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Author SHA1 Message Date
Evan Mattson ae7fa3389c Python: Bump Python package versions for 1.14.0 release (#7661)
* 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
2026-08-14 11:06:35 +09:00
Evan Mattson 4aa737eee5 Python: [BREAKING] Require building functional workflow instances (#7521)
* 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
2026-08-14 00:31:52 +00:00
Evan Mattson 8c4da3c3b9 Python: Harden AG-UI approval lifecycle and resume semantics (#7594)
* 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
2026-08-14 00:28:14 +00:00
Evan Mattson 5fafa18569 Python: track agent-hooks feature usage (#7558) 2026-08-14 00:06:33 +00:00
Tao Chen ee27065359 Python: Update agentserver to x.1.0b1 (#7621)
* 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
2026-08-14 00:02:17 +00:00
Atharva Vichare 9645d33cde Python: Fix Cosmos memory provider calling renamed add_cosmos toolkit API (#7635)
* 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>
2026-08-13 21:25:38 +00:00
pratik wayase 9a06fa3f42 Python: fix(python): add release_session API to prevent BackgroundAgentsProvider memory leaks (#7450)
* fix: add release_session API to prevent BackgroundAgentsProvider memory leaks

* fix: address Copilot review comments on release_session

* fix(harness): make background agent session release race-safe and bounded

* fix (harness): address release_session and review feedback
2026-08-13 19:35:36 +00:00
westey 6d25fb1e9c Remove clear and package source mapping from nuget.config to allow user level config inheritance (#7646) 2026-08-13 16:09:41 +00:00
westey e926ad2859 Python: fix streaming transcript duplication with message injection and per-service-call persistence (#7605)
* Fix ordering issue when streaming with content injection and per-service-call persistence

* Update spec

* Address PR comment

* revert uv.lock changes
2026-08-13 15:04:58 +00:00
Giles Odigwe 7cfa905486 Python: scope under-specified approve-for-session permission decisions (#7607)
* 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
2026-08-13 01:13:19 +00:00
Ruiming Zhao 3221011427 fix(core): warn when advertised MCP archives are rejected (#7622) 2026-08-13 00:44:55 +00:00
Chinmay V 3aadac53c8 Python: fix(redis): honour a max_messages retention limit of zero (#7470)
* 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>
2026-08-13 00:44:20 +00:00
Ruiming Zhao 35c6b880f7 Python: Preserve Mistral prompt-cache usage details (#7597)
* Fix Mistral cached token usage

Map prompt cache hits from Mistral chat usage into the standard usage details. Add regression coverage for regular and streaming responses.

* Validate Mistral cached token usage

* fix(mistral): satisfy strict cached token typing

Narrow prompt token details before reading cached_tokens so the Mistral package passes strict Pyright without changing runtime validation.\n\nAddresses https://github.com/microsoft/agent-framework/pull/7597#discussion_r3750712320
2026-08-13 00:43:50 +00:00
Vaibhav Patel 3a5d00be54 Python: add checkpointing support to AgentFrameworkWorkflow.run() in agent-framework-ag-ui (#6646)
* 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>
2026-08-12 22:07:52 +00:00
westey 56d13bce4e .NET: Add BackgroundAgentsProvider.ReleaseSessionAsync to cancel and release per-session background tasks (#7602)
* 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>
2026-08-12 14:25:59 +00:00
Luis Rodriguez 27d82b1567 Python: Ignore non-project workspace glob matches (#7509)
* Python: Ignore non-project workspace glob matches

* test: collect workspace script tests

* test: remove standalone script test

---------

Co-authored-by: Luis Rodriguez <25299418+luisangelrod@users.noreply.github.com>
Co-authored-by: Luis Rodriguez <luis.rodriguez@bcpos.com>
2026-08-11 19:14:18 +00:00
Giles Odigwe 5e52c6a718 Python: Fix ClaudeAgent reusing one SDK client across distinct fresh sessions (#7404)
* 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
2026-08-11 19:04:08 +00:00
Giles Odigwe 30996433ac Python: Restore Gemini thought_signature on approval replays (#7546)
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
2026-08-11 19:03:57 +00:00
Tao Chen e85b3c8ba8 Python: Fix FHA session ID translation (#7608)
* Fix FHA session ID traslation

* Fix tests

* Address comments and fix tests

* Fix typing

* Show how to use user created sessions

* Update README
2026-08-11 17:30:56 +00:00
Roger Barreto 4ca093371e .NET: Add Options for Hosted Agent to Allow Backend Storage (#7572)
* 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
2026-08-11 16:22:26 +00:00
Peter Ibekwe 8a0731ad92 .NET: Prevent telemetry serialization failures from failing workflows (#7612)
* Prevent telemetry serialization failures from failing workflows

* Address PR comments
2026-08-11 15:16:40 +00:00
Peter Ibekwe 6fff2c9b1f Fix misleading workflow protocol attribute diagnostics (#7609) 2026-08-11 15:16:21 +00:00
Saurish 0d75365331 .NET: Add Cosmos NoSQL vector memory sample (#7552)
* .NET: Add Cosmos NoSQL vector memory sample

* Address Cosmos memory sample review feedback

* Fix Cosmos NoSQL memory sample build

---------

Co-authored-by: nos-redacted <nosxredacted@gmail.com>
2026-08-11 10:33:06 +00:00
Peter Ibekwe db979b616a Python: Improve Json parsing for declarative workflow (#7550)
* Json parsing improvement

* Fix PR comments

* Address PR comments.
2026-08-11 05:09:28 +00:00
Tao Chen d0a4165f17 [BREAKING] Python: Migrate FHA to responses==2.0.0b1 and add Foundry state store (#7533)
* Migrate FHA to responses==2.0.0b1 and add Foundry state store

* Fix session id error

* Fix tests

* Improve tests

* Fix copilot comments

* Address comments

* Revert sample changes

* Address comments

* Add ContextScopedStoreProvider

* Fix type check

* Fix type check

* Export ContextScopedStoreProvider
2026-08-10 05:51:59 +00:00
dependabot[bot] 4357ff5742 Bump postcss (#7529)
Bumps [postcss](https://github.com/postcss/postcss) from 8.5.22 to 8.5.25.
- [Release notes](https://github.com/postcss/postcss/releases)
- [Changelog](https://github.com/postcss/postcss/blob/main/CHANGELOG.md)
- [Commits](https://github.com/postcss/postcss/compare/8.5.22...8.5.25)

---
updated-dependencies:
- dependency-name: postcss
  dependency-version: 8.5.25
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-08-10 02:47:05 +00:00
dependabot[bot] 221f4b6df1 Bump postcss from 8.5.15 to 8.5.25 in /python/packages/devui/frontend (#7493)
Bumps [postcss](https://github.com/postcss/postcss) from 8.5.15 to 8.5.25.
- [Release notes](https://github.com/postcss/postcss/releases)
- [Changelog](https://github.com/postcss/postcss/blob/main/CHANGELOG.md)
- [Commits](https://github.com/postcss/postcss/compare/8.5.15...8.5.25)

---
updated-dependencies:
- dependency-name: postcss
  dependency-version: 8.5.25
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-08-10 02:46:59 +00:00
dependabot[bot] a9f7b2b788 Bump pyrefly from 1.1.1 to 1.2.0 in /python (#7541)
Bumps [pyrefly](https://github.com/facebook/pyrefly) from 1.1.1 to 1.2.0.
- [Release notes](https://github.com/facebook/pyrefly/releases)
- [Commits](https://github.com/facebook/pyrefly/compare/1.1.1...1.2.0)

---
updated-dependencies:
- dependency-name: pyrefly
  dependency-version: 1.2.0
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-08-10 02:44:59 +00:00
dependabot[bot] adcc3de654 Bump js-yaml from 4.3.0 to 4.3.1 in /python/packages/devui/frontend (#7554)
Bumps [js-yaml](https://github.com/nodeca/js-yaml) from 4.3.0 to 4.3.1.
- [Changelog](https://github.com/nodeca/js-yaml/blob/4.3.1/CHANGELOG.md)
- [Commits](https://github.com/nodeca/js-yaml/compare/4.3.0...4.3.1)

---
updated-dependencies:
- dependency-name: js-yaml
  dependency-version: 4.3.1
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-08-10 02:42:11 +00:00
dependabot[bot] 034f5fa119 Bump zuban from 0.9.0 to 0.9.1 in /python (#7545)
Bumps [zuban](https://github.com/zubanls/zubanls-python) from 0.9.0 to 0.9.1.
- [Release notes](https://github.com/zubanls/zubanls-python/releases)
- [Commits](https://github.com/zubanls/zubanls-python/compare/v0.9.0...v0.9.1)

---
updated-dependencies:
- dependency-name: zuban
  dependency-version: 0.9.1
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-08-10 02:41:55 +00:00
Evan Mattson 48e547506b Python: Make encrypted reasoning opt-in for Foundry chat (#7536)
* Python: Make Foundry encrypted reasoning opt-in

* Python: Opt hosted replay test into encrypted reasoning
2026-08-10 02:36:48 +00:00
Peter Ibekwe 5eb3eb745e Improve string parsing in declarative workflows (#7535) 2026-08-07 18:07:56 +00:00
SergeyMenshykh c987529df3 .NET: [BREAKING] Rename to AgentIsolationKeyProvider (#7567)
* 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
2026-08-07 14:59:07 +00:00
westey aaaa56bc60 .NET: Store executable function calls bypassed by declaration-only tool calls (#7388)
* 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>
2026-08-07 13:52:31 +00:00
Roger Barreto 18ceb182b1 .NET: Give a hosted agent a single source of conversation history (#7525)
* 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.
2026-08-07 10:02:23 +00:00
westey ec32e86646 .NET: Aggregate usage across looping agents and chat clients (#7539)
* 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>
2026-08-07 10:00:27 +00:00
SergeyMenshykh 94bbfb2ac8 .NET: Harden file skill discovery (#7540)
* .NET: Harden file skill discovery

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 8ff072fa-42d6-44b0-b226-2182cfc7639c

* .NET: Handle inaccessible skill directories

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 8ff072fa-42d6-44b0-b226-2182cfc7639c

---------

Copilot-Session: 8ff072fa-42d6-44b0-b226-2182cfc7639c
2026-08-07 09:53:56 +00:00
Giles Odigwe 4b1afd9052 Python: surface Gemini thought summaries as reasoning content (#7488)
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
2026-08-07 03:08:25 +00:00
Giles Odigwe 45c515b8a7 Python: fix CopilotStudioAgent LineTooLong on large activities (#7417)
* 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
2026-08-07 03:08:06 +00:00
Giles Odigwe b2a2fcbd87 Python: Add response/request customization hooks to OpenAIChatCompletionClient (#7028)
* 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
2026-08-07 02:51:06 +00:00
MohammadHaroonAbuomar 7302d0bf23 Python: agent-hooks interception contract as a first-class experimental core feature (#7515)
* 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>
2026-08-07 00:25:09 +00:00
westey 422160eabe Python: Add windows junction detection for skills (#7507)
* Add windows junction detection for skills

* Address PR comment
2026-08-06 09:08:37 +00:00
westey 5a1d96df67 Python: Separate mem0 storage and search scopes (#7531)
* Separate mem0 storage and search scopes

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-08-06 08:32:36 +00:00
Atharva Vichare 74a144085a .NET: Bound the tool-approval auto-approval loop (#7472) (#7474)
* .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>
2026-08-05 19:33:51 +00:00
Evan Mattson 594954700a Python: Fix AG-UI conversation correlation across runs (#7430)
* Add single agent AGUI sample

* Fix AG-UI conversation correlation across runs

* Address PR review and code quality feedback

* Correlate AG-UI chat spans across runs

---------

Co-authored-by: Tao Chen <taochen@microsoft.com>
2026-08-05 17:17:53 +00:00
SergeyMenshykh a4d4eafa5e Add CodeQL suppression comment for DevUI proxy validation (#7505)
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
2026-08-05 11:33:41 +00:00
SergeyMenshykh da056275e6 .NET: [Experimental] Extend A2A task store with isolation key scoping (#7504)
* .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
2026-08-05 11:33:11 +00:00
Peter Ibekwe 1da571860a Updating version for dotnet release 1.17.0 (#7514) 2026-08-04 21:04:06 +00:00
Peter Ibekwe d56e81357e Fail declarative workflows when an agent returns an error (#7497) 2026-08-04 17:23:49 +00:00
Evan Mattson 5f3ca8f93c Python: Fix AG-UI approval resume at the protocol boundary (#7480)
* Python: Fix Ollama approval resume message handling

* Python: Reject empty Ollama approval resume payload

* Python: Keep AG-UI approval controls out of provider input

* Python: Do not trust pending AG-UI tool results
2026-08-04 15:12:33 +00:00
Scarab Systems 4d3c7844d6 Python: Bound tool result compaction summaries (#7396)
* 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>
2026-08-04 15:05:02 +00:00
Eduard van Valkenburg 07511b80c9 Python: Prevent orphaned local approval responses (#7462)
* Python: Prevent orphaned local approval responses

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 0efaca91-a0a7-46f4-9b81-022385607fe4

* Python: Clarify approval serialization boundaries

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 0efaca91-a0a7-46f4-9b81-022385607fe4

---------

Copilot-Session: 0efaca91-a0a7-46f4-9b81-022385607fe4
2026-08-04 01:05:08 +00:00
CTW_CTWalk e84b5a07c1 Python: fix LocalEvaluator reporting zero-check items as passed (#7399)
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
2026-08-04 00:23:08 +00:00
Evan Mattson 84d5a5eec1 Consolidate Dependabot dependency updates (#7445)
* Bump AgentMemory from 1.2.0 to 1.3.0

---
updated-dependencies:
- dependency-name: AgentMemory
  dependency-version: 1.3.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* .NET: consolidate #7280 AgentMemory.AgentFramework 1.3.0

* Bump github/codeql-action/init from 4.37.0 to 4.37.3

Bumps [github/codeql-action/init](https://github.com/github/codeql-action) from 4.37.0 to 4.37.3.
- [Release notes](https://github.com/github/codeql-action/releases)
- [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/github/codeql-action/compare/99df26d4f13ea111d4ec1a7dddef6063f76b97e9...e4fba868fa4b1b91e1fdab776edc8cfbe6e9fb81)

---
updated-dependencies:
- dependency-name: github/codeql-action/init
  dependency-version: 4.37.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump astral-sh/setup-uv from 8.3.2 to 9.0.0

Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 8.3.2 to 9.0.0.
- [Release notes](https://github.com/astral-sh/setup-uv/releases)
- [Commits](https://github.com/astral-sh/setup-uv/compare/11f9893b081a58869d3b5fccaea48c9e9e46f990...c771a70e6277c0a99b617c7a806ffedaca235ff9)

---
updated-dependencies:
- dependency-name: astral-sh/setup-uv
  dependency-version: 9.0.0
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump github/codeql-action/analyze from 4.37.0 to 4.37.3

Bumps [github/codeql-action/analyze](https://github.com/github/codeql-action) from 4.37.0 to 4.37.3.
- [Release notes](https://github.com/github/codeql-action/releases)
- [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/github/codeql-action/compare/99df26d4f13ea111d4ec1a7dddef6063f76b97e9...e4fba868fa4b1b91e1fdab776edc8cfbe6e9fb81)

---
updated-dependencies:
- dependency-name: github/codeql-action/analyze
  dependency-version: 4.37.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump actions/cache from 5.0.5 to 6.1.0

Bumps [actions/cache](https://github.com/actions/cache) from 5.0.5 to 6.1.0.
- [Release notes](https://github.com/actions/cache/releases)
- [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md)
- [Commits](https://github.com/actions/cache/compare/27d5ce7f107fe9357f9df03efb73ab90386fccae...55cc8345863c7cc4c66a329aec7e433d2d1c52a9)

---
updated-dependencies:
- dependency-name: actions/cache
  dependency-version: 6.1.0
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump actions/checkout from 6.0.2 to 7.0.1

Bumps [actions/checkout](https://github.com/actions/checkout) from 6.0.2 to 7.0.1.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/de0fac2e4500dabe0009e67214ff5f5447ce83dd...3d3c42e5aac5ba805825da76410c181273ba90b1)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: 7.0.1
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump astral-sh/setup-uv in /.github/actions/python-setup

Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 8.3.2 to 9.0.0.
- [Release notes](https://github.com/astral-sh/setup-uv/releases)
- [Commits](https://github.com/astral-sh/setup-uv/compare/11f9893b081a58869d3b5fccaea48c9e9e46f990...c771a70e6277c0a99b617c7a806ffedaca235ff9)

---
updated-dependencies:
- dependency-name: astral-sh/setup-uv
  dependency-version: 9.0.0
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump ty from 0.0.60 to 0.0.64 in /python

Bumps [ty](https://github.com/astral-sh/ty) from 0.0.60 to 0.0.64.
- [Release notes](https://github.com/astral-sh/ty/releases)
- [Changelog](https://github.com/astral-sh/ty/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ty/compare/0.0.60...0.0.64)

---
updated-dependencies:
- dependency-name: ty
  dependency-version: 0.0.65
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump prek from 0.4.10 to 0.4.11 in /python

Bumps [prek](https://github.com/j178/prek) from 0.4.10 to 0.4.11.
- [Release notes](https://github.com/j178/prek/releases)
- [Changelog](https://github.com/j178/prek/blob/master/CHANGELOG.md)
- [Commits](https://github.com/j178/prek/compare/v0.4.10...v0.4.11)

---
updated-dependencies:
- dependency-name: prek
  dependency-version: 0.4.11
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump uv from 0.11.29 to 0.11.32 in /python

Bumps [uv](https://github.com/astral-sh/uv) from 0.11.29 to 0.11.32.
- [Release notes](https://github.com/astral-sh/uv/releases)
- [Changelog](https://github.com/astral-sh/uv/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/uv/compare/0.11.29...0.11.32)

---
updated-dependencies:
- dependency-name: uv
  dependency-version: 0.12.0
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump ruff from 0.15.22 to 0.16.0 in /python

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.15.22 to 0.16.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.15.22...0.16.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.16.0
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* update uv-build requirement in /python

---
updated-dependencies:
- dependency-name: uv-build
  dependency-version: 0.12.0
  dependency-type: direct:development
...

Signed-off-by: dependabot[bot] <support@github.com>

* Python: align workspace pins for #7436-#7439

* Python: support ty 0.0.64 diagnostics for #7436

* Python: apply Ruff 0.16 formatting for #7439

* Update workflow action version annotations

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-08-04 00:01:14 +00:00
Tao Chen 8d379168b2 Python: Improve python sample validation workflow (#7350)
* 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
2026-08-03 22:28:53 +00:00
Evan Mattson 18997c2fde Python: Give the AG-UI Thread Snapshot lifecycle a single owner module (#7479)
* 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.
2026-08-03 21:15:39 +00:00
Vaibhav Patel 5cc1b8e3c3 Python: Add hosted agent sample for the agent harness (#7010)
* 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>
2026-08-03 20:55:47 +00:00
Evan Mattson 9ce55cae00 Python: Remove dead AG-UI orchestration helpers and flatten subpackage (#7426)
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.
2026-08-03 20:46:05 +00:00
Evan Mattson 06c0fc2b10 Python: forward Azure AI Search query-source identity (#7278)
* Forward Azure AI Search query-source identity

* Address query source credential review feedback
2026-08-03 20:45:40 +00:00
Henry Su a74811edec fix(python): preserve falsey EditTableV2 items (#7380) 2026-08-03 16:54:29 +00:00
Peter Ibekwe 9c8151699a Fix Handoff orchestration sample not responding to user input (#7442) 2026-08-03 16:40:16 +00:00
NekoPunch f5dfb1413e Python: Add Mistral chat client (#7392)
* 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
2026-08-03 06:29:09 +00:00
Chris Gillum 43309018be .NET and Python: Extract Durable Task and Azure Functions integrations (#7465)
* Extract Durable Task and Azure Functions integrations

Remove the migrated implementations, samples, tests, documentation, and repository wiring now owned by microsoft/agent-framework-durable-extension. Preserve Python compatibility through the agent_framework.azure shim and agent-framework-core[all], and leave customer-facing redirects to the new repository.

