prefix) — a workflow keeps the label and title prefix in sync automatically.
EOF
PR_NUMBER="$(gh pr list --head "${BRANCH}" --base main --state open --json number --jq '.[0].number')"
Microsoft Agent Framework (MAF) is an open, multi-language framework for building **production-grade AI agents and multi-agent workflows** in **.NET and Python**.
@@ -161,19 +161,19 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
### Python
- [Getting Started](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
- [Getting Started](./python/samples/01-get-started): progressive tutorial from hello-world to workflows
- [Hosting](./python/samples/04-hosting): A2A, self-hosted protocol helpers, and Foundry hosted agents. Durable Task and Azure Functions samples are in the [Durable Agent Framework extension](https://github.com/microsoft/agent-framework-durable-extension/tree/main/python/samples).
- [End-to-End](./python/samples/05-end-to-end): full applications, evaluation, and demos
### .NET
- [Getting Started](./dotnet/samples/01-get-started): progressive tutorial from hello agent to hosting
- [Getting Started](./dotnet/samples/01-get-started): progressive tutorial from hello agent to workflows
- [Agent Concepts](./dotnet/samples/02-agents/Agents): basic agent creation and tool usage
- [Agent Providers](./dotnet/samples/02-agents/AgentProviders): samples showing different agent providers
- [Workflows](./dotnet/samples/03-workflows): advanced multi-agent patterns and workflow orchestration
- [Hosting](./dotnet/samples/04-hosting): A2A and Foundry hosted agents. Durable agent and workflow samples are in the [Durable Agent Framework extension](https://github.com/microsoft/agent-framework-durable-extension/tree/main/dotnet/samples).
- [End-to-End](./dotnet/samples/05-end-to-end): full applications and demos
## Community & Feedback
@@ -199,6 +199,7 @@ For environment variable configuration specific to each sample, refer to the REA
## Contributor Resources
- [Contributing Guide](./CONTRIBUTING.md)
- [Code of Conduct](./CODE_OF_CONDUCT.md)
- [Python Development Guide](./python/DEV_SETUP.md)
Microsoft Agent Framework is a comprehensive multi-language (C#/.NET and Python) framework for building, orchestrating, and deploying AI agents and multi-agent workflows. The system takes user instructions and conversation inputs and produces intelligent responses through AI agents that can integrate with various LLM providers (OpenAI, Azure OpenAI, Azure AI Foundry). It provides both simple chat agents and complex multi-agent workflows with graph-based orchestration.
Microsoft Agent Framework is a comprehensive multi-language (C#/.NET and Python) framework for building, orchestrating, and deploying AI agents and multi-agent workflows. The system takes user instructions and conversation inputs and produces intelligent responses through AI agents that can integrate with various LLM providers (OpenAI, Azure OpenAI, Microsoft Foundry). It provides both simple chat agents and complex multi-agent workflows with graph-based orchestration.
**What can Microsoft Agent Framework do?**
@@ -12,7 +12,7 @@ The framework offers:
- **Multi-Agent Orchestration**: Group chat, sequential, concurrent, and handoff patterns
- **Graph-based Workflows**: Connect agents and deterministic functions using data flows with streaming, checkpointing, time-travel, and Human-in-the-loop
- **Extensibility Framework**: Extend with native functions, A2A, Model Context Protocol (MCP)
- **LLM Integration**: Support for OpenAI, Azure OpenAI, Azure AI Foundry, and other providers
- **LLM Integration**: Support for OpenAI, Azure OpenAI, Microsoft Foundry, and other providers
- **Runtime Support**: Both in-process and distributed agent execution
**What is/are Microsoft Agent Framework's intended use(s)?**
# - Knowledge Agent: Performs generic web searches.
# - Coder Agent: Able to write and execute code.
# - Weather Agent: Provides weather information.
#
# Example input:
# Find the current temperatures in Seattle and San Francisco, calculate the difference in Celsius and Fahrenheit, and recommend what clothing to pack for each city.
#
kind:Workflow
maxTurns:500
@@ -264,14 +267,14 @@ trigger:
output:
messages:Local.Plan
input:
arguments:
team:=Local.TeamDescription
messages:|-
=UserMessage(
"Please briefly explain what went wrong on this last run (the root cause of the failure),
and then come up with a new plan that takes steps and/or includes hints to overcome prior challenges and especially avoids repeating the same mistakes.
As before, the new plan should be concise, be expressed in bullet-point form, and consider the following team composition
(do not involve any other outside people since we cannot contact anyone else):
{Local.TeamDescription}")
As before, the new plan should be concise, be expressed in bullet-point form, and only involve the team members already described
(do not involve any other outside people since we cannot contact anyone else).")
@@ -113,7 +113,7 @@ Implement a hybrid strategy where common tools use generic `AITool`-derived abst
### AI Agent Tool Types Availability
Tool Type | Azure AI Foundry Agent Service | OpenAI Assistant API | OpenAI ChatCompletion API | OpenAI Responses API | Amazon Bedrock Agents | Google | Anthropic | Description
Tool Type | Microsoft Foundry Agent Service | OpenAI Assistant API | OpenAI ChatCompletion API | OpenAI Responses API | Amazon Bedrock Agents | Google | Anthropic | Description
-- | -- | -- | -- | -- | -- | -- | -- | --
Function Calling | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Enables custom, stateless functions to define specific agent behaviors.
Code Interpreter | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | Allows agents to execute code for tasks like data analysis or problem-solving.
@@ -25,7 +25,7 @@ See various features that would need to be supported via this type of mechanism,
- Also see [the openai human-in-the-loop guide](https://openai.github.io/openai-agents-js/guides/human-in-the-loop/#approval-requests).
- Also see [the openai MCP guide](https://openai.github.io/openai-agents-js/guides/mcp/#optional-approval-flow).
- Also see [MCP Approval Requests from OpenAI](https://platform.openai.com/docs/guides/tools-remote-mcp#approvals).
- Also see [Azure AI Foundry MCP Approvals](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/model-context-protocol-samples?pivots=rest#submit-your-approval).
- Also see [Microsoft Foundry MCP Approvals](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/model-context-protocol-samples?pivots=rest#submit-your-approval).
- Also see [MCP Elicitation requests](https://modelcontextprotocol.io/specification/draft/client/elicitation)
The option is similar the the "6.1.3 Continuation Token of System.ClientModel.ContinuationToken Type" option but suggests using a
The option is similar to the "6.1.3 Continuation Token of System.ClientModel.ContinuationToken Type" option but suggests using a
custom type for the continuation token instead of the `System.ClientModel.ContinuationToken` type.
**Pros**
@@ -1203,7 +1203,7 @@ response = await agent.CancelRunAsync(response.ResponseId, new AgentCancelRunOpt
In case an agent supports either or both cancellation and deletion of long-running operations, it will override the corresponding methods.
Otherwise, it won't override them, and the base implementations will return null by default.
Some agents, for example Azure AI Foundry Agents, require the thread identifier to cancel a run. To accommodate this requirement, the `CancelRunAsync` method
Some agents, for example Microsoft Foundry Agents, require the thread identifier to cancel a run. To accommodate this requirement, the `CancelRunAsync` method
accepts an optional `AgentCancelRunOptions` parameter that allows callers to specify the thread associated with the run they want to cancel.
```csharp
@@ -1574,7 +1574,7 @@ the thread is provided with background operations consistently for all runs.
</details>
<details>
<summary>Azure AI Foundry Agents</summary>
<summary>Microsoft Foundry Agents</summary>
- Create a thread and run the agent against it and wait for it to complete using polling:
1. **New `agent-framework-openai` package** with dependencies on `agent-framework-core`, `openai`, and `packaging` only.
2. **Class renames**: `OpenAIResponsesClient` → `OpenAIChatClient` (Responses API), `OpenAIChatClient` → `OpenAIChatCompletionClient` (Chat Completions API). Old names remain as deprecated aliases.
3. **Deprecated classes**: `OpenAIAssistantsClient`, all `AzureOpenAI*Client` classes, `AzureAIClient`, `AzureAIAgentClient`, and `AzureAIProjectAgentProvider` are marked deprecated.
4. **New `FoundryChatClient`** in azure-ai for Azure AI Foundry Responses API access, built on `RawFoundryChatClient(RawOpenAIChatClient)`.
4. **New `FoundryChatClient`** in azure-ai for Microsoft Foundry Responses API access, built on `RawFoundryChatClient(RawOpenAIChatClient)`.
5. **All deprecated `AzureOpenAI*` classes** consolidated into a single file (`_deprecated_azure_openai.py`) in the azure-ai package for clean future deletion.
6. **Core's `agent_framework.openai` and `agent_framework.azure` namespaces** become lazy-loading gateways, preserving backward-compatible import paths while removing hard dependencies.
7. **Unified `model` parameter** replaces `model_id` (OpenAI), `deployment_name` (Azure OpenAI), and `model_deployment_name` (Azure AI) across all client constructors. The term `model` is intentionally generic: it naturally maps to an OpenAI model name *and* to an Azure OpenAI deployment name, making it straightforward to use `OpenAIChatClient` with either OpenAI or Azure OpenAI backends (via `AsyncAzureOpenAI`). Environment variables are similarly unified (e.g., `OPENAI_MODEL` instead of separate `OPENAI_CHAT_MODEL_ID` / `OPENAI_CHAT_COMPLETION_MODEL_ID`).
8. **`FoundryAgent`** replaces the pattern of `Agent(client=AzureAIClient(...))` for connecting to pre-configured agents in Azure AI Foundry (PromptAgents and HostedAgents). The underlying `RawFoundryAgentChatClient` is an implementation detail — most users interact only with `FoundryAgent`. `AzureAIAgentClient` is separately deprecated as it refers to the V1 Agents Service API. See below for design rationale.
8. **`FoundryAgent`** replaces the pattern of `Agent(client=AzureAIClient(...))` for connecting to pre-configured agents in Microsoft Foundry (PromptAgents and HostedAgents). The underlying `RawFoundryAgentChatClient` is an implementation detail — most users interact only with `FoundryAgent`. `AzureAIAgentClient` is separately deprecated as it refers to the V1 Agents Service API. See below for design rationale.
### Foundry Agent Design: `FoundryAgentClient` vs `FoundryAgent`
informed: Agent Framework team, Foundry Evals team
---
# Agent Evaluation Architecture with Azure AI Foundry Integration
# Agent Evaluation Architecture with Microsoft Foundry Integration
## Context and Problem Statement
Azure AI Foundry provides a rich evaluation service for AI agents — built-in evaluators for agent behavior (task adherence, intent resolution), tool usage (tool call accuracy, tool selection), quality (coherence, fluency, relevance), and safety (violence, self-harm, prohibited actions). Results are viewable in the Foundry portal with dashboards and comparison views.
Microsoft Foundry provides a rich evaluation service for AI agents — built-in evaluators for agent behavior (task adherence, intent resolution), tool usage (tool call accuracy, tool selection), quality (coherence, fluency, relevance), and safety (violence, self-harm, prohibited actions). Results are viewable in the Foundry portal with dashboards and comparison views.
However, using Foundry Evals with an agent-framework agent today requires significant manual effort. Developers must:
@@ -445,7 +445,7 @@ These factorings produce different scores for the same conversation. The framewo
### Azure AI: FoundryEvals
`Evaluator` implementation backed by Azure AI Foundry:
`Evaluator` implementation backed by Microsoft Foundry:
```python
class FoundryEvals:
@@ -812,4 +812,4 @@ public sealed class EvalItem
## More Information
- [Foundry Evals documentation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-approach-gen-ai) — Azure AI Foundry evaluation overview
- [Foundry Evals documentation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-approach-gen-ai) — Microsoft Foundry evaluation overview
@@ -43,6 +43,11 @@ FIDES (Flow Integrity Deterministic Enforcement System) is a label-based securit
3. **Variable Indirection** — `ContentVariableStore` and `VariableReferenceContent` for physical isolation of untrusted content from the LLM context.
4. **Quarantined Execution** — `quarantined_llm` and `inspect_variable` tools for isolated processing of untrusted data with audit logging.
In addition, remote MCP integrations are secured through two mechanisms:
- **Hint-based tool auto-labeling**: MCP `ToolAnnotations` (`readOnlyHint`, `openWorldHint`, etc.) are mapped to FIDES tool properties (`source_integrity`, `accepts_untrusted`, `max_allowed_confidentiality`).
- **Server `_meta.ifc` result labels**: MCP result metadata is parsed into per-item `security_label` values, so provider-supplied IFC labels are enforced by middleware.
### Consequences
- Good, because it provides deterministic security guarantees about what untrusted content can influence.
- Attaches labels via `additional_properties` (no schema changes).
- Leverages `SerializationMixin` for label persistence.
- Integrates MCP hint/result metadata through `additional_properties` keys (`max_allowed_confidentiality`, `source_integrity`, `__mcp_result_meta__`) without transport-specific policy code in core middleware.
### MCP-Specific Security Notes
- `SecureMCPToolProxy` applies `apply_mcp_security_labels(...)` automatically when connecting an MCP tool or URL.
- For servers like the GitHub MCP server (with `X-MCP-Features: ifc_labels`), `_meta.ifc` labels are considered authoritative for per-result label assignment.
- Tools that are not explicitly `readOnlyHint=True` are treated as potential sinks and default to `max_allowed_confidentiality=PUBLIC` to prevent exfiltration.
Enable Agent Framework users to consume Foundry **toolboxes** — named, versioned bundles of tool definitions stored server-side in an Azure AI Foundry project — directly from `FoundryChatClient`, without dropping to the raw `azure-ai-projects` SDK.
Enable Agent Framework users to consume Foundry **toolboxes** — named, versioned bundles of tool definitions stored server-side in a Microsoft Foundry project — directly from `FoundryChatClient`, without dropping to the raw `azure-ai-projects` SDK.
A user who has configured a toolbox in the Foundry portal (or via the raw SDK) should be able to load it into an agent with a single call:
status: superseded by [ADR-0030](0030-hosted-platform-context-agentserver-2.0.md)
contact: rogerbarreto
date: 2026-05-07
date: 2026-06-29
deciders: rogerbarreto
consulted: []
informed: []
@@ -9,6 +9,8 @@ informed: []
# Hosted session identity context for Foundry Hosting
> **Superseded by [ADR-0030](0030-hosted-platform-context-agentserver-2.0.md).**`Azure.AI.AgentServer.*` 2.0.0 (responses protocol `2.0.0`) replaced `ResponseContext.Isolation` (`UserIsolationKey` / `ChatIsolationKey`, headers `x-agent-user-isolation-key` / `x-agent-chat-isolation-key`) with `ResponseContext.PlatformContext` (`UserIdKey` / `CallId`, headers `x-agent-user-id` / `x-agent-foundry-call-id`). The chat isolation key was removed and `HostedSessionContext` is now user-only. This ADR is retained as the historical record of the original design.
## Context and Problem Statement
Server-hosted Foundry agents need a way to scope per-user state (most notably `FoundryMemoryProvider` memories) by the end user that initiated the request. The Foundry platform already injects `x-agent-user-isolation-key` and `x-agent-chat-isolation-key` headers on every Responses request, but the agent-framework hosting layer did not surface those values to `AIContextProvider` instances. The provider's `stateInitializer` only received an `AgentSession?` with no identity attached, so per-user scoping was impossible without out-of-band plumbing.
# Python minimal hosting core and pluggable channels
# Python protocol helpers and optional execution state
## Context and Problem Statement
Agent Framework has several protocol-specific hosting surfaces. App authors who want one agent or workflow on multiple protocols must compose servers, routes, middleware, session handling, and lifecycle code by hand.
Agent Framework needs to help applications expose agents and workflows over external protocols such as OpenAI
Responses, Telegram, Activity Protocol, and future transports.
We will introduce a small Python hosting core that owns the common server shape and leaves protocol details inside channel packages. The first public contract must be intentionally narrow so Python can ship a base contract before adding identity linking, proactive delivery, or multicast behavior. Other language implementations may reuse the same conceptual boundary, but this ADR records the Python decision.
FastAPI, Starlette, Azure Functions, Django, Telegram SDKs, Bot Framework SDKs, and other app frameworks already own
Chosen option: **3. Ship protocol helpers plus optional execution state**.
`AgentFrameworkHost` owns:
Protocol packages own:
- one application object,
- one hostable target (`SupportsAgentRun` agent-compatible object or a `Workflow`), and
- one or more channels.
- parsing protocol-native input into Agent Framework run input and options;
- rendering `AgentResponse`, `AgentResponseUpdate`, workflow results, or workflow updates back into protocol-native
response/event payloads;
- protocol-specific isolation/session id helper functions when useful, such as `telegram_session_id(update)`;
- protocol-specific typing/update event helpers where the protocol has a native concept.
Channels own:
Application or web-framework code owns:
- contributed routes, middleware, commands, and lifecycle callbacks,
- protocol-native request parsing into `ChannelRequest`,
- protocol-native rendering of the originating response, and
- any channel-specific authentication or signature validation.
- HTTP route declaration and route grouping;
- dependency injection;
- authentication and authorization;
- middleware;
- background tasks and webhook acknowledgement policy;
- native protocol SDK clients and outbound calls;
- command registration and command dispatch;
- request/response status codes and framework-specific error handling;
- choosing the isolation/session id source for the current deployment and route.
The host owns:
The application builder can make the server exactly as they see fit, but this is outside the responsibilities of this proposed scheme.
This might include implementing other known API surfaces from vendors like OpenAI, such as creating conversations, vector stores, deleting things, etc.
If they want they can build the full OpenAI API, but it will include code that does not rely on agent-framework-hosting, which is fine.
They are responsible for what they expose.
- route/lifecycle aggregation,
- invocation of the target,
- `ChannelSession(isolation_key=...)` to `AgentSession` resolution and caching,
- `reset_session(isolation_key=...)`,
- host-level middleware, including Foundry isolation middleware only when the Foundry hosting environment flag is present,
- invocation of per-channel hooks (`ChannelRunHook`, `ChannelResponseHook`, `ChannelStreamUpdateHook`), and
- workflow checkpoint wiring through an explicit `checkpoint_location`.
The optional execution-state helpers, if provided, are limited to shared execution state:
`ChannelIdentity`, when present, is request metadata only. In v1 it is not a linking, authorization, or delivery key.
- `AgentState`: one `SupportsAgentRun`-compatible target plus a `SessionStore`;
- `WorkflowState`: one `Workflow`, `WorkflowBuilder`-shaped builder, orchestration builder, or workflow factory;
- `SessionStore`: plain async storage (`get` / `set` / `delete`) by an app-selected id.