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd

* Fix feature registry validation after extraction

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd

* Narrow external feature package paths

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd

---------

Copilot-Session: 6181dcf9-857b-43ea-9fd2-fcd6b175ffdd
2026-08-03 03:02:07 +00:00
Joshua Nwachinemere 10fe3c4c72 Python: Ignore excluded tool results during compaction (#7391)
* Python: Ignore excluded tool results during compaction

* fix: avoid extra compaction message pass
2026-08-03 01:38:21 +00:00
Chris Gillum c073ed9f74 docs: ADR-0032 — propose durable/Azure Functions repo extraction (#7247)
* 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
2026-07-31 19:43:26 +00:00
Giles Odigwe e39a8a2e79 Python: Bump Python package versions for 1.13.0 release (#7443)
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
2026-07-30 22:47:07 +00:00
Giles Odigwe 25ec4c3b5c Python: Support archive-type MCP skills (source, toolbox, sample) (#7121)
* 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
2026-07-30 20:57:51 +00:00
SergeyMenshykh 3ad861f0b2 reference code of conduct in readme (#4998)
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2026-07-30 18:39:05 +00:00
SergeyMenshykh 2aa267e028 .NET: Updating version for dotnet release 1.16.0 (#7441)
Co-authored-by: SergeyMenshykh <SergeMenshikh@outlook.com>
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 28ce674d-8c40-4d49-864c-5d02894fe762
2026-07-30 16:36:36 +00:00
Peter Ibekwe 6a3d535204 .NET: Add regression tests and sample guidance for stable agent IDs in checkpointed workflows (#7415)
* Add regression tests and sample guidance for stable agent IDs in checkpointed workflows

* Updated tests to address PR comments

* Improve test for checkpoint state.
2026-07-30 16:21:18 +00:00
westey 73f48d255e .NET: Add FileMemoryProvider sample to 02-agents/AgentWithMemory (#7401)
* Add FileMemoryProvider sample

* Address PR comments
2026-07-30 15:26:51 +00:00
Eduard van Valkenburg 28389df805 Python: Move SessionStore to core and persist Foundry Responses sessions (#7306)
* Python: Move session persistence into core

Move SessionStore and durable msgspec-backed storage into core, restore sessions in Foundry Responses hosting with per-user isolation, and document the serialization design.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Python: Address session persistence review feedback

Harden scoped file paths and corruption recovery, preserve session serialization compatibility, clarify dependency placement, and add reproducible benchmark evidence.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Python: Preserve session snapshot compatibility

Deep-copy in-memory session writes and retain existing Telegram session keys so stored conversations continue resolving.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Python: Simplify Foundry session isolation

Add experimental FoundrySessionStore backed by Agent Server request context, remove resolver plumbing, and centralize v2 user isolation for sessions, checkpoints, and approvals.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Python: Reduce Foundry session helper layering

Inline the single-use request user accessor while keeping separate context validation, fingerprint, and directory helpers for their distinct callers.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Python: Clarify Foundry request context validation

Separate fail-fast request validation from context retrieval so Responses no longer appears to discard a returned context.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Python: Share Foundry request context helpers

Move protocol validation and user-scope derivation into a dedicated request-context module, leaving the session-store module focused on storage.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Restore Foundry checkpoint storage paths

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Simplify Foundry session storage paths

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Persist Foundry sessions under hosted home

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Make hosted path test platform independent

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Address session persistence review feedback

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Isolate Foundry session path handling

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Clarify Foundry session path terminology

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Align Foundry sessions with Responses continuity

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Finalize Foundry Responses session persistence

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Add session store feature usage telemetry

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

* Fix hosted per-call history persistence

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c

---------

Copilot-Session: 3e5c81ad-75e8-4e92-a883-8bbd676c6c8c
2026-07-30 13:04:08 +00:00
HaoJun 143386fecc Python: fix(core): restrict unpickler module-prefix allowlist to types only (#5923)
* fix(core): harden restricted pickle attribute resolution

* fix(core): validate nested pickle types against allowlist

---------

Co-authored-by: White-Mouse <15983334+White-Mouse@users.noreply.github.com>
Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2026-07-30 10:46:15 +00:00
westey 47c8e29b64 Python: Add FileMemoryProvider context provider sample (#7428)
* Add FileMemoryProvider sample

* Address PR comments
2026-07-30 10:46:11 +00:00
Evan Mattson 51615d5468 Fix DevFlow review comment trigger (#7434)
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
2026-07-30 19:41:21 +09:00
Giles Odigwe 12b2893bac Python: Apply header_provider headers to the MCP initialize handshake and other ambient requests (#7305)
* 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
2026-07-30 10:31:11 +00:00
Yufeng He 93e8cb2de3 Python: make SerializationMixin.from_dict enforce the documented type check (#7256)
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>
2026-07-30 10:28:27 +00:00
Eduard van Valkenburg b64a2e2f82 Python: add feature-usage User-Agent telemetry (#7420)
* Python: add first-pass feature usage telemetry

Add the 128-bit feature accumulator, package-local indexes, activation markers, and destination-scoped User-Agent emission for the initial Python implementation slice.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: track declarative feature usage

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: complete feature usage telemetry

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: report core version in User-Agent

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: configure Lab telemetry import path

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: preserve telemetry transport behavior

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: preserve caller-owned Foundry transports

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

* Python: remove stale Anthropic test import

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f

---------

Copilot-Session: 346bf168-b668-4c4a-a8db-a67282ee5e5f
2026-07-30 10:24:34 +00:00
Yufeng He 962b86ddbb Python: preserve model emission order in AG-UI MESSAGES_SNAPSHOT (#7239)
* 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>
2026-07-30 10:19:40 +00:00
Tao Chen 80eb2570c7 Remove indices in FHA sample names (#7405) 2026-07-30 10:17:26 +00:00
Dineshsuriya D d42c78c8cf Python: Add GitHub Copilot BYOK sample (#7336)
* 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>
2026-07-30 10:15:17 +00:00
dependabot[bot] d9e1990484 Bump postcss (#7315)
Bumps [postcss](https://github.com/postcss/postcss) from 8.5.15 to 8.5.23.
- [Release notes](https://github.com/postcss/postcss/releases)
- [Changelog](https://github.com/postcss/postcss/blob/main/CHANGELOG.md)
- [Commits](https://github.com/postcss/postcss/compare/8.5.15...8.5.23)

---
updated-dependencies:
- dependency-name: postcss
  dependency-version: 8.5.23
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-07-30 10:06:27 +00:00
dependabot[bot] fa7cc021c6 Bump postcss (#7314)
Bumps [postcss](https://github.com/postcss/postcss) from 8.5.15 to 8.5.23.
- [Release notes](https://github.com/postcss/postcss/releases)
- [Changelog](https://github.com/postcss/postcss/blob/main/CHANGELOG.md)
- [Commits](https://github.com/postcss/postcss/compare/8.5.15...8.5.23)

---
updated-dependencies:
- dependency-name: postcss
  dependency-version: 8.5.23
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-07-30 10:06:05 +00:00
Thota Sai Karthik 4d67eefa5f Python: Fix FoundryAgent inheriting OPENAI_CHAT_MODEL for agent-reference requests (#7283)
* 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>
2026-07-30 10:05:33 +00:00
Scarab Systems 32928e645b Python: Bound summarization input before provider call (#7375)
* 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>
2026-07-30 10:01:57 +00:00
Evan Mattson d07edffaed Python: Fix Actions token environment (#7427)
* 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
2026-07-30 18:57:23 +09:00
Nadjib Attig 59fe8bedeb Python: Sanitize author_name for the Chat Completions message name field (#7127)
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>
2026-07-30 09:47:57 +00:00
Yufeng He 31d6af1447 Python: fix Anthropic streaming double-counting token usage (#7162)
* 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
2026-07-30 09:45:21 +00:00
Chinedum Echeta ae6923c8b1 Python: feat(observability): add support for OpenAI cache write tokens in usage details (#7369)
* feat(observability): add support for OpenAI cache write tokens in usage details

* feat(openai): add cache write tokens handling in usage details

* Fix test
2026-07-30 09:12:30 +00:00
Eduard van Valkenburg 99dcf3c133 Python: Preserve declaration-only streaming metadata (#7409)
* Python: Preserve declaration-only streaming metadata

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Chore: retrigger PR checks

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Reconcile remaining function-loop spec gaps

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
2026-07-30 09:11:51 +00:00
Eduard van Valkenburg 95ec5b7d36 Python: Preserve approval decisions under OpenAI continuation (#7407)
* Python: Preserve approval decisions under OpenAI continuation

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Chore: retrigger PR checks

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
2026-07-30 08:29:05 +00:00
Eduard van Valkenburg 0937233d86 Python: Remove tool content returned after invocation limits (#7408)
* Python: Remove tool content returned after invocation limits

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Chore: retrigger PR checks

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Preserve provider-owned content after limits

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Isolate post-limit spec update

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
2026-07-30 08:27:57 +00:00
Eduard van Valkenburg 572a9621bd Python: Keep call and result occurrences atomic in compaction (#7406)
* Python: Keep call and result occurrences atomic in compaction

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Clarify ambiguous compaction reannotation

Document why incremental reannotation retains all prior duplicate candidates and strengthen the regression that keeps ambiguous results unpaired without changing existing groups.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Handle assistant-embedded compaction results

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
2026-07-30 07:58:07 +00:00
Evan Mattson df723d768f Update GH Actions workflows (#7424)
* 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
2026-07-30 16:59:28 +09:00
Eduard van Valkenburg e344f456ae Python: Correlate AG-UI confirm_changes snapshots by call id (#7411)
* Python: Correlate AG-UI confirm_changes snapshots by call id

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Chore: retrigger PR checks

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Require real results for accepted confirmations

Keep accepted confirm_changes snapshot payloads inert unless approval resolution produced a matching function result, while retaining explicit rejection cleanup.

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
2026-07-30 07:08:03 +00:00
Eduard van Valkenburg e18a64569c Python: Defer provider-injected approvals to in-run execution (#7410)
* 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
2026-07-30 06:48:36 +00:00
pratik wayase ce5ee8a9c7 Python: fix(foundry-hosting): root hosted checkpoints under durable home dire… (#7220)
* 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>
2026-07-30 00:35:19 +00:00
Giles Odigwe 4a7af303af Bump .NET SDK from 10.0.301 to 10.0.302 (#7376)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 63ac3381-c9ca-47af-b0e7-9a09c0a5b2be
2026-07-29 20:44:42 +00:00
Eduard van Valkenburg 5987a6791b Python: Improve function approval resume and replay (#7345)
* Python: Harden function approval resume and replay

Make approval resume immutable and occurrence-aware, return grouped approved and rejected results consistently, preserve pending approval history without model-orphaned calls, and align streaming, non-streaming, and AG-UI result boundaries.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1

* Python: Clarify function invocation orchestration

Simplify approval-resolution setup and add phase-level comments around the key function invocation orchestration paths.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1
EOF && git push origin python-approval-resume-contract

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1ee1d250-d1e2-4c6f-8c36-aae0d94fe7a1
2026-07-29 20:01:42 +00:00
Tao Chen 0e6a104192 [BREAKING] Python: Allow workflow checkpoint full replayability (#7374)
* 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
2026-07-29 19:25:35 +00:00
Binit Mohanty ce2208c62c Python: Fix OpenAIChatCompletionClient passing raw JSON-Schema dict response_format through unwrapped (#7199)
* 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>
2026-07-29 16:35:02 +00:00
Roger Barreto 0ca6a3e652 .NET: Add source (ZIP) deploy oriented hosted agent samples (#7372)
* 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
2026-07-29 15:34:54 +00:00
Peter Ibekwe 5543bc94fc Fix and re-enable flaky InputWaiter timeout test (#7377) 2026-07-29 15:24:54 +00:00
Eduard van Valkenburg 5f84917f15 docs: ADR-0033 feature-usage bitmask in the User-Agent (#6500)
* 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
2026-07-29 14:37:13 +00:00
Yufeng He 33bc9c063c Python: extract keywords from non-English text for topic selection (#7130)
_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.
2026-07-29 02:34:58 +00:00
Alexander Nachtmann 8d22bb9177 .NET: Add Anthropic-backed live tests for OpenAI Responses hosting helpers (#7362)
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>
2026-07-28 21:12:42 +00:00
Dineshsuriya D ad20ab009b .NET: Add GitHub Copilot BYOK sample (#7337)
* .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>
2026-07-28 20:48:57 +00:00
Peter Ibekwe aa53182dc0 .NET: Preserve table state across declarative EditTable operations  (#7353)
* Preserve table state across declarative EditTable operations 

* Address PR comments.
2026-07-28 18:44:38 +00:00
Scarab Systems 3daa3b2c2d Python: Fix Gemini harness tool declarations (#7322)
* 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>
2026-07-28 17:49:14 +00:00
Eduard van Valkenburg 7514122d59 Python: isolate dependency-bound validation (#7342)
* Python: isolate dependency-bound validation

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: e446e833-ea7c-44e8-8b73-e730e30160af

* Python: remove unused validator import

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: e446e833-ea7c-44e8-8b73-e730e30160af

* Python: keep core dependency validation isolated

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: e446e833-ea7c-44e8-8b73-e730e30160af

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: e446e833-ea7c-44e8-8b73-e730e30160af
2026-07-28 17:15:42 +00:00
Roger Barreto 859f56fb49 .NET: Skip flaky InputWaiterTests timeout test blocking the merge queue (#7361)
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
2026-07-28 15:50:47 +00:00
SergeyMenshykh 2694120383 Forward A2A MessageSendParams.Configuration in the A2A adapter (#7365)
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.

Closes microsoft/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
2026-07-28 15:25:38 +00:00
westey 7b6d257988 Python: Add TodoProvider and AgentModeProvider samples (#7309)
* 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
2026-07-27 20:28:58 +00:00
Henry Su acbcdaa086 Python: fix(python): handle callable class middleware safely in _determine_middleware_type (#6697) (#7333)
* fix(python): handle callable class middleware safely in _determine_middleware_type (#6697)

* test(python): type-annotate test middleware lists to pass test-typing checks
2026-07-27 19:39:47 +00:00
KXH 0b0dcaa5af fix(dotnet): preserve table after EditTable add (#7324)
Signed-off-by: KXH <shepherdlaurie238@gmail.com>
2026-07-27 18:29:32 +00:00
Copilot 35a8891d67 .NET: Add Microsoft.Agents.AI.LocalCodeAct to release solution filter (#7343)
* Initial plan

* Add Microsoft.Agents.AI.LocalCodeAct to release solution filter

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
2026-07-27 14:18:31 +00:00
westey 28e02d4669 Create session for tool approval agent when non-present (#7310) 2026-07-27 11:07:43 +00:00
Sriraj 5ccd7784a6 Python: Reject Windows junctions in FileSystemAgentFileStore (#7291)
* Python: reject file-store junctions during enumeration

* Python: clarify file-store probe diagnostics
2026-07-27 10:47:57 +00:00
pratik wayase 754cbe5976 Python: [Feature]: Support OpenAI instructions in Responses API (#7292)
* Python: Support OpenAI instructions in Responses API

* fix: address PR comments and fix typing for OpenAIChatOptions
2026-07-27 10:30:34 +00:00
Giles Odigwe c6442de528 .NET: Graduate GitHub Copilot agent to stable (#7313)
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
2026-07-24 22:46:19 +00:00
Tao Chen 0df184e7dd Python: Fix sub-workflow checkpoint restore to preserve sub-workflow state (#7097)
* 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>
2026-07-24 17:21:41 +00:00
westey d0a0d5a3df .NET: Add TodoProvider and AgentModeProvider samples (#7262)
* Add samples for todo and mode providers

* Address PR review: add Step21 to samples index and trim slash-command input

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Print agent mode after each turn in AgentMode sample

Reflects mode changes the agent makes itself via the mode_set tool.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-07-24 12:57:10 +00:00
westey c59a65da5e .NET: fix InMemoryChatHistoryProvider persisting when service stores history (#7284)
* Fix chat history storage bug

* Improve error messaging

* Address PR comment
2026-07-24 10:44:52 +00:00
Evan Mattson e90b6de5a7 Python: Improve python package management operations (#7274)
* improve package mgmt timings

* Address Python release validation review feedback
2026-07-23 22:14:10 +00:00
Atharva Vichare d98ac29115 Python: Fix duplicate function call on approval round-trip (#7267) (#7271)
* 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>
2026-07-23 21:27:26 +00:00
1333 changed files with 61899 additions and 81935 deletions
+2 -1
View File
@@ -2,6 +2,7 @@ dirs:
- .
excludedFiles:
- ./python/CHANGELOG.md
- "**/SKILL.md"
ignorePatterns:
- pattern: "/github/"
- pattern: "./actions"
@@ -26,7 +27,7 @@ ignorePatterns:
- pattern: "https:\/\/dotnet.microsoft.com"
- pattern: "https://github.com/Rel1cx/eslint-react"
# excludedDirs:
# Folders which include links to localhost, since it's not ignored with regular expressions
# Folders which include links to localhost, since it's not ignored with regular expressions
baseUrl: https://github.com/microsoft/agent-framework/
aliveStatusCodes:
- 200
-2
View File
@@ -1,7 +1,5 @@
# Code ownership assignments
# https://docs.github.com/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
python/packages/azurefunctions/ @microsoft/agentframework-durabletask-developers
python/packages/durabletask/ @microsoft/agentframework-durabletask-developers
python/samples/getting_started/azure_functions/ @microsoft/agentframework-durabletask-developers
python/samples/getting_started/durabletask/ @microsoft/agentframework-durabletask-developers
@@ -1,48 +0,0 @@
name: Azure Functions Integration Test Setup
description: Prepare local emulators and tools for Azure Functions integration tests
runs:
using: "composite"
steps:
- name: Start Durable Task Scheduler Emulator
shell: bash
run: |
if [ "$(docker ps -aq -f name=dts-emulator)" ]; then
echo "Stopping and removing existing Durable Task Scheduler Emulator"
docker rm -f dts-emulator
fi
echo "Starting Durable Task Scheduler Emulator"
docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 -e DTS_USE_DYNAMIC_TASK_HUBS=true mcr.microsoft.com/dts/dts-emulator:latest
echo "Waiting for Durable Task Scheduler Emulator to be ready"
timeout 30 bash -c 'until curl --silent http://localhost:8080/healthz; do sleep 1; done'
echo "Durable Task Scheduler Emulator is ready"
- name: Start Azurite (Azure Storage emulator)
shell: bash
run: |
if [ "$(docker ps -aq -f name=azurite)" ]; then
echo "Stopping and removing existing Azurite (Azure Storage emulator)"
docker rm -f azurite
fi
echo "Starting Azurite (Azure Storage emulator)"
docker run -d --name azurite -p 10000:10000 -p 10001:10001 -p 10002:10002 mcr.microsoft.com/azure-storage/azurite
echo "Waiting for Azurite (Azure Storage emulator) to be ready"
timeout 30 bash -c 'until curl --silent http://localhost:10000/devstoreaccount1; do sleep 1; done'
echo "Azurite (Azure Storage emulator) is ready"
- name: Start Redis
shell: bash
run: |
if [ "$(docker ps -aq -f name=redis)" ]; then
echo "Stopping and removing existing Redis"
docker rm -f redis
fi
echo "Starting Redis"
docker run -d --name redis -p 6379:6379 redis:latest
echo "Waiting for Redis to be ready"
timeout 30 bash -c 'until docker exec redis redis-cli ping | grep -q PONG; do sleep 1; done'
echo "Redis is ready"
- name: Install Azure Functions Core Tools
shell: bash
run: |
echo "Installing Azure Functions Core Tools"
npm install -g azure-functions-core-tools@4 --unsafe-perm true
func --version
+1 -1
View File
@@ -17,7 +17,7 @@ runs:
using: "composite"
steps:
- name: Set up uv
uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990 # v8.3.2
uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9 # v9.0.0
with:
version-file: "python/pyproject.toml"
enable-cache: true
@@ -0,0 +1,19 @@
name: Save Sample Playbooks
description: >
Save the cached sample-validation playbooks. Split out from
sample-validation-setup (which only restores) so the save runs even when the
validation step fails. Combining restore+save via actions/cache would skip the
save on a failing job (post-if: success()), so freshly authored playbooks for
samples that failed validation would never persist. Invoke this with
'if: not-cancelled' after the validation step in each job.
runs:
using: "composite"
steps:
- name: Save sample playbooks cache
uses: actions/cache/save@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0
with:
# Must match the restore path/key in sample-validation-setup/action.yml and the
# sample_validation --playbooks-dir default (samples/sample_validation/playbooks).
path: python/samples/sample_validation/playbooks/
key: sample-playbooks-${{ github.job }}-${{ github.run_id }}
@@ -36,15 +36,31 @@ runs:
shell: bash
run: copilot --version && copilot -p "What can you do in one sentence?"
- name: Set up python and install the project
uses: ./.github/actions/python-setup
with:
python-version: ${{ inputs.python-version }}
os: ${{ inputs.os }}
- name: Restore sample playbooks
# Restore-only. The matching save is a separate step in each job that runs with
# `if: ${{ !cancelled() }}` (see .github/actions/sample-validation-save-playbooks).
# A combined actions/cache would skip its post-job save on a failing job
# (post-if: success()), so playbooks authored for samples that failed validation
# would never persist. Keyed per job so each validate-* job keeps its own playbooks.
uses: actions/cache/restore@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0
with:
# Must match the sample_validation --playbooks-dir default, which resolves to
# samples/sample_validation/playbooks (see python/scripts/sample_validation/__main__.py).
# If a job overrides --playbooks-dir, update this path to match.
path: python/samples/sample_validation/playbooks/
key: sample-playbooks-${{ github.job }}-${{ github.run_id }}
restore-keys: |
sample-playbooks-${{ github.job }}-
- name: Azure CLI Login
uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2
with:
client-id: ${{ inputs.azure-client-id }}
tenant-id: ${{ inputs.azure-tenant-id }}
subscription-id: ${{ inputs.azure-subscription-id }}
- name: Set up python and install the project
uses: ./.github/actions/python-setup
with:
python-version: ${{ inputs.python-version }}
os: ${{ inputs.os }}
@@ -1,17 +0,0 @@
---
applyTo: "dotnet/src/Microsoft.Agents.AI.DurableTask/**,dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/**"
---
# Durable Task area code instructions
The following guidelines apply to pull requests that modify files under
`dotnet/src/Microsoft.Agents.AI.DurableTask/**` or
`dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/**`:
## CHANGELOG.md
- 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]".
+5 -3
View File
@@ -9,12 +9,14 @@
* @param {object} opts.core - GitHub Actions core toolkit
* @param {string} opts.teamSlug - Team slug to check membership against
* @param {string|number} opts.issueNumber - Issue or pull request number to resolve author for
* @param {string} [opts.username] - Explicit user to check instead of the issue or pull request author
* @returns {Promise<{author: string|null, isTeamMember: boolean}>}
*/
async function checkTeamMembership({ github, context, core, teamSlug, issueNumber }) {
let author =
async function checkTeamMembership({ github, context, core, teamSlug, issueNumber, username = '' }) {
let author = username.trim() || (
context.payload.issue?.user?.login ??
context.payload.pull_request?.user?.login;
context.payload.pull_request?.user?.login
);
if (!author) {
const number = Number(issueNumber);
@@ -74,6 +74,28 @@ const BASE_OPTS = { teamSlug: 'my-team', issueNumber: '123' };
// ---------------------------------------------------------------------------
describe('author resolution', () => {
it('uses an explicit username instead of the issue author', async () => {
const { github, context, core } = createMocks({
payloadIssue: { user: { login: 'issue-author' } },
});
let issuesGetCalled = false;
github.rest.issues.get = async () => {
issuesGetCalled = true;
return { data: { user: { login: 'api-user' } } };
};
const result = await checkTeamMembership({
github,
context,
core,
...BASE_OPTS,
username: 'comment-author',
});
assert.equal(result.author, 'comment-author');
assert.equal(issuesGetCalled, false);
});
it('resolves author from event payload', async () => {
const { github, context, core } = createMocks({
payloadIssue: { user: { login: 'payload-user' } },
+3 -3
View File
@@ -32,13 +32,13 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@99df26d4f13ea111d4ec1a7dddef6063f76b97e9 # v4
uses: github/codeql-action/init@e4fba868fa4b1b91e1fdab776edc8cfbe6e9fb81 # v4
with:
languages: ${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
@@ -64,6 +64,6 @@ jobs:
# ./location_of_script_within_repo/buildscript.sh
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@99df26d4f13ea111d4ec1a7dddef6063f76b97e9 # v4
uses: github/codeql-action/analyze@e4fba868fa4b1b91e1fdab776edc8cfbe6e9fb81 # v4
with:
category: "/language:${{matrix.language}}"
+44 -10
View File
@@ -6,6 +6,9 @@ on:
- opened
- reopened
- ready_for_review
issue_comment:
types:
- created
workflow_dispatch:
inputs:
pr_number:
@@ -20,7 +23,7 @@ permissions:
pull-requests: write
concurrency:
group: devflow-pr-review-${{ github.repository }}-${{ github.event.pull_request.number || inputs.pr_number || github.run_id }}
group: devflow-pr-review-${{ github.repository }}-${{ github.event.pull_request.number || github.event.issue.number || inputs.pr_number || github.run_id }}
cancel-in-progress: true
env:
@@ -28,9 +31,20 @@ env:
DEVFLOW_REF: main
TARGET_REPO_PATH: ${{ github.workspace }}/target-repo
DEVFLOW_PATH: ${{ github.workspace }}/devflow
MODEL_CONFIG_PATH: ${{ github.workspace }}/devflow/config.ci.yaml
jobs:
team_check:
if: >-
github.event_name != 'issue_comment' ||
(
github.event.issue.pull_request &&
github.event.comment.body == '/review' &&
(
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'OWNER'
)
)
runs-on: ubuntu-latest
environment: github-app-auth
outputs:
@@ -44,6 +58,7 @@ jobs:
shell: bash
env:
PR_HTML_URL: ${{ github.event.pull_request.html_url }}
PR_NUMBER_COMMENT: ${{ github.event.issue.number }}
PR_NUMBER_EVENT: ${{ github.event.pull_request.number }}
PR_NUMBER_INPUT: ${{ inputs.pr_number }}
run: |
@@ -52,6 +67,9 @@ jobs:
if [[ "${GITHUB_EVENT_NAME}" == "pull_request_target" ]]; then
pr_number="${PR_NUMBER_EVENT}"
pr_url="${PR_HTML_URL}"
elif [[ "${GITHUB_EVENT_NAME}" == "issue_comment" ]]; then
pr_number="${PR_NUMBER_COMMENT}"
pr_url="https://github.com/${GITHUB_REPOSITORY}/pull/${pr_number}"
else
pr_number="${PR_NUMBER_INPUT}"
pr_url="https://github.com/${GITHUB_REPOSITORY}/pull/${pr_number}"
@@ -67,7 +85,7 @@ jobs:
echo "repo=${GITHUB_REPOSITORY}" >> "$GITHUB_OUTPUT"
- name: Checkout GitHub automation
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ github.event_name == 'pull_request_target' && github.event.pull_request.base.sha || github.sha }}
sparse-checkout: |
@@ -91,10 +109,11 @@ jobs:
repository: ${{ github.repository }}
fallback-token: ${{ secrets.GH_ACTIONS_PR_WRITE }}
- name: Check PR author team membership
- name: Check review requester team membership
id: check
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8
env:
MEMBERSHIP_USER: ${{ github.event_name == 'issue_comment' && github.event.comment.user.login || '' }}
TEAM_NAME: ${{ secrets.DEVELOPER_TEAM }}
PR_NUMBER: ${{ steps.pr.outputs.pr_number }}
with:
@@ -107,19 +126,33 @@ jobs:
core,
teamSlug: process.env.TEAM_NAME,
issueNumber: process.env.PR_NUMBER,
username: process.env.MEMBERSHIP_USER,
});
core.setOutput('is_team_member', isTeamMember ? 'true' : 'false');
if (isTeamMember) {
core.info(`Author ${author} is a team member; proceeding with review.`);
core.info(`User ${author} is a team member; proceeding with review.`);
} else {
core.info(`Author ${author} is not a member of ${process.env.TEAM_NAME}; skipping review.`);
core.info(`User ${author} is not a member of ${process.env.TEAM_NAME}; skipping review.`);
}
- name: React to authorized review command
if: ${{ github.event_name == 'issue_comment' && steps.check.outputs.is_team_member == 'true' }}
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8
with:
github-token: ${{ steps.github-auth.outputs.token }}
script: |
await github.rest.reactions.createForIssueComment({
...context.repo,
comment_id: context.payload.comment.id,
content: 'eyes',
});
review:
runs-on: ubuntu-latest
needs: team_check
if: ${{ needs.team_check.outputs.is_team_member == 'true' }}
permissions:
copilot-requests: write
contents: read
issues: write
pull-requests: write
@@ -132,7 +165,7 @@ jobs:
steps:
# Safe checkout: base repo only, not the untrusted PR head.
- name: Checkout target repo base
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ github.event_name == 'pull_request_target' && github.event.pull_request.base.sha || github.sha }}
fetch-depth: 0
@@ -141,7 +174,7 @@ jobs:
# Private DevFlow checkout: the PAT/token grants access to this repo's code.
- name: Checkout DevFlow
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
repository: ${{ env.DEVFLOW_REPOSITORY }}
ref: ${{ env.DEVFLOW_REF }}
@@ -156,7 +189,7 @@ jobs:
python-version: "3.13"
- name: Set up uv
uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990 # v8.3.2
uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9 # v9.0.0
with:
version: "0.11.x"
enable-cache: true
@@ -169,8 +202,8 @@ jobs:
id: review
working-directory: ${{ env.DEVFLOW_PATH }}
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GH_COPILOT_TOKEN: ${{ secrets.GH_COPILOT_TOKEN }}
DEVFLOW_TOKEN: ${{ secrets.DEVFLOW_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
SK_REPO_PATH: ${{ env.TARGET_REPO_PATH }}
AGENT_REPO_PATH: ${{ env.TARGET_REPO_PATH }}
PR_URL: ${{ needs.team_check.outputs.pr_url }}
@@ -178,4 +211,5 @@ jobs:
uv run python scripts/trigger_pr_review.py \
--pr-url "$PR_URL" \
--github-username "$GITHUB_ACTOR" \
--review-compare \
--no-require-comment-selection
+7 -118
View File
@@ -38,10 +38,9 @@ jobs:
dotnetChanges: ${{ steps.filter.outputs.dotnet }}
cosmosDbChanges: ${{ steps.filter.outputs.cosmosdb }}
foundryHostingChanges: ${{ steps.filter.outputs.foundryHosting }}
functionsChanged: ${{ steps.filter.outputs.functions }}
coreChanged: ${{ steps.filter.outputs.core }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- uses: dorny/paths-filter@7b450fff21473bca461d4b92ce414b9d0420d706 # v4.0.2
id: filter
with:
@@ -66,13 +65,6 @@ jobs:
- 'dotnet/Directory.Packages.props'
- 'dotnet/tests/Foundry.Hosting.IntegrationTests/scripts/it-build-image.ps1'
- '.github/workflows/dotnet-build-and-test.yml'
functions:
- 'dotnet/src/Microsoft.Agents.AI.DurableTask/**'
- 'dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/**'
- 'dotnet/tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/**'
- 'dotnet/tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/**'
- '.github/actions/azure-functions-integration-setup/**'
- '.github/workflows/dotnet-build-and-test.yml'
core:
- 'dotnet/src/Microsoft.Agents.AI/**'
- 'dotnet/src/Microsoft.Agents.AI.Abstractions/**'
@@ -111,7 +103,7 @@ jobs:
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
sparse-checkout: |
@@ -185,7 +177,7 @@ jobs:
runs-on: ${{ matrix.os }}
environment: ${{ matrix.environment }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
sparse-checkout: |
@@ -242,7 +234,6 @@ jobs:
-OutputPath dotnet/filtered-unit.slnx
./dotnet/eng/scripts/New-FilteredSolution.ps1 @commonArgs `
-TestProjectNameIncludeFilter "*IntegrationTests*" `
-TestProjectNameExcludeFilter "*DurableTask.IntegrationTests*","*AzureFunctions.IntegrationTests*" `
-OutputPath dotnet/filtered-integration.slnx
- name: Run Unit Tests
@@ -364,7 +355,7 @@ jobs:
env:
configuration: Release
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
sparse-checkout: |
@@ -440,113 +431,11 @@ jobs:
AZURE_SEARCH_INDEX_NAME: ${{ secrets.AZURE_SEARCH_INDEX_NAME }}
# IT_HOSTED_AGENT_IMAGE was exported into $GITHUB_ENV by the previous step.
# DurableTask and AzureFunctions integration tests (ubuntu/net10.0 only).
# Split from main dotnet-test job for path-based filtering and parallelism.
dotnet-test-functions:
needs: [paths-filter]
if: >
github.event_name != 'pull_request' &&
(needs.paths-filter.outputs.functionsChanged == 'true' ||
needs.paths-filter.outputs.coreChanged == 'true' ||
github.event_name == 'schedule' ||
github.event_name == 'workflow_dispatch')
runs-on: ubuntu-latest
environment: integration
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
with:
persist-credentials: false
sparse-checkout: |
.
.github
dotnet
python
declarative-agents
- name: Free runner disk space
uses: ./.github/actions/free-runner-disk-space
- name: Setup dotnet
uses: actions/setup-dotnet@c2fa09f4bde5ebb9d1777cf28262a3eb3db3ced7 # v5.2.0
with:
global-json-file: ${{ github.workspace }}/dotnet/global.json
- name: Build functions integration test projects
shell: bash
working-directory: dotnet
run: |
dotnet build ./tests/Microsoft.Agents.AI.DurableTask.IntegrationTests -c Release -f net10.0 --warnaserror
dotnet build ./tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests -c Release -f net10.0 --warnaserror
- name: Azure CLI Login
uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- name: Set up Durable Task and Azure Functions Integration Test Emulators
uses: ./.github/actions/azure-functions-integration-setup
id: azure-functions-setup
- name: Run Functions Integration Tests
shell: pwsh
working-directory: dotnet
run: |
# Run DurableTask integration tests
dotnet test `
--project ./tests/Microsoft.Agents.AI.DurableTask.IntegrationTests `
-f net10.0 `
-c Release `
--no-build -v Normal `
--report-xunit-trx `
--report-junit `
--results-directory ../IntegrationTestResults/ `
--ignore-exit-code 8 `
--filter-not-trait "Category=IntegrationDisabled" `
--parallel-algorithm aggressive `
--max-threads 2.0x
# Run AzureFunctions integration tests
dotnet test `
--project ./tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests `
-f net10.0 `
-c Release `
--no-build -v Normal `
--report-xunit-trx `
--report-junit `
--results-directory ../IntegrationTestResults/ `
--ignore-exit-code 8 `
--filter-not-trait "Category=IntegrationDisabled" `
--parallel-algorithm aggressive `
--max-threads 2.0x
env:
# OpenAI Models
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_CHAT_MODEL_NAME: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_REASONING_MODEL_NAME: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
# Azure OpenAI Models
AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
# Microsoft Foundry
AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
- name: Upload functions test results
if: always()
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
with:
name: dotnet-test-results-functions-net10.0-ubuntu-latest
path: IntegrationTestResults/**/*.junit
if-no-files-found: ignore
# This final job is required to satisfy the merge queue. It must only run (or succeed) if no tests failed
dotnet-build-and-test-check:
if: always()
runs-on: ubuntu-latest
needs: [dotnet-build, dotnet-test, dotnet-foundry-hosted-it, dotnet-test-functions]
needs: [dotnet-build, dotnet-test, dotnet-foundry-hosted-it]
steps:
- name: Get Date
shell: bash
@@ -593,13 +482,13 @@ jobs:
github.event_name != 'pull_request' &&
(contains(join(needs.*.result, ','), 'success') ||
contains(join(needs.*.result, ','), 'failure'))
needs: [dotnet-test, dotnet-test-functions]
needs: [dotnet-test]
runs-on: ubuntu-latest
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
sparse-checkout: |
+1 -1
View File
@@ -30,7 +30,7 @@ jobs:
steps:
- name: Check out code
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
persist-credentials: false
@@ -21,8 +21,6 @@ on:
required: true
AZUREAI__ENDPOINT:
required: true
COPILOT_GITHUB_TOKEN:
required: true
OPENAI__APIKEY:
required: true
@@ -32,6 +30,7 @@ permissions:
jobs:
dotnet-integration-tests:
permissions:
copilot-requests: write
contents: read
id-token: write
strategy:
@@ -44,7 +43,7 @@ jobs:
environment: integration
timeout-minutes: 60
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -84,10 +83,6 @@ jobs:
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- name: Set up Durable Task and Azure Functions Integration Test Emulators
if: matrix.os == 'ubuntu-latest'
uses: ./.github/actions/azure-functions-integration-setup
- name: Run Integration Tests
shell: bash
run: |
@@ -103,7 +98,7 @@ jobs:
env:
COSMOSDB_ENDPOINT: https://localhost:8081
COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
OpenAI__ApiKey: ${{ secrets.OPENAI__APIKEY }}
OpenAI__ChatModelId: ${{ vars.OPENAI__CHATMODELID }}
OpenAI__ChatReasoningModelId: ${{ vars.OPENAI__CHATREASONINGMODELID }}
+1 -1
View File
@@ -41,7 +41,7 @@ jobs:
environment: 'integration'
timeout-minutes: 90
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
sparse-checkout: |
@@ -25,7 +25,7 @@ jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6
with:
@@ -42,7 +42,7 @@ jobs:
python-changes: ${{ steps.detect-changes.outputs.python }}
steps:
- name: Check out trusted workflow helpers
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ github.sha }}
persist-credentials: false
@@ -102,6 +102,7 @@ jobs:
needs: resolve-ref
if: needs.resolve-ref.outputs.dotnet-changes == 'true'
permissions:
copilot-requests: write
contents: read
id-token: write
uses: ./.github/workflows/dotnet-integration-tests.yml
@@ -112,7 +113,6 @@ jobs:
AZURE_TENANT_ID: ${{ secrets.AZURE_TENANT_ID }}
AZURE_SUBSCRIPTION_ID: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
AZUREAI__ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
OPENAI__APIKEY: ${{ secrets.OPENAI__APIKEY }}
python-integration-tests:
@@ -120,6 +120,7 @@ jobs:
needs: resolve-ref
if: needs.resolve-ref.outputs.python-changes == 'true'
permissions:
copilot-requests: write
contents: read
id-token: write
uses: ./.github/workflows/python-integration-tests.yml
@@ -130,6 +131,5 @@ jobs:
AZURE_CLIENT_ID: ${{ secrets.AZURE_CLIENT_ID }}
AZURE_TENANT_ID: ${{ secrets.AZURE_TENANT_ID }}
AZURE_SUBSCRIPTION_ID: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
FOUNDRY_MODELS_API_KEY: ${{ secrets.FOUNDRY_MODELS_API_KEY }}
OPENAI__APIKEY: ${{ secrets.OPENAI__APIKEY }}
+8 -7
View File
@@ -29,6 +29,7 @@ env:
DEVFLOW_REF: main
TARGET_REPO_PATH: ${{ github.workspace }}/target-repo
DEVFLOW_PATH: ${{ github.workspace }}/devflow
MODEL_CONFIG_PATH: ${{ github.workspace }}/devflow/config.ci.yaml
jobs:
team_check:
@@ -67,7 +68,7 @@ jobs:
echo "repo=${GITHUB_REPOSITORY}" >> "$GITHUB_OUTPUT"
- name: Checkout scripts
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
sparse-checkout: |
.github/actions/github-app-token
@@ -125,6 +126,7 @@ jobs:
}}
environment: integration
permissions:
copilot-requests: write
contents: read
id-token: write
issues: write
@@ -133,7 +135,7 @@ jobs:
steps:
# Safe checkout: base repo only.
- name: Checkout target repo base
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
persist-credentials: false
@@ -141,7 +143,7 @@ jobs:
# Private DevFlow (maf-dashboard) checkout.
- name: Checkout DevFlow
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
repository: ${{ env.DEVFLOW_REPOSITORY }}
ref: ${{ env.DEVFLOW_REF }}
@@ -156,7 +158,7 @@ jobs:
python-version: "3.13"
- name: Set up uv
uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990 # v8.3.2
uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9 # v9.0.0
with:
version: "0.11.x"
enable-cache: true
@@ -176,7 +178,7 @@ jobs:
id: spam
working-directory: ${{ env.DEVFLOW_PATH }}
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
DEVFLOW_TOKEN: ${{ secrets.DEVFLOW_TOKEN }}
SK_REPO_PATH: ${{ env.TARGET_REPO_PATH }}
AGENT_REPO_PATH: ${{ env.TARGET_REPO_PATH }}
@@ -201,8 +203,7 @@ jobs:
id: repro
working-directory: ${{ env.DEVFLOW_PATH }}
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GH_COPILOT_TOKEN: ${{ secrets.GH_COPILOT_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
# Not seen by the agent prompt; used only to push a paper-trail
# branch back to maf-dashboard at run end.
DEVFLOW_TOKEN: ${{ secrets.DEVFLOW_TOKEN }}
+1 -1
View File
@@ -17,7 +17,7 @@ jobs:
issues: write
steps:
- name: Checkout GitHub automation
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
sparse-checkout: |
.github/actions/github-app-token
+1 -1
View File
@@ -22,7 +22,7 @@ jobs:
steps:
- name: Checkout scripts
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ github.event.pull_request.base.sha }}