### Trust boundary for `isolation_key`
The store does not create sessions. `AgentState` provides the target-aware `get_or_create_session(...)` helper because
only the state object has both the store and the resolved agent target. Workflow checkpointing should use the existing
`CheckpointStorage` abstraction directly; app/state code may keep a small cursor (`session_id -> checkpoint_id`) when it
needs to resume a workflow for a session.
The host treats `ChannelSession.isolation_key` as a session partition key, not as proof of identity. Channels or host middleware must authenticate and authorize any externally supplied value before passing it to the host. For example, a Responses caller must not be allowed to choose an arbitrary `previous_response_id` or header-derived key unless the platform or middleware has already established that the caller owns that conversation. The host deliberately does not infer that trust from the string itself.
These objects are **not** app objects, channel registries, or route owners. They do not own FastAPI/Starlette setup,
route contribution, protocol dispatch, command projection, or native SDK calls.
### Hook ownership
### Helper naming and families
Channels provide hook configuration and protocol-native context. The host invokes those hooks as part of the common invocation pipeline:
Helpers should be protocol-specific, not generic. Avoid a generic `protocol_to_run(...)` name in public samples because it
hides the protocol-specific contract behind a second abstraction.
- `ChannelRunHook` runs after channel parsing and before target invocation.
- `ChannelResponseHook` runs after target invocation and before the originating channel serializes its response.
- `ChannelStreamUpdateHook` is applied by the host while the channel consumes streamed updates because streaming serialization is protocol-specific.
Protocol packages should consider these helper families. This table is a set of examples, not a required protocol or
checklist. Not every protocol needs every helper, but when a protocol has the concept the naming should stay consistent:
`ChannelStreamUpdateHook` is an update hook, not a final-response sanitizer. Channels that use it for redaction or filtering must also apply equivalent policy to any final response they render. Channels choose whether the response is streaming before run hooks execute.
| Helper family | Shape | Purpose |
| --- | --- | --- |
| Run conversion | `<protocol>_to_run(...)` | Convert one protocol-native call/update/request into `Agent.run` or `Workflow.run` values. |
| Final rendering | `<protocol>_from_run(...)` | Convert a final `AgentResponse` / workflow result into protocol-native response payloads or operations. |
| Stream rendering | `<protocol>_from_streaming_run(...)` | Convert `ResponseStream` / workflow updates into protocol-native events or operations. |
| Session id extraction | `<protocol>_session_id(...)` | Extract the protocol's natural continuation/partition key from the call, if present. |
| Command/action parsing | `<protocol>_command(...)` | Parse a protocol-native command/action/operation name without deciding app policy. |
This keeps hook call conventions centralized while leaving protocol payload parsing and response formatting in channel packages.
`state_dir` is limited to host-owned local files for reset-session aliases and workflow checkpoint path derivation. It does not store linked identities, active-channel state, response-routing state, continuation records, durable runner queues, or delivery attempts. Those storage concerns belong to ADR-0028.
The app still owns what a parsed command means. For example, a Telegram `/new`, Discord slash command, Bot Framework
command activity, or A2A cancellation/request action may parse through a command/action helper, but the route or SDK
handler decides whether that command clears a session, cancels a task, calls an agent, or is ignored.
Additional helper functions can be protocol-specific when the concept is not broadly shared. Examples include
These areas are follow-up enhancements covered by [ADR-0028](0028-hosting-linking-multicast-enhancements.md). They are not prerequisites for shipping or using the v1 host.
The app builder owns these concerns with normal web-framework, SDK, platform, or application code:
- authentication, authorization policy, and allowlists;
- deciding whether identities across protocols map to the same `session_id`;
- non-originating sends using native SDK clients;
- background work, durable execution, retry, and replay when app code owns the work;
- routing between multiple agents.
This is easier in the protocol-helper model than it was in the host/channel model: app code already owns the native SDK
clients, route handlers, authenticated caller context, session id selection, and outbound send calls. An app can link
channels by choosing the same authorized `session_id` for multiple protocols, and can do non-originating delivery by
calling the destination protocol's native client directly. That does not make a reusable framework feature safe by
default; it just means the app-specific version no longer has to fight a host abstraction.
### Future framework work
The following require a reviewed identity, storage, delivery, replay, and observability model before becoming reusable
framework features:
- reusable cross-channel identity linking;
- framework-owned proactive or non-originating delivery;
- fan-out, multicast, selected-channel, active-channel, or all-linked delivery;
- framework-owned delivery observability, dead-letter handling, and replay semantics;
- cross-channel confidentiality and link policy.
These possible framework enhancements are tracked by [ADR-0028](0028-hosting-linking-multicast-enhancements.md). They are
not prerequisites for shipping or using the v1 protocol-helper surface. ADR-0028 was written against the earlier
host/channel framing and must be revised to align with this protocol-helper and execution-state boundary before those
enhancements are implemented.
## Consequences
Positive:
- The host/channel model can be implemented and tested without designing a security-sensitive identity graph.
- Existing and new channel packages can share one Starlette app, middleware stack, lifecycle, and target invocation path.
- Session continuity is explicit and debuggable: two channels share history only when they produce the same `isolation_key`.
- Hook invocation is centralized in the host, so channels do not each invent the call convention.
- The released surface is smaller and easier to inspect: helpers plus state, not a channel framework.
- Protocol helpers can be used from FastAPI, Starlette, Azure Functions, Django, CLI tools, tests, or native SDK webhook
handlers.
- App authors can use the authentication, dependency injection, lifecycle, and background-task tools they already know.
- Session continuity stays explicit and debuggable.
- Workflow checkpointing can still be centralized if needed without making protocol packages own routing.
Negative:
- Apps that need OAuth linking, allowlists, proactive messages, or multicast must continue to implement those behaviors outside the v1 host.
- Some richer cross-channel scenarios from the original design move to a separate decision and validation cycle.
- The host must document `isolation_key` trust clearly because it now provides the shared session boundary.
## Validation Gates
Before this ADR is accepted:
- A sample can expose one target on multiple channels with one `AgentFrameworkHost` and no handwritten Starlette route composition.
- Built-in channel tests prove that routes, commands, startup, and shutdown callbacks are contributed by channels and aggregated by the host.
- Session tests prove that identical `ChannelSession.isolation_key` values resolve to the same cached `AgentSession`, and `reset_session` rotates that mapping.
- Channel tests prove that each channel renders only its own originating response; there is no host-level push, multicast, or active-channel delivery path.
- Workflow tests or samples use an explicit `checkpoint_location`.
- Foundry isolation middleware is documented and covered by integration or contract tests, including the non-Foundry case where raw isolation headers are ignored.
- The v1 API and packages do not expose the removed symbols or packages listed in [Non-goals for v1](#non-goals-for-v1).
- The Python spec is updated to match this simplified contract and uses "public", "stable", or "released" terminology for Agent Framework APIs.
- Multi-protocol samples include explicit route/client code.
- Apps that want a batteries-included ASGI app must write or depend on an app-specific wrapper.
- Existing unreleased code and docs that mention channels, contribution, or hooks must be revised before release.
## More Information
- Follow-up linking and multicast ADR: [ADR-0028](0028-hosting-linking-multicast-enhancements.md)
- Follow-up linking and multicast ADR: [ADR-0028](0028-hosting-linking-multicast-enhancements.md). That ADR still uses
some earlier host/channel terminology and must be aligned before implementation work starts.
## Appendix: Developer experience sketch
The examples below are sketches, not runtime-ready sample code. They show the minimum shape a developer would need to
build: where protocol helpers are called, where app-owned auth/authorization belongs, where state is loaded/stored, and
where native framework code remains in charge.
### Optional execution state
`AgentState` and `WorkflowState` stay small: they are target-specific state holders, not app hosts.
```python
from typing import Protocol
from agent_framework import AgentSession, SupportsAgentRun, Workflow
This document explores design options for two SEP-2640 features. The decisions are not yet finalized.
- **Part 1: MCP Resource Template Skills** - skills described by a URI template with variables that must be resolved before loading.
- **Part 2: Direct Skill References** - reading `skill://` URIs referenced directly (e.g., in server instructions) without being listed in the index.
## Part 1: MCP Resource Template Skills
### Context and Problem Statement
The `AgentMcpSkillsSource` currently only supports `skill-md` type entries from `skill://index.json` (support for `archive` type is planned). The SEP-2640 specification also defines `mcp-resource-template` entries: **parameterized skill namespaces** described by a URI template with variables (e.g., `{product}`) that resolve to concrete `SKILL.md` URIs. Rather than materializing every skill in the index, the template's variables must be resolved before a skill can be loaded.
| `url` | Concrete URI to `SKILL.md` | URI template with variables |
| `description` | Describes the skill | Describes the addressable skill space |
### Use Cases
Template skills address two scenarios where listing concrete skills is impractical:
- **Large skill catalogs** - too many skills to enumerate every entry in the index.
- **Dynamically generated skills** - skill content generated on the fly from parameters, so the set of valid skills is not known at index-creation time.
### How Template Skills Are Consumed
Per SEP-2640, the consumption flow relies on the MCP `completion/complete` method:
1. **Server registers a resource template** - The MCP server registers the same `url` value (e.g., `skill://docs/{product}/SKILL.md`) as an MCP [resource template](https://modelcontextprotocol.io/specification/2025-11-25/server/resources#resource-templates), wiring template variables to the [completion API](https://modelcontextprotocol.io/specification/2025-11-25/server/utilities/completion).
2. **Host reads `skill://index.json`** - Discovers the template entry with `type: "mcp-resource-template"`.
3. **Host surfaces template in UI** - Presents the template as an interactive discovery point where the user fills in variables.
4. **Host calls `completion/complete`** - For each template variable (e.g., `{product}`), the host calls the MCP completion API to get possible values from the server:
5. **User selects a value** - The user picks a value (e.g., `"billing"`) from the list.
6. **Host resolves the URI** - The template `skill://docs/{product}/SKILL.md` becomes the concrete URI `skill://docs/billing/SKILL.md`.
7. **Host reads the resolved skill** - Calls `resources/read` with the concrete URI and proceeds as with any `skill-md` skill.
### Potential Implementation Options
### Option 1: Callback on `AgentMcpSkillsSource` for Variable Value Selection
Add a callback to `AgentMcpSkillsSource` (or its options) that is invoked for each `mcp-resource-template` entry to let the caller select variable values.
return (selected, IncludeSkill: selected is not null);
};
})
.Build();
```
**Pros:**
- Simple implementation
- Easy to understand and use
**Cons:**
- Cannot be used in server-side scenarios where there is no interactive user at skill-discovery time
- Does not integrate with the agent's conversational flow
---
### Option 2: Integrate into Agent Conversation via `ChatClientAgent` Decorator
Model the template variable resolution as a request/response interaction within the agent's conversational loop.
**Flow:**
1. A `DelegatingAIAgent` decorator (e.g., `McpTemplateSkillResolutionAgent`) intercepts `RunAsync`/`RunStreamingAsync` calls and checks whether the inner agent has an `AgentSkillsProvider` with an `AgentMcpSkillsSource` containing unresolved template entries. The check is performed via `GetService<AgentMcpSkillsSource>()` on the `AgentSkillsProvider`, which delegates to a `GetService` method on the `AgentSkillsSource` base class.
2. The decorator calls an internal member on `AgentMcpSkillsSource` to get the list of `mcp-resource-template` entries from the index. The `AgentMcpSkillsSource` needs to be extended with an internal member that exposes unresolved template entries separately from concrete skills.
3. For each template entry, the decorator calls an internal member on `AgentMcpSkillsSource` to retrieve possible values for the template's variables via the MCP `completion/complete` API.
4. For each variable needing resolution, the decorator returns an `McpResourceTemplateValueRequestContent` (inherits from MEAI's `InputRequestContent`) in the agent response - bypassing the call to the inner agent. The content carries the template description, variable name, and possible values.
5. The user app receives the response, identifies the `McpResourceTemplateValueRequestContent` content type, and displays UI to the user showing the variable name and possible values, or forwards it further downstream if the user app is a service.
6. The user selects a value, and the user app calls the agent again with a corresponding `McpResourceTemplateValueResponseContent` (inherits from MEAI's `InputResponseContent`) containing the selected value. The `RequestId` property (inherited from the base classes) correlates the response with the original request.
7. The decorator identifies the response content and provides the resolved values to `AgentMcpSkillsSource` so it can use them when constructing concrete skills.
8. Having resolved all template variables, the decorator calls `RunAsync`/`RunStreamingAsync` on the inner agent.
9. The inner agent invokes the `AgentSkillsProvider`, which calls `AgentMcpSkillsSource.GetSkillsAsync()`. The source now has all resolved variable values and constructs concrete `AgentMcpSkill` instances from the resolved URIs, so it can provide the skill content if requested by the model.
**API sketch:**
```csharp
// New content types inheriting from MEAI's InputRequestContent/InputResponseContent:
public sealed class McpResourceTemplateValueRequestContent : InputRequestContent
{
public string TemplateDescription { get; }
public string VariableName { get; }
public IReadOnlyList<string> PossibleValues { get; }
public string TemplateUrl { get; }
}
public sealed class McpResourceTemplateValueResponseContent : InputResponseContent
{
public string SelectedValue { get; }
public string TemplateUrl { get; }
}
// Decorator usage:
var provider = new AgentSkillsProviderBuilder()
.UseMcpSkills(mcpClient)
.Build();
AIAgent agent = new ChatClientAgent(chatClient, new ChatClientAgentOptions
{
AIContextProviders = [provider],
});
agent = new McpTemplateSkillResolutionAgent(agent);
```
**Pros:**
- Works in server-side scenarios
- Fits the existing `DelegatingAIAgent` decorator pattern
- Can be composed with other decorators (tool approval, etc.)
**Cons:**
- Complex implementation
- Requires user app awareness of the new content types
- Users need to know that an additional decorator is required for handling MCP template skills, in addition to registering the MCP skills source
- Resolved template variable values must be persisted across conversation turns so the decorator does not re-prompt on subsequent agent runs within the same session
**Note:** This writeup is high-level and may miss details that could change the design. A POC would be needed to validate the approach.
### Open Questions
1. **Completion API limit** - The MCP completion API returns at most 100 values per request and provides no offset/cursor mechanism for enumeration. If a variable has more than 100 possible values, it's unclear how to retrieve the rest - the API only supports prefix-based filtering (typeahead), not bulk pagination.
2. **Multi-variable templates** - A template like `skill://{org}/{product}/SKILL.md` has multiple variables. Should they be resolved sequentially (org first, then product - since product values may depend on org) or presented together?
3. **Caching** - Should resolved template values be saved in the `AgentSession` so the user isn't re-prompted on every agent run? How should they be persisted between sessions?
---
## Part 2: Direct Skill References
This part covers how to let the model read `skill://` URIs referenced directly (e.g., in an MCP server's `instructions`, in a resource, or in another skill's content) without being listed in `skill://index.json`.
### How MCP Skills and Relative Links Work Today
The `AgentMcpSkillsSource` discovers skills by reading the well-known `skill://index.json` resource from the MCP server:
"description": "Convert between world currencies using live rates.",
"url": "skill://currency-converter/SKILL.md"
}
]
}
```
For each `skill-md` entry it creates an `AgentMcpSkill` instance - frontmatter (name/description) comes straight from the entry. The `AgentSkillsProvider` lists the discovered skills in the model's context (name + description):
```xml
<available_skills>
<skill>
<name>unit-converter</name>
<description>Convert between common units.</description>
</skill>
<skill>
<name>currency-converter</name>
<description>Convert between world currencies using live rates.</description>
</skill>
</available_skills>
```
It also provides functions to the model so it can load a skill and access its resources:
```csharp
// Loads the full content of a specific skill.
load_skill(string skillName)
// Reads a resource associated with a skill (references, assets, dynamic data).
The model calls `load_skill("unit-converter")` and receives the skill content:
```markdown
---
name: unit-converter
description: Convert between common units.
---
## Usage
For the full conversion table, see references/units-table.md.
```
The skill body references `references/units-table.md` by relative path. The model calls `read_skill_resource("unit-converter", "references/units-table.md")` and receives the resource content:
```markdown
# Unit Conversion Table
| From | To | Factor |
| miles | km | 1.60934 |
| kg | lbs | 2.20462 |
```
### Direct Reference Examples
A `skill://` URI can appear in any of these locations:
**Server instructions** - the MCP server advertises a skill the model should load:
```text
Follow our coding standards. Load skill://code-standards/SKILL.md for details.
```
**A skill body** - a skill's `SKILL.md` links to a sibling resource:
```markdown
---
name: code-standards
description: Coding standards and conventions.
---
## Naming
Follow the naming rules in skill://code-standards/references/naming.md.
```
**A resource** - the linked resource holds the actual content:
```markdown
# Naming Rules
- Use PascalCase for public members and type names.
- Use camelCase for locals and parameters.
- Prefix interfaces with `I` (e.g. `ISkillReader`).
- Suffix async methods with `Async`.
For examples, see skill://code-standards/references/naming-examples.md.
```
How can the model access content by direct reference?
### Function for Reading Direct Skill References
### Option 1: Extend existing `load_skill` and `read_skill_resource` functions
```csharp
// Added optional 'origin' and a direct skill:// URI is passed in 'skillName'.
| `skill://` link (skill) | `read_skill_uri(uri: "skill://commit-guidelines/SKILL.md", origin:"DirectRefServer")` |
| `skill://` link (resource) | `read_skill_uri(uri: "skill://commit-guidelines/examples/COMMIT_EXAMPLES.md", origin: "DirectRefServer")` |
**Pros:**
- Purely additive - no changes to existing functions needed; `read_skill_uri` can be deferred and added later when direct `skill://` reference support is needed.
- Granular approval: each function can have its own approval gate (like the existing `ScriptApproval` for `run_skill_script`), making per-operation approval for skill loading, resource reading, and direct URI access straightforward to add.
- Both `uri` and `origin` are required - no silent misuse through optional parameters.
- Clean split: `load_skill`/`read_skill_resource` for named skills, `read_skill_uri` for `skill://` links - no parameter ambiguity.
**Cons:**
- Three read functions (`load_skill`, `read_skill_resource`, `read_skill_uri`), not counting `run_skill_script`: larger tool surface than a single-function design.