sparse-checkout: |
+1 -1
View File
@@ -16,7 +16,7 @@ jobs:
steps:
- name: Checkout scripts
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
sparse-checkout: .github/scripts
fetch-depth: 1
+2 -2
View File
@@ -27,7 +27,7 @@ jobs:
is_team_member: ${{ steps.check.outputs.is_team_member }}
steps:
- name: Checkout scripts
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ github.event.pull_request.base.sha }}
sparse-checkout: |
@@ -82,7 +82,7 @@ jobs:
if: ${{ needs.team_check.outputs.is_team_member == 'false' }}
steps:
- name: Checkout scripts
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ github.event.pull_request.base.sha }}
sparse-checkout: |
+1 -1
View File
@@ -19,7 +19,7 @@ jobs:
runs-on: ubuntu-22.04
# check out the latest version of the code
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
+5 -5
View File
@@ -31,7 +31,7 @@ jobs:
env:
UV_PYTHON: ${{ matrix.python-version }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
- name: Set up python and install the project
@@ -42,7 +42,7 @@ jobs:
os: ${{ runner.os }}
env:
UV_CACHE_DIR: /tmp/.uv-cache
- uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5
- uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0
with:
path: ~/.cache/prek
key: prek|${{ matrix.python-version }}|${{ hashFiles('python/.pre-commit-config.yaml') }}
@@ -68,7 +68,7 @@ jobs:
env:
UV_PYTHON: ${{ matrix.python-version }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
- name: Set up python and install the project
@@ -97,7 +97,7 @@ jobs:
env:
UV_PYTHON: ${{ matrix.python-version }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
- name: Set up python and install the project
@@ -128,7 +128,7 @@ jobs:
env:
UV_PYTHON: ${{ matrix.python-version }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
- name: Set up python and install the project
@@ -25,7 +25,7 @@ jobs:
# installability starts differing across supported Python versions.
UV_PYTHON: "3.13"
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
fetch-depth: 0
+2 -2
View File
@@ -24,9 +24,9 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up uv
uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990 # v8.3.2
uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9 # v9.0.0
with:
version-file: "python/pyproject.toml"
enable-cache: true
+16 -83
View File
@@ -25,8 +25,6 @@ on:
required: true
AZURE_SUBSCRIPTION_ID:
required: true
COPILOT_GITHUB_TOKEN:
required: true
FOUNDRY_MODELS_API_KEY:
required: false
OPENAI__APIKEY:
@@ -50,7 +48,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -83,7 +81,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -129,7 +127,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -180,7 +178,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -194,7 +192,7 @@ jobs:
run: curl -fsSL https://ollama.com/install.sh | sh
working-directory: .
- name: Cache Ollama models
uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0
with:
path: ~/.ollama/models
key: ollama-models-qwen2.5-1.5b-nomic-embed-text-v1
@@ -234,11 +232,12 @@ jobs:
fallback_url: ${{ env.LOCAL_MCP_URL }}
- name: Prefer local MCP URL when available
run: echo "LOCAL_MCP_URL=${{ steps.local-mcp.outputs.effective_url }}" >> "$GITHUB_ENV"
- name: Test with pytest (Anthropic, Hyperlight, Ollama, MCP integration)
- name: Test with pytest (Anthropic, Hyperlight, Mistral, Ollama, MCP integration)
run: >
uv run pytest --import-mode=importlib
packages/anthropic/tests
packages/hyperlight/tests
packages/mistral/tests
packages/ollama/tests
packages/core/tests/core/test_mcp.py
packages/hosting-mcp/tests
@@ -275,73 +274,6 @@ jobs:
done
kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" 2>/dev/null || true
# Azure Functions + Durable Task integration tests
python-tests-functions:
name: Python Integration Tests - Functions
permissions:
contents: read
id-token: write
runs-on: ubuntu-latest
environment: integration
timeout-minutes: 60
env:
UV_PYTHON: "3.11"
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FUNCTIONS_WORKER_RUNTIME: "python"
DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
AzureWebJobsStorage: "UseDevelopmentStorage=true"
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
with:
python-version: ${{ env.UV_PYTHON }}
os: ${{ runner.os }}
- name: Azure CLI Login
uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- name: Set up Azure Functions Integration Test Emulators
uses: ./.github/actions/azure-functions-integration-setup
id: azure-functions-setup
- name: Test with pytest (Functions + Durable Task integration)
run: >
uv run pytest --import-mode=importlib
packages/azurefunctions/tests/integration_tests
packages/durabletask/tests/integration_tests
-m integration
-n logical --dist worksteal
-x
--timeout=480 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 5
--junitxml=pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
with:
name: test-results-functions
path: ./python/pytest.xml
if-no-files-found: ignore
# Foundry integration tests
python-tests-foundry:
name: Python Integration Tests - Foundry
@@ -365,7 +297,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -415,7 +347,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -470,7 +402,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -506,15 +438,18 @@ jobs:
name: Python Integration Tests - GitHub Copilot
runs-on: ubuntu-latest
environment: integration
permissions:
copilot-requests: write
contents: read
timeout-minutes: 60
env:
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
GITHUB_COPILOT_TIMEOUT: "120"
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -552,7 +487,6 @@ jobs:
python-tests-openai,
python-tests-azure-openai,
python-tests-misc-integration,
python-tests-functions,
python-tests-foundry,
python-tests-foundry-hosting,
python-tests-cosmos,
@@ -563,7 +497,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
@@ -617,7 +551,6 @@ jobs:
python-tests-openai,
python-tests-azure-openai,
python-tests-misc-integration,
python-tests-functions,
python-tests-foundry,
python-tests-foundry-hosting,
python-tests-cosmos,
+2 -2
View File
@@ -24,7 +24,7 @@ jobs:
outputs:
pythonChanges: ${{ steps.filter.outputs.python}}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- uses: dorny/paths-filter@7b450fff21473bca461d4b92ce414b9d0420d706 # v4.0.2
id: filter
with:
@@ -63,7 +63,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
+18 -97
View File
@@ -36,13 +36,12 @@ jobs:
openaiChanged: ${{ steps.filter.outputs.openai }}
azureChanged: ${{ steps.filter.outputs.azure }}
miscChanged: ${{ steps.filter.outputs.misc }}
functionsChanged: ${{ steps.filter.outputs.functions }}
foundryChanged: ${{ steps.filter.outputs.foundry }}
foundryHostingChanged: ${{ steps.filter.outputs.foundry_hosting }}
cosmosChanged: ${{ steps.filter.outputs.cosmos }}
githubCopilotChanged: ${{ steps.filter.outputs.github_copilot }}
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- uses: dorny/paths-filter@7b450fff21473bca461d4b92ce414b9d0420d706 # v4.0.2
id: filter
with:
@@ -68,6 +67,7 @@ jobs:
misc:
- 'python/packages/anthropic/**'
- 'python/packages/hyperlight/**'
- 'python/packages/mistral/**'
- 'python/packages/ollama/**'
- 'python/packages/core/agent_framework/_mcp.py'
- 'python/packages/core/tests/core/test_mcp.py'
@@ -76,9 +76,6 @@ jobs:
- '.github/actions/setup-local-mcp-server/**'
- '.github/workflows/python-merge-tests.yml'
- '.github/workflows/python-integration-tests.yml'
functions:
- 'python/packages/azurefunctions/**'
- 'python/packages/durabletask/**'
foundry:
- 'python/packages/foundry/**'
- 'python/samples/**/providers/foundry/**'
@@ -110,7 +107,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -157,7 +154,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -218,7 +215,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -288,7 +285,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -299,7 +296,7 @@ jobs:
run: curl -fsSL https://ollama.com/install.sh | sh
working-directory: .
- name: Cache Ollama models
uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5.0.5
uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0
with:
path: ~/.ollama/models
key: ollama-models-qwen2.5-1.5b-nomic-embed-text-v1
@@ -339,11 +336,12 @@ jobs:
fallback_url: ${{ env.LOCAL_MCP_URL }}
- name: Prefer local MCP URL when available
run: echo "LOCAL_MCP_URL=${{ steps.local-mcp.outputs.effective_url }}" >> "$GITHUB_ENV"
- name: Test with pytest (Anthropic, Hyperlight, Ollama, MCP integration)
- name: Test with pytest (Anthropic, Hyperlight, Mistral, Ollama, MCP integration)
run: >
uv run pytest --import-mode=importlib
packages/anthropic/tests
packages/hyperlight/tests
packages/mistral/tests
packages/ollama/tests
packages/core/tests/core/test_mcp.py
packages/hosting-mcp/tests
@@ -390,84 +388,6 @@ jobs:
path: ./python/pytest.xml
if-no-files-found: ignore
# Azure Functions + Durable Task integration tests
python-tests-functions:
name: Python Tests - Functions Integration
needs: paths-filter
if: >
github.event_name != 'pull_request' &&
needs.paths-filter.outputs.pythonChanges == 'true' &&
(github.event_name != 'merge_group' ||
needs.paths-filter.outputs.functionsChanged == 'true' ||
needs.paths-filter.outputs.coreChanged == 'true')
runs-on: ubuntu-latest
environment: integration
env:
UV_PYTHON: "3.11"
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FUNCTIONS_WORKER_RUNTIME: "python"
DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
AzureWebJobsStorage: "UseDevelopmentStorage=true"
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
with:
python-version: ${{ env.UV_PYTHON }}
os: ${{ runner.os }}
- name: Azure CLI Login
if: github.event_name != 'pull_request'
uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- name: Set up Azure Functions Integration Test Emulators
uses: ./.github/actions/azure-functions-integration-setup
id: azure-functions-setup
- name: Test with pytest (Functions + Durable Task integration)
run: >
uv run pytest --import-mode=importlib
packages/azurefunctions/tests/integration_tests
packages/durabletask/tests/integration_tests
-m integration
-n logical --dist worksteal
-x
--timeout=480 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 5
--junitxml=pytest.xml
working-directory: ./python
- name: Surface failing tests
if: always()
uses: pmeier/pytest-results-action@20b595761ba9bf89e115e875f8bc863f913bc8ad # v0.7.2
with:
path: ./python/pytest.xml
summary: true
display-options: fEX
fail-on-empty: false
title: Functions integration test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
with:
name: test-results-functions
path: ./python/pytest.xml
if-no-files-found: ignore
python-tests-foundry:
name: Python Integration Tests - Foundry
needs: paths-filter
@@ -493,7 +413,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -554,7 +474,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -625,7 +545,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -675,15 +595,18 @@ jobs:
needs.paths-filter.outputs.coreChanged == 'true')
runs-on: ubuntu-latest
environment: integration
permissions:
copilot-requests: write
contents: read
timeout-minutes: 60
env:
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
GITHUB_COPILOT_TIMEOUT: "120"
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
@@ -727,7 +650,6 @@ jobs:
python-tests-openai,
python-tests-azure-openai,
python-tests-misc-integration,
python-tests-functions,
python-tests-foundry,
python-tests-foundry-hosting,
python-tests-cosmos,
@@ -738,7 +660,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
uses: ./.github/actions/python-setup
with:
@@ -789,7 +711,6 @@ jobs:
python-tests-openai,
python-tests-azure-openai,
python-tests-misc-integration,
python-tests-functions,
python-tests-foundry,
python-tests-foundry-hosting,
python-tests-cosmos,
+1 -1
View File
@@ -23,7 +23,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
+278 -64
View File
@@ -8,11 +8,11 @@ on:
env:
# Configure a constant location for the uv cache
UV_CACHE_DIR: /tmp/.uv-cache
# GitHub Copilot configuration
GITHUB_COPILOT_MODEL: claude-opus-4.6
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
GITHUB_TOKEN: ${{ github.token }}
GITHUB_COPILOT_MODEL: auto
permissions:
copilot-requests: write
contents: read
id-token: write
@@ -23,13 +23,13 @@ jobs:
environment: integration
env:
# Required configuration for get-started samples
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -48,6 +48,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 01-get-started --save-report --report-name 01-get-started
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -57,12 +61,13 @@ jobs:
validate-02-agents:
name: Validate 02-agents
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
# Foundry configuration
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
@@ -70,19 +75,19 @@ jobs:
AZURE_OPENAI_CHAT_MODEL: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
AZURE_OPENAI_EMBEDDING_MODEL: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME || vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
# OpenAI configuration
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
# GitHub MCP
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
# Observability
ENABLE_INSTRUMENTATION: "true"
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -108,7 +113,11 @@ jobs:
- name: Run sample validation
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents --exclude providers --save-report --report-name 02-agents
cd scripts && uv run python -m sample_validation --subdir 02-agents --exclude providers harness tools --save-report --report-name 02-agents
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
@@ -117,20 +126,110 @@ jobs:
name: validation-report-02-agents
path: python/samples/sample_validation/reports/
validate-02-agents-openai:
name: Validate 02-agents/providers/openai
validate-02-agents-harness:
name: Validate 02-agents/harness
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Optional: enables the Foundry memory path in harness samples
FOUNDRY_EMBEDDING_MODEL: ${{ vars.FOUNDRY_EMBEDDING_MODEL || '' }}
FOUNDRY_MEMORY_STORE: ${{ vars.FOUNDRY_MEMORY_STORE || '' }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
with:
azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
os: ${{ runner.os }}
- name: Create .env for samples
run: |
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
echo "FOUNDRY_EMBEDDING_MODEL=$FOUNDRY_EMBEDDING_MODEL" >> .env
echo "FOUNDRY_MEMORY_STORE=$FOUNDRY_MEMORY_STORE" >> .env
- name: Run sample validation
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/harness --save-report --report-name 02-agents-harness
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
with:
name: validation-report-02-agents-harness
path: python/samples/sample_validation/reports/
validate-02-agents-tools:
name: Validate 02-agents/tools
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
with:
azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
os: ${{ runner.os }}
- name: Create .env for samples
run: |
echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
- name: Run sample validation
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/tools --save-report --report-name 02-agents-tools
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
with:
name: validation-report-02-agents-tools
path: python/samples/sample_validation/reports/
validate-02-agents-openai:
name: Validate 02-agents/providers/openai
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_MODEL: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -151,6 +250,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/openai --save-report --report-name 02-agents-openai
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -160,6 +263,7 @@ jobs:
validate-02-agents-azure:
name: Validate 02-agents/providers/azure
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
@@ -170,7 +274,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -190,6 +294,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure --save-report --report-name 02-agents-azure
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -199,6 +307,7 @@ jobs:
validate-02-agents-anthropic:
name: Validate 02-agents/providers/anthropic
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
@@ -208,7 +317,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -227,6 +336,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/anthropic --save-report --report-name 02-agents-anthropic
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -236,13 +349,14 @@ jobs:
validate-02-agents-github-copilot:
name: Validate 02-agents/providers/github_copilot
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -256,6 +370,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/github_copilot --save-report --report-name 02-agents-github-copilot
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -274,7 +392,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -288,6 +406,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/amazon --save-report --report-name 02-agents-amazon
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -306,7 +428,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -320,6 +442,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/ollama --save-report --report-name 02-agents-ollama
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -329,19 +455,19 @@ jobs:
validate-02-agents-foundry:
name: Validate 02-agents/providers/foundry
if: false # Temporarily disabled - provider folder also contains the local Foundry sample
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME || '' }}
FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION || '' }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -362,6 +488,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry --save-report --report-name 02-agents-foundry
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -383,7 +513,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -404,6 +534,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/copilotstudio --save-report --report-name 02-agents-copilotstudio
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -419,7 +553,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -433,6 +567,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/custom --save-report --report-name 02-agents-custom
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -442,16 +580,17 @@ jobs:
validate-03-workflows:
name: Validate 03-workflows
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -470,6 +609,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 03-workflows --save-report --report-name 03-workflows
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -477,21 +620,68 @@ jobs:
name: validation-report-03-workflows
path: python/samples/sample_validation/reports/
validate-04-hosting:
name: Validate 04-hosting
if: false # Temporarily disabled because of sample complexity
validate-04-hosting-foundry-hosted-agents:
name: Validate 04-hosting (foundry-hosted-agents)
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Foundry hosted agent configuration
AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.FOUNDRY_MODEL }}
FOUNDRY_PROJECT_ID: ${{ vars.FOUNDRY_PROJECT_ID }}
AZURE_CONTAINER_REGISTRY_ENDPOINT: ${{ vars.AZURE_CONTAINER_REGISTRY_ENDPOINT }}
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
TOOLBOX_ENDPOINT: ${{ vars.TOOLBOX_ENDPOINT }}
FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_HOSTED_AGENT_NAME }}
MEMORY_STORE_NAME: ${{ vars.FOUNDRY_HOSTED_AGENT_MEMORY_STORE }}
AZURE_SEARCH_ENDPOINT: ${{ vars.AZURE_SEARCH_ENDPOINT }}
AZURE_SEARCH_INDEX_NAME: ${{ vars.FOUNDRY_HOSTED_AGENT_SEARCH_INDEX_NAME }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
with:
azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
os: ${{ runner.os }}
- name: Run sample validation
# Maximum parallel workers is set to 1 because all samples use the same port
run: |
cd scripts && uv run python -m sample_validation --subdir 04-hosting/foundry-hosted-agents --save-report --report-name 04-hosting-foundry-hosted-agents --max-parallel-workers 1
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
with:
name: validation-report-04-hosting-foundry-hosted-agents
path: python/samples/sample_validation/reports/
validate-04-hosting-other:
name: Validate 04-hosting (other)
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# A2A configuration
A2A_AGENT_HOST: http://localhost:5001/
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -503,13 +693,17 @@ jobs:
- name: Run sample validation
run: |
cd scripts && uv run python -m sample_validation --subdir 04-hosting --save-report --report-name 04-hosting
cd scripts && uv run python -m sample_validation --subdir 04-hosting --exclude foundry-hosted-agents --save-report --report-name 04-hosting-other
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
with:
name: validation-report-04-hosting
name: validation-report-04-hosting-other
path: python/samples/sample_validation/reports/
validate-05-end-to-end:
@@ -518,8 +712,8 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
@@ -534,7 +728,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -548,6 +742,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir 05-end-to-end --save-report --report-name 05-end-to-end
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -560,21 +758,21 @@ jobs:
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# OpenAI configuration
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
OPENAI_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -594,9 +792,16 @@ jobs:
echo "OPENAI_CHAT_COMPLETION_MODEL=$OPENAI_CHAT_COMPLETION_MODEL" >> .env
echo "OPENAI_CHAT_MODEL=$OPENAI_CHAT_MODEL" >> .env
- name: Pre-install AutoGen dependencies for migration samples
run: uv pip install "autogen-agentchat" "autogen-ext[openai]"
- name: Run sample validation
run: |
cd scripts && uv run python -m sample_validation --subdir autogen-migration --save-report --report-name autogen-migration
cd scripts && uv run python -m sample_validation --subdir autogen-migration --save-report --report-name autogen-migration --agent-timeout 600
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
@@ -607,23 +812,25 @@ jobs:
validate-semantic-kernel-migration:
name: Validate semantic-kernel-migration
if: false # Temporarily disabled - to free up Copilot quota for other jobs
runs-on: ubuntu-latest
environment: integration
env:
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
# Azure OpenAI configuration for AF
AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
AZURE_OPENAI_MODEL: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
# Azure OpenAI configuration for SK
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
# OpenAI key
OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_CHAT_COMPLETION_MODEL: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_CHAT_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
OPENAI_MODEL: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
# OpenAI configuration for SK
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
# Copilot Studio
COPILOTSTUDIOAGENT__ENVIRONMENTID: ${{ secrets.COPILOTSTUDIOAGENT__ENVIRONMENTID }}
COPILOTSTUDIOAGENT__SCHEMANAME: ${{ secrets.COPILOTSTUDIOAGENT__SCHEMANAME }}
@@ -633,7 +840,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Setup environment
uses: ./.github/actions/sample-validation-setup
@@ -661,6 +868,10 @@ jobs:
run: |
cd scripts && uv run python -m sample_validation --subdir semantic-kernel-migration --save-report --report-name semantic-kernel-migration
- name: Save sample playbooks
if: ${{ !cancelled() }}
uses: ./.github/actions/sample-validation-save-playbooks
- name: Upload validation report
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
if: always()
@@ -675,6 +886,8 @@ jobs:
needs:
- validate-01-get-started
- validate-02-agents
- validate-02-agents-harness
- validate-02-agents-tools
- validate-02-agents-openai
- validate-02-agents-azure
- validate-02-agents-anthropic
@@ -685,12 +898,13 @@ jobs:
- validate-02-agents-copilotstudio
- validate-02-agents-custom
- validate-03-workflows
- validate-04-hosting
- validate-04-hosting-foundry-hosted-agents
- validate-04-hosting-other
- validate-05-end-to-end
- validate-autogen-migration
- validate-semantic-kernel-migration
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Download all validation reports
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
@@ -20,7 +20,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Download coverage report
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
with:
+1 -1
View File
@@ -25,7 +25,7 @@ jobs:
env:
UV_PYTHON: "3.11"
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
# Save the PR number to a file since the workflow_run event
# in the coverage report workflow does not have access to it
- name: Save PR number
+1 -1
View File
@@ -31,7 +31,7 @@ jobs:
run:
working-directory: python
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Set up python and install the project
id: python-setup
uses: ./.github/actions/python-setup
+1 -1
View File
@@ -33,7 +33,7 @@ jobs:
issues: write
pull-requests: write
steps:
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
- name: Get GitHub automation token
id: github-auth
+5 -4
View File
@@ -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
- [Agent Concepts](./python/samples/02-agents): deep-dive samples by topic (tools, middleware, providers, etc.)
- [Workflows](./python/samples/03-workflows): workflow creation and integration with agents
- [Hosting](./python/samples/04-hosting): A2A, Azure Functions, Durable Task hosting
- [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, Durable Agents, Durable Workflows
- [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)
- [Design Documents](./docs/design)
- [Architectural Decision Records](./docs/decisions)
@@ -0,0 +1,104 @@
---
status: Accepted
contact: cgillum
date: 2026-07-21
deciders: cgillum, vrdmr, chetantoshniwal
consulted: westey-m, eavanvalkenburg, kshyju, larohra, ahmedmuhsin
informed:
---
# Extract Durable Task and Azure Functions hosting into a separate repository
## Context and Problem Statement
The Durable Task and Azure Functions hosting integrations (`agent-framework-durabletask`,
`agent-framework-azurefunctions`, plus their samples, docs, and CI) currently live in the
`microsoft/agent-framework` (MAF) monorepo. They carry heavyweight specialized dependencies
(Azure Functions runtime, Durable Task) and need integration-test infrastructure (Functions Core
Tools, Azurite, a DTS emulator) that the core repo otherwise does not.
This ADR proposes moving them into a dedicated repository
([`microsoft/agent-framework-durable-extension`](https://github.com/microsoft/agent-framework-durable-extension))
and considers how to do so without breaking existing users who import them today.
## Decision Drivers
- **Independent lifecycle** — the hosting integrations should be able to version and release on their
own cadence, decoupled from core (extends [ADR-0008](0008-python-subpackages.md)'s goal of keeping
heavyweight/optional dependencies out of the main package).
- **Dependency & CI isolation** — keep core lean and its PR pipeline free of heavyweight hosting
dependencies and integration-test prerequisites.
- **Ownership** — a dedicated repo would give the integrations their own issues, CODEOWNERS, and
contribution flow.
- **No breaking change** — existing `from agent_framework.azure import …` code and
`pip install agent-framework[all]` should keep working (stable-import-path guarantee, ADR-0008).
## Considered Options
1. **Keep in the MAF repo** (status quo).
2. **Move out, drop the core shim** — the extension becomes standalone; core stops re-exporting the
types and removes them from `[all]`.
3. **Move out, keep core's backward-compat shim + `[all]`** (proposed) — the code would live in the
new repo; core would still lazily re-export the entry-point types from `agent_framework.azure` and
keep both packages in the `[all]` extra (resolved from PyPI).
## Decision Outcome
Proposed choice: **Option 3.** Extract the integrations for lifecycle, dependency, and ownership
isolation, while preserving the existing import surface so the move is invisible to consumers.
Option 1 forgoes the isolation benefits; Option 2 achieves them but would be a breaking change for
existing imports and the `[all]` extra.
### Consequences
- Good — would give independent release cadence, a leaner/faster core repo and CI, and clear
ownership for the hosting integrations.
- Good — no user-visible break: existing imports and `agent-framework[all]` would continue to work
unchanged.
- Neutral — type *definitions* would live once in the extension; the core shim would re-export only a
curated subset of entry-point types (no metadata duplication). The extension's own samples/docs
would import directly from `agent_framework_durabletask` / `agent_framework_azurefunctions`; the
shim would be compatibility-only.
- Neutral — users may still open GitHub issues against the core repo for problems in the extension,
but the extension's own repo would be the primary place for issues and PRs. These issues would
need to be triaged and transferred to the extension repo.
- Neutral — **.NET public API boundary.** The extension should prefer the smallest stable public core
API over friend-assembly access where the capability is useful to external hosts or tooling. For
workflow routing metadata, the agreed first step is to expose a read-only `Workflow.Edges` view plus
public `EdgeData.Connection` and `FanOutEdgeData`, while keeping graph construction internal
([#7448](https://github.com/microsoft/agent-framework/issues/7448),
[#7459](https://github.com/microsoft/agent-framework/pull/7459)). This reduces internal coupling but
adds a public API compatibility commitment. Any remaining internal dependencies would still need to
be evaluated individually before retaining `InternalsVisibleTo`.
- Bad — **Python version coordination.** Core's shim correctness would track the extension's publish
cadence. In the other direction, when an extension package adopts a new core API, maintainers would
need to choose per feature between raising its minimum core version (simpler, but forces every
extension user to upgrade) and conditional imports with fallback behavior (preserves support for
older core versions, but adds implementation and testing complexity).
## Validation
Compliance would be validated by:
- Python: `uv lock --check` passing with both packages resolving from PyPI; the shim entry-point
symbols importing at runtime after `uv sync --all-extras`; `pyright` staying clean on
`agent_framework/azure/__init__.pyi`; and extension tests running against both the minimum supported
and current core versions when conditional compatibility behavior is used.
- .NET: tests from an external assembly confirming that workflow routing metadata is inspectable
through the agreed public surface while graph construction remains internal.
A known risk is **publish-lag**: if a symbol is added to core's shim before the extension has
published a release that exports it, that symbol would not resolve at runtime. The mitigation would
be to omit any such symbol from the shim until the extension publishes it, then add the entry and
re-lock.
## More Information
- Related: [ADR-0008](0008-python-subpackages.md) (vendor namespaces + stable import paths),
[ADR-0021](0021-provider-leading-clients.md) (lazy-loading gateways),
[issue #7448](https://github.com/microsoft/agent-framework/issues/7448) and
[PR #7459](https://github.com/microsoft/agent-framework/pull/7459) (.NET workflow routing API).
- Follow-ups: during extraction, keep the shim's re-exported symbols in sync with each newly
published extension release (adding any symbol only once the extension publishes it); document the
direct-import convention in the extension's samples READMEs so samples are not switched back to the
shim.
@@ -0,0 +1,642 @@
---
status: accepted
contact: eavanvalkenburg
date: 2026-07-22
deciders: eavanvalkenburg, chetantoshniwal
consulted: TaoChenOSU, moonbox3, peibekwe, rogerbarreto, westey-m
informed:
---
# Feature-usage bitmask in the User-Agent
## Context and Problem Statement
We can see which Agent Framework packages are installed and that *some* framework
call happened (via the existing `agent-framework-python/{version}` User-Agent),
but we have no usage-based signal about **which features are actually exercised**
at runtime, nor which are used *together* (e.g. workflows + MCP + Foundry). How
can we collect a lightweight, privacy-respecting signal of feature usage for the
traffic we can actually read, without standing up new event pipelines?
The detailed mechanism is in [SPEC-004](../specs/004-feature-usage-telemetry.md);
the per-language bit tables are in
[feature-usage-bit-registry.md](../specs/feature-usage-bit-registry.md).
## Decision Drivers
- **Transparency** — openly documented, human-decodable, user-controllable. No
hidden or obfuscated telemetry.
- **First-party scope / no third-party leakage** — emission requires both an
explicitly approved client/pipeline family and an approved actual HTTPS origin
on every request (including redirects). Credentials or an Azure setting alone
never approve a custom gateway/origin.
- **Live signal** — read the process's observed-feature set *so far* at request
send time, rather than freezing it at client construction.
- **Low cost / few moving parts** — reuse telemetry already in the request path;
bounded fixed-width processing; as little machinery as the job needs.
- **Privacy** — encode only coarse "observed at least once" Boolean feature
state, never counts; no identifiers, arguments, prompts, payloads,
model/deployment names, endpoints, or customer-defined names.
- **Use, not presence** — package-level indexes mean a capability reached its
first meaningful activation, not that a package was installed/imported or a
DI container constructed an unused service.
- **Versioning discipline** — v1 is a point-in-time decision. Adding bits later is
easier than removing or redefining them, so the initial table should lean toward
fewer bits and avoid forcing v2 shortly after launch.
- **Allocation discipline** — each bit represents a stable framework-owned
capability with a concrete product/support question and an actual-use mark
point; implementation detail and speculative distinctions stay out.
## Considered Options
The options below are grouped by the decisions that matter: the **transport**,
the **granularity**, and the **registry sharing model**.
### Transport
#### A. User-Agent token, first-party only, per request (chosen)
Stamp a `(feat=...)` comment onto the UA, but only on approved Azure/Foundry
client pipelines, and re-evaluate it per request.
- Good, reuses telemetry already sent to approved backends we can read.
- Good, request-time stamping reflects the live mask (not frozen at construction).
- Good, first-party scoping means no fingerprint leaks to third-party providers.
- Good, two-factor destination approval (pipeline + actual origin) denies custom
`base_url` gateways and strips the token on unapproved redirect hops.
- Good, maps onto .NET's existing per-request UA pipeline policies unchanged.
- Neutral, v1 stamps only pipelines the framework creates or can configure
through supported public hooks. It does not mutate caller-owned clients or
reach into private SDK pipelines.
- Bad, no signal for traffic that never hits a first-party endpoint (accepted —
we couldn't read it anyway).
#### B. User-Agent token on all clients
- Good, simplest to wire (one static header).
- Bad, sends a deployment fingerprint to OpenAI/Anthropic/AWS/Google logs we
cannot read — privacy leak for zero benefit.
- Bad, baked into static `default_headers`, so it freezes at client construction
and reports a near-empty mask.
#### C. OpenTelemetry span/resource attribute
- Good, precise per-call usage; no UA change.
- Bad (**privacy — the main reason to hold it**), a span attribute broadcasts the
feature-combination fingerprint into the user's **general** telemetry pipeline,
which is typically exported to third-party APM vendors (Datadog, Honeycomb, …).
That re-introduces exactly the fingerprint leakage the first-party-only UA
scoping (A) was chosen to avoid — just into a different set of third parties.
- Bad (secondary), also a cardinality footgun (a growing, combinatorial value
must never become a metric dimension).
- Neutral, for the team's own goal it reaches us only if the user exports to
Azure Monitor and we query it.
- **Deferred, not rejected.** The version prefix lets us add it later **if** the
User-Agent path cannot answer a concrete query and there is an acceptable
scoped/redacted variant.
#### D. Bespoke usage events
- Good, richest detail and flexibility.
- Bad, new data flow and cost; larger privacy surface; heavy to build and review;
overkill for a coarse "which features" signal.
#### E. Install/import-time signal only (status quo-ish)
- Good, zero new runtime work.
- Bad, measures installation, not usage; cannot capture feature combinations —
does not solve the problem.
### Accumulation scope
#### S1. Process-global, monotonic mask (chosen)
A single mask per process; bits are OR-ed in as features are first used and never
cleared. The token reflects "what this process has used so far."
- **Binary interpretation:** a set bit means the feature was observed at least
once in this process before the request was sent. A bit repeated on later
requests is the same Boolean observation, not another feature use. It cannot be
summed into invocation, request, agent, user, or tenant counts.
- Good, fits our **mixed feature lifecycle**: many features are *not* bound to an
outbound service request — an agent/workflow may first run or build, a
context/history provider may first participate in a session, and a host may
start serving before the request that later emits the token. A process-wide
mask can carry those activations forward.
- Good, trivial and cheap: one OR under a lock (Python) / one atomic OR into one
of two 64-bit lanes (.NET); no per-request state plumbing.
- Good, deliberately coarse for privacy: it avoids emitting a sequence of exact
per-call feature combinations that could reconstruct a workload's behavioral
trace.
- Neutral, coarser than per-call — early requests carry fewer bits than later
ones, and the token says "this process used X", not "this call used X" or "X
was used this many times."
For example, at time 1 Agent A can use MCP and a Foundry chat client. At time 2,
Agent B in the same worker can make a normal Foundry chat call without MCP. The
time-2 request still carries the MCP bit because MCP was previously observed in
that process. It does **not** say Agent B used MCP, nor count a second MCP use.
#### S2. Per-request set, reset between calls (botocore's model — rejected)
AWS botocore scopes its `m/` feature codes to a `contextvars` set that is reset
between requests, giving exact per-call attribution (and it deliberately no-ops
when called outside a request context to avoid features bleeding across requests).
See [Prior art](#prior-art).
- Good, exact per-call attribution directly in the User-Agent.
- Bad, **assumes every feature is exercised inside a single service request**
true for botocore (an SDK natively bound to AWS service calls), but *not* for
us. Our features split into request-scoped ones (a chat call, an MCP tool
invocation) and decidedly non-request ones (workflow build/start, provider
participation, hosting startup). The latter have no service request to attach to, so a
per-request set would simply miss them.
- Bad, needs `contextvars` propagation through every async/threaded path and a
reset discipline, plus enable/disable calls around every scoped operation; the
bleed-guard botocore documents is the warning sign.
- Bad, creates a more detailed per-call behavioral trace, increasing the privacy
sensitivity and review burden compared with a coarse process-lifetime Boolean.
- Note, per-call attribution for the request-scoped subset is better served by
the deferred OTel span path (option C) than by reshaping the UA token.
### Granularity
The mechanism can support several granularities. The remaining decision before
implementation is how detailed v1 should be. The estimates below are
intentionally rough; v1 uses a fixed 128-bit bound to leave useful headroom
without making the registry unbounded.
#### F0. Package-level bits
One bit per package, set on first use of a package-owned public API, client,
provider, or tool. It is **not** set on install, import, or assembly load.
Examples that get bits:
- `agent-framework-core` when `Agent`, `AgentSession`, `Workflow`, etc. is used.
- `agent-framework-tools` when a `LocalShellTool` or `DockerShellTool` first
executes/probes its shell capability.
- `agent-framework-foundry` when a `FoundryChatClient`, `FoundryAgent`, etc.
performs its first Foundry operation.
- `agent-framework-openai` when `OpenAIChatClient`,
`OpenAIEmbeddingClient`, etc. performs its first provider operation.
- `agent-framework-azure-ai-search` when `AzureAISearchContextProvider` is used.
- `agent-framework-azure-cosmos` when `CosmosHistoryProvider` is used.
- `agent-framework-redis` when `RedisContextProvider` or `RedisHistoryProvider`
is used.
Examples that do **not** get separate bits: merely installed dependencies;
imports or DI construction with no activation; `Agent` vs `AgentSession` vs
`InMemoryHistoryProvider`; `FunctionTool` vs `MCPStdioTool` vs `LocalShellTool`
vs `DockerShellTool`; `FoundryChatClient` vs `FoundryAgent`; `OpenAIChatClient`
vs `OpenAIEmbeddingClient`.
Rough estimate: Python ~25-35 bits; .NET ~15-25 bits.
- Good, lowest specificity and simplest registry.
- Good, clearly measures usage rather than dependency inventory if bits are set
only at package-owned public API/client/provider/tool use sites.
- Bad, does not answer which major capability within a package is used.
#### F1. Package + major capability bits
Package bits plus selected major capabilities that are product-distinct and stable
across implementations.
Examples that get bits:
- `agent-framework-core` plus `Agent`.
- `AgentSession` plus `InMemoryHistoryProvider` / `FileHistoryProvider` as one
history capability.
- `Workflow` / `FunctionalWorkflow` as one workflow capability.
- `FunctionTool`; MCP transports as one MCP capability; shell tools as one shell
capability.
- Skills provider plus stable source types: file, in-memory/programmatic, and
MCP-backed skills (with .NET inline/class skill distinctions).
- Foundry chat/agent/embedding capabilities; OpenAI chat/embedding capabilities.
Examples that do **not** get separate bits: `InMemoryHistoryProvider` vs
`FileHistoryProvider`; `WorkflowBuilder`, `AgentExecutor`, `FunctionExecutor`, or
`FanOutEdgeGroup`; `MCPStdioTool` vs `MCPStreamableHTTPTool` vs
`MCPWebsocketTool`; `LocalShellTool` vs `DockerShellTool` vs
`ShellEnvironmentProvider` vs `ShellPolicy`; `OpenAIChatClient` vs
`OpenAIChatCompletionClient`; skill-source decorators such as caching, filtering,
deduplication, and aggregation.
Rough estimate: Python ~60-70 indexes; .NET ~45-55 indexes. The current candidate
registry is at 63 Python / 52 .NET assigned indexes.
- Good, likely answers the first product adoption questions while staying compact.
- Good, fits comfortably within 128 bits while leaving room for additive package
and feature growth.
- Neutral, some provider internals remain collapsed until a later additive bit is
justified.
#### F2. Public construct / concrete type bits
One bit per public construct that users intentionally instantiate or configure.
Examples that get bits:
- `Agent`, `AgentSession`, `InMemoryHistoryProvider`, `FileHistoryProvider`.
- `Workflow`, `WorkflowBuilder`, `FunctionalWorkflow`.
- `FunctionTool`, `MCPStdioTool`, `MCPStreamableHTTPTool`, `MCPWebsocketTool`.
- `LocalShellTool`, `DockerShellTool`, `ShellEnvironmentProvider`, `ShellPolicy`.
- `FoundryChatClient`, `FoundryAgent`, `OpenAIChatClient`,
`OpenAIChatCompletionClient`, `OpenAIEmbeddingClient`.
Examples that do **not** get separate bits: `Agent.run` vs
`Agent.run_streamed`; workflow edge/executor internals such as `AgentExecutor`,
`FunctionExecutor`, or `FanOutEdgeGroup`; `LocalShellTool` persistent vs
stateless mode; `ShellPolicy` allowlist vs denylist configuration; `FunctionTool`
approval mode or result parser choices.
Rough estimate: Python ~70-100 bits; .NET ~55-80 bits.
- Good, concrete and directly tied to public API use.
- Neutral, fits within 128 bits at the current estimate, but consumes much of the
deliberate growth reserve.
- Bad, adds many call sites and more fingerprint specificity for v1.
#### F3. Construct subtype / configuration bits
Split important constructs by mode, transport, storage, or workflow primitive
when that distinction matters.
Examples that get bits:
- `InMemoryHistoryProvider` and `FileHistoryProvider` separately.
- `FunctionalWorkflow`, `WorkflowBuilder`, `AgentExecutor`, `FunctionExecutor`.
- `FanOutEdgeGroup`, `FanInEdgeGroup`, `SwitchCaseEdgeGroup`.
- `LocalShellTool` persistent, `LocalShellTool` stateless, `DockerShellTool`.
- `MCPStdioTool`, `MCPStreamableHTTPTool`, `MCPWebsocketTool`;
`OpenAIChatClient` vs `OpenAIChatCompletionClient`.
Examples that do **not** get separate bits: exact session id or persisted history
file path; exact shell command, workdir, timeout, or output cap; exact MCP server
command, URL, or tool names from the server; exact workflow graph shape or edge
count; model/deployment names, prompts, tool arguments, payloads.
Rough estimate: Python ~110-150 bits; .NET ~85-125 bits.
- Good, useful where mode-level distinctions are decision-relevant.
- Bad, trades simplicity for precision, increases fingerprint specificity, and
may exhaust or exceed 128 bits in Python.
#### F4. Option / behavior flag bits
The most detailed framework-owned option: bits for specific modes and behavior
switches, still excluding customer/runtime values.
Examples that get bits:
- Agent streaming used vs non-streaming used.
- `FunctionTool` `approval_mode="always_require"` vs `"never_require"`.
- `FunctionTool` `SKIP_PARSING` / result-parser path used.
- MCP sampling configured; MCP long-running task support used.
- `LocalShellTool` `clean_env` / `confine_workdir`; `DockerShellTool` container
mode.
Examples that do **not** get separate bits: function names wrapped by
`FunctionTool`; approval rule arguments or approval decisions; MCP remote tool
names or schemas; shell command text or policy regex patterns; prompt/message
content, model names, URLs, tenant/user/session identifiers.
Rough estimate: Python 150+ bits; .NET 120+ bits.
- Good, maximum framework-owned detail.
- Bad, exceeds or nearly exhausts 128 bits and is too detailed for v1 without a
concrete decision that requires it.
### Registry sharing model
#### H. Per-language bit lists (chosen)
Each SDK owns an independent list; the decoder picks the list using the language
already present in the UA product token.
- Good, **no cross-language coordination**: each SDK numbers and evolves its
features independently; adding a Python feature never touches .NET numbering.
- Good, no null placeholders for one-SDK features, no "same bit, same meaning"
rule, no SDK-aware decode caveats.
- Good, decoding is trivial: language (from UA) + version -> list -> AND.
- Neutral, two small lists to maintain instead of one (but they were going to
diverge anyway — the packages differ).
#### I. Single shared cross-language registry
- Good, one list, one number space.
- Bad, forces synchronized numbering and null placeholders for features that
exist in only one SDK, plus SDK-aware decode rules.
- Bad, the synchronization is pure accidental complexity — **the language is
already in the User-Agent**, so sharing the number space buys nothing.
### Registry maintenance
#### J. Package-local indexes + parity/no-overlap test (chosen)
- Good, each package owns private `FeatureIndex` declarations only for its own
rows; adding an optional-provider index does not require a core release after
the marker API exists.
- Good, one repository test compares the package-local declarations with the
per-language table and rejects missing rows, wrong ids, out-of-range indexes,
and any duplicate/overlapping index.
- Good, no build step, no generator to own.
#### K. Code-generate the enums from the registry
- Bad, a generator + drift test + schema test to maintain a short list of
integer constants; likely justified only if v1 deliberately chooses the most