### Option 3: Collapse `load_skill` and `read_skill_resource` into a single `read_resource` function
```csharp
// Single entrypoint for all skill content. 'uri' is required; 'origin' is optional.
read_resource(string uri, string? origin = null)
```
- `uri` - what to read: a skill name, a relative resource path, or a `skill://` link.
- `origin` - determines how `uri` is interpreted:
- **omitted** → load skill by name (`uri` is the skill name).
- **skill name** → read a relative resource (`uri` is the path within that skill).
- **server name** → read content by the `skill://` link (`uri` is handled by the source identified by the `[Origin: X]` marker).
Dispatch is ordered: null `origin` routes to Case 1; if `origin` names a known skill, routes to Case 2; otherwise tries to find an `ISkillUriReader` whose `CanRead` returns true for `origin` (Case 3).
| `skill://` link (skill) | `read_resource(uri: "skill://commit-guidelines/SKILL.md", origin: "DirectRefServer")` |
| `skill://` link (resource) | `read_resource(uri: "skill://commit-guidelines/examples/COMMIT_EXAMPLES.md", origin: "DirectRefServer")` |
**Pros:**
- Minimal tool surface: one read function instead of two or three (not counting `run_skill_script`) reduces token usage and gives the model fewer choices.
**Cons:**
- No per-operation approval: all cases (skill loading, resource reading, direct URI access) share one function, so approval cannot be scoped to individual operations.
- Unreliable on gpt-4.1-mini: omits `origin` when reading `skill://` links, passes skill name as `origin` when loading a plain skill (should be omitted), and hallucinates resource names (e.g. `API_SPECIFICATION.md`) that do not exist.
---
### Origin Marker
A `skill://` URI does not carry an origin, but the model needs to provide one when reading it. The `origin` is what routes the read call to the source that can handle the URI - the provider uses it to pick the matching source. Since the URI itself carries no such hint, the MCP source injects an `[Origin: ...]` marker wherever a `skill://` URI appears, so the model can read it back and pass it as the `origin` argument.
The marker is only added when the content actually contains `skill://` references. If a piece of content (server instructions, a skill body, or a resource) has no `skill://` URIs, there is nothing for the model to read back, so no marker is injected.
Into **server instructions**, which may mention `skill://` URIs directly:
```
[Origin: code-standards-server]
Follow our coding standards. Load skill://code-standards/SKILL.md for details.
```
Into **skill bodies**, since a `SKILL.md` may reference other `skill://` URIs (a resource file or a related skill):
```
[Origin: code-standards-server]
# Code Standards
For naming conventions, load skill://code-standards/references/naming.md.
```
Into **skill resources**, since a resource may itself reference further `skill://` URIs:
```
[Origin: code-standards-server]
# Naming Rules
- Use PascalCase for public members and type names.
- Use camelCase for locals and parameters.
For examples, see skill://code-standards/references/naming-examples.md.
```
---
### Read-by-URI Capability: Interface vs Base Class Virtual Methods
Now let's look at how an `AgentSkillsSource` can opt in to reading `skill://` URIs and signal that capability to the provider.
### Option 1: New `ISkillUriReader` interface
```csharp
public interface ISkillUriReader
{
// Returns true if this reader can handle the given skill:// URI from the given origin.
bool CanRead(string uri, string origin);
// Reads and returns the content for the given skill:// URI.
The provider may treat a source implementing `ISkillUriReader` as the signal to advertise `read_skill_uri`: if at least one registered source implements the interface, the function is exposed to the model; otherwise it is not.
### Option 2 (Proposed): Virtual methods on `AgentSkillsSource` base class
```csharp
public abstract class AgentSkillsSource
{
// New members for reading by URI.
// Whether this source can read by URI; drives whether read_skill_uri is advertised. Off by default.
public virtual bool SupportsReadByUri => false;
// Returns true if this source can handle the given skill:// URI from the given origin.
public virtual bool CanReadByUri(string uri, string origin) => false;
// Reads and returns the content for the given skill:// URI.
// Reads content by skill:// URI from the MCP server.
public override Task<string?> ReadByUriAsync(string uri, string origin, CancellationToken cancellationToken)
=> /* resolve uri via the MCP server identified by origin */;
```
All sources inherit the methods, so there is no type signal - `SupportsReadByUri` fills that role. The function is advertised when any registered source returns `true`.
### Comparison
| Aspect | Option 1: Interface | Option 2: Base class virtual methods |
| Discovery | Service locator | Direct call on source |
| Advertising signal | Interface implementation | `SupportsReadByUri` flag |
| Adding new members | Breaking change | Non-breaking |
| Complexity | Higher | Lower |
---
### Include MCP Server Instructions Into Agent Instructions
MCP server instructions may contain the `skill://` references the model needs, so we want to surface them in the agent's instructions. But they can also carry system prompts or behavioral directives irrelevant to the agent, polluting context - so inclusion is **opt-in** via the `IncludeServerInstructions` option:
```csharp
public sealed class AgentMcpSkillsSourceOptions
{
// When true, the MCP server's instructions are injected into the agent instructions. Off by default.
public bool IncludeServerInstructions { get; set; }
Following direct `skill://` references is **disabled by default** and activated via an option. When enabled, the provider advertises the read function to the model, and the source injects the `[Origin: ...]` marker into all content provided by the MCP server that contains `skill://` references. When disabled, no function is advertised and no marker is injected.
### Template Variable Resolution: Callback vs Decorator (Part 1)
**Postponed.** Deferring this decision until:
- We have a concrete list of scenarios that require template variable resolution.
- The skills-over-MCP spec is released (it is still a draft, so the design may change).
- There is a strong signal of demand from users or the ecosystem.
### Function for Reading Direct Skill References (Part 2)
**Postponed.** Leaning toward **Option 2 - dedicated `read_skill_uri` function alongside existing ones** (purely additive, and each function can have its own approval gate for granular per-operation approval), but deferring the decision until:
- The skills-over-MCP spec is released (it is still a draft, so the design may change).
- There is a strong signal of demand from users or the ecosystem.
### Read-by-URI Capability: Interface vs Base Class (Part 2)
**Postponed.** Leaning toward **Option 2 - virtual methods on `AgentSkillsSource`** (non-breaking, lower complexity, and a natural fit with the existing base class hierarchy), but deferring the decision until:
- The skills-over-MCP spec is released (it is still a draft, so the design may change).
- There is a strong signal of demand from users or the ecosystem.
The method naming (`SupportsReadByUri`, `CanReadByUri`, `ReadByUriAsync`) should also be abstracted a little more before adoption, so the same members can be reused when a similar direct-reference concept is needed for other skill types (e.g. file skills).
[ADR-0026](0026-hosted-session-identity-context.md) sourced the hosted-agent end-user identity from `ResponseContext.Isolation` (an `IsolationContext` typed `UserIsolationKey` / `ChatIsolationKey`), injected by the platform as the `x-agent-user-isolation-key` and `x-agent-chat-isolation-key` headers.
`Azure.AI.AgentServer.*` 2.0.0 (responses protocol `2.0.0`) removes that surface. `ResponseContext.Isolation` is gone; the platform now exposes `ResponseContext.PlatformContext` (a `PlatformContext` typed `UserIdKey` and `CallId`), populated from the `x-agent-user-id` and `x-agent-foundry-call-id` headers. The chat isolation key no longer exists, and a new per-request **call id** is introduced that first-party Foundry services (the toolbox proxy in particular) require on outbound calls to resolve the server-side-stored caller context. The hosting layer in `Microsoft.Agents.AI.Foundry.Hosting` had to migrate to this contract without changing the public shape that samples and providers depend on.
## Decision Drivers
- Track the breaking `Azure.AI.AgentServer.*` 2.0.0 surface (`PlatformContext` replacing `Isolation`) while keeping the same per-user partitioning guarantees from ADR-0026.
- Keep the change **internal**: existing hosted samples and `AIContextProvider`s must not need code changes. `session.GetHostedContext().UserId`, `HostedSessionIsolationKeyProvider`, and `AddFoundryResponses` stay source-compatible.
- Forward the new per-request call id verbatim on outbound calls to Foundry first-party services so per-user toolbox OAuth consent and other server-side caller-context lookups keep working.
- Remain resilient on protocol `1.0.0`: when only the legacy headers are present, `UserIdKey` still resolves and `CallId` is simply absent.
- Preserve the strict-resume tamper defense from ADR-0026 with identity now reduced to user only.
## Considered Options
For the identity source:
1. **Map `ResponseContext.PlatformContext.UserIdKey`** into the existing `HostedSessionContext` (user only), keeping ADR-0026's storage shape and read accessor.
2. Keep a `ChatId` slot on `HostedSessionContext` for backward source-compatibility, populated from `CallId` or left null.
For the call id propagation:
A. **A request-scoped ambient (`HostedCallContext`, an `AsyncLocal<string?>`)** set by the handler and re-applied before each egress point, read by the outbound delegating handler.
B. Thread the call id through every method signature down to the toolbox bearer handler.
For session keying (previously implied by the conversation/chat pairing):
I. **`HostedConversationKey`** resolving a stable partition from `conversation_id ?? partition(previous_response_id) ?? partition(responseId)`.
II. Continue keying on the container session id (`FOUNDRY_AGENT_SESSION_ID`).
## Decision Outcome
Chosen: **Option 1** for identity, **Option A** for call id, **Option I** for session keying.
Rationale:
- **`ChatId` dropped (Option 2 rejected).** The platform no longer supplies a chat key; carrying a synthetic one would invent identity the trust boundary does not provide. `HostedSessionContext` becomes user-only (`HostedSessionContext(string userId)` / `UserId`), and the strict-resume check validates `UserId` alone. The corresponding `HostedFoundryMemoryProviderScopes` values `PerChat` and `PerUserAndChat` are removed; `PerUser` is retained.
- **Ambient call id (Option B rejected).** Writing `HostedCallContext.CallId` inside the streaming `async IAsyncEnumerable` iterator is reverted across each `yield`, so a single up-front assignment is lost before the toolbox/MCP egress runs. The handler therefore captures `context.PlatformContext?.CallId` once and **re-applies it immediately before each egress point**; `FoundryToolboxBearerTokenHandler` forwards it as `x-agent-foundry-call-id`. The ambient is request-scoped and never leaks into the caller's execution context (guarded by a unit test).
- **`HostedConversationKey` (Option II rejected).** One container serves many conversations for its lifetime, so the container session id cannot key per-conversation state. The partition key is derived from the conversation/`previous_response_id`/minted response id instead.
Implementation summary in `Microsoft.Agents.AI.Foundry.Hosting`:
| Type | Visibility | Change vs ADR-0026 |
|---|---|---|
| `HostedSessionContext` | public sealed | Now user-only (`UserId`); `ChatId` removed. |
Package manifests bump the responses container protocol to `2.0.0` (invocations stays `1.0.0`).
## Consequences
Positive:
- Per-user memory partitioning and the strict-resume tamper defense from ADR-0026 are preserved with no public API churn for samples or providers.
- Per-user toolbox OAuth consent and other server-side caller-context lookups keep working because the per-request call id is forwarded on egress.
- Works unchanged on protocol `1.0.0` (no call id) and `2.0.0`.
Negative:
- `HostedSessionContext.ChatId` and the `PerChat` / `PerUserAndChat` memory scopes are removed; any out-of-tree consumer that referenced them must move to user-scoped partitioning.
- The call id must be re-applied before every egress point because of the async-iterator `AsyncLocal` revert; a missed re-apply silently drops the header. This is covered by unit tests.
## Out of scope
- HMAC tamper signatures over the persisted context remain unimplemented; equality comparison against `ResponseContext.PlatformContext` on every request is sufficient because the platform sets the header at the trust boundary.
- The per-request `User` field on `CreateResponse` is still intentionally not consumed.
| `AgentState` (target + store, get-or-create, callable/awaitable target) | `AgentSessionStore` (get-or-create + save + serialize + isolation) + DI container (target lifetime + async setup) | create-on-miss lives in the store; per-run instance and deferred/async target come from DI, so no separate holder is needed |
existing whole-User-Agent opt-out also suppresses the mask.
- Good, first-party-only + request-time stamping gives a live mask and no
third-party fingerprint leak.
- Good, 128 bits leaves useful v1 headroom; .NET remains lock-free by storing two
independently atomic 64-bit lanes; per-language lists remove all cross-language
sync; package-local enums avoid both codegen and provider→core release coupling.
- Neutral, the token's reach equals eligible framework-configured first-party
traffic; broader per-call signal (OTel) can be added later if needed.
- Neutral, every set bit is a repeated Boolean observation after first use;
request rows carrying it are not feature invocation counts.
- Neutral, v1 granularity is intentionally a separate choice; the registry should
start with fewer bits unless a more detailed bit answers a concrete question.
- Bad, each feature must add an activation mark, first-party clients need a
per-request destination-aware hook, and the registry validator must scan all
package-local index declarations.
## Prior art
SDK telemetry-in-the-User-Agent is well-established; this design is closest to
AWS's, and conventional in the rest. Summary of what comparable SDKs do:
| SDK | What's in the UA / headers | Usage-based? | Opt-out | Closest to ours? |
| --- | --- | --- | --- | --- |
| **AWS botocore** | structured UA with an `m/` token: a per-request set of **short feature codes** for features actually exercised (`WAITER`→`B`, `PAGINATOR`→`C`, retry mode, checksums, credential source, …) | **Yes** — registered at call time via `register_feature_id`, contextvar-scoped per request | `AWS_SDK_UA_APP_ID` sets app id (no opt-out for `m/`) | **Yes — direct analog** |
The token carries a **per-language** version (`feat=v1.<hex>`); a version bump is
independent for Python and .NET.
- **Additive growth stays on v1 — no bump.** Allocating a new feature to a
reserved/unused bit is backward-compatible: an older decoder simply sees an
unknown bit and ignores it. Normal package growth never needs a new
version.
- **A bump (v2) is required only for breaking changes:** renumbering or
re-partitioning existing bits, changing the *meaning* of an already-assigned
index, or widening beyond 128-bit. Within a version an index is **never** reused or
reassigned — that invariant is what lets old decoders stay correct.
- **The draft 64→128 change is still v1.** No v1 token or enum has shipped, so
this pre-implementation repartition establishes the initial contract rather
than migrating an existing one.
- **Mixed-version coexistence is the norm.** A fleet runs many SDK releases at
once, so `v1` and `v2` tokens appear simultaneously for a long time (old SDKs
keep emitting `v1`). The decoder keeps **every** published `(language,
version)` table and selects by the token's version; the `v1` table is retained
indefinitely for historical decode.
- **Unknown version → do not guess.** A decoder without the `vN` table must
record "unknown registry version" rather than decode against an older table —
bit meanings may differ across versions, so mis-attribution is worse than
no data.
- **Producing v2:** publish the v2 table alongside v1, update the affected
package-local `FeatureIndex` declarations and SDK version constant, and emit
`v2` from the release that ships them. Prefer staying on v1 (additive) and
reserving a clean v2 for an eventual deliberate re-partition.
## Limitations
| Limitation | Caused by (choice) | Why we accepted it |
| --- | --- | --- |
| **No signal for self-hosted or third-party-only traffic.** If a process never calls Azure/Foundry, we see nothing. | First-party-only emission (A) | We can't read third-party logs anyway, and must not leak a fingerprint into them. Reach traded for privacy. |
| **Not every first-party client is stampable.** Caller-supplied `AIProjectClient` / OpenAI clients and toolkit-owned clients may not expose a supported per-request policy hook. | Supported-hook-only emission (A) | V1 does not mutate caller-owned clients or private SDK pipelines. Those features may still appear on another eligible request from the same process-global mask. |
| **Custom origins intentionally receive no feature token.** A customer gateway may use Azure credentials or Azure-named settings but route to a non-approved origin. | Two-factor destination classification (A) | Credentials and configuration names are not proof of telemetry ownership. Unknown/custom origins and cross-origin redirects are denied by default. |
| **No OTel / per-call signal in v1.** | OTel deferred (C) — primarily on **privacy** and availability grounds | A broadly-emitted span attribute would push the fingerprint into the user's general telemetry / third-party APM vendors, undoing the first-party-only scoping. It also requires customer/user OTel setup, and even Foundry users may not export data where we can query it. Left open only if there is a compelling reason to add. |
| **Mask reflects "usage so far," not the whole session.** Early requests carry fewer bits than later ones. | Process-global accumulator + request-time stamping | Honest and still useful as a Boolean process-lifetime observation. Repeated request rows must not be summed as additional uses. Reading the mask at request time makes it *grow* rather than freeze. |
| **No per-agent / per-call attribution.** The mask is one process-wide value — "this process used X", not "this agent/call used X". | Process-global monotonic scope (S1) | A deliberate choice, not a transport limit: botocore *does* per-call attribution in the UA via a per-request `contextvars` set, but many AF activations (workflow build/start, provider participation, hosting startup) occur outside the service request that later emits the token. Per-call detail remains deferred to OTel. |
| **Shared processes intentionally carry usage across agents and tenants.** A request can include bits first set by another workload in the same worker. | Process-global monotonic scope (S1) | The token must be interpreted only as process-level "used so far," never as request/user/tenant attribution. Privacy review must explicitly accept this. |
| **Bits are binary, sticky observations — not countable events.** Once set, a bit appears on every later eligible request from that process, so raw request counts repeat the same observation and long-lived/high-traffic processes dominate. | Monotonic mask stamped at request time | The signal supports coarse observed-feature and co-occurrence questions only. It cannot provide first-use counts, unique-process counts, request attribution, or feature invocation frequency. |
| **Granularity may be too coarse or too detailed.** The chosen level may miss useful distinctions or create more specificity than needed. | v1 granularity choice (F0-F4) | This is the main remaining decision. Adding bits later is easier than removing/redefining them, so v1 should lean toward fewer bits that answer known questions. |
| **.NET snapshots span two atomic lanes.** A bit can be marked between the low/high reads, so one request may omit that just-added bit. | 128-bit width without a global lock | The mask is monotonic: the snapshot cannot invent or clear a bit, and the next request includes the addition. This matches the existing "usage so far" timing semantics. |
| **Fingerprinting risk is reduced, not eliminated.** A feature-combination mask is still a deployment signature, and it transits intermediaries (proxies/CDNs) even when first-party-scoped. | Emitting any feature-combination value | Scope + opt-out + coarse granularity mitigate it; v1 should avoid unnecessary detailed bits. |
## Open Questions (for decider discussion)
These are unresolved and should be decided before implementation:
1. **Which v1 granularity level (F0-F4)?** This is the primary remaining choice.