detailed L3/L4 granularities.
### Representation (how the mask is rendered as text)
All examples below encode the same mask — bits 0, 2, 32, 48, 56 set
(agent + workflow + sequential-orchestration + foundry.chat_client + openai, in
the Python v1 list) = decimal `72339073309605893`.
#### L. Decimal — `feat=v1.72339073309605893`
- Good, human-familiar; trivial to parse.
- Neutral, no visual alignment to four-bit groups; slightly longer than hex for
large masks. No advantage over hex.
#### M. Hex (chosen) — `feat=v1.101000100000005`
- Good, compact (≤32 chars for a 128-bit mask).
- Good, decodes with one stdlib call in every language (`int(x, 16)` /
two 64-bit lane parses in .NET); each hex character corresponds to four
consecutive bit positions.
- Good, lowercase, no `0x` prefix, no leading zeros — unambiguous and stable.
A grouped variant such as `feat=v1.101.0001.0000.0005` was also considered.
Separators make the value longer and must be removed before `int(x, 16)` can
parse it, while the ordinary hex digits already preserve fixed four-bit groups.
#### N. Binary — `feat=v1.100000001000000000000000100000000000000000000000000000101`
- Good, directly shows every zero/one position.
- Bad, grows to 128 payload characters and is difficult to scan reliably.
#### O. Bit-list — `feat=v1.0,2,32,48,56`
- Good, most directly human-readable ("which bits").
- Bad, needs delimiter handling and grows with the number of set bits; a full
128-bit list is substantially larger than every fixed-width representation.
#### P. Alphabet / base-N (e.g. Crockford base32 `feat=v1.208004000005`, base62 `feat=v1.5LJRx1i6xJ`)
- Good, shortest representation.
- Bad, needs a custom alphabet + decode table on both ends; base62 is
case-sensitive (fragile through case-normalizing intermediaries); not
directly readable. Premature optimization for a value that is already ≤32
chars in hex.
All forms are ASCII. The table shows total bytes added to the existing
User-Agent, including the leading space and `(feat=v1.)` wrapper:
| Representation | Example (5 bits) | All current Python rows (63) | All current .NET rows (52) | Full 128-bit v1 |
| --- | ---: | ---: | ---: | ---: |
| Hex | 26 | 34 | 30 | 43 |
| Grouped hex | 29 | 39 | 34 | 50 |
| Decimal | 28 | 38 | 34 | 50 |
| Binary | 68 | 100 | 86 | 139 |
| Bit-list | 23 | 189 | 156 | 412 |
| Crockford base32 | 23 | 29 | 26 | 37 |
| Base62 | 21 | 26 | 24 | 33 |
There is no defensible average before rollout, and the design does not depend on
one: a process-global mask may eventually contain every assigned row. There is
no smaller per-request bit budget because the bits are not request-scoped; the
registry allocation tenet controls how many distinctions v1 assigns. Client
processing is bounded by the fixed 128-bit width: marking performs one
lock/atomic OR, and request-time stamping reads the mask, formats at most 32 hex
characters, and replaces one User-Agent comment. It performs no registry scan,
network call, or per-feature enable/disable bookkeeping.
## Decision Outcome
Chosen: **a request-time-stamped, first-party-only User-Agent `(feat=...)` token (A),
with a 128-bit process-global monotonic accumulator (S1), per-language bit lists
(H), package-local index enums kept honest by parity and no-overlap tests (J),
rendered as lowercase hex (M).**
This is a bounded design with enough v1 headroom. A 128-bit
**process-global, monotonic** mask accumulates from universal
`mark_feature_used()` calls (so it spans build/start/participation activations
that aren't bound to any service request — the per-request set model (S2) can't);
the token is **stamped per request** only when both the client/pipeline and the
actual HTTPS origin are approved, so custom origins and cross-origin redirects
cannot inherit the fingerprint; each
SDK owns an independent bit list selected by the language already in the UA; the
mask is rendered as hex (`feat=v1.101000100000005`). The dedicated
`AGENT_FRAMEWORK_FEATURE_MASK_DISABLED` opt-out drops only the mask while
keeping the base SDK identity/version User-Agent. Python's existing
`AGENT_FRAMEWORK_USER_AGENT_DISABLED` continues to suppress its entire
contribution, including the mask; this decision does not introduce a matching
whole-User-Agent switch in .NET. OTel (C) is deferred — mainly because a
broadly-emitted span attribute would leak the fingerprint into the user's
general telemetry, against the first-party-only stance and would require
user-side OTel setup that may still not make the data available to us — but left
open behind the version prefix. Per-request scoping (S2), a shared registry (I),
codegen for the initial registry (K), and the decimal/grouped-hex/binary/bit-list/
base-N representations (L, M variant, N, O, P) are rejected as complexity or
length the problem does not require.
The remaining choice before implementation is the **v1 granularity level** among
F0-F4. This is a point-in-time decision: adding new bits later is easier than
removing or redefining them, because removals/redefinitions require a new
registry version and historical decode tables. For v1, prefer the least detailed
level that answers the known product/support questions so we do not force a v2
shortly after launch. The refreshed candidate registry uses **63 Python indexes and
52 .NET indexes**, leaving 65 and 76 positions respectively. That headroom supports
normal growth; it does not waive the registry's
[allocation tenet](../specs/feature-usage-bit-registry.md#allocation-tenet).
### Consequences
- Good, adds a bounded-cost usage signal with no new data flow and few moving
parts.
- Good, transparent (public registry, human-decodable token) and disabled by a
dedicated `AGENT_FRAMEWORK_FEATURE_MASK_DISABLED` mask-only opt-out. Python's
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** |
| **OpenAI / Anthropic** (Stainless) | sidecar `X-Stainless-*` headers: lang, package version, OS, arch, runtime, runtime version; plus per-request `x-stainless-retry-count`, `x-stainless-read-timeout` | Mostly static identity (retry/timeout are per-request) | none | No (static identity) |
| **Azure SDK** (`azure-core`) | `User-Agent: azsdk-python-{pkg}/{ver} Python/{pyver} ({platform})` | No | `AZURE_TELEMETRY_DISABLED` (tracing spans only, **not** the UA) | No |
| **Google API core** | `x-goog-api-client: gl-python/… grpc/… gax/… gapic/…` | No | none | No |
| **LangSmith** | `User-Agent: langsmith-py/{ver}`; usage lives in trace payloads | No (header) | opt-in via `LANGSMITH_TRACING_V2`/`LANGCHAIN_TRACING_V2`; `…HIDE_INPUTS/OUTPUTS` | No |
Takeaways that shaped (or validate) our choices:
- **AWS `m/` is the precedent for usage-based feature flags in a first-party
User-Agent.** It validates the core idea. Its key *difference* is the encoding:
AWS uses a **comma-separated set of 12 char short codes** (open-ended, no bit
coordination, but variable length), whereas we use a fixed-width **hex
bitmask** (compact, bounded, decode-by-AND, but needs per-language bit
allocation). We keep the bitmask for boundedness and trivial AND-decoding;
AWS's short-code set is recorded as a viable alternative if bit-position
coordination ever becomes painful (it would also drop the fixed 128-bit bound).
- **A fixed-width bitmask gives bounded token size for free.** botocore must cap
the `m/` component at 1024 bytes and truncate at delimiter boundaries (with a
fallback log) precisely *because* its short-code set is unbounded. Our 128-bit
hex is ≤32 chars by construction — no size cap, no truncation logic.
- **Scope is where we diverge most — and deliberately.** botocore collects
features into a per-request `contextvars` set that is **reset between
requests**, and no-ops outside a request context to prevent cross-request
bleed. That works because every botocore feature is exercised *inside* an AWS
service request. We are more general: some features are request-scoped (a chat
call, an MCP tool invocation) but many are **not bound to any request**
(workflow build/start, provider participation, hosting startup). So we use a
**process-global, monotonic** mask (option S1), which is the only scope that can
represent the non-request features. Our mask therefore intentionally "bleeds"
(accumulates) for the life of the process — the opposite of botocore's reset —
and that is the intended semantic, not the bug botocore guards against.
- **The mechanism is private; the wire format is the contract.** botocore marks
its whole user-agent module private and "subject to abrupt breaking changes."
Same for us: the Python/.NET helpers are internal, and only the emitted token +
the per-language registry tables are the stable, decodable contract.
- **First-party-only emission** is stricter than any of the above; the closest in
spirit is Stainless headers, which only reach the owning API. We make the
client/pipeline allowlist explicit (initially Foundry/Azure OpenAI) rather than
attempting to infer safety from arbitrary request URLs. Other Azure clients
join only after telemetry access is confirmed.
- **Opt-out naming.** `AZURE_TELEMETRY_DISABLED` is the family precedent for our
`AGENT_FRAMEWORK_*_DISABLED` names. Separately, the cross-tool `DO_NOT_TRACK`
convention (honored by e.g. HuggingFace Hub) is worth considering — see Open
Questions.
Sources: botocore [`useragent.py`](https://github.com/boto/botocore/blob/develop/botocore/useragent.py)
(`_USERAGENT_FEATURE_MAPPINGS`, `register_feature_id`, `_build_feature_metadata`);
openai-python [`_base_client.py` `platform_headers()`](https://github.com/openai/openai-python/blob/main/src/openai/_base_client.py);
anthropic-sdk-python [`_base_client.py`](https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/_base_client.py);
azure-core [`_universal.py` `UserAgentPolicy`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/azure/core/pipeline/policies/_universal.py);
google-api-core [`client_info.py`](https://github.com/googleapis/python-api-core/blob/main/google/api_core/client_info.py);
langsmith-sdk [`client.py`](https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/client.py) /
[`utils.py`](https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/utils.py);
huggingface_hub [`constants.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/constants.py).
## Registry versioning and migration (v1 → v2)
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
registers the capability.
## More Information
- Mechanism & API: [SPEC-004](../specs/004-feature-usage-telemetry.md)
- Per-language bit tables, encoding, opt-out, governance: [feature-usage-bit-registry.md](../specs/feature-usage-bit-registry.md)
- Existing accumulator pattern: `python/packages/core/agent_framework/_telemetry.py`
- .NET emission policies: `dotnet/src/Microsoft.Agents.AI.Foundry/AgentFrameworkUserAgentPolicy.cs`,
`dotnet/src/Microsoft.Agents.AI.Foundry.Hosting/HostedAgentUserAgentPolicy.cs`
@@ -0,0 +1,308 @@
---
status: proposed
contact: eavanvalkenburg
date: 2026-07-24
deciders: eavanvalkenburg, chetantoshnival, taochenosu, moonbox3, giles17
---
# Python session storage and serialization
## Context and Problem Statement
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 reproducible harness is
[`python/scripts/session_serialization_benchmark.py`](../../python/scripts/session_serialization_benchmark.py):
```bash
cd python
uv run --with orjson python scripts/session_serialization_benchmark.py
```
| Codec | File size | Encode median (ms) | Decode median (ms) | Round-trip median (ms) | Disk round-trip median (ms) |
| --- | ---: | ---: | ---: | ---: | ---: |
| Standard library JSON | 1.57 MiB | 33.503 | 14.316 | 55.261 | 75.226 |
| orjson | 1.57 MiB | 25.808 | 11.754 | 39.398 | 63.319 |
| Pydantic JSON | 1.57 MiB | 28.330 | 18.344 | 53.522 | 77.096 |
| msgspec JSON | 1.57 MiB | 26.019 | 11.379 | **38.060** | 62.230 |
| msgspec MessagePack | **1.45 MiB** | **25.134** | **11.201** | 38.512 | **58.112** |
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.
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@@ -1,48 +0,0 @@
# AGENTS.md
Instructions for AI coding agents working on durable agents documentation.
## Scope
This directory contains feature documentation for the durable agents integration. The source code and samples live elsewhere:
- .NET implementation: `dotnet/src/Microsoft.Agents.AI.DurableTask/` and `dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions/`
- Python implementation: `python/packages/durabletask/` and `python/packages/azurefunctions/` (package `agent-framework-azurefunctions`)
- .NET samples: `dotnet/samples/04-hosting/DurableAgents/`
- Python samples: `python/samples/04-hosting/durabletask/`
- 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 Taskcompatible host. Show host-agnostic patterns (plain orchestration functions, `IServiceCollection` registration) before Azure Functionsspecific 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:
```bash
markdownlint docs/features/durable-agents/ --disable MD013
```
## When to update these docs
- A new durable agent feature is added (e.g., a new orchestration pattern, hosting model, or configuration option).
- The public API surface changes in a way that affects how developers use durable agents.
- New sample directories are added — update the sample links in README.md.
- The official Microsoft Learn documentation is restructured — update external links.
+7 -237
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@@ -1,239 +1,9 @@
# Durable agents
# Durable Agents Have Moved
## Overview
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 |
| Multi-agent orchestrations | Manual coordination | Deterministic, checkpointed workflows |
| 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 Taskcompatible 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).
- Optionally exposes agents as MCP tools.
**C# example:**
```csharp
using IHost app = FunctionsApplication
.CreateBuilder(args)
.ConfigureFunctionsWebApplication()
.ConfigureDurableAgents(options => options.AddAIAgent(agent))
.Build();
app.Run();
```
**Python example:**
```python
app = AgentFunctionApp(agents=[agent])
```
### Console apps / generic hosts
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));
})
.Build();
```
**Python example:**
```python
worker = DurableAIAgentWorker(TaskHubGrpcWorker(host_address="localhost:4001"))
worker.add_agent(agent)
worker.start()
```
## Deterministic multi-agent orchestrations
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.
**C#:**
```csharp
static async Task<string> WritingOrchestration(TaskOrchestrationContext context)
{
// Get a durable agent reference — works in any host (console app, Azure Functions, etc.)
DurableAIAgent writer = context.GetAgent("WriterAgent");
// Create a session to maintain conversation context across multiple calls
AgentSession session = await writer.CreateSessionAsync();
// First call: generate an initial draft
AgentResponse<TextResponse> draft = await writer.RunAsync<TextResponse>(
message: "Write a concise inspirational sentence about learning.",
session: session);
// Second call: refine the draft — the agent sees the full conversation history
AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
message: $"Improve this further while keeping it under 25 words: {draft.Result.Text}",
session: session);
return refined.Result.Text;
}
```
**Python:**
```python
def writing_orchestration(context, _):
agent_ctx = DurableAIAgentOrchestrationContext(context)
# 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.
- **Performance metrics** Monitor agent response times, token usage, and orchestration duration.
- **Debugging** Trace tool invocations and external event handling.
## Samples
- **.NET** [Console app samples](../../../dotnet/samples/04-hosting/DurableAgents/ConsoleApps/) and [Azure Functions samples](../../../dotnet/samples/04-hosting/DurableAgents/AzureFunctions/) covering single-agent, chaining, concurrency, conditionals, human-in-the-loop, long-running tools, MCP tool exposure, and reliable streaming.
- **Python** [Durable Task samples](../../../python/samples/04-hosting/durabletask/) covering single-agent, multi-agent, streaming, chaining, concurrency, conditionals, and human-in-the-loop.
## Packages
| Language | Package | Source |
| --- | --- | --- |
| .NET | `Microsoft.Agents.AI.DurableTask` | [`dotnet/src/Microsoft.Agents.AI.DurableTask`](../../../dotnet/src/Microsoft.Agents.AI.DurableTask) |
| .NET | `Microsoft.Agents.AI.Hosting.AzureFunctions` | [`dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions`](../../../dotnet/src/Microsoft.Agents.AI.Hosting.AzureFunctions) |
| Python | `agent-framework-durabletask` | [`python/packages/durabletask`](../../../python/packages/durabletask) |
| Python | `agent-framework-azurefunctions` | [`python/packages/azurefunctions`](../../../python/packages/azurefunctions) |
## Further reading
- [Azure Functions (Durable) — Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/azure-functions)
- [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/durable-task-scheduler)
- [Durable Entities](https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-entities)
- [Session TTL](durable-agents-ttl.md)
- [.NET source](https://github.com/microsoft/agent-framework-durable-extension/tree/main/dotnet/src)
- [.NET samples](https://github.com/microsoft/agent-framework-durable-extension/tree/main/dotnet/samples)
- [Python source](https://github.com/microsoft/agent-framework-durable-extension/tree/main/python/packages)
- [Python samples](https://github.com/microsoft/agent-framework-durable-extension/tree/main/python/samples)
- [Durable agent documentation](https://github.com/microsoft/agent-framework-durable-extension/tree/main/docs/features/durable-agents)
@@ -1,147 +0,0 @@
# Time-To-Live (TTL) for durable agent sessions
## Overview
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
// Agent with custom TTL of 1 day
options.AddAIAgent(shortLivedAgent, timeToLive: TimeSpan.FromDays(1));
// Agent with custom TTL of 90 days
options.AddAIAgent(longLivedAgent, timeToLive: TimeSpan.FromDays(90));
// Agent using global default (14 days)
options.AddAIAgent(defaultAgent);
});
// Disable TTL for specific agents by setting TTL to null
services.ConfigureDurableAgents(
options =>
{
options.DefaultTimeToLive = TimeSpan.FromDays(14);
// Agent with no TTL (never expires)
options.AddAIAgent(permanentAgent, timeToLive: null);
});
```
## How TTL works
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.
@@ -172,7 +172,7 @@ parsing a structured payload into a typed record), without coupling the holder t
- Authorize and bind the candidate id to the authenticated principal/tenant before using it as an
`AgentSessionStore` key or a workflow checkpoint session id.
- For multi-user hosts, wrap the store with `IsolationKeyScopedAgentSessionStore` (for example via
`UseClaimsBasedSessionIsolation(...)`), so the session namespace is scoped per principal.
`UseClaimsBasedAgentIsolation(...)`), so the session namespace is scoped per principal.
- Persist session/checkpoint state only after the run or stream has completed.
## E2E Code Samples
+500
View File
@@ -0,0 +1,500 @@
---
status: proposed
contact: eavanvalkenburg
date: 2026-07-22
deciders: eavanvalkenburg
consulted:
informed:
---
# Feature-usage telemetry via an accumulating bitmask
> Companion design for [ADR-0033](../decisions/0033-feature-usage-bitmask-user-agent.md).
> The per-language bit tables, encoding, opt-out, and governance live in
> [feature-usage-bit-registry.md](feature-usage-bit-registry.md). The registry
> allocates indexes; package-local `FeatureIndex` declarations implement them.
## What is the goal of this feature?
Give the Agent Framework team a lightweight signal about **which framework
features are actually exercised** at runtime (not merely installed), so we can
prioritise investment based on real usage. We emit a single small number — a
*feature mask* — on the User-Agent that already goes out with each request.
**Reach is deliberately bounded.** The mask accumulates from *all* feature usage,
but the `feat=` token is only stamped through an explicit allowlist of
**first-party Azure/Foundry client pipelines** whose User-Agent telemetry the
team can ingest (initially Foundry/Azure OpenAI). We do **not** send the token to
third-party providers (OpenAI direct, Anthropic, Bedrock, Gemini, Ollama,
Mistral), or to an Azure service merely because its hostname is first-party;
doing so would leak a deployment fingerprint into logs we cannot read (see
[Emission](#emission)).
The current candidate uses package-level bits plus selected major capabilities:
one bit per orchestration pattern (sequential / concurrent / group-chat /
magentic / handoff), **one bit per built-in context/history provider**, selected
skill source types, and separate Foundry chat/agent/memory/evals/toolbox bits
(plus embedding in Python).
See the
[registry](feature-usage-bit-registry.md). ADR-0033 still leaves final v1
granularity open. The refreshed candidate assigns 63 Python indexes and 52 .NET
indexes. V1 uses 128 bits, leaving 65 Python and 76 .NET positions for additive
growth.
Success metric: within one release after rollout, ≥80% of **eligible,
framework-created** first-party (Foundry) requests carry a **non-empty** feature
token whose mask reflects features activated **after** client construction (i.e.
the token is live, not frozen — see the request-time stamping requirement
below). This measures transport coverage, not feature invocation volume.
Secondary: ability to describe which process-lifetime feature bits are observed
together in eligible traffic (e.g. "requests observed from processes that have
used workflows"). Repeated requests carrying a bit are not additional uses.
This is done **transparently**: the bit registry is public, the emitted value is
human-decodable, and a dedicated `AGENT_FRAMEWORK_FEATURE_MASK_DISABLED`
disables the mask while preserving the base User-Agent. Python's existing
`AGENT_FRAMEWORK_USER_AGENT_DISABLED` continues to suppress its entire
User-Agent contribution, mask included.
## What is the problem being solved?
Today we only know which packages are *installed* (from package telemetry) or
that *some* Agent Framework call happened (the existing
`agent-framework-python/{version}` User-Agent). We have no usage-based signal
about feature combinations, and no way to tell that, say, a process uses
workflows + MCP + Foundry together. Collecting this through bespoke events would
add cost and new data flows; folding a tiny accumulating integer into telemetry
we already send is far cheaper and easier to reason about for privacy.
## Mechanism
### Process-global accumulator in `core`
The accumulator and its helpers live in the existing
`agent_framework/_telemetry.py` (alongside `get_user_agent()` /
`prepend_agent_framework_to_user_agent()`), so the User-Agent machinery stays in
one module. It owns a process-global 128-bit accumulator. Python's arbitrary-size
`int` stores it directly. A **dedicated**
`AGENT_FRAMEWORK_FEATURE_MASK_DISABLED` that drops **only** the feature mask
while keeping the base `agent-framework-python/{version}` User-Agent is
introduced by this design. The existing Python
`AGENT_FRAMEWORK_USER_AGENT_DISABLED` continues to drop the whole User-Agent
contribution, mask included:
```python
# agent_framework/_telemetry.py (same module as get_user_agent)
# IS_TELEMETRY_ENABLED already defined here (AGENT_FRAMEWORK_USER_AGENT_DISABLED)
FEATURE_MASK_DISABLED_ENV_VAR = "AGENT_FRAMEWORK_FEATURE_MASK_DISABLED"
REGISTRY_VERSION = 1
_feature_mask = 0
_feature_mask_lock = threading.Lock()
def _feature_mask_enabled() -> bool:
"""Mask is on unless the UA is disabled or the dedicated flag is set."""
if not IS_TELEMETRY_ENABLED:
return False
return os.environ.get(FEATURE_MASK_DISABLED_ENV_VAR, "false").lower() not in ("true", "1")
def mark_feature_used(index: int) -> None:
"""OR a feature bit into the process-global mask.
Called the first time a feature is exercised. Cheap and idempotent;
a no-op when the feature mask is disabled.
"""
global _feature_mask
if not _feature_mask_enabled():
return
if not 0 <= index < 128:
raise ValueError(f"Feature index must be in range 0..127, got {index}")