Adding bits later is easier than removing or redefining bits, so v1 should
choose the least detailed level that answers known questions and avoids a quick
v2.
2. **Privacy approval for the v1 User-Agent signal.** Before implementation,
confirm that a transparent, opt-out, first-party-only feature-combination
fingerprint is acceptable, including the exact client allowlist, retention,
access, and permitted product queries. This is a rollout precondition.
3. **When (if ever) to add the OTel path?** Held back mainly for **privacy** and
data availability: a span attribute broadcasts the fingerprint into the user's
general telemetry and onward to third-party APM vendors, contradicting the
first-party-only stance, and it requires user-side OTel setup that may not make
the data available to us even for Foundry users. It also carries a
metric-cardinality hazard. Revisit only if the User-Agent path cannot answer a
concrete question.
4. **Honor the cross-tool `DO_NOT_TRACK` convention?** Several ecosystems treat
`DO_NOT_TRACK=1` as a universal telemetry opt-out (HuggingFace Hub honors it;
see [Prior art](#prior-art)). Should our mask opt-out also respect
`DO_NOT_TRACK` (in addition to `AGENT_FRAMEWORK_FEATURE_MASK_DISABLED` and
Python's pre-existing whole-UA flag)? Cheap to add and
community-friendly, but it widens the opt-out surface and needs a clear
precedence rule. Recommend yes; confirm with the deciders.
### Decided
- **Dedicated opt-out flag — included.** In addition to the existing
Python `AGENT_FRAMEWORK_USER_AGENT_DISABLED` (drops the whole UA), v1 ships
`AGENT_FRAMEWORK_FEATURE_MASK_DISABLED`, which drops **only** the feature mask
while keeping the base SDK identity/version User-Agent. This lets a
privacy-conscious user withhold the usage signal without losing the
support/compat value of the SDK-version header. .NET adopts the dedicated
mask-only flag; adding a .NET whole-User-Agent switch is outside this decision.
- **Caller-owned clients are not modified.** V1 stamps only framework-created
clients or clients with a supported public policy/hook registration point. It
does not patch private pipelines; injected clients are an explicit coverage
limitation.
- **Destination approval is explicit and redirect-aware.** An eligible pipeline
still emits only to a reviewed HTTPS origin. Custom origins are default-deny,
and the token is removed on an unapproved redirect hop.
- **Telemetry does not replace transport defaults.** Framework-created OpenAI
clients use the SDK's default async HTTP client with the request hook added,
preserving redirect, timeout, connection-limit, and pooling behavior.
- **Marking uses activation, not DI construction.** Operational surfaces mark on
first real use; a constructor marks only when construction itself exercises or
Python does not have a broadly shared session-store API in
`agent-framework-core`. The alpha `agent-framework-hosting` package has a small process-local `SessionStore`, but that
type is hosting-specific, in-memory only, and unavailable to packages such as Foundry Hosting without taking a
dependency on the hosting helper package.
The alpha implementation is a prototype, not a compatibility constraint. This decision may replace its location,
names, method shape, and behavior if another design is preferable.
The existing file-backed persistence surfaces solve narrower problems:
- `FileHistoryProvider` stores conversation `Message` records, not complete `AgentSession` snapshots;
- `FileCheckpointStorage` stores workflow checkpoints; and
- the Responses provider stores protocol history, but not Agent Framework runtime state carried in
`AgentSession.state`.
`AgentSession.to_dict()` / `from_dict()` already provide a dictionary snapshot shape. Session state may contain
framework or application-defined objects, and `register_state_type` provides dynamic type restoration, but the
registration and collision behavior is not yet strong enough to serve as a durable, cold-start persistence contract.
The framework therefore needs to decide:
- where a reusable in-memory and file-backed session store belongs;
- how a complete `AgentSession` should be serialized atomically and validated;
- how custom nested state types are registered and restored after process restart; and
- how to provide the required readable JSON format while leaving room for an optional optimized binary format.
## Decision Drivers
### Session-store ownership and API
- Make session storage reusable by core, hosting, and provider packages without creating dependency cycles.
- Keep the smallest public API that supports in-memory use, durable implementations, and application-defined stores.
- Define the minimum async operations required for lookup, replacement, and deletion.
- Decide explicitly whether reads return shared instances or independent snapshots suitable for branching.
- Simpler is better
### Serialization and type restoration
- Provide readable JSON serialization as a required capability.
- Treat an optimized binary format as a nice-to-have only when the chosen JSON implementation supports it without a
separate state model or substantial additional complexity.
- Perform one typed encode and decode operation per file write/read.
- Preserve dynamic registration of nested state types by the provider modules that own them.
- Fail before persistence when an object cannot be restored after a cold start.
- Keep the existing serialized `{"type": "<id>", ...}` representation compatible.
## Decision 1: Session-store ownership and API shape
### Keep `SessionStore` in `agent-framework-hosting`
- Good: keeps the abstraction local to app-owned hosting scenarios.
- Bad: Foundry Hosting and other packages cannot reuse it without depending on the hosting helper package.
- Bad: a generic session snapshot store is not inherently or only a web-hosting concern.
- Bad: durable implementations would either be duplicated or placed in an unrelated package.
### Add an abstract store plus separate in-memory and file implementations
For example, define a `SessionStore` protocol/ABC with `InMemorySessionStore` and `FileSessionStore`.
- Good: clearly separates the contract from implementations.
- Good: implementation names state their storage behavior explicitly.
- Neutral: follows a familiar repository/adapter pattern.
- Bad: introduces an additional public type and rename for a three-method experimental API.
- Bad: callers must choose an implementation even for the default in-memory case.
- Bad: the abstraction adds little value while every implementation still needs the same method overrides.
### Move the concrete store to core and use it as the overridable base
Move `SessionStore` to `agent-framework-core`, retain its in-memory behavior, and implement `FileSessionStore` by
overriding the same async methods.
- Good: one public type is both the useful default and the extension point.
- Good: existing custom stores can continue subclassing and overriding `get` / `set` / `delete`.
- Good: core and provider packages can share the API without depending on hosting helpers.
- Good: `FileSessionStore` remains a focused subclass while the base stays free of file-system concerns.
- Bad: the class name does not explicitly say "in memory" when used without overrides.
## Decision 2: Serialization and type restoration
Once a file-backed store exists, it needs an on-disk format and a reliable way to reconstruct the complete
`AgentSession`, including nested framework and application-defined state. Serialization belongs to each durable store
implementation rather than the `SessionStore` API: the default in-memory store does not serialize, and custom stores
remain free to choose another protocol.
The alternatives below compare top-level snapshot validation, JSON encoding/decoding cost, and how each option
interacts with the dynamic custom-state registry. Binary storage is not a primary selection criterion.
### Considered options
The standard-library and optimized-JSON options are not mutually exclusive. A store can default to `json` while
accepting caller-supplied `dumps` / `loads` callables for `orjson` or another compatible implementation. This is the
pre-msgspec `FileHistoryProvider` design; those hooks remain only as a deprecated compatibility path.
### Standard library `json`
- Good: no additional dependency and familiar readable output.
- Good: accepts the existing dictionary snapshots without a schema.
- Good: can remain the fallback/default behind pluggable `dumps` / `loads`.
- Neutral: custom state restoration still requires the framework registry.
- Bad: slower encoding and decoding than optimized native implementations.
- Bad: provides no typed snapshot validation during file reads.
### Optimized drop-in JSON libraries such as `orjson`
- Good: substantially faster JSON encoding and decoding than the standard library.
- Good: can preserve the existing dictionary-oriented snapshot and custom `dumps` / `loads` shape.
- Good: can be an opt-in codec without making the optimized package a framework dependency.
- Neutral: returns bytes when encoding, which the file stores can already handle.
- Neutral: custom state restoration still requires the framework registry.
- Bad: remains an untyped top-level decode; the framework must separately validate the session snapshot shape.
- Bad: choosing one drop-in implementation as a core dependency adds a dependency without providing typed construction.
### Pydantic `model_dump` / `model_validate`
- Good: Pydantic is already a core dependency.
- Good: a typed session snapshot model can validate top-level fields and provide `model_dump_json` /
`model_validate_json` for file serialization.
- Good: validation errors include useful field paths.
- Neutral: the dynamic `state` field remains `dict[str, Any]`, so custom nested state restoration still requires the
framework registry.
- Neutral: the public `AgentSession` does not need to become a Pydantic model; an internal snapshot model can bridge it.
- Bad: benchmarked encode/decode includes model construction and dumping overhead on every operation.
- Bad: core dependency on Pydantic run the risk of us not being able to use different versions or users of the framework being unable to upgrade or having additional extra code dealing with major version bumps in Pydantic.
### msgspec typed/tagged unions only
- Good: msgspec owns validation and reconstruction end to end.
- Neutral: works well for a closed set of framework-owned `msgspec.Struct` types.
- Bad: every external type must be known when the decoder schema is constructed; dynamic registration is lost.
### msgspec codecs plus an explicit dynamic registry
- Good: one typed file encode/decode and dynamic nested custom types.
- Good: it satisfies the required readable JSON format.
- Neutral: the same typed snapshot can also support optional MessagePack as a low-cost implementation detail.
- Good: the registry can enforce stable IDs, codec completeness, and collision handling.
- Neutral: a single state-payload hook still recursively applies registry codecs.
- Bad: msgspec cannot infer dynamic types from JSON without the framework's type tags.
## Benchmark Evidence
A benchmark using a large `AgentSession` with 2,000 `Message` objects stored through
`InMemoryHistoryProvider`, nested standard dictionaries, registered custom classes, and registered Pydantic models
measured the complete `AgentSession.to_dict()` / codec / `AgentSession.from_dict()` path.
The JSON encodings produced the same 1.57 MiB file size. msgspec JSON had the best median JSON round-trip latency,
slightly ahead of orjson, while also supporting typed top-level decoding. Pydantic validation added measurable decode
and disk-round-trip overhead without eliminating the dynamic state registry.
MessagePack reduced file size to 92.2% of JSON (about 7.8% smaller) and produced the best encode, decode, and disk
round-trip medians. Its in-memory round-trip median was effectively tied with msgspec JSON. This supports offering it
as a nice-to-have, but it is not required to justify choosing msgspec for JSON.
These results are workload- and machine-dependent. The small differences between optimized JSON implementations are
not the basis for the architectural choice. The benchmark instead confirms that the typed design does not impose a
material regression for this representative payload:
- use msgspec JSON as the readable default;
- optionally offer msgspec MessagePack when storage size or disk latency matters;
- retain the explicit registry for dynamic custom state in both formats;
- do not add orjson solely for a small JSON performance difference without typed decoding; and
- do not use Pydantic as the file codec when its validation overhead does not replace the registry.
## Decision Outcome
### Decision 1: Move the concrete overridable store to core
`SessionStore` moves to `agent-framework-core` as an experimental public API. It remains a concrete in-memory store and
the default used by `AgentState` in the `hosting` package. Its async `get`, `set`, and `delete` methods remain overridable for custom storage
implementations.
`FileSessionStore` subclasses `SessionStore` and provides durable atomic file persistence. No separate
`InMemorySessionStore`, protocol, or ABC is introduced. `agent-framework-hosting` consumes the core type and no longer
owns or re-exports `SessionStore` (this will be a breaking change in the `hosting` package).
Actual `SessionStore` and `FileSessionStore` operations mark Python feature-usage index 17,
`core.session_store`, following ADR-0033's use-not-presence policy. Construction and import alone do not mark the bit.
`SessionStore` accepts opaque non-empty keys so custom backends can use their native key contracts. `FileSessionStore`
accepts opaque keys up to 128 characters and encodes values that are not portable filename stems; this supports
provider IDs such as `telegram:<bot-id>:<chat-id>` without permitting path traversal. `AgentState` remains
storage-agnostic and passes keys through unchanged; each store implementation owns backend-specific validation or
normalization. Protocol-specific hosts such as Foundry may still derive their own stable storage key before calling the
store.
Foundry Hosting exposes an experimental `FoundrySessionStore`, which is the
default `ResponsesHostServer` store when hosted; local hosting defaults to the
in-memory `SessionStore`. `FoundrySessionStore` currently subclasses
`FileSessionStore`, stores snapshots under
`/.sessions/<user-id>/<conversation-id-or-response-id>.json`, and derives the
validated user partition from
`azure.ai.agentserver.core.get_request_context()`. A Foundry session controls
hosted compute and filesystem lifetime and may host multiple users and
Responses conversations, so its ID is not used as the MAF session identifier.
Stored-conversation requests read and write one snapshot under
`conversation_id`. Response-chain requests read under `previous_response_id`
and write the updated, loaded MAF session under the current `response_id`, which
allows branching without overwriting the parent snapshot. Because Foundry does
not infer `agent_session_id` from `previous_response_id`, response-chain callers
must also reuse the prior response's hosted session ID so the request reaches
the same persistent `$HOME`; conversation objects bind a stable hosted session
automatically.
The Foundry-specific type is the host configuration seam; its implementation
may later move from files to a Foundry storage API without changing the generic
core store contract. The session file API maps `/` to the hosted `$HOME`
directory, so this API path is persisted on disk under `$HOME/.sessions`.
### Decision 2: Use msgspec codecs plus an explicit dynamic registry
Chosen option: **msgspec codecs plus an explicit dynamic registry**.
`FileSessionStore` uses a typed internal `msgspec.Struct` snapshot with reusable JSON and MessagePack encoders/decoders.
JSON is the required and default format. Because msgspec can reuse the same typed snapshot and registry hooks,
`serialization_format="msgpack"` is also exposed as an optional compact binary convenience. The complete state
dictionary is wrapped in one custom field; its encode/decode hooks recursively translate explicitly registered types
to and from the existing tagged mappings in either format.
The dependency range is `msgspec>=0.20.0,<0.22`: version 0.20.0 added Python 3.14 support, and the upper bound limits
core to the tested 0.20/0.21 minor lines.
Three dependency placements were considered:
1. Make msgspec a standard core dependency.
2. Make msgspec optional in core but standard in Foundry hosting.
3. Make msgspec optional in both packages.
Option 3 moves installation failures to application developers even though durable session persistence is required for
the primary `ResponsesHostServer` API to preserve Agent Framework state. Option 2 removes that burden from Foundry
hosting but makes core's shared `_sessions` module and public types conditionally defined or lazily imported without
removing msgspec from the default Foundry installation. Option 1 is therefore selected: msgspec is a standard core
dependency, giving both core file providers and Foundry hosting one predictable implementation path.
Core already depends on the native `pydantic-core` extension, so native-wheel availability is not a new packaging
constraint. The msgspec project is also actively tracking upcoming Python support; its merged
[`Add 3.15-dev to CI` PR](https://github.com/msgspec/msgspec/pull/1037) exercises Python 3.15 development builds. This gives confidence that they will add support for new python version quickly.
The public `AgentSession` remains a normal framework class. The msgspec Struct is an internal persistence DTO rather
than the inheritance base for runtime sessions. The Struct gives persistence one typed encode/decode operation, validates
the snapshot envelope, and carries an explicit payload version. The benchmark's small timing spread was not used to
choose the Struct.
`register_state_type` supports stable type IDs and optional codecs, rejects collisions, and provides defaults for
`to_dict` / `from_dict` classes and Pydantic models. Type IDs share one process-wide registry, so provider packages
should use stable package-qualified identifiers and register their own state types at module import time; consumers do
not need to know those implementation details. One recursive serializer is shared by `AgentSession.to_dict()` and the
durable codecs. The established implicit Pydantic registration behavior remains temporarily for compatibility, but now
emits `DeprecationWarning`. Same-process round-trips continue to work; cold-start deserialization is not guaranteed
without explicit provider registration. Unknown persisted type IDs remain raw dictionaries.
File snapshots are quarantined only when their bytes cannot be parsed as the selected JSON or MessagePack format.
Schema errors, unsupported snapshot versions, and registered state-decoder failures leave the original file in place so
an application fix, rollback, or compatible reader can recover it.
`FileHistoryProvider` also adds msgspec JSON as its default JSON Lines codec. It supports the same explicit
`serialization_format="msgpack"` choice using length-prefixed append-only MessagePack records. Its existing `dumps` /
`loads` extension points remain temporarily for JSON compatibility, emit `DeprecationWarning` when supplied, and do
not apply to MessagePack. New code uses the built-in codecs. The default JSON reader falls back to the standard library
for legacy JSON Lines containing `NaN` or infinity, and writes those non-finite values with the standard library so
existing history semantics are preserved.
## Follow-up Work
Audit the remaining file-backed stores to determine whether they benefit from the same typed msgspec treatment and
optional JSON / MessagePack formats. `FileCheckpointStorage` is the first candidate because it persists large,
structured workflow state and currently uses JSON plus custom checkpoint value encoding. Its existing
`WorkflowCheckpoint.version` field already provides a payload-shape discriminator.
Checkpoint migration should be reader-first. A compatibility release can detect the codec from the first byte, widen
the two `glob("*.json")` readers to discover future formats, and continue writing only JSON. A later release can add
opt-in MessagePack writes while retaining JSON as the default. The payload `version` should describe the checkpoint
shape rather than the codec, which is discoverable from the bytes. MessagePack should not become the default while
mixed-version fleets may share one checkpoint directory: older readers silently ignore non-JSON files and could resume
from no checkpoint instead of surfacing an incompatibility.
`MemoryContextProvider` is another candidate because its file-backed path combines `MemoryFileStore` state with
transcript files and still exposes `history_dumps` / `history_loads` passthroughs to the deprecated
`FileHistoryProvider` codec hooks.
The follow-up should measure real framework payloads before changing formats, preserve compatibility or define a clear
migration path for existing files, and consider whether each store needs readable JSON, compact binary storage, append
semantics, or atomic whole-file replacement. Other candidates include file-backed todo state, but each should be
evaluated independently rather than adopting msgspec by default solely for consistency.
# .NET agent-hooks enforcement: composed factory over three seams
## Context and Problem Statement
The [AGENT-HOOKS-0.1](https://github.com/responsibleai/agent-hooks) interception contract shipped for Python as a first-class experimental core feature (#7515): a middleware bundle emitting eight interception points with three-verdict, fail-closed enforcement, transform write-back, buffered streaming, and verdict-before-durability persistence gating. The .NET side needs the same semantics, but the .NET framework has no category-based middleware lists — interception is decorator composition (`DelegatingAIAgent`, Microsoft.Extensions.AI `DelegatingChatClient`, the function-invocation middleware seam). How should the contract's indivisibility and enforcement properties be realized in that model?
## Decision Drivers
- Identical enforcement semantics to the merged Python feature (same spec, same fail-closed rules), diverging only where the .NET seam model requires it — never by weakening an enforcement property.
- Partial installation of the enforcement must be impossible or loudly rejected, not silently degraded.
- Denied content must never become durable; transformed content must persist post-transform.
- No changes to existing framework source; the optional native-runtime dependency (`ResponsibleAI.AgentHooks`) must not be referenced by core packages.
## Decision Outcome
**A single factory (`AsAIAgentWithAgentHooks`, per-run and host-owned-session overloads) in a new package `Microsoft.Agents.AI.AgentHooks` composes the full enforcement itself** instead of exposing middleware values:
- **Seam order (fixed by construction):**`AgentHooksAgent` (agent seam: `agent_startup`/`input`/`output`/`agent_shutdown`, per-run `AsyncLocal` state, buffered streaming, persistence gate) → framework function-invocation middleware (`pre_tool_call`/`post_tool_call`) → `ChatClientAgent` with its default pipeline → `AgentHooksChatClient`**below**`FunctionInvokingChatClient` (so `pre_model_call`/`post_model_call` bracket every model service call of the tool loop individually).
- **Indivisibility:** the seam decorators are `internal`; only the factory composes them. Two pipeline-replacement affordances of `ChatClientAgent` are rejected loudly (fail closed): a caller-supplied per-run `ChatClientFactory` (the framework's own function-middleware factory is recognized and allowed — it wraps, not replaces), and a supplied chat client that already contains a `FunctionInvokingChatClient` (it would execute tools below the verdicts).
- **Verdict-before-durability:** end-of-run history and context-provider writes defer behind the `output` verdict via gating provider wrappers installed by the factory (dropped on deny, flushed post-transform with verdicted-message substitution for streamed runs). The implicit default `InMemoryChatHistoryProvider` is materialized and gated, with the history-conflict flags set to mimic implicit-default semantics. Per-service-call persistence sits above the chat seam, so it is covered by its own `post_model_call` verdict. Per-run provider overrides are wrapped in both `AdditionalProperties` dictionaries, copy-on-write. Nested agents persist inline at their own boundaries (they have their own providers) — no run-identity bookkeeping is needed, unlike Python.
- **Fail-closed error behavior:** interceptor crashes/timeouts surface as `host_error:*` denies; enforcement-layer failures at the tool seam halt the run through `FunctionInvocationContext.Terminate` (the loop's only loud escape — thrown exceptions are converted to tool errors by the loop, which would fail open); wire projections run inside the guarded blocks; failure notifications to providers are redacted (empty request messages) once a deny/halt stands.
- **Streaming:** fully buffered per the spec's `buffered_output` semantics — zero egress ahead of a verdict; transformed responses re-derive the released updates (preserving continuation tokens) so egress never diverges from verdicted content.
### Considered Alternatives
- **Port Python's middleware-value model (a `MiddlewareBundle` type):** rejected — .NET has no middleware list to put a bundle into; indivisibility via runtime validation is weaker than construction ownership.
- **Core-framework persistence gate (as Python added in `_sessions.py`):** rejected — unnecessary in .NET; construction ownership of the provider instances gives the same property with zero core changes.
- **Per-run `ChatClientFactory` as the chat-seam install point:** rejected — it wraps the whole pipeline above the function-invocation loop, so per-model-call points would be impossible.
## Consequences
- Good: zero existing-source changes; the optional native dependency is isolated in one leaf package; enforcement properties are structural rather than convention-based.
- Accepted: the package ships in the release solution filter as an **alpha** package (maintainer decision on the PR) — the version suffix follows the maturity of the `ResponsibleAI.AgentHooks` dependency it is built on, and the whole surface stays `[Experimental]`; a sample follows once the API shape settles.
- Known limitations (documented on the factory): hosted (service-executed) tools never reach the function seam and are intercepted via the `post_model_call` content projection; service-managed (conversation-id) history is durable at the service and ungateable; the deferred-OTel decorator sits above the chat seam, so sensitive-data request spans observe pre-transform content; a chat-seam projection failure fails the run closed but without a synthesized `host_error` record (SDK affordance gap, responsibleai/agent-hooks#70).
- The trust model is the spec's: cooperative contract, not a security boundary — the misuse rejections catch accidental foot-guns loudly, not in-process adversaries.
FIDES now secures remote MCP integration end-to-end:
- **Tool labels from hints**: `apply_mcp_security_labels(...)` maps MCP hints (`readOnlyHint`, `openWorldHint`) to FIDES tool properties.
- **Safe sink defaults**: tools not explicitly marked `readOnlyHint=True` are treated as potential sinks and receive `max_allowed_confidentiality=public`.
- **Result labels from metadata**: MCP result `_meta` is propagated via `__mcp_result_meta__`; `_meta.ifc` is parsed into `security_label` per result item.
- **`SecureMCPToolProxy` convenience**: wraps MCP tools/URLs and applies this labeling automatically on connect.
This behavior is used with the GitHub MCP server when `X-MCP-Features: ifc_labels` is passed, which causes the server to return IFC labels in `_meta` (for example `{"ifc": {"integrity": "untrusted", "confidentiality": "public"}}`).
- Official docs (Microsoft Learn): <https://learn.microsoft.com/agent-framework/integrations/azure-functions>
## Document structure
| File | Purpose |
| --- | --- |
| `README.md` | Main technical overview: architecture, hosting models, orchestration patterns, and links to samples. |
| `durable-agents-ttl.md` | Deep-dive on session Time-To-Live (TTL) configuration and behavior. |
Add new sibling documents when a topic is too detailed for the README (e.g., a new feature like reliable streaming or MCP tool exposure). Keep the README focused on orientation and link out to siblings for depth.
## Writing guidelines
- **Audience**: Developers already familiar with the Microsoft Agent Framework who want to understand what durability adds and how to use it.
- **Host-agnostic first**: Durable agents work in console apps, Azure Functions, and any Durable Task–compatible host. Show host-agnostic patterns (plain orchestration functions, `IServiceCollection` registration) before Azure Functions–specific patterns. Avoid giving the impression that Azure Functions is the only hosting option.
- **Both languages**: Always include C# and Python examples side by side. Keep them equivalent in functionality.
- **Callout syntax**: Use GitHub-flavored callouts (`> [!NOTE]`, `> [!IMPORTANT]`, `> [!WARNING]`) rather than bold-text callouts (`> **Note:** ...`).
- **Line length**: Do not wrap long lines. Rely on text viewers / renderers for line wrapping.
- **Tables**: Use spaces around pipes in separator rows (`| --- |` not `|---|`).
- **Code snippets**: Keep them minimal and self-contained. Omit boilerplate (using statements, environment variable reads) unless the snippet is specifically about setup.
- **Cross-references**: Link to Microsoft Learn for conceptual background (Durable Entities, Durable Task Scheduler, Azure Functions). Link to sibling docs within this directory for feature deep-dives.
## Linting
Run markdownlint on all documents before committing, with line-length checks disabled:
Durable Task and Azure Functions integrations for Microsoft Agent Framework are now maintained in the [Durable Agent Framework extension repository](https://github.com/microsoft/agent-framework-durable-extension).
Durable agents extend the standard Microsoft Agent Framework with **durable state management** powered by the Durable Task framework. An ordinary Agent Framework agent runs in-process: its conversation history lives in memory and is lost when the process ends. A durable agent persists conversation history and execution state in external storage so that sessions survive process restarts, failures, and scale-out events.
| Capability | Ordinary agent | Durable agent |
| --- | --- | --- |
| Conversation history | In-memory only | Durably persisted |
| Failure recovery | State lost on crash | Automatically resumed |
| Multi-instance scale-out | Not supported | Any worker can resume a session |
| Human-in-the-loop | Must keep process alive | Can wait days/weeks with zero compute |
| Hosting | Any process | Console app, Azure Functions, or any Durable Task–compatible host |
> [!NOTE]
> For a step-by-step tutorial and deployment guidance, see [Azure Functions (Durable)](https://learn.microsoft.com/agent-framework/integrations/azure-functions) on Microsoft Learn.
## How durable agents work
Durable agents are implemented on top of [Durable Entities](https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-entities) (also called "virtual actors"). Each **agent session** maps to one entity instance whose state contains the full conversation history. When you send a message to a durable agent, the following happens:
1. The message is dispatched to the entity identified by an `AgentSessionId` (a composite of the agent name and a unique session key).
2. The entity loads its persisted `DurableAgentState`, which includes the complete conversation history.
3. The entity invokes the underlying `AIAgent` with the full conversation history, collects the response, and appends both the request and the response to the state.
4. The updated state is persisted back to durable storage automatically.
Because the entity framework serializes access to each entity instance, concurrent messages to the same session are processed one at a time, eliminating race conditions.
### Agent session identity
Every durable agent session is identified by an `AgentSessionId`, which has two components:
- **Name**– the registered name of the agent (case-insensitive).
- **Key**– a unique session key (case-sensitive), typically a GUID.
The session ID is mapped to an underlying Durable Task entity ID with a `dafx-` prefix (e.g., `dafx-joker`). This naming convention is consistent across both .NET and Python implementations.
## Architecture
### .NET
The .NET implementation consists of two NuGet packages:
| Package | Purpose |
| --- | --- |
| `Microsoft.Agents.AI.DurableTask` | Core durable agent types: `DurableAIAgent`, `AgentEntity`, `DurableAgentSession`, `AgentSessionId`, `DurableAgentsOptions`, and the state model. |
| `Microsoft.Agents.AI.Hosting.AzureFunctions` | Azure Functions hosting integration: auto-generated HTTP endpoints, MCP tool triggers, entity function triggers, and the `ConfigureDurableAgents` extension method on `FunctionsApplicationBuilder`. |
Key types:
- **`DurableAIAgent`** – A subclass of `AIAgent` used *inside orchestrations*. Obtained via `context.GetAgent("agentName")`, it routes `RunAsync` calls through the orchestration's entity APIs so that each call is checkpointed.
- **`DurableAIAgentProxy`** – A subclass of `AIAgent` used *outside orchestrations* (e.g., from HTTP triggers or console apps). It signals the entity via `DurableTaskClient` and polls for the response.
- **`AgentEntity`** – The `TaskEntity<DurableAgentState>` that hosts the real agent. It loads the registered `AIAgent` by name, wraps it in an `EntityAgentWrapper`, feeds it the full conversation history, and persists the result.
- **`DurableAgentSession`** – An `AgentSession` subclass that carries the `AgentSessionId`.
- **`DurableAgentsOptions`** – Builder for registering agents and configuring TTL.
### Python
The core Python implementation is in the `agent-framework-durabletask` package (`python/packages/durabletask`). Azure Functions hosting (including `AgentFunctionApp`) is in the separate `agent-framework-azurefunctions` package (`python/packages/azurefunctions`).
Key types:
- **`DurableAIAgent`** – A generic proxy (`DurableAIAgent[TaskT]`) implementing `SupportsAgentRun`. Returns a `TaskT` from `run()` — either an `AgentResponse` (client context) or a `DurableAgentTask` (orchestration context, must be `yield`ed).
- **`DurableAIAgentWorker`** – Wraps a `TaskHubGrpcWorker` and registers agents as durable entities via `add_agent()`.
- **`DurableAIAgentClient`** – Wraps a `TaskHubGrpcClient` for external callers. `get_agent()` returns a `DurableAIAgent[AgentResponse]`.
- **`DurableAIAgentOrchestrationContext`** – Wraps an `OrchestrationContext` for use inside orchestrations. `get_agent()` returns a `DurableAIAgent[DurableAgentTask]`.
- **`AgentEntity`** – Platform-agnostic agent execution logic that manages state, invokes the agent, handles streaming, and calls response callbacks.
## Hosting models
### Azure Functions
The recommended production hosting model. A single call to `ConfigureDurableAgents` (C#) or `AgentFunctionApp` (Python) automatically:
- Registers agent entities with the Durable Task worker.
- Generates HTTP endpoints at `/api/agents/{agentName}/run` for each registered agent.
- Supports `thread_id` query parameter / JSON field and the `x-ms-thread-id` response header for session continuity.
- Supports fire-and-forget via the `x-ms-wait-for-response: false` header (returns HTTP 202).
For self-hosted or non-serverless scenarios, register durable agents via `IServiceCollection.ConfigureDurableAgents` (.NET) or `DurableAIAgentWorker` (Python) with explicit Durable Task worker and client configuration.
**C# example:**
```csharp
IHost host = Host.CreateDefaultBuilder(args)
.ConfigureServices(services =>
{
services.ConfigureDurableAgents(
options => options.AddAIAgent(agent),
workerBuilder: b => b.UseDurableTaskScheduler(connectionString),
clientBuilder: b => b.UseDurableTaskScheduler(connectionString));
Durable agents can be composed into deterministic, checkpointed workflows using Durable Task orchestrations. The orchestration framework replays orchestrator code on failure, so completed agent calls are not re-executed.
### Patterns
| Pattern | Description |
| --- | --- |
| **Sequential (chaining)** | Call agents one after another, passing outputs forward. |
| **Parallel (fan-out/fan-in)** | Run multiple agents concurrently and aggregate results. |
| **Conditional** | Branch orchestration logic based on structured agent output. |
| **Human-in-the-loop** | Pause for external events (approvals, feedback) with optional timeouts. |
### Using agents in orchestrations
Inside an orchestration function, obtain a `DurableAIAgent` via the orchestration context. Each agent gets its own session (created with `CreateSessionAsync` / `create_session`), and you can call the same agent multiple times on the same session to maintain conversation context across sequential invocations.
# Get a durable agent reference — works in any host (standalone worker, Azure Functions, etc.)
writer = agent_ctx.get_agent("WriterAgent")
# Create a session to maintain conversation context across multiple calls
session = writer.create_session()
# First call: generate an initial draft
draft = yield writer.run(
messages="Write a concise inspirational sentence about learning.",
session=session,
)
# Second call: refine the draft — the agent sees the full conversation history
refined = yield writer.run(
messages=f"Improve this further while keeping it under 25 words: {draft.text}",
session=session,
)
return refined.text
```
> [!IMPORTANT]
> In .NET, `DurableAIAgent.RunAsync<T>` deliberately avoids `ConfigureAwait(false)` because the Durable Task Framework uses a custom synchronization context — all continuations must run on the orchestration thread.
## Streaming and response callbacks
Durable agents do not support true end-to-end streaming because entity operations are request/response. However, **reliable streaming** is supported via response callbacks:
- **`IAgentResponseHandler`** (.NET) or **`AgentResponseCallbackProtocol`** (Python) – Implement this interface to receive streaming updates as the underlying agent generates them (e.g., push tokens to a Redis Stream for client consumption).
- The entity still returns the complete `AgentResponse` after the stream is fully consumed.
- Clients can reconnect and resume reading from a cursor-based stream (e.g., Redis Streams) without losing messages.
See the **Reliable Streaming** samples for a complete implementation using Redis Streams.
## Session TTL (Time-To-Live)
Durable agent sessions support automatic cleanup via configurable TTL. See [Session TTL](durable-agents-ttl.md) for details on configuration, behavior, and best practices.
## Observability
When using the [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/durable-task-scheduler) as the durable backend, you get built-in observability through its dashboard:
- **Conversation history**– View complete chat history for each agent session.
- **Orchestration visualization**– See multi-agent execution flows, including parallel branches and conditional logic.
The durable agents automatically maintain conversation history and state for each session. Without automatic cleanup, this state can accumulate indefinitely, consuming storage resources and increasing costs. The Time-To-Live (TTL) feature provides automatic cleanup of idle agent sessions, ensuring that sessions are automatically deleted after a period of inactivity.
## What is TTL?
Time-To-Live (TTL) is a configurable duration that determines how long an agent session state will be retained after its last interaction. When an agent session is idle (no messages sent to it) for longer than the TTL period, the session state is automatically deleted. Each new interaction with an agent resets the TTL timer, extending the session's lifetime.
## Benefits
- **Automatic cleanup**: No manual intervention required to clean up idle agent sessions
- **Cost optimization**: Reduces storage costs by automatically removing unused session state
- **Resource management**: Prevents unbounded growth of agent session state in storage
- **Configurable**: Set TTL globally or per-agent type to match your application's needs
## Configuration
TTL can be configured at two levels:
1. **Global default TTL**: Applies to all agent sessions unless overridden
2. **Per-agent type TTL**: Overrides the global default for specific agent types
Additionally, you can configure a **minimum deletion delay** that controls how frequently deletion operations are scheduled. The default value is 5 minutes, and the maximum allowed value is also 5 minutes.
> [!NOTE]
> Reducing the minimum deletion delay below 5 minutes can be useful for testing or for ensuring rapid cleanup of short-lived agent sessions. However, this can also increase the load on the system and should be used with caution.
### Default values
- **Default TTL**: 14 days
- **Minimum TTL deletion delay**: 5 minutes (maximum allowed value, subject to change in future releases)
### Configuration examples
#### .NET
```csharp
// Configure global default TTL and minimum signal delay
services.ConfigureDurableAgents(
options =>
{
// Set global default TTL to 7 days
options.DefaultTimeToLive = TimeSpan.FromDays(7);
// Add agents (will use global default TTL)
options.AddAIAgent(myAgent);
});
// Configure per-agent TTL
services.ConfigureDurableAgents(
options =>
{
options.DefaultTimeToLive = TimeSpan.FromDays(14); // Global default
The following sections describe how TTL works in detail.
### Expiration tracking
Each agent session maintains an expiration timestamp in its internally managed state that is updated whenever the session processes a message:
1. When a message is sent to an agent session, the expiration time is set to `current time + TTL`
2. The runtime schedules a delete operation for the expiration time (subject to minimum delay constraints)
3. When the delete operation runs, if the current time is past the expiration time, the session state is deleted. Otherwise, the delete operation is rescheduled for the next expiration time.
### State deletion
When an agent session expires, its entire state is deleted, including:
- Conversation history
- Any custom state data
- Expiration timestamps
After deletion, if a message is sent to the same agent session, a new session is created with a fresh conversation history.
## Behavior examples
The following examples illustrate how TTL works in different scenarios.