with _feature_mask_lock:
_feature_mask |= 1 << index
def get_feature_token() -> str | None:
"""Return ``v<version>.<hex_mask>`` for the accumulated mask, or None."""
if not _feature_mask_enabled() or _feature_mask == 0:
return None
return f"v{REGISTRY_VERSION}.{_feature_mask:x}"
```
- **Per package/feature, usage-based:** `mark_feature_used()` is called at the
feature's first meaningful activation, never at import/install time. For
operational clients, tools, providers, and hosts, activation is the first
public operation that exercises the capability. Construction is a valid mark
point only when construction itself performs the capability (for example,
registering/starting runtime resources), not merely because a DI container
instantiated an otherwise-unused object.
- **Process-global and monotonic — intentionally never reset.** Unlike a
per-request scheme (e.g. botocore's `contextvars` feature set that resets
between calls), our mask spans the whole process because many features are not
bound to any service request — an agent or workflow may first run, a provider
may first participate in a session, and a host may start serving independently
of the later request that emits the token. The single global
mask is the only scope that can represent them, and its monotonic "usage so
far" growth is the intended semantic, not a bleed bug. Concurrency-safe via the
module lock (Python) / two atomic 64-bit lanes in .NET.
- **Binary and non-countable.** A set bit means "this feature was observed at
least once in this process before this request." Repeating that bit on every
later eligible request does not represent additional uses and must not be
interpreted as request, invocation, agent, user, or tenant counts.
- **No scoped enable/disable bookkeeping.** Making the mask exact per operation
would add hot-path state changes, context propagation, and reset/error-path
handling. It would also produce a more detailed behavioral trace and therefore
increase privacy sensitivity. V1 deliberately keeps the coarser process-level
Boolean.
- **Token is safe by construction.** The emitted value is `v{int}.{hex}`
characters limited to `[0-9a-fv.]` — so no header-injection sanitization is
required. A 128-bit mask is at most 32 hex characters (contrast botocore,
which must sanitize and cap arbitrary component strings).
- **Private API.** `mark_feature_used`, `get_feature_token`, `apply_feature_token`
and the mask itself are internal helpers; only the emitted token and the
per-language registry tables are the stable, decodable contract.
- **No import cycles:** the accumulator lives in core, while each package owns
private index constants for its own features and calls the core marker. Core
never imports optional packages.
### Interpretation contract
At time 1, Agent A in a worker can use MCP and a Foundry chat client. At time 2,
Agent B in the same worker can make a normal Foundry chat call without MCP. The
time-2 request still carries the MCP bit because MCP was observed earlier in the
process.
That request means only "this process has used MCP." It does not mean Agent B
used MCP, that MCP was used on the time-2 request, or that two requests carrying
the bit equal two MCP uses. Without a separate stable process identifier, the
signal also cannot produce unique-process counts. Supported analysis is limited
to coarse observed-feature prevalence and feature co-occurrence, with the
request-weighting limitation called out explicitly.
### Bit constants
The registry is the allocation authority. Each package defines a private,
hand-written `FeatureIndex` IntEnum (or equivalent constants) containing only
the rows it owns. Core owns core indexes plus the accumulator; optional packages
can allocate and ship new indexes without requiring a core release after the
marker API exists.
```python
# agent_framework_foundry/_feature_usage.py
from enum import IntEnum
from agent_framework._telemetry import mark_feature_used # pyright: ignore[reportAttributeAccessIssue]
class FeatureIndex(IntEnum):
FOUNDRY_CHAT_CLIENT = 48
class RawFoundryChatClient:
async def _send_request(self) -> None:
mark_feature_used(FeatureIndex.FOUNDRY_CHAT_CLIENT)
...
```
A repository validation test reads every package-local declaration and the
matching language/version table. It fails when an index is out of range, missing
from the registry, duplicated/overlapping across packages, or mapped to the wrong
id. For reference, in v1 `FoundryChatClient` → index 48,
`FoundryAgent` → index 49, Foundry memory → index 50.
### Usage activation points
- **Clients/embeddings/evals:** first outbound operation.
- **Tools/MCP:** first connection, discovery, or invocation that exercises the
tool surface.
- **Context/history providers:** first provider hook or load/save operation, not
constructor-only registration.
- **Agents/workflows/orchestrations:** first run/build/start operation that
activates the defined runtime.
- **Hosting:** first serve/start/route activation.
- **Constructor marking:** allowed only when construction itself performs one of
those activations or acquires/registers the runtime resource.
## Emission
**One path in v1: the User-Agent `feat=` token, stamped at request time on an
explicit allowlist of first-party Azure/Foundry client pipelines only.**
Marking (`mark_feature_used`) is **universal** — every feature sets its index
regardless of provider. Only **emission** is scoped. A user who never calls a
first-party endpoint emits no token; this is the honest, intended behaviour (no
third-party leakage, no signal we couldn't read anyway).
The existing base User-Agent behavior (`agent-framework-python/{version}` plus
any dynamically detected hosting prefix) is unchanged; packages continue using
their current `default_headers`, `user_agent`, suffix, or policy mechanisms.
`get_user_agent()` stays base-only (no `feat=`). The `feat=` token is
**separate**, added **only** by eligible Azure/Foundry clients, and
**re-evaluated on each request** so it reflects the mask accumulated so far. A
helper stamps it:
This request-time read does not make the signal request-scoped. The payload
remains the process-global Boolean history described above.
```python
# agent_framework/_telemetry.py
def apply_feature_token(user_agent: str) -> str:
"""Append/refresh the live ``(feat=v<ver>.<hex>)`` comment on a UA string.
Re-reads the current mask on every call, so newly accumulated bits are
reflected immediately. Idempotent: replaces an existing ``(feat=...)``
comment rather than appending a second.
"""
token = get_feature_token() # None when disabled or mask == 0
base = _strip_feature_comment(user_agent)
return f"{base} (feat={token})" if token else base
```
Emission requires **both**:
1. an explicitly approved framework client/pipeline family; and
2. the actual request's normalized HTTPS origin matching that family's reviewed
first-party origin allowlist.
Credentials, `use_azure`, or an Azure-named setting alone do not approve a
destination. Approval depends on the **resolved origin**: customer-specific
subdomains on reviewed Azure/Foundry suffixes remain eligible even when supplied
through `base_url` / `AZURE_OPENAI_BASE_URL`, while customer gateways and unknown
OpenAI-compatible origins are denied by default. The check runs on every actual
request, including redirect hops; a cross-origin or otherwise unapproved redirect
removes `(feat=...)` before sending.
Eligible first-party clients install a **request hook** that performs this
classification and calls `apply_feature_token()`:
- **OpenAI-SDK clients created by Agent Framework**: construct the underlying
client with
`http_client=DefaultAsyncHttpxClient(event_hooks={"request": [_stamp_feat_hook]})`.
Using OpenAI's `DefaultAsyncHttpxClient` preserves the SDK's redirect,
connection-limit, and timeout defaults; a plain `httpx.AsyncClient` must not
replace them. The hook adds or removes the token based on the approved pipeline
plus actual-origin classification. Caller-supplied clients/transports are not
replaced or patched.
- **azure-core pipeline clients**: start with `AIProjectClient` paths whose
telemetry is confirmed ingestible. When Agent Framework constructs/configures
an approved pipeline, add a separate per-call `SansIOHTTPPolicy` whose
`on_request` performs the same actual-origin check and calls
`apply_feature_token()` on
`request.http_request.headers["User-Agent"]`. Do not stamp `SearchClient`,
`CosmosClient`, or another Azure client merely because it is first-party; add
it to the allowlist only after confirming the data path. This mirrors .NET's
request-time `PipelinePolicy` exactly.
This fixes the frozen-at-construction problem: the token is materialised at
**send time**, not client-init time, so it carries features activated after the
client was created. It also confines the token to first-party endpoints. Caller-owned
clients are not patched, and toolkit-owned clients without a supported public
hook are outside v1 coverage.
Encoding uses the RFC 7231 **comment** form `(feat=v1.<hex>)` (metadata, not a
product token), placed after the agent-framework product token, e.g.:
```text
foundry-hosting/agent-framework-python/1.2.3 (feat=v1.2a)
```
### OpenTelemetry — not in v1
An OTel span attribute carrying the same value was considered but **deferred —
primarily for privacy, not complexity**. Unlike the first-party-only UA token, a
span attribute broadcasts the feature-combination fingerprint into the user's
**general** telemetry pipeline, which is commonly exported to third-party APM
vendors (Datadog, Honeycomb, …) — re-introducing exactly the leakage the
first-party scoping was chosen to avoid. (It also carries a cardinality footgun:
a monotonically-growing, combinatorial value must never become a metric
dimension.) The version prefix leaves the door open to add it later **if** the
User-Agent path cannot answer a concrete query and there is an acceptable
scoped/redacted variant; v1 ships the UA path only. See
[ADR-0033 → option C](../decisions/0033-feature-usage-bitmask-user-agent.md#considered-options).
## API Changes
New **internal cross-package** surface in
`agent_framework._telemetry` (not exported from `agent_framework`):
- `mark_feature_used(index: int) -> None`
- `get_feature_token() -> str | None` — returns `v<ver>.<hex>` or `None`.
- `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.
agent = Agent(client=FoundryChatClient(...), instructions="...")
# Agent use marks bit 0; FoundryChatClient marks bit 48
await agent.run("Hello")
# Outgoing request to Foundry carries:
# User-Agent: agent-framework-python/1.2.3 (feat=v1.<mask-at-send-time>)
# Third-party client: NO feat token is added (no first-party hook).
other = Agent(client=OpenAIChatClient(...), instructions="...")
await other.run("Hi")
# Outgoing request to OpenAI carries only:
# User-Agent: agent-framework-python/1.2.3
```
Drop only the feature mask (keep the base User-Agent):
```bash
AGENT_FRAMEWORK_FEATURE_MASK_DISABLED=true python app.py
# Foundry request User-Agent: agent-framework-python/1.2.3 (no (feat=...) comment)
```
Python only: use the existing flag to drop its entire User-Agent contribution
(mask included):
```bash
AGENT_FRAMEWORK_USER_AGENT_DISABLED=true python app.py
```
## .NET mapping
- Core owns `FeatureUsage.MarkUsed(int index)` plus the core package's private
index declaration. Each optional assembly owns a private `FeatureIndex` enum
containing only its allocated rows. These are index positions `0..127`, not
`[Flags]` values; `MarkUsed` performs the shift.
- Store the 128-bit mask as **two `long` lanes** (`low` for bits 063, `high`
for 64127). Marking touches one lane with `Interlocked.Or` where available
and a small `Interlocked.CompareExchange` loop on `netstandard2.0` / `net472`.
Read each lane atomically. Since bits only move from zero to one, a concurrent
two-lane snapshot may miss a just-added bit but can never invent or clear one;
the next request includes it.
- Format without depending on `UInt128`: if `high == 0`, emit `low` as lowercase
hex; otherwise emit `high` without leading zeros followed by `low:x16`. Cast
each signed lane to `ulong` before formatting so bits 63 and 127 are preserved.
Reject indexes outside `0..127`.
- **Emission is stamped at request time and first-party-scoped**, matching
Python. The
existing `AgentFrameworkUserAgentPolicy` / `HostedAgentUserAgentPolicy`
pipeline policies already run per request — extend them to apply the same
approved-pipeline + actual-origin classifier, append/refresh the `(feat=...)`
comment only for approved destinations, and remove it on unapproved redirect
hops. Do not register it on third-party `IChatClient`s.
- Same **wire format** (`v<version>.<hex>` comment, hex encoding) and the same
dedicated mask-only opt-out (`AGENT_FRAMEWORK_FEATURE_MASK_DISABLED`). The
**mask is decoded per language**: indexes are not shared, so a decoder must
read the language from the UA product token and select that language's table
before decoding. (.NET's policy was already request-time, so there is no
Python/.NET timing asymmetry.) Adding a .NET whole-User-Agent opt-out is
outside this design.
## Keeping the bitmap in sync
[feature-usage-bit-registry.md](feature-usage-bit-registry.md) is the published
allocation contract. Package-local `FeatureIndex` declarations are the runtime
implementation. There is deliberately **no shared numbering across languages**
and **no machine-readable registry file**.
One repository validation test gathers every package-local declaration for one
language/version and parses the matching Markdown table. It asserts:
1. every declared index is within `0..127`;
2. every `(index, id)` exactly matches one registry row;
3. the union of declarations has no duplicate/overlapping indexes;
4. every non-reserved registry row is declared exactly once.
Adding an optional-package feature therefore changes that package and the
registry, not core. If a programmatic decoder is built later, export the table
to JSON then.
### Decoding
```
UA: agent-framework-python/1.2.3 (feat=v1.2a)
│ │ └ hex mask
│ └ version
└ language → pick the Python table (version 1)
```
Read language → pick the table; read `vN` → pick that version; `AND` the hex mask
against each bit. Unknown bits (from a newer SDK than the decoder's copy of the
table) are ignored.
## Implementation plan (post-approval)
1. **Privacy approval** — confirm the first-party-only feature-combination
signal, retention, access, allowed queries, and opt-out behavior before code
ships.
2. **Core accumulator** — in `agent_framework/_telemetry.py` add the 128-bit
mask, lock, `mark_feature_used(index)`, `get_feature_token`, and
`apply_feature_token`; `get_user_agent()` stays base-only.
3. **Package-local indexes + validation** — add private `FeatureIndex`
declarations to packages and a repository test for exact registry parity,
complete coverage, range, and zero overlap.
4. **First-party request-time hooks** — use OpenAI's
`DefaultAsyncHttpxClient` for framework-created clients and the separate
azure-core `SansIOHTTPPolicy`. Require approved pipeline **and** approved
actual origin on every request/redirect hop. Verify custom origins and
cross-origin redirects never carry the token.
5. **Mark feature usage** — call `mark_feature_used(FeatureIndex.X)` at the
first meaningful activation. Operational clients/providers/tools mark on
their first real operation; build/start points mark compositional features.
Constructor-only marking requires construction itself to exercise the
capability.
6. **.NET parity** — package-local index enums plus the two atomic 64-bit lanes
with `Interlocked.Or` / compare-exchange fallback; extend existing request-time
Foundry UA policies through the shared destination classifier and formatter.
7. **Docs & tests** — update package `AGENTS.md`/skills; tests for **both**
Python opt-out paths (dedicated mask-only and existing whole-UA), the
dedicated .NET mask-only opt-out, first-party scoping, and the live
(non-frozen) UA.
## Limitations & open questions
The decision-level limitations and unresolved trade-offs — reach, per-process
(not per-call) attribution, v1 granularity, fingerprinting residue, and the OTel
question — are owned by the ADR (the dedicated mask-only opt-out is now decided
and included). See
**[ADR-0033 → Limitations](../decisions/0033-feature-usage-bitmask-user-agent.md#limitations)**
and **[Open Questions](../decisions/0033-feature-usage-bitmask-user-agent.md#open-questions-for-decider-discussion)**.
This spec is the implementation reference; it does not re-litigate those choices.
Implementation-only note:
- **Per-request hook overhead is negligible** (a flag check, one Python integer
snapshot or two atomic .NET lane reads, and a string concat per first-party
request), but benchmark the hot path once if a high-QPS Foundry scenario is in
scope.
@@ -0,0 +1,583 @@
---
status: proposed
contact: eavanvalkenburg
date: 2026-07-27
deciders: eavanvalkenburg
---
# Python function-calling loop contract and validation matrix
## Scope
This specification defines the required behavior and validation coverage for the Python function-calling loop.
It covers:
- normal local function execution;
- streaming and non-streaming response aggregation;
- tool approval request and resume;
- approved, rejected, mixed, and replayed approval rounds;
- reasoning content and opaque reasoning signatures bound to function calls;
- history persistence and service-side continuation;
- error, user-input, middleware-termination, and loop-limit paths;
- provider and transport serialization of function calls and results.
The primary implementation is in `python/packages/core/agent_framework/_tools.py`. History replay behavior in
`python/packages/core/agent_framework/_sessions.py`, provider serializers, hosting packages, and UI transports are
part of the same contract when they carry function-call loop content.
## Change sensitivity
This code is high risk. Small changes can produce duplicate side effects, orphaned calls or results, invalid
provider histories, invisible streaming results, stale approval authority, or loops that never terminate.
Dropping reasoning content that a service binds to a tool call can also make an otherwise balanced call/result
transcript invalid.
Any change to the function-calling loop or its approval/history/serialization paths must:
1. identify every affected row in the scenario matrix below;
2. add or update the corresponding regression tests;
3. validate streaming updates, streaming finalization, and non-streaming output where applicable;
4. validate both model-bound history and caller-visible responses;
5. run the full core package tests plus every affected provider or transport package;
6. run source typing, test typing, and syntax checks for every affected package;
7. receive extra review focused on call/result pairing, exactly-once execution, and history replay.
A passing narrow regression test is not sufficient evidence for changes in this area.
### Contribution ownership
Issues involving this code must not be picked up by external contributors without first checking with the Agent
Framework core team. The core team must confirm the intended behavior, affected scenario-matrix rows, ownership
across core/providers/transports, and the required validation scope before implementation starts.
## Flow diagrams and code map
### Main function-calling flow
The main control flow deliberately has separate streaming and non-streaming methods. They share policy helpers, but
their output mechanics differ: one returns an aggregated `ChatResponse`; the other yields `ChatResponseUpdate`
items and is finalized by `ResponseStream`.
The diagrams use only the generic distinction between **local tools**, which Agent Framework executes, and
**hosted-service tools**, whose calls and approval decisions are owned by a remote service. Provider-specific wire
formats and regression tests appear later in the scenario matrix.
```mermaid
flowchart TD
Entry["FunctionInvocationLayer.get_response(...)"]
Setup["Prepare middleware, options, session, budget state,<br/>and execute_function_calls partial"]
Enabled{"Function invocation enabled?"}
Direct["Delegate directly to super().get_response(...)"]
Mode{"stream?"}
NonStream["_get_response_with_function_invocation(...)"]
Stream["_stream_response_with_function_invocation(...)"]
Resolve["_resolve_approval_responses(...)<br/>runs once before the model-iteration loop"]
ApprovalAction{"approval action"}
Immediate["Return/yield terminal result or user-input request<br/>without another model call"]
ApprovalPolicy["Record approval executions;<br/>apply stop/function-call-limit policy"]
Model["Call super_get_response(...)<br/>response may contain reasoning + function_call"]
Process["_process_model_function_calls(...)"]
FunctionAction{"function-processing action"}
Execute["_execute_function_calls(...)"]
Try["_try_execute_function_calls(...)"]
Single["_execute_single_function_call(...)"]
Handle["_handle_function_call_results(...)"]
PostCallPolicy["Record executions; apply error/function-call-limit policy;<br/>reset required tool choice"]
Advance["_prepare_messages_for_next_iteration(...)"]
More{"iteration budget remains?"}
Final["Final model call with tool_choice = none<br/>and deterministic fallback if needed"]
Output["Return ChatResponse or complete ResponseStream"]
Entry --> Setup --> Enabled
Enabled -- no --> Direct
Enabled -- yes --> Mode
Mode -- no --> NonStream
Mode -- yes --> Stream
NonStream --> Resolve
Stream --> Resolve
Resolve --> ApprovalAction
ApprovalAction -- return --> Immediate --> Output
ApprovalAction -- stop --> ApprovalPolicy
ApprovalAction -- continue --> ApprovalPolicy
ApprovalPolicy --> More
Model --> Process
Process --> Execute --> Try --> Single --> Handle --> FunctionAction
FunctionAction -- return --> Output
FunctionAction -- stop --> PostCallPolicy
FunctionAction -- continue --> PostCallPolicy
PostCallPolicy --> Advance
Advance --> More
More -- yes --> Model
More -- no --> Final --> Output
```
Code-reading landmarks:
- `get_response(...)` owns setup and selects the response mode.
- `_get_response_with_function_invocation(...)` owns non-streaming aggregation.
- `_stream_response_with_function_invocation(...)` owns streamed emission/finalization.
- `_resolve_approval_responses(...)` handles only inbound approval decisions.
- `_process_model_function_calls(...)` handles only calls from a completed model response.
- `_try_execute_function_calls(...)` decides approval/declaration/execution behavior for a batch.
- `_replace_approval_contents_with_results(...)` is the occurrence-aware approval transcript normalizer.
- `FunctionInvocationLayer._update_function_invocation_continuation_state(...)` updates continuation state after
every service response. Provider layers may override it to carry provider-specific continuation metadata into
the next service call, but must delegate to the base implementation so generic conversation continuation remains
synchronized with the active `AgentSession`.
### Approval pause and resume
```mermaid
sequenceDiagram
participant Caller
participant History as HistoryProvider
participant Layer as FunctionInvocationLayer
participant Tool
participant Model
Caller->>Layer: Initial user request
Layer->>Model: Messages + tools
Model-->>Layer: reasoning content + function_call
Layer->>Layer: Tool requires approval
Layer-->>Caller: function_call + function_approval_request
Caller->>Layer: function_approval_response
Layer->>Layer: Copy caller-owned messages
Layer->>Layer: _resolve_approval_responses(...)
alt approved
Layer->>Tool: Execute exactly once
Tool-->>Layer: result or exception
Layer->>Layer: Create terminal function_result
else rejected
Layer->>Layer: Create synthetic rejection function_result
end
Layer-->>Caller: Terminal result message/update
alt tool requests more user input
Layer-->>Caller: User-input request with assistant role
else middleware terminates
Layer-->>Caller: Termination result
else error limit reached
Layer->>Model: Normalized reasoning/call/result history, tools disabled
Model-->>Layer: Final assistant response
Layer-->>Caller: Final assistant response
else continue normally