### Example 1: Agent session expires after TTL
1. Agent configured with 30-day TTL
2. User sends message at Day 0 → agent session created, expiration set to Day 30
3. No further messages sent
4. At Day 30 → Agent session is deleted
5. User sends message at Day 31 → New agent session created with fresh conversation history
### Example 2: TTL reset on interaction
1. Agent configured with 30-day TTL
2. User sends message at Day 0 → agent session created, expiration set to Day 30
3. User sends message at Day 15 → Expiration reset to Day 45
4. User sends message at Day 40 → Expiration reset to Day 70
5. Agent session remains active as long as there are regular interactions
## Logging
The TTL feature includes comprehensive logging to track state changes:
- **Expiration time updated**: Logged when TTL expiration time is set or updated
- **Deletion scheduled**: Logged when a deletion check signal is scheduled
- **Deletion check**: Logged when a deletion check operation runs
- **Session expired**: Logged when an agent session is deleted due to expiration
- **TTL rescheduled**: Logged when a deletion signal is rescheduled
These logs help monitor TTL behavior and troubleshoot any issues.
## Best practices
1. **Choose appropriate TTL values**: Balance between storage costs and user experience. Too short TTLs may delete active sessions, while too long TTLs may accumulate unnecessary state.
2. **Use per-agent TTLs**: Different agents may have different usage patterns. Configure TTLs per-agent based on expected session lifetimes.
3. **Monitor expiration logs**: Review logs to understand TTL behavior and adjust configuration as needed.
4. **Test with short TTLs**: During development, use short TTLs (e.g., minutes) to verify TTL behavior without waiting for long periods.
## Limitations
- TTL is based on wall-clock time, not activity time. The expiration timer starts from the last message timestamp.
- Deletion checks are durably scheduled operations and may have slight delays depending on system load.
- Once an agent session is deleted, its conversation history cannot be recovered.
- TTL deletion requires at least one worker to be available to process the deletion operation message.
# Python protocol helpers and optional execution state
## Scope
This specification is the Python implementation plan for [ADR-0027](../decisions/0027-hosting-channels.md). It documents the simplified v1 host/channel contract only.
This specification is the Python implementation plan for
[ADR-0027](../decisions/0027-hosting-channels.md). It documents the helper-first v1 contract for Python hosting.
The v1 contract is:
- `AgentFrameworkHost` owns one Starlette app, one hostable target, and one or more channels.
- A hostable target is either a `SupportsAgentRun`-compatible agent or a `Workflow`.
- Channels contribute routes, middleware, commands, and lifecycle callbacks.
- Channels parse protocol-native input into `ChannelRequest`.
- Channels render their own originating response.
- Session continuity is explicit: a channel supplies `ChannelSession(isolation_key=...)`, and the host resolves/caches an `AgentSession` for that key.
- The host invokes `ChannelRunHook` and `ChannelResponseHook`; channels provide hook configuration and protocol context.
The host does not link identities, route responses to other channels, run background continuations, or multicast in v1. Those enhancements are tracked in [ADR-0028](../decisions/0028-hosting-linking-multicast-enhancements.md).
- protocol packages expose helper functions that convert protocol-native input to Agent Framework run values;
- protocol packages expose helper functions that convertAgent Framework run results or streams back to protocol-native
| `agent-framework-hosting-a2a` | `agent_framework_hosting_a2a` | A2A`Message` to run conversion and Agent Framework output to A2A `Part` conversion. |
| `agent-framework-hosting-mcp` | `agent_framework_hosting_mcp` | Agent and workflow MCP tool adapters, MCP tool arguments to run conversion, and Agent Framework output to MCP `ContentBlock` conversion. |
| `agent-framework-hosting-responses` | `agent_framework_hosting_responses` | Responses helpers: request parsing, session id extraction, response id creation, response rendering, streaming rendering. |
| `agent-framework-hosting-telegram` | `agent_framework_hosting_telegram` | Telegram Bot API helpers: update parsing, chat/session/command/media extraction, final rendering, and streaming edit rendering. |
| Future protocol packages | e.g. `agent_framework_hosting_activity_protocol` | Protocol-specific helpers such as `activity_to_run(...)`, `activity_from_run(...)`, `activity_session_id(...)`, and command/media helpers when useful. |
Channel packages may depend on their native SDKs. The core hosting package should not depend on channel SDKs or on top-level legacy protocol hosts.
The core hosting package must not depend on protocol SDKs. Protocol packages may depend on their native protocol SDKs if
needed, but helper functions should stay usable from plain app code and tests.
## Key Types
## Helper naming and families
### `AgentFrameworkHost`
Helper names are protocol-specific. Avoid a generic `protocol_to_run(...)` public surface.
The host constructor accepts:
Protocol packages may provide the following helper families when the protocol has the concept:
- `target`: one `SupportsAgentRun`-compatible object or one `Workflow`
- `channels`: one or more `Channel` instances
- optional Starlette middleware
- optional `state_dir`
- optional workflow `checkpoint_location`
| Helper family | Shape | Purpose |
| --- | --- | --- |
| Run conversion | `<protocol>_to_run(...)` | Convert one protocol-native call/update/request into `Agent.run` or `Workflow.run` values. |
| Final rendering | `<protocol>_from_run(...)` | Convert a final `AgentResponse` or workflow result into protocol-native response payloads or operations. |
| Stream rendering | `<protocol>_from_streaming_run(...)` | Convert `ResponseStream` or workflow updates into protocol-native events or operations. |
| Session id extraction | `<protocol>_session_id(...)` | Extract the protocol's natural continuation/partition key from the call, if present. |
| Command/action parsing | `<protocol>_command(...)` | Parse a protocol-native command/action/operation name without deciding app policy. |
The host exposes:
Examples:
- `app`: the canonical Starlette ASGI application
-`serve(...)`: a convenience wrapper for local serving
- `reset_session(isolation_key: str)`: rotate the cached `AgentSession` for a host-tracked conversation
It is not a store for identity links, continuations, active-channel state, delivery attempts, or multicast payloads.
## `agent-framework-hosting` state helpers
Externally supplied isolation keys are trusted only after the channel or host middleware has authenticated and authorized the caller. The host uses `isolation_key` as a partition key; the string itself is not proof of identity or ownership.
### `SessionStore`
### `Channel`
A channel implements a small protocol:
- declare a stable channel id/name,
- contribute routes, middleware, commands, and lifecycle callbacks,
- parse inbound protocol data into `ChannelRequest`,
- call the host through `ChannelContext.run(...)` or `ChannelContext.run_stream(...)`, and
- serialize the returned result to the originating protocol response.
Channels own protocol authentication, signature validation, native command registration, and protocol-specific error bodies.
### `ChannelContribution`
`ChannelContribution` is the channel's host-facing contribution:
- Starlette routes and optional middleware,
- native command descriptors,
- startup and shutdown callbacks, and
- any channel-local metadata needed by the package.
The host aggregates contributions but does not interpret protocol payloads.
### `ChannelRequest`
`ChannelRequest` is the host-neutral request envelope produced by a channel. It carries:
- target input,
- optional `ChannelSession`,
- optional `ChannelIdentity`,
- options and attributes produced by the channel, and
- request metadata useful to hooks and context providers.
The host may pass attributes through to context providers and middleware. Channels should treat attributes as a documented extension bag, not as a cross-channel delivery contract.
### `ChannelSession`
`ChannelSession(isolation_key=...)` is the only v1 session-continuity mechanism.
When a request contains an isolation key:
1. The host looks up or creates the cached `AgentSession` for that key.
2. The target runs with that `AgentSession` when the target is an agent.
3. `reset_session(isolation_key)` rotates the alias so the next request starts a new conversation.
If two channels produce the same isolation key on the same host, they share the same cached session. If they produce different keys, they do not share session state.
### `ChannelIdentity`
`ChannelIdentity` is optional request metadata such as channel id, native user id, tenant id, claims, or display attributes.
In v1, `ChannelIdentity` does not link channels, authorize callers, select delivery destinations, or imply that two identities should share an `AgentSession`. A channel that wants shared history must still produce the same `ChannelSession.isolation_key`.
### Hooks
Hooks are optional and channel-owned:
- `ChannelRunHook`: runs after channel parsing and before host invocation; returns the `ChannelRequest` to execute.
- `ChannelResponseHook`: runs after target completion and before the originating channel renders a one-shot response.
- `ChannelStreamUpdateHook`: the host applies it to streamed updates before the originating channel serializes the stream.
Common uses include adapting chat text into workflow inputs, enforcing deployment-specific options, flattening rich output for text-only protocols, or filtering streamed updates for a protocol. Stream update hooks are update-only; they do not automatically sanitize `get_final_response()` output. Channels choose their response transport from the parsed protocol request before invoking run hooks.
### `HostedRunResult`
`HostedRunResult[T]` wraps the target's full-fidelity result plus the resolved `AgentSession | None`.
- Agent targets produce `HostedRunResult[AgentResponse]`.
- Workflow targets produce `HostedRunResult[WorkflowRunResult]`.
The host does not flatten, filter, or translate the result. Each channel decides how much of the result its protocol can carry.
## Host Behavior
1. `AgentFrameworkHost` builds one Starlette app and asks each channel for its contribution.
2. A channel route receives a protocol-native request.
3. The channel validates/parses the native payload and creates `ChannelRequest`.
4. The channel passes the request, optional `ChannelRunHook`, and protocol-native context to the host.
5. The host invokes `ChannelRunHook`, if configured, and receives the prepared request.
6. The host resolves an `AgentSession` from `ChannelSession.isolation_key` when present.
7. The host invokes the agent or workflow target.
8. The host wraps the result in `HostedRunResult` or the streaming equivalent.
9. The host invokes `ChannelResponseHook`, if configured, for non-streaming/final response shaping.
10. The host applies stream update hooks while the channel consumes streams; the channel renders the originating protocol response.
There is no host-level route from one channel's request to another channel's response in v1.
## Workflow Checkpoints
Workflow checkpointing is explicit. Apps either configure checkpoint storage on the workflow itself or pass a `checkpoint_location` to the host so the workflow dispatch path can use the intended file location.
`state_dir` may provide a conventional location for workflow checkpoint files, but checkpointing is still opt-in and separate from agent session history. Checkpoints are workflow-runtime state, not channel state and not identity-link state.
## Foundry Isolation Middleware
V1 keeps Foundry isolation as middleware rather than as a channel-linking feature.
The middleware is installed only when the Foundry hosting environment flag is present. In that environment it reads Foundry-provided isolation values at the trusted hosting boundary, exposes them as read-only request context for Foundry-aware history or memory providers, and rejects unsafe session resumes when the live isolation context does not match persisted session context. Outside Foundry, raw isolation headers are ignored unless an app supplies its own trusted middleware.
This middleware does not create cross-channel identity links and does not authorize non-Foundry channels.
## Current Channels
### Responses
`ResponsesChannel` exposes the OpenAI-compatible Responses API shape. It maps request body fields such as input, options, and conversation identifiers into `ChannelRequest`, and it renders Responses-compatible one-shot or streaming responses.
Responses session continuity uses a channel-selected `isolation_key`, commonly derived from a response/conversation id, caller-provided session id, Foundry isolation context, or deployment-specific request metadata.
### Invocations
`InvocationsChannel` exposes an invocation endpoint for server-side callers and tools. It maps the request body into `ChannelRequest` and renders the invocation result on the same HTTP response.
Invocations is useful for typed workflow inputs because a `ChannelRunHook` can translate the request body into the workflow's expected input type.
### Telegram
`TelegramChannel` supports webhook or polling transport, native command registration, and message rendering back to the originating Telegram chat.
The channel chooses a default `isolation_key` from Telegram-native data such as chat id, user id, or a configured user/chat scope. A `/new` or equivalent command may call `reset_session` for that isolation key.
### Activity Protocol
`ActivityChannel` supports Activity Protocol requests, typically through Azure Bot Service for Teams, Web Chat, and other Bot Framework-fronted surfaces.
The channel maps incoming `Activity` objects to `ChannelRequest` and renders a reply activity to the originating conversation. Proactive Activity delivery, active-channel routing, and all-linked fan-out are not v1 host semantics.
### Discord
`DiscordChannel` supports Discord messages, slash commands, and interactions as channel-native input.
The channel maps Discord-native user, guild, channel, thread, and interaction data into `ChannelRequest` metadata and a configured `ChannelSession.isolation_key`. It renders the result to the originating Discord response path.
The hook adapts channel-native input to the workflow's typed input. Checkpoints use the explicit workflow checkpoint location, not identity-link or delivery storage.
- `apply_feature_token(user_agent: str) -> str` — live, idempotent UA stamper
used by first-party request hooks.
- `FEATURE_MASK_DISABLED_ENV_VAR` constant — the dedicated mask-only opt-out env
var name (`AGENT_FRAMEWORK_FEATURE_MASK_DISABLED`).
Each package also adds a private package-local `FeatureIndex` declaration for
the rows it owns. The dedicated mask-only opt-out and Python's existing
whole-User-Agent opt-out gate the Python mask; see [Opt-out](#opt-out).
Behavioural change to existing API:
- `get_user_agent()` / `prepend_agent_framework_to_user_agent()` are
**unchanged** — they keep returning the base UA with no `feat=` token. The
token is added only by first-party request hooks via
`apply_feature_token()`.
No breaking changes: when the mask is empty or disabled, for any non-first-party
client, or for an injected client outside the supported-hook set, output is
byte-for-byte identical to today.
## Opt-out
The dedicated mask-only opt-out is shared by both SDKs. Python also retains its
pre-existing whole-User-Agent opt-out:
| Env var | SDKs | Effect |
| --- | --- | --- |
| `AGENT_FRAMEWORK_FEATURE_MASK_DISABLED` | Python and .NET | disables **only** the feature mask; the base `agent-framework-<lang>/{version}` User-Agent is still sent |
| `AGENT_FRAMEWORK_USER_AGENT_DISABLED` | Python (existing behavior) | disables the **entire** Python AF User-Agent contribution, mask included |
The flags accept `true`/`1` (case-insensitive). The dedicated flag lets a
privacy-conscious user keep contributing the SDK identity/version (useful for
support and compat triage) while withholding the feature-usage signal. The mask
is also disabled implicitly whenever Python's whole User-Agent is disabled. A
new whole-User-Agent opt-out for .NET is outside this design.
## E2E example
```python
from agent_framework import Agent
from agent_framework_foundry import FoundryChatClient
from agent_framework_openai import OpenAIChatClient
# First-party (Foundry) client: request hook stamps the live feat token.
When `load_messages=False`, no history is replayed and the history filter is intentionally not invoked. Callers
that manually replay messages own the equivalent rule: do not resend an approval response after its terminal result.
## Normative contract
### Function calls and results
- Every actionable local `function_call` produces exactly one terminal `function_result`, unless execution pauses
for a new user-input request or the run is aborted by `MiddlewareFailure`.
- An ordinary exception raised by function middleware or a tool body becomes one terminal error `function_result`
and the loop continues; `MiddlewareFailure` is the loop's only fail-closed escape: it is never converted into a
tool result, the in-flight parallel batch is cancelled, no further tool call starts, no further model turn is
consumed, and the exception propagates to the caller (for streaming runs, when the stream is consumed). On a
service-managed conversation the loop first settles the aborted batch — one error `function_result` per dangling
call (approval-response wrappers unwrap to their underlying calls; hosted-tool approvals are left to their own
provider protocol), submitted with `tool_choice="none"` in a single extra request — so the hosted thread is not
left ending in unresolved function calls that the service would reject on the session's next request; the
persisted continuation then advances to the settlement response (for response-ID continuations the settled
endpoint is the new handle; for conversation-object ids the advance is a no-op) and the settlement response is
otherwise discarded. Settlement covers the approval-resolution phase too: a fatal abort while an approved tool is
replayed settles the original, already-persisted calls. Without a service-managed conversation no extra request
is made. Batch
cancellation is cooperative: an async sibling stops at its next suspension point, while a synchronous tool body
already executing in a worker thread cannot be interrupted and may complete its side effects — its result is
discarded either way and never reaches the transcript, the model, or history. Middleware must not catch
`MiddlewareFailure` — swallowing it converts a fail-closed abort back into a running, possibly unguarded loop.
- Parallel calls retain model order in the returned transcript.
- Reused `call_id` values are correlated by logical occurrence, not one global value per id.
- A completed function call/result pair is inert on later turns.
- Informational-only and declaration-only calls are not executed as local tools.
### Reasoning-bound calls
- Reasoning content or opaque reasoning metadata that a service binds to a function call is part of the same logical
group as that call and its terminal result.
- Active function loops preserve the reasoning content, function call, function result, and final assistant output
in caller-visible responses.
- Framework-managed/stateless replay includes the service-required reasoning representation before the paired call.
- Service-managed continuation may omit inline reasoning/call items only when the hosted service already owns them.
- Missing non-reconstructable reasoning fails explicitly before a provider request instead of silently dropping the
content.
- Foundry clients do not request `reasoning.encrypted_content` implicitly; callers may opt in explicitly when the
selected deployment supports encrypted reasoning.
- Compaction preserves or excludes the complete reasoning/call/result group atomically.
### Approval request and resume
- A tool that requires approval does not execute before an approved response.
- With an `AgentSession`, every surfaced local or hosted approval request is stored as an immutable snapshot in one
active model batch. A new surfaced batch replaces an abandoned batch instead of accumulating session state.
- Approval request IDs use the provider function `call_id`, whose conversation-level uniqueness is required for
function-call/result correlation. Duplicate request IDs within one batch are rejected as malformed.
- An inbound response is honored only when its request id matches the pending server-held snapshot.
- Approval requests replayed in inbound message history do not create, replace, or resurrect approval authority.
- The executable call id, tool name, arguments, and local or hosted tool metadata are sourced from the recorded
request, never from the response payload.
- A matched approval response consumes its pending entry once. Unmatched, duplicate, and replayed responses do not
reach local execution.
- Tool lookup uses the recorded name against the current registry. A same-name implementation upgrade is allowed;
removing the name prevents local execution.
- Only the strict boolean `True` grants approval. Missing decisions and non-boolean values are rejection, not consent.
- Direct chat-client invocation without an `AgentSession` preserves pass-through compatibility, matching .NET;
authorization sinks still require strict `True`.
- An approved tool executes exactly once.
- A rejected tool executes zero times and produces one synthetic rejection `function_result` using the original
function `call_id`.
- The resumed response contains the newly resolved approved and rejected terminal results before any final assistant
message.
- Streaming yields the same logical result content and ordering as non-streaming output and
`ResponseStream.get_final_response()`.
- The function invocation layer normalizes a private copy of caller messages. It must not mutate the caller's
approval `Message`, approval `Content`, or an earlier returned response.
- Approval-time `UserInputRequiredException` and `MiddlewareTermination` return immediately without another model
call.
### Approval control content
- `function_approval_request` and `function_approval_response` are control-plane contents, not durable model
transcript items.
- A current hosted approval response must be sent once on the immediate resume request.
- AG-UI removes a local approval response from its request and snapshot replay when a terminal result belongs to an
already-consumed occurrence, including result-before-response replay. A client-authored result in the occurrence
that is still registered as pending does not prove completion: AG-UI removes that result, keeps the validated
response for local execution, and leaves hosted approval responses as provider protocol data.
- Hosted AG-UI approval interrupts expose an accept/reject decision only; argument edits are rejected because the
hosted provider executes the server-owned request rather than client-edited arguments.
- AG-UI tool approval resumes accept the standard `approved` decision and full-replacement `editedArgs` payload.
Existing MAF clients remain compatible through the `accepted` decision alias and direct partial argument edits.
- An AG-UI `cancelled` resume is a valid terminal decision, not a run error. In a resume covering parallel open
interrupts, resolved siblings still execute and cancelled calls do not. An identical cancellation retry during
the retained terminal window also completes normally without restoring authority.
- AG-UI Approval State capacity is enforced independently for each trusted application scope. Abandoned pending
authority expires after its configured window, and indeterminate execution records remain non-retryable until
their separate safety window permits reclamation. Reclamation never recreates approval authority.
- A server-issued approval request must not be replayed inline during service-side continuation.
- History providers may retain approval control contents in their backing store for audit, but base history replay
filters them before later model calls.
- Callers that manually own and replay message history without a loading `HistoryProvider` must likewise omit a
previously submitted approval response from later continuation requests.
### History and continuation
- Model-bound history contains one function call/result pair per completed logical occurrence.
- Append-only history must not replay stale approval request/response wrappers to the model.
- Framework-managed and service-managed continuation must preserve the same logical call/result transcript.
- A streaming response rebuilt from updates by an intermediate middleware must carry over the inner response's
conversation id and its internal-conversation-id marker, so framework-managed continuation appends only the latest
message instead of replaying a transcript the provider already holds. The rebuilt response mirrors the inner
conversation id exactly, including clearing it, and never retains an id emitted by an earlier service call in the
same turn.
- A trusted terminal result consumes the corresponding approval authority in explicit stateless replay; a result in a
server-registered pending occurrence cannot consume that authority before local execution.
## Scenario-to-test matrix
### Normal function invocation
| Scenario | Required invariant | Primary regression test |
|---|---|---|
| Single non-streaming call | Call, result, and final assistant message are returned in order. | `packages/core/tests/core/test_function_invocation_logic.py::test_base_client_with_function_calling` |
| String input | Flexible string input follows the same loop behavior. | `test_base_client_with_function_calling_string_input` |
| Multiple sequential rounds | Each round retains one call/result pair. | `test_base_client_with_function_calling_resets` |
| Streaming call | Call chunks, one result update, and final text are emitted in order. | `test_base_client_with_streaming_function_calling` |
| Reasoning-bound call | Finalized output retains reasoning, function call, function result, and final text. | `test_streaming_function_calling_response_includes_reasoning_and_tool_results` |
| Calls across response messages | Every actionable call is executed once. | `test_base_client_executes_function_calls_across_multiple_response_messages` |
| Parallel calls | Results retain the corresponding call ids and execution count. | `test_max_function_calls_limits_parallel_invocations`, `test_streaming_multiple_function_calls_parallel_execution` |
| Informational-only call | The call is returned but not executed or approved. | `test_informational_only_function_call_is_not_invoked`, `test_informational_only_function_call_does_not_request_approval`, `test_streaming_informational_only_function_call_is_not_invoked` |
| Declaration-only call | The call is surfaced as user input and is not executed; streaming arguments appear once while finalized request metadata remains available. | `test_declaration_only_tool`, `test_streaming_declaration_only_tool_preserves_metadata_without_duplicate_arguments` |
| Function invocation disabled | The client bypasses the invocation loop without losing invocation kwargs. | `test_function_invocation_config_enabled_false`, `test_function_invocation_config_enabled_false_preserves_invocation_kwargs`, `test_streaming_function_invocation_config_enabled_false` |
| Runtime tool changes | Added tools become available on the next iteration and retain approval behavior. | `test_add_tools_available_next_iteration`, `test_add_tools_with_approval_required_tool` |
### Approval pause and resume
| Scenario | Required invariant | Primary regression test |
|---|---|---|
| Initial approval request | Assistant response contains the original call and approval request; tool does not execute. | `test_approval_requests_in_assistant_message`, `test_streaming_approval_request_generated`, `test_streaming_approval_requests_in_assistant_message` |
| Approved non-streaming resume | Result precedes final text; tool executes once; inputs remain unchanged. | `packages/core/tests/core/test_harness_tool_approval.py::test_approval_resume_returns_result_without_mutating_inputs[non-streaming-approved]` |
| Rejected non-streaming resume | Rejection result precedes final text; tool executes zero times; inputs remain unchanged. | `test_approval_resume_returns_result_without_mutating_inputs[non-streaming-rejected]` |
| Approved streaming resume | Result update precedes final text and final response matches non-streaming shape. | `test_approval_resume_returns_result_without_mutating_inputs[streaming-approved]`, `test_streaming_approval_resume_yields_terminal_result_before_model_text[approved]` |
| Rejected streaming resume | Rejection result update precedes final text and tool executes zero times. | `test_approval_resume_returns_result_without_mutating_inputs[streaming-rejected]`, `test_streaming_approval_resume_yields_terminal_result_before_model_text[rejected]` |
| Mixed approved/rejected batch | Every call gets one correctly correlated terminal result. | `packages/core/tests/core/test_function_invocation_logic.py::test_rejected_approval` |
| Persisted approval replay | Resume executes with the prior call available. | `test_persisted_approval_messages_replay_correctly` |
| Hosted approval pass-through | Hosted requests/responses are bound to the recorded provider request and are not processed as local calls. | `test_hosted_tool_approval_response`, `test_hosted_mcp_approval_response_passthrough`, `test_session_approval_binding_reconstructs_hosted_response`, `test_mixed_local_and_hosted_approval_flow` |
| Approval-time user input | Every user-input request from one approved execution returns in order with assistant role and no extra model call; the execution consumes one call-budget unit. | `packages/core/tests/core/test_harness_tool_approval.py::test_approval_resume_returns_all_user_input_requests_without_another_model_call`, `packages/core/tests/core/test_function_invocation_logic.py::test_approval_resume_user_input_counts_toward_function_call_budget` |
| Mixed terminal result and follow-up input | Completed siblings remain tool-role while only follow-up input requests use assistant-role messages/updates. | `packages/core/tests/core/test_function_invocation_logic.py::test_approval_resume_separates_terminal_results_from_follow_up_requests`, `packages/openai/tests/openai/test_openai_chat_completion_client.py::test_mixed_approval_resume_roles_serialize_function_result_as_tool` |
| Approval-time middleware termination | Terminal result returns with no extra model call in either response mode. | `packages/core/tests/core/test_function_invocation_logic.py::test_approval_resume_honors_middleware_termination` |
| Approval re-entry after iteration budget | Pending approved calls resolve once even when prior model calls consumed `max_iterations`. | `packages/core/tests/core/test_harness_tool_approval.py::test_auto_approval_resolves_after_iteration_budget_is_exhausted` |
| Approval resume with reasoning | Model-bound resume history retains reasoning before the call and terminal result in both modes. | `packages/core/tests/core/test_harness_tool_approval.py::test_approval_resume_replays_reasoning_with_function_call_group` |
| Session-bound substituted response | A response is rebound to the immutable recorded call and cannot replace its call id, tool name, or arguments. | `packages/core/tests/core/test_function_invocation_logic.py::test_session_approval_binding_rebinds_consumes_and_rejects_duplicates` |
| Truthy non-boolean decision | Strings, integers, null, and other non-booleans do not authorize execution. | `packages/core/tests/core/test_function_invocation_logic.py::test_session_approval_binding_treats_truthy_non_boolean_as_rejection`, `packages/core/tests/core/test_types.py::test_function_approval_response_deserialization_rejects_non_boolean_decisions`, `packages/ag-ui/tests/ag_ui/test_message_adapters.py::test_function_approval_requires_real_boolean`, `packages/ag-ui/tests/ag_ui/test_approval_result_event.py::test_resolve_approval_responses_treats_non_boolean_decision_as_rejection` |
| Active batch replacement | A newly surfaced model batch replaces abandoned approval authority instead of growing session state. | `packages/core/tests/core/test_function_invocation_logic.py::test_session_approval_binding_replaces_abandoned_batch` |
| Duplicate request id | Ambiguous request IDs within one active batch fail explicitly. | `packages/core/tests/core/test_function_invocation_logic.py::test_session_approval_batch_rejects_duplicate_request_ids` |
| Tool registry changes | Same-name upgrades may execute the recorded operation; removing the recorded name executes nothing. | `packages/core/tests/core/test_harness_tool_approval.py::test_approval_resume_allows_same_name_tool_upgrade`, `test_approval_resume_does_not_execute_when_recorded_tool_disappears` |
### Approval correlation and replay
| Scenario | Required invariant | Primary regression test |
|---|---|---|
| Result matching without placeholders | Results match calls by id even when the result list is reordered. | `test_replace_approval_contents_with_results_uses_result_call_ids_without_placeholders` |
| Reused id after completion | A later round with the same id creates a second valid pair. | `test_replace_approval_contents_with_results_allows_reused_call_id_after_completion` |
| Replayed approval wrapper | A duplicated wrapper does not restore another function call. | `test_replace_approval_contents_with_results_deduplicates_replayed_approval_request` |
| Historical resolved response plus new round | The old response is removed from normalized input and is not converted into a rejection result. | `test_replace_approval_contents_with_results_ignores_already_resolved_response` |
| Multiple reused-id rounds | Approved and rejected rounds retain separate call/result occurrences. | `test_replace_approval_contents_with_results_correlates_reused_call_id_occurrences` |
| Multi-content result with reused id | Every content produced by one execution stays with that approval occurrence and cannot bleed into the next reused-id round. | `test_replace_approval_contents_with_results_keeps_multi_content_group_with_reused_call_id` |
| Follow-up request closes one occurrence | A user-input follow-up consumes only the preceding approval authority and leaves a later reused-id response pending. | `test_collect_approval_responses_consumes_matching_follow_up_request_occurrence` |
| Rejected placeholder | Rejection replaces the pending placeholder instead of adding a second result. | `test_replace_approval_contents_with_results_replaces_rejected_placeholder` |
| Results reordered with placeholders | Results still match the correct call ids. | `test_replace_approval_contents_with_results_uses_result_call_ids_for_placeholders` |
| Missing result call id | A malformed result does not steal another approval's result. | `test_replace_approval_contents_with_results_skips_results_without_call_id` |
| Empty approval message cleanup | Fully consumed approval messages are removed from normalized model input. | `test_replace_approval_contents_with_results_prunes_emptied_messages` |
| Later stateless turn | A prior terminal approval response cannot execute again. | `test_resolved_approval_response_is_inert_on_later_stateless_turn` |
| Unbound or duplicate response | A response with no pending session request is removed; one request authorizes at most one response. | `test_session_approval_binding_rebinds_consumes_and_rejects_duplicates` |
| Forged inbound request history | A caller-supplied request wrapper cannot replace the server snapshot or resurrect consumed authority. | `test_session_approval_binding_does_not_trust_inbound_request_history` |
| Pending history turn | An unresolved approval batch is omitted atomically from unrelated model input while a later decision can still resume it once. | `packages/core/tests/core/test_harness_tool_approval.py::test_pending_approval_from_file_history_stays_resumable_without_model_orphan` |
| Duplicate function-call prevention | Approval normalization does not create a second call for one round. | `test_no_duplicate_function_calls_after_approval_processing` |
| Rejection call id | Rejection result uses the function call id, not only the approval id. | `test_rejection_result_uses_function_call_id` |
### Mixed batches and approval middleware
| Scenario | Required invariant | Primary regression test |
|---|---|---|
| Safe and approval-required calls in one batch | Hidden safe calls replay only with the matching visible approval. | `packages/core/tests/core/test_harness_tool_approval.py::test_mixed_batch_hides_already_approved_request_until_approval_replay` |
| Unrelated turn before approval | Hidden calls do not execute on an unrelated turn. | `test_hidden_mixed_batch_requests_do_not_replay_on_unrelated_turn` |
| Multiple abandoned batches | Hidden calls replay only for the matching batch. | `test_hidden_mixed_batch_requests_replay_only_for_matching_visible_approval` |
| Queued approvals | One unresolved approval is surfaced per run without premature execution. | `test_tool_approval_middleware_queues_multiple_approval_requests`, `test_tool_approval_middleware_queues_streamed_approval_requests` |
| Middleware state plus hidden core state | State saves do not discard hidden mixed-batch calls. | `test_tool_approval_middleware_preserves_hidden_mixed_batch_requests` |
| Auto-approval callback | Callback receives the original function call and executes the approved set once. | `test_tool_approval_middleware_auto_approval_rule_receives_function_call` |
| Shared call budget | Auto-approved re-entry does not reset `max_function_calls`, and every executed approval group counts even when it pauses for input. | `test_tool_approval_middleware_auto_approved_loops_share_function_call_budget`, `test_approval_resume_user_input_counts_toward_function_call_budget` |
| Standing tool rule | Tool-level approval applies only to later matching tools. | `test_tool_approval_middleware_always_approve_tool_rule` |
| Forged standing rule | An unbound or substituted hosted response cannot create a standing middleware approval rule for caller-selected metadata. | `test_tool_approval_middleware_drops_forged_standing_approval`, `test_tool_approval_middleware_rebinds_hosted_standing_approval` |
| Hosted server boundary | Standing approval does not cross `server_label`. | `test_tool_approval_middleware_standing_rules_include_hosted_server_boundary` |
| Argument-scoped rule | Exact arguments are required; empty arguments are not tool-wide. | `test_tool_approval_middleware_always_approve_tool_with_arguments_rule`, `test_tool_approval_middleware_empty_arguments_rule_is_not_tool_wide` |
| Provider-injected approval tool | A tool added during `before_run` defers to in-run resolution, executes once, and emits one result. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_deferred_provider_tool_executes` |
| AG-UI provider boundary | Completed local approval controls from AG-UI request and snapshot replay are absent from raw chat-client input while deferred and hosted approvals keep their respective in-run/provider paths. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_does_not_forward_resolved_local_approval_control_to_chat_client`, `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_deferred_provider_tool_executes`, `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_canonical_resume_preserves_hosted_approval_for_provider`, `packages/ag-ui/tests/ag_ui/test_run.py::test_filter_local_approval_responses_for_provider_removes_duplicate_completed_controls`, `packages/ag-ui/tests/ag_ui/test_run.py::test_filter_local_approval_responses_for_provider_pairs_reused_call_ids_by_occurrence`, `packages/ag-ui/tests/ag_ui/test_run.py::test_canonical_hosted_approval_resume_rejects_edited_arguments_without_mutating_pending` |
| AG-UI standard approval payload | Agent and workflow tool approvals emit canonical `tool_call` interrupts. `approved` plus full-replacement `editedArgs` executes once and replays idempotently, while legacy `accepted` plus direct partial edits remains supported. Hosted approvals remain decision-only. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_resume_entry_applies_standard_full_replacement_edited_args`, `test_endpoint_agent_approval_replayed_standard_edited_resume_is_idempotent`, `test_endpoint_agent_approval_resume_entry_applies_edited_arguments`, `test_workflow_endpoint_emits_canonical_tool_approval_interrupt`, `test_workflow_endpoint_accepts_canonical_tool_approval_resume`, `test_workflow_endpoint_applies_canonical_approval_edited_args`, `test_workflow_endpoint_accepts_legacy_partial_approval_edits`, `test_workflow_endpoint_hosted_approval_rejects_argument_edits` |
| AG-UI cancellation | A cancelled interrupt executes zero times and completes normally, including an identical retry during retained cancellation state; resolved siblings in the same complete resume still execute once. Workflow cancellation clears both runner correlation and the owning agent executor's pending request so later approvals remain resumable. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_cancelled_resume_entry_completes_without_execution`, `test_endpoint_agent_approval_replayed_cancellation_completes_idempotently`, `test_endpoint_agent_approval_mixed_cancelled_and_resolved_resume_executes_resolved_tool`, `test_endpoint_workflow_request_info_cancelled_resume_completes_normally`, `test_workflow_endpoint_cancelled_agent_approval_does_not_block_next_approval` |
| AG-UI shared workflow interrupt ownership | A direct shared `Workflow` request-info interrupt can only be resolved or cancelled by the Snapshot Scope and AG-UI thread that created it. Ownership follows the authoritative pending request occurrence, and explicitly threaded cold checkpoint resumes fail closed when ownership is unavailable. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_workflow_request_info_rejects_resume_from_different_thread`, `test_endpoint_workflow_request_info_rejects_resume_from_different_scope`, `test_endpoint_workflow_request_info_rejects_cancellation_from_different_thread`, `test_endpoint_workflow_request_info_remains_owned_after_client_disconnect`, `test_endpoint_workflow_request_info_rejects_unowned_pending_interrupt`, `test_endpoint_workflow_checkpoint_resume_rejects_threaded_resume_after_restart` |
| AG-UI approval retention and capacity | Pending authority expires automatically, indeterminate outcomes remain non-retryable until their safety window permits reclamation, and one trusted scope cannot consume another scope's occurrence quota. | `packages/ag-ui/tests/ag_ui/test_approval_lifecycle.py::test_abandoned_pending_occurrence_expires_and_releases_capacity`, `test_indeterminate_occurrence_is_reclaimed_after_its_safety_window`, `test_capacity_is_enforced_per_trusted_scope` |
| AG-UI local executor unavailable on resume | A claimed local occurrence whose executor disappeared releases its unstarted claim, reports temporary unavailability, and remains safely retryable. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_resume_remains_retryable_when_local_tool_is_temporarily_unavailable` |
| AG-UI forwarded execution interruption | A provider failure, cancellation, or stream close after forwarding an approval recovers the open occurrence as indeterminate when no idempotency key proves retry safety. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_hosted_approval_becomes_indeterminate_when_provider_stream_fails` |
### Errors, control flow, and limits
| Scenario | Required invariant | Primary regression test |
|---|---|---|
| Rejected execution | Rejection is a normal terminal result, not an exception to the caller. | `test_unapproved_tool_execution_raises_exception` |
| Approved tool exception | Generic and detailed error modes preserve one result and one execution. | `test_approved_function_call_with_error_without_detailed_errors`, `test_approved_function_call_with_error_with_detailed_errors` |
| Approved validation error | Validation failure returns one result without invoking the function body. | `test_approved_function_call_with_validation_error` |
| Consecutive error cap | Error threshold stops repeated failures, submits collected results, and makes only the required final no-tool model call. | `test_function_invocation_config_max_consecutive_errors`, `test_streaming_function_invocation_config_max_consecutive_errors`, `test_approval_resume_error_limit_forces_final_no_tool_response` |
| Middleware termination | Normal non-approval loop stops without a second model call. | `test_terminate_loop_single_function_call`, `test_terminate_loop_multiple_function_calls_one_terminates`, `test_terminate_loop_streaming_single_function_call` |
| Middleware failure (fatal) | `MiddlewareFailure` from function middleware or a tool body propagates to the caller without becoming a tool result; the tool does not execute (pre-invocation) or its result never feeds another model call (post-invocation); the cause chain is preserved; ordinary exceptions still become tool-error results and the loop continues. | `packages/core/tests/core/test_middleware_with_agent.py::TestMiddlewareFailure::test_failure_before_tool_aborts_run`, `test_failure_after_tool_aborts_run_before_next_model_turn`, `test_failure_cause_chain_reaches_caller`, `test_failure_from_tool_escapes_without_middleware`, `test_failure_streaming_reaches_stream_consumer`, `test_ordinary_exception_still_becomes_tool_error` |
| Middleware failure batch cancellation | A fatal signal fails the whole parallel batch: in-flight sibling tool invocations are cancelled and awaited before the failure propagates. Cancellation is cooperative — an async sibling stops at its next suspension point; a synchronous tool body already executing in a worker thread cannot be interrupted and may complete its side effects, but its result is discarded and never reaches the transcript, the model, or history, and failure propagation is not delayed behind it. | `TestMiddlewareFailure::test_failure_cancels_concurrent_sibling_tool`, `test_failure_with_sync_sibling_discards_late_result` |
| Middleware failure on a service-managed conversation | The continuation state is already persisted when the batch fails, so before propagating, the loop settles the hosted thread: one error `function_result` per dangling call, sent with `tool_choice="none"` in one extra request; the persisted continuation advances to the settlement response (required for response-ID continuations, a no-op for conversation-object ids) and the settlement response is otherwise discarded; a settlement failure never masks the abort. Without a service-managed conversation no extra request is made. | `TestMiddlewareFailure::test_failure_settles_dangling_calls_on_service_conversation`, `test_failure_settles_service_conversation_streaming`, `test_failure_settlement_advances_response_id_continuation`, `test_failure_without_service_conversation_makes_no_settlement_request` |
| Middleware failure during approved-tool replay | A fatal abort while the approval-resolution phase replays an approved tool escapes loudly (never absorbed into a rejection result), the tool's original — already service-persisted — call is settled the same way, and the continuation advances; both response modes. | `TestMiddlewareFailure::test_failure_during_approved_replay_settles_and_escapes`, `test_failure_during_approved_replay_streaming` |
| Maximum iterations | No orphan calls; a final no-tool response or deterministic fallback is returned. | `test_max_iterations_limit`, `test_max_iterations_no_orphaned_function_calls`, `test_max_iterations_makes_final_toolchoice_none_call`, `test_max_iterations_blank_final_fallback_synthesizes_message`, streaming equivalents |
| Maximum function calls | Parallel overshoot is bounded after the batch; every executed result group counts even without a `function_result`; blank final responses get fallback content. | `test_max_function_calls_limits_parallel_invocations`, `test_max_function_calls_single_calls_per_iteration`, `test_user_input_request_multiple_contents_propagate`, `test_approval_resume_user_input_counts_toward_function_call_budget`, `test_max_function_calls_blank_final_fallback_synthesizes_message`, streaming equivalent |
| Provider tool content after an active limit | Locally actionable calls and local approval requests returned despite `tool_choice="none"` are removed in both response modes. Provider-executed informational call/result pairs, hosted approval requests, and metadata-only streaming updates remain visible; fallback text never replaces retained transcript content. | `test_function_invocation_limit_drops_unexecutable_tool_content`, `test_streaming_function_invocation_limit_drops_unexecutable_tool_content`, `test_streaming_function_invocation_limit_preserves_metadata_after_tool_content_is_dropped`, `test_function_invocation_limit_preserves_provider_executed_tool_pair`, `test_streaming_function_invocation_limit_preserves_provider_executed_tool_pair`, `test_function_invocation_limit_appends_fallback_after_provider_executed_tool_pair`, `test_streaming_function_invocation_limit_appends_fallback_after_provider_executed_tool_pair`, `test_function_invocation_limit_preserves_hosted_approval_request`, `test_streaming_function_invocation_limit_preserves_hosted_approval_request` |
| Conversation continuation | Conversation id updates between iterations and is cleared on stop where required. | `test_conversation_id_updated_in_options_between_tool_iterations`, `test_function_invocation_stop_clears_conversation_id_non_stream`, `test_streaming_function_invocation_stop_clears_conversation_id` |
### History and provider serialization
| Scenario | Required invariant | Primary regression test |
|---|---|---|
| Append-only history replay | Resolved approval wrappers do not reach a later model call; one call/result pair remains. | `packages/core/tests/core/test_harness_tool_approval.py::test_approval_resume_filters_resolved_control_items_from_file_history` |
| Pending placeholder history | An approval response remains replayable while its only result is `[APPROVAL_PENDING]`. | `packages/core/tests/core/test_sessions.py::test_filter_approval_controls_keeps_response_for_pending_placeholder` |
| Pending hosted history replay | Stateless hosted approval requests remain replayable until a response is recorded, then both controls become inert. | `packages/openai/tests/openai/test_openai_chat_client.py::test_stateless_history_preserves_pending_hosted_approval_request_until_response` |
| Non-history provider plus session | Local history is still auto-injected for approval resume. | `packages/core/tests/core/test_agents.py::test_non_history_context_provider_still_injects_inmemory` |
| Hosted per-service-call persistence | A host-managed transcript remains available throughout a local function-call loop without being persisted into the framework session and replayed on the next hosted request. | `packages/foundry_hosting/tests/test_responses.py::TestAgentSessionPersistence::test_per_service_call_persistence_preserves_function_loop_history` |
| Streaming message injection with per-service-call persistence | A streaming response rebuilt from updates mirrors the inner conversation id exactly, including clearing it, and keeps its internal marker, so the next iteration appends only the latest message rather than replaying the whole turn on top of provider-held history, and never persists a conversation id from an earlier injected service call. | `packages/core/tests/core/test_middleware_with_chat.py::TestChatMiddleware::test_message_injection_middleware_streaming_preserves_inner_continuation_state`, `test_message_injection_middleware_streaming_keeps_service_conversation_id_external`, `test_message_injection_middleware_streaming_clears_conversation_id_when_final_call_has_none`, `test_message_injection_middleware_conversation_id_matches_across_streaming_modes`, `packages/core/tests/core/test_harness_agent.py::test_streaming_harness_tool_call_does_not_duplicate_transcript` |
| Service-side approval decision | Stored hosted request is skipped; the current approved or rejected hosted response is sent, while local approval controls are omitted from provider input. | `packages/openai/tests/openai/test_openai_chat_client.py::test_prepare_messages_strips_approval_request_but_keeps_response_under_storage`, `test_prepare_messages_drops_local_approval_controls` |
| OpenAI approval serialization | Hosted approval id and decision serialize to `mcp_approval_response`; local approvals remain in-process. | `test_prepare_message_for_openai_with_function_approval_response`, `test_prepare_content_for_opentool_approval_response`, `test_function_approval_response_with_mcp_tool_call` |
| Stored function call/result | Service-side storage drops server-issued calls but keeps new outputs. | `test_prepare_options_with_conversation_id_strips_server_issued_items`, `test_prepare_messages_for_openai_full_conversation_with_reasoning` |
| Stateless reasoning replay | Replay reconstructs reasoning, call, and result together; missing required reasoning fails before the request. | `test_tool_loop_store_false_replays_encrypted_reasoning_group`, `test_stateless_request_rejects_non_replayable_reasoning_bound_mcp_output`, `test_prepare_messages_for_openai_full_conversation_with_reasoning` |
| Foundry encrypted reasoning opt-in | Foundry clients omit `reasoning.encrypted_content` by default and preserve an explicit caller opt-in. | `packages/foundry/tests/foundry/test_foundry_chat_client.py::test_get_response_does_not_request_encrypted_reasoning_by_default`, `test_get_response_preserves_explicit_encrypted_reasoning_opt_in`, `packages/foundry/tests/foundry/test_foundry_agent.py::test_foundry_agent_basic_call_does_not_request_unsupported_encrypted_reasoning`, `test_foundry_agent_preserves_caller_requested_encrypted_reasoning`, `packages/foundry_hosting/tests/test_responses_int.py::TestReasoningHostedMcpReplay::test_second_turn_replays_mcp_call_with_encrypted_reasoning` |
| Opaque reasoning signature replay | Provider-specific opaque reasoning metadata is captured and restored on reconstructed calls. | `packages/gemini/tests/test_gemini_client.py::test_function_call_part_captures_thought_signature_as_reasoning_content`, `test_reconstructed_function_call_replays_thought_signature_from_reasoning_content` |
| Chat Completions approval wrappers | Framework approval wrappers are not sent as chat messages. | `packages/openai/tests/openai/test_openai_chat_completion_client.py` approval serialization tests |
| AG-UI approval result event | Approved result emits once with content and persists in snapshot. | `packages/ag-ui/tests/ag_ui/test_approval_result_event.py::test_approved_call_emits_one_live_result_under_original_identity`, `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_resume_persists_replayable_tool_results`, `test_endpoint_agent_approval_replayed_resume_entry_reprojects_retained_result` |
| AG-UI rejection/mixed decision | Transport emits only the events defined for approved and rejected calls without duplicates. | `packages/ag-ui/tests/ag_ui/test_approval_result_event.py::test_rejected_call_does_not_execute_or_emit_live_result`, `test_mixed_batch_preserves_approved_result_identity_and_order`, `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_agent_approval_rejection_releases_already_approved_sibling` |
| AG-UI approval-time follow-up | The full grouped user-input pause remains in message history and emits no synthetic `TOOL_CALL_RESULT`. | `packages/ag-ui/tests/ag_ui/test_approval_result_event.py::test_approval_follow_up_group_remains_in_history_without_live_tool_result` |
| AG-UI approval execution failure | A grouped executor failure becomes one deterministic terminal error result for the approved call. | `packages/ag-ui/tests/ag_ui/test_approval_result_event.py::test_approval_execution_failure_emits_one_terminal_error_result` |
| AG-UI no-approval path | Ordinary tool results do not gain an extra approval result event. | `packages/ag-ui/tests/ag_ui/test_approval_result_event.py::test_no_approval_path_emits_no_approval_specific_duplicate_result` |
| AG-UI client-tool request isolation | Client tool declarations are validated before use and remain request-scoped; a rejected collision or earlier successful request cannot change a later request's server-tool execution. | `packages/ag-ui/tests/ag_ui/test_endpoint.py::test_endpoint_failed_client_tool_collision_does_not_affect_next_request`, `test_endpoint_client_tools_do_not_persist_into_next_request` |
| AG-UI `confirm_changes` snapshot | An accepted synthetic confirmation is replaced only when its original function call has a real result; rejection is cleaned explicitly, and missing accepted results remain inert. | `packages/ag-ui/tests/ag_ui/test_confirm_changes_snapshot.py` |
| AG-UI malformed `confirm_changes` metadata | Non-list tool-call metadata and malformed argument JSON are ignored without guessing a target call. | `test_confirm_changes_target_ignores_non_list_tool_calls`, `test_confirm_changes_target_rejects_malformed_arguments_json` |
| Compaction pair integrity | Adjacent and non-adjacent pairs, including assistant-embedded results and completed reused-id occurrences, remain atomic without pairing ambiguous or out-of-order ids. | `packages/core/tests/core/test_compaction.py::test_group_annotations_keep_tool_call_and_tool_result_atomic`, `test_group_annotations_include_reasoning_in_tool_call_group`, `test_group_annotations_pair_nonadjacent_function_result_by_call_id`, `test_group_annotations_pair_multiple_nonadjacent_results_with_declaration`, `test_group_annotations_pair_completed_reused_call_id_occurrences`, `test_group_annotations_close_assistant_embedded_result_before_reused_call_id`, `test_sliding_window_does_not_retain_orphan_result_after_assistant_embedded_result`, `test_sliding_window_keeps_reused_call_id_occurrences_atomic`, `test_group_annotations_do_not_pair_ambiguous_duplicate_call_ids` |
## Required coverage gaps
These scenarios are required but are not fully covered by merged tests on `main`:
| Gap | Tracking |
|---|---|
| Service-owned `previous_response_id` continuation cannot execute a terminal approval again on a later turn. | #6851 |
Do not mark these rows covered by nearby tests; each needs a dedicated regression at the owning layer.
## Minimum validation commands
Run from `python/` for any core function-loop change:
```bash
uv run poe test -P core
uv run poe syntax -P core
uv run poe pyright -P core
uv run poe test-typing -P core
```
Also run every affected package. Common approval-loop changes require:
```bash
uv run poe test -P openai
uv run poe syntax -P openai
uv run poe pyright -P openai
uv run poe test-typing -P openai
uv run poe test -P ag-ui
uv run poe test -P declarative
uv run --directory packages/foundry_hosting poe test
```
Run focused regression files first while iterating, but do not substitute them for the full package commands above.
## Review checklist
Before accepting an update, reviewers must confirm:
- the changed behavior is represented in this specification;
- the matrix names a regression test for every affected scenario;
- approved tools cannot execute twice;
- rejected tools cannot execute;
- no call or result becomes orphaned or duplicated;
- call/result matching does not assume `call_id` is globally unique forever;
- reasoning content or opaque signatures remain in the same logical group as the paired call/result, or replay fails
explicitly before sending a lossy provider request;
- caller messages and previous responses remain immutable;
- streaming updates and final response agree with non-streaming output;
- history replay does not reintroduce approval authority;
- full package, syntax, source typing, and test typing checks were run.
## Related issues
- #7241 — approval-resolution result streaming
- #7522 — first-class fatal signal (`MiddlewareFailure`) for function middleware
- #7267 / #7271 and #7304 — replayed calls and reused ids
<PackageVersion Include="MessagePack" Version="3.1.7" /> <!-- Transitive dependency of Aspire pinned to newer version due to vulnerability in 2.5.192 -->
"The output should show the agent starting with only a RequestTools function and dynamically loading additional tools (weather, time, temperature) as needed.",
"The output should contain weather information for Seattle and London, the current time in New York, and a Fahrenheit-to-Celsius temperature conversion.",
"The output should demonstrate both non-streaming and streaming modes.",
"The output should not contain error messages or stack traces.",
"The output should show an agent using a shell tool to print the current working directory.",
"The output should demonstrate that in stateless mode side effects (such as changing directory) do not carry between calls, while in persistent mode the working directory and an environment variable (DEMO_TOKEN set to 'hello-world') carry across calls.",
"The output should include a captured environment snapshot describing the OS, shell, and working directory.",
"The output should not contain error messages or stack traces.",
"The output should show an agent planning a team offsite by breaking the work into a todo list.",
"The output should show the todo list being updated as progress is reported (for example marking items complete after the venue is booked and invites are sent) and adjusted when the plan changes to skip catering and add a group hike.",
"The current todo list should be printed after each turn, showing item status.",
"The output should not contain error messages or stack traces.",
"The output should acknowledge that the user is vegetarian and travels with a dog, indicating the agent stored these preferences.",
"The memory files section should list at least one memory file written by the agent, such as a file about the user's preferences.",
"The second conversation should recommend a hotel and a restaurant in Paris that are consistent with the remembered preferences, for example a pet-friendly hotel and a restaurant with vegetarian options, even though it is a new session.",
"The output should not contain error messages or stack traces.",
"Second session (recalling prior chat history from Cosmos DB):",
],
ExpectedOutputDescription=
[
"The output should contain two joke responses.",
"The first joke should be about a pirate (as explicitly requested).",
"The second joke should also be pirate-themed or similar to what the user likes, since chat history from the first session should be recalled from Cosmos DB.",
"The output should not contain error messages or stack traces.",
ExpectedOutputDescription=["The output should show a computer automation session processing simulated browser screenshots with iteration steps and a final response describing search results."],
Inputs=["My laptop won't start","The laptop is now working, thank you!"],
InputDelayMs=5000,
ExpectedOutputDescription=["The output should show a customer support workflow processing a laptop issue, with agent responses providing troubleshooting or support."],
},
@@ -405,26 +405,16 @@ internal static class WorkflowSamples
ExpectedOutputDescription=["The output should show a declarative workflow executing generated code, processing a math question and producing a result."],
ExpectedOutputDescription=["The output should show a workflow calling function tools (e.g. a menu plugin) to answer a question about restaurant specials."],
@@ -443,7 +433,7 @@ internal static class WorkflowSamples
ExpectedOutputDescription=["The output should show a workflow invoking a function tool (e.g. a menu plugin) to answer a question about the soup of the day."],
@@ -473,8 +463,8 @@ internal static class WorkflowSamples
Inputs=["How do I use Azure OpenAI with my data?"],
InputDelayMs=3000,
ExpectedOutputDescription=["The output should show a workflow using Foundry Toolbox MCP tools to search Microsoft Learn documentation and web search to provide a summary of results."],
@@ -484,8 +474,8 @@ internal static class WorkflowSamples
Inputs=["Search for .NET tutorials on Microsoft Learn"],
InputDelayMs=3000,
ExpectedOutputDescription=["The output should show a workflow using MCP tools to search Microsoft Learn documentation and provide a summary of results."],
@@ -495,8 +485,8 @@ internal static class WorkflowSamples
ExpectedOutputDescription=["The output should show a student-teacher workflow where a student asks a math question and a teacher provides the answer."],
@@ -517,8 +507,8 @@ internal static class WorkflowSamples
ExpectedOutputDescription=["The output should show a workflow using an MCP tool with approval to search Microsoft Learn, followed by an exit from the input loop."],
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