Layer->>Model: Normalized reasoning/call/result history
Model-->>Layer: Final assistant response or another function_call
Layer-->>Caller: Final assistant response / continued loop
end
Layer-->>History: Persist caller input + returned response
Note over History: Later model replay filters approval request/response wrappers
```
The terminal result is caller-visible in both modes. The private normalized message copy is model-visible. The
original caller input and earlier response remain unchanged.
### Reasoning-bound function-call groups
Some hosted services bind reasoning content or an opaque reasoning signature to the function call that follows it.
For those services, reasoning is not optional decoration; it is part of the provider-valid function-call group.
```mermaid
flowchart TD
Response["Assistant response:<br/>reasoning content + function_call"]
Group["One logical reasoning/function-call group"]
Owner{"local or hosted-service tool?"}
Local["Local execution"]
Hosted["Hosted service owns tool execution/state"]
Result["Terminal function_result or hosted result"]
Continuation{"continuation mode"}
Stateless["Stateless or framework-history replay"]
Replayable{"reasoning payload/signature<br/>is replayable?"}
Replay["Replay reasoning + call + result atomically"]
Reject["Fail before the service call;<br/>do not send a lossy transcript"]
Service["Hosted-service continuation"]
Reference["Reference service-stored reasoning/call;<br/>send only the new result or approval decision"]
Compact{"compaction needed?"}
Atomic["Keep or exclude the complete<br/>reasoning/call/result group"]
Caller["Caller-visible response retains reasoning<br/>with the function-call turn"]
Response --> Group --> Owner
Group --> Caller
Owner -- local --> Local --> Result
Owner -- hosted service --> Hosted --> Result
Result --> Compact
Compact -- yes --> Atomic --> Continuation
Compact -- no --> Continuation
Continuation -- stateless / local history --> Stateless --> Replayable
Replayable -- yes --> Replay
Replayable -- no --> Reject
Continuation -- service-managed --> Service --> Reference
```
The generic contract is:
- reasoning content remains ordered immediately before or alongside the function call it explains;
- a terminal result does not replace or discard the reasoning/call portion of the active group;
- stateless replay includes the service-required reasoning payload or opaque signature;
- if required reasoning cannot be reconstructed, the adapter fails before sending invalid or lossy history;
- service-managed continuation may rely on the hosted service's stored reasoning/call items and send only new
outputs or approval decisions;
- compaction keeps or removes the entire reasoning/call/result group atomically.
In the code, core response aggregation preserves reasoning `Content` items, compaction annotations bind reasoning to
the tool-call group, and provider adapters serialize or reconstruct the provider-specific reasoning representation.
### Approval correlation, replay, and reused ids
`call_id` is not globally unique forever. The normalizer therefore tracks open logical occurrences in transcript
order instead of keeping one global result per id.
```mermaid
flowchart TD
Scan["Scan normalized messages in order"]
Kind{"content type"}
Call["function_call:<br/>open a call occurrence"]
Request["function_approval_request"]
Bind{"unbound call occurrence<br/>with same call_id?"}
BindExisting["Bind request id to existing occurrence<br/>and remove wrapper"]
Duplicate{"same request identity<br/>already restored?"}
DropDuplicate["Remove replayed duplicate wrapper"]
Restore["Restore embedded function_call<br/>as a new occurrence"]
Placeholder["function_result with APPROVAL_PENDING:<br/>attach placeholder to open occurrence"]
Completed["terminal function_result:<br/>close earliest open occurrence"]
Response["function_approval_response"]
Pending{"response still pending?"}
RemoveOld["Remove already-resolved historical response"]
Decision{"approved?"}
Approved["Pop next execution result for this call_id"]
Rejected["Create synthetic rejection result"]
HasPlaceholder{"occurrence has placeholder?"}
Replace["Replace placeholder and remove response wrapper"]
ReplaceResponse["Replace response wrapper with terminal content"]
Close["Close occurrence; append terminal content<br/>to resumed response"]
Next["Continue scan"]
Scan --> Kind
Kind -- function_call --> Call --> Next
Kind -- approval request --> Request --> Bind
Bind -- yes --> BindExisting --> Next
Bind -- no --> Duplicate
Duplicate -- yes --> DropDuplicate --> Next
Duplicate -- no --> Restore --> Next
Kind -- pending placeholder --> Placeholder --> Next
Kind -- terminal result --> Completed --> Next
Kind -- approval response --> Response --> Pending
Pending -- no --> RemoveOld --> Next
Pending -- yes --> Decision
Decision -- yes --> Approved --> HasPlaceholder
Decision -- no --> Rejected --> HasPlaceholder
HasPlaceholder -- yes --> Replace --> Close --> Next
HasPlaceholder -- no --> ReplaceResponse --> Close --> Next
Next --> Kind
```
This flow corresponds to `_ApprovalCallOccurrence`, `_collect_approval_responses(...)`, and
`_replace_approval_contents_with_results(...)`.
### History and service-side continuation
```mermaid
flowchart LR
Store["History backing store<br/>(may retain approval wrappers for audit)"]
Load{"HistoryProvider.load_messages?"}
Filter["_filter_approval_control_messages(...)"]
Context["SessionContext model history:<br/>function_call + terminal function_result"]
Current["Current caller input:<br/>new function_approval_response"]
Layer["FunctionInvocationLayer private copy"]
Local{"local or hosted-service approval?"}
LocalResult["Execute locally and normalize to function_result"]
Hosted["Hosted-service adapter"]
StoredRequest["Prior service-issued approval request"]
NewResponse["Current hosted approval decision"]
Skip["Do not replay the stored request inline"]
Send["Send the approval decision exactly once"]
Later["Later turn"]
Manual["Manual-history caller"]
Store --> Load
Load -- yes --> Filter --> Context --> Layer
Load -- no --> Layer
Current --> Layer
Layer --> Local
Local -- local --> LocalResult --> Later
Local -- hosted service --> Hosted
StoredRequest --> Hosted --> Skip
NewResponse --> Hosted --> Send --> Later
Later --> Store
Manual -. owns equivalent filtering .-> Layer
```
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.
- 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.
- 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 not processed as local calls. | `test_hosted_tool_approval_response`, `test_hosted_mcp_approval_response_passthrough`, `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` |
### 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` |
| Reused-id placeholders | Placeholder results consume approved results by occurrence. | `test_replace_approval_contents_with_results_correlates_reused_call_id_placeholders` |
| 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` |
| 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` |
| Restored approval state | Serialized `ToolApprovalState` restores mixed-batch behavior. | `test_mixed_batch_accepts_restored_tool_approval_state` |
| 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` |
| 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 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` |
| Approved success | Successful approved execution returns one result. | `test_approved_function_call_successful_execution` |
| 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` |
| Unknown call handling | Configured false returns an error result; configured true raises. | `test_function_invocation_config_terminate_on_unknown_calls_false`, `test_function_invocation_config_terminate_on_unknown_calls_true`, streaming equivalents |
| 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` |
| 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` |
| OpenAI end-to-end hosted approval | Hosted request parses, response sends, and continuation completes. | `test_end_to_end_mcp_approval_flow` |
| 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 `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 --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
- #7267 / #7271 and #7304 — replayed calls and reused ids
- #7043 — provider-injected approval execution
- #6828 — AG-UI `confirm_changes` snapshot correlation
- #7212 — non-adjacent and reused-id compaction integrity
- #7125 — service-side approval response serialization
- #7045 — post-limit tool-content transcript integrity
- #6973 — declaration-only streaming metadata and argument integrity
- #6851 — duplicate side effects after approval continuation
- #7383 — bind approval responses to framework-issued requests after this foundation merges
- #6963 / #7095 — opaque reasoning-signature replay
- #6074 / #7233 — reasoning-paired tool-call replay
- #6450 / #6794 — provider message and tool-result serialization
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# Feature-usage bit registry (per-language)
> **Status:** draft, accompanies [ADR-0033](../decisions/0033-feature-usage-bitmask-user-agent.md)
> and [SPEC-004](004-feature-usage-telemetry.md).
> **Version:** `1` per language · **Width:** 128-bit
This document is the proposed human-readable registry for the feature-usage
mask. Until ADR-0033 is accepted and the index declarations ship, these tables
are a **candidate mapping**, not a stable wire contract. The table is the
allocation authority and published decoder contract; package-local private
`FeatureIndex` declarations implement the rows they own. There is no generated
artifact.
This telemetry is intentionally **transparent**: this registry is public, the
emitted value is human-decodable, and a dedicated env var disables the mask
without removing the base User-Agent. Python's existing whole-User-Agent opt-out
also suppresses its mask; see [Opt-out](#opt-out).
## What is collected
A single 128-bit integer (the *feature mask*) describing **which Agent Framework
features were exercised** in a process — not which packages are installed. The
candidate below uses package-level bits plus selected major capabilities: core
agent/workflow/MCP features, stable skill source types, each orchestration
pattern, each individual built-in context/history provider, and distinct Foundry
surfaces. ADR-0033 still leaves the final v1 granularity open. A feature sets its
index at first meaningful activation; the SDK shifts that index, ORs the mask,
and emits the value.
No identifiers, arguments, prompts, payloads, or user data are encoded — only the
coarse Boolean \"this feature was observed at least once in this process\" per
registered bit. A repeated bit on later requests is the same observation, not
another use and not a count.
## Allocation tenet
**An index represents a stable, framework-owned capability whose adoption answers a
concrete product or support question.** It has a clear actual-use mark point in a
public entry path, and the privacy review covers the resulting distinction.
Keep imports, installation state, aliases, wrappers, internal helpers, and
implementation decorators such as caching/filtering/deduplication within their
own capability bit. Customer/runtime values — names, prompts, arguments, URLs,
identifiers, configuration choices — never become bits. A proposed distinction
without a concrete query and named decision owner waits.
Operational clients, tools, providers, and hosts mark on their first real public
operation/participation. Constructor marking is reserved for cases where
construction itself activates or registers the capability; DI instantiation
alone is not usage.
Ids use the package/integration name for a package-level signal and add a
capability suffix only when the row tracks a narrower surface. They describe the
registered feature, not an inheritance hierarchy: for example, Python
`hosting` is the base `agent-framework-hosting` package, while `hosting.a2a` is
the separate hosting-A2A integration.
## Per-language, not shared
The two tables below are **independent**. Feature indexes are **not** shared across
languages — Python bit 13 and .NET bit 13 do not mean the same thing. This is
deliberate: the User-Agent product token already names the language
(`agent-framework-python` vs `agent-framework-dotnet`), so a decoder selects the
right table from the UA and decodes against it. Each SDK numbers and evolves its
features independently — no cross-language synchronization, no null placeholders,
no \"same bit, same meaning\" rule.
## Encoding
- **Width:** 128-bit unsigned integer per language.
- **Versioning:** the emission carries the version so a decoder knows the bit
mapping in effect (version is per language).
- **User-Agent:** the mask is an RFC 7231 **comment** (metadata, not a product
token), placed after the agent-framework product token:
```text
agent-framework-python/1.2.3 (feat=v1.<hex_mask>)
```
where `<hex_mask>` is lowercase hex, no leading zeros, no `0x` prefix. Example
for bits 0, 1, 5 set (`0b100011 = 0x23`):
```text
agent-framework-python/1.2.3 (feat=v1.23)
```
- **Decoding:** read the **language** from the product token, pick that table;
read `vN`, pick that version; test `mask & (1 << index)` for each row. Unknown indexes
(newer SDK than the decoder's copy) are ignored.
## Emission scope (where the mask is sent)
- **Marking is universal:** every feature sets its index at first meaningful
activation, regardless of provider.
- **User-Agent `(feat=...)` comment — approved first-party clients only,
stamped at request time.** Added only when both the **Azure / Foundry**
client/pipeline family and the actual HTTPS origin are approved, re-evaluated
on every request and redirect hop. Custom origins are default-deny and an
unapproved redirect removes the token. It is
**never** sent to third-party providers — a feature fingerprint must not leak
into logs we cannot read. See [SPEC-004](004-feature-usage-telemetry.md#emission).
- **OpenTelemetry: not in v1.** Deferred primarily for privacy (a span attribute
would broadcast the fingerprint into the user's general telemetry / third-party
APM vendors). Left open behind the version prefix; see
[ADR-0033](../decisions/0033-feature-usage-bitmask-user-agent.md#considered-options).
## Index table — Python (`agent-framework-python`, version 1)
Layout: core features 031, orchestration patterns 3247, and
provider/integration packages from 48.
The provider/integration block is intentionally **not** partitioned by vendor
ownership. Some packages span first- and third-party services, ownership can
change, and protocols/storage integrations do not fit a stable first/third-party
taxonomy. Index ranges are allocation space, not privacy or emission policy;
the explicit destination allowlist independently ensures that the mask is sent
only to approved first-party endpoints.
| Index | Id | Feature | Activated at (representative) |
| --- | --- | --- | --- |
| 0 | `core.agent` | Agent | `agent_framework.Agent` |
| 1 | `core.harness_agent` | Harness agent | `agent_framework.create_harness_agent` |
| 2 | `core.workflow` | Workflow engine (custom graphs) | `agent_framework.WorkflowBuilder` |
| 3 | `core.mcp` | MCP tool (any transport) | `agent_framework.MCPStdioTool` |
| 4 | `core.tool_approval` | Tool-approval harness | `agent_framework.ToolApprovalMiddleware` |
| 5 | `core.memory_provider` | Memory context provider | `agent_framework.MemoryContextProvider` |
| 6 | `core.skills_provider` | Skills provider | `agent_framework.SkillsProvider` |
| 7 | `core.file_access_provider` | File-access provider | `agent_framework.FileAccessProvider` |
| 8 | `core.compaction_provider` | Context compaction provider | `agent_framework.CompactionProvider` |
| 9 | `core.todo_provider` | Todo provider | `agent_framework.TodoProvider` |
| 10 | `core.agent_mode_provider` | Agent-mode provider | `agent_framework.AgentModeProvider` |
| 11 | `core.background_agents_provider` | Background-agents provider | `agent_framework.BackgroundAgentsProvider` |
| 12 | `core.in_memory_history_provider` | In-memory history provider | `agent_framework.InMemoryHistoryProvider` |
| 13 | `core.file_history_provider` | File history provider | `agent_framework.FileHistoryProvider` |
| 14 | `core.file_skills_source` | File-backed skills | `agent_framework.FileSkillsSource` |
| 15 | `core.in_memory_skills_source` | In-memory / programmatic skills | `agent_framework.InMemorySkillsSource` |
| 16 | `core.mcp_skills_source` | MCP-backed skills | `agent_framework.MCPSkillsSource` |
| 17 | `core.session_store` | Agent session store | `agent_framework.SessionStore` / `FileSessionStore` |
| 18 | `core.agent_hooks` | Agent Hooks middleware | `agent_framework.create_agent_hooks_middleware` |
| 1931 | _reserved_ | core growth | — |
| 32 | `orchestration.sequential` | Sequential orchestration | `agent_framework_orchestrations.SequentialBuilder` |
| 33 | `orchestration.concurrent` | Concurrent orchestration | `agent_framework_orchestrations.ConcurrentBuilder` |
| 34 | `orchestration.group_chat` | Group-chat orchestration | `agent_framework_orchestrations.GroupChatBuilder` |
| 35 | `orchestration.magentic` | Magentic orchestration | `agent_framework_orchestrations.MagenticBuilder` |
| 36 | `orchestration.handoff` | Handoff orchestration | `agent_framework_orchestrations.HandoffBuilder` |
| 3747 | _reserved_ | orchestration growth | — |
| 48 | `foundry.chat_client` | Foundry chat client | `agent_framework_foundry.RawFoundryChatClient` |
| 49 | `foundry.agent` | Foundry agent | `agent_framework_foundry.FoundryAgent` |
| 50 | `foundry.memory` | Foundry memory provider | `agent_framework_foundry.FoundryMemoryProvider` |
| 51 | `foundry.embedding` | Foundry embedding client | `agent_framework_foundry.RawFoundryEmbeddingClient` |
| 52 | `foundry.evals` | Foundry evaluations | `agent_framework_foundry.FoundryEvals` |
| 53 | `foundry.toolbox` | Foundry Toolbox MCP tool | `agent_framework_foundry_hosting.FoundryToolbox` |
| 54 | `foundry_local` | Foundry Local client | `agent_framework_foundry_local.FoundryLocalClient` |
| 55 | `foundry_hosting` | Foundry hosting layer | `agent_framework_foundry_hosting.ResponsesHostServer` / `InvocationsHostServer` |
| 56 | `openai` | OpenAI clients | `agent_framework_openai` |
| 57 | `anthropic` | Anthropic clients | `agent_framework_anthropic` |
| 58 | `bedrock` | AWS Bedrock clients | `agent_framework_bedrock` |
| 59 | `gemini` | Gemini chat client | `agent_framework_gemini` |
| 60 | `mistral` | Mistral embedding client | `agent_framework_mistral` |
| 61 | `ollama` | Ollama clients | `agent_framework_ollama` |
| 62 | `claude` | Claude Agent SDK agent | `agent_framework_claude` |
| 63 | `copilotstudio` | Copilot Studio agent | `agent_framework_copilotstudio` |
| 64 | `github_copilot` | GitHub Copilot agent | `agent_framework_github_copilot` |
| 65 | `azure_ai_search` | Azure AI Search context provider | `agent_framework_azure_ai_search` |
| 66 | `azure_cosmos` | Azure Cosmos history / checkpoint store | `agent_framework_azure_cosmos` |
| 67 | `azure_contentunderstanding` | Azure Content Understanding context provider | `agent_framework_azure_contentunderstanding.ContentUnderstandingContextProvider` |
| 68 | `redis` | Redis context / history provider | `agent_framework_redis` |
| 69 | `mem0` | Mem0 memory provider | `agent_framework_mem0.Mem0ContextProvider` |
| 70 | `purview` | Purview client | `agent_framework_purview.PurviewClient` |
| 71 | `a2a` | A2A agent / executor | `agent_framework_a2a.A2AAgent` / `A2AExecutor` |
| 72 | `ag_ui` | AG-UI chat client / agent | `agent_framework_ag_ui` |
| 73 | `chatkit` | ChatKit integration | `agent_framework_chatkit` |
| 74 | `devui` | DevUI served | `agent_framework_devui.serve` |
| 75 | `declarative.agent` | Declarative agent definitions | `agent_framework_declarative.AgentFactory` |
| 76 | `declarative.workflow` | Declarative workflow definitions | `agent_framework_declarative.WorkflowFactory` |
| 77 | `durabletask` | Durable task runtime | `agent_framework_durabletask` |
| 78 | `azurefunctions` | Azure Functions agent host | `agent_framework_azurefunctions` |
| 79 | `tools.shell` | Shell tools | `agent_framework_tools.shell.LocalShellTool` / `DockerShellTool` |
| 80 | `monty` | Monty CodeAct provider | `agent_framework_monty.MontyCodeActProvider` |
| 81 | `hyperlight` | Hyperlight CodeAct provider | `agent_framework_hyperlight.HyperlightCodeActProvider` |
| 82 | `azure_cosmos_memory` | Azure Cosmos DB semantic-memory provider | `agent_framework_azure_cosmos_memory.CosmosMemoryContextProvider` |
| 83 | `hosting` | App-owned agent/workflow hosting state | `agent_framework_hosting.AgentState` / `WorkflowState` |
| 84 | `hosting.a2a` | A2A hosting converters | `agent_framework_hosting_a2a.a2a_to_run` / `a2a_from_run` |
| 85 | `hosting.mcp` | MCP hosting adapters | `agent_framework_hosting_mcp.AgentMCPTool` / `WorkflowMCPTool` |
| 86 | `hosting.responses` | OpenAI Responses hosting converters | `agent_framework_hosting_responses.responses_to_run` |
| 87 | `hosting.telegram` | Telegram hosting converters | `agent_framework_hosting_telegram.telegram_to_run` |
| 88 | `lab` | Experimental Agent Framework Lab features | `agent_framework.lab` feature entry points |
| 89127 | _reserved_ | future packages | — |
## Index table — .NET (`agent-framework-dotnet`, version 1)
| Index | Id | Feature | Activated at (representative) |
| --- | --- | --- | --- |
| 0 | `core.agent` | Agent | `Microsoft.Agents.AI.ChatClientAgent` |
| 1 | `core.harness_agent` | Harness agent | `Microsoft.Agents.AI.HarnessAgent` |
| 2 | `core.workflow` | Workflow engine (custom graphs) | `Microsoft.Agents.AI.Workflows.WorkflowBuilder` |
| 3 | `core.tool_approval` | Tool-approval agent | `Microsoft.Agents.AI.ToolApprovalAgent` |
| 4 | `core.chat_history_memory_provider` | Chat-history memory provider | `Microsoft.Agents.AI.ChatHistoryMemoryProvider` |
| 5 | `core.file_memory_provider` | File memory provider | `Microsoft.Agents.AI.FileMemoryProvider` |
| 6 | `core.text_search_provider` | Text-search provider | `Microsoft.Agents.AI.TextSearchProvider` |
| 7 | `core.file_access_provider` | File-access provider | `Microsoft.Agents.AI.FileAccessProvider` |
| 8 | `core.skills_provider` | Skills provider | `Microsoft.Agents.AI.AgentSkillsProviderBuilder` |
| 9 | `core.compaction_provider` | Context compaction provider | `Microsoft.Agents.AI.Compaction.CompactionProvider` |
| 10 | `core.todo_provider` | Todo provider | `Microsoft.Agents.AI.TodoProvider` |
| 11 | `core.agent_mode_provider` | Agent-mode provider | `Microsoft.Agents.AI.AgentModeProvider` |
| 12 | `core.background_agents_provider` | Background-agents provider | `Microsoft.Agents.AI.BackgroundAgentsProvider` |
| 13 | `core.in_memory_history_provider` | In-memory history provider | `Microsoft.Agents.AI.InMemoryChatHistoryProvider` |
| 14 | `core.mcp` | MCP tasks / skills integration | `Microsoft.Agents.AI.Mcp.McpClientTaskExtensions` |
| 15 | `core.file_skills_source` | File-backed skills | `Microsoft.Agents.AI.AgentFileSkillsSource` |
| 16 | `core.in_memory_skills_source` | In-memory skills | `Microsoft.Agents.AI.AgentInMemorySkillsSource` |
| 17 | `core.inline_skill` | Inline programmatic skill | `Microsoft.Agents.AI.AgentInlineSkill` |
| 18 | `core.class_skill` | Class-based programmatic skill | `Microsoft.Agents.AI.AgentClassSkill` |
| 19 | `core.mcp_skills_source` | MCP-backed skills | `Microsoft.Agents.AI.AgentSkillsProviderBuilderMcpExtensions.UseMcpSkills` |
| 2031 | _reserved_ | core growth | — |
| 32 | `orchestration.sequential` | Sequential orchestration | `Microsoft.Agents.AI.Workflows.SequentialWorkflowBuilder` |
| 33 | `orchestration.concurrent` | Concurrent orchestration | `Microsoft.Agents.AI.Workflows.ConcurrentWorkflowBuilder` |
| 34 | `orchestration.group_chat` | Group-chat orchestration | `Microsoft.Agents.AI.Workflows.GroupChatWorkflowBuilder` |
| 35 | `orchestration.magentic` | Magentic orchestration | `Microsoft.Agents.AI.Workflows.MagenticWorkflowBuilder` |
| 36 | `orchestration.handoff` | Handoff orchestration | `Microsoft.Agents.AI.Workflows.HandoffWorkflowBuilder` |
| 3747 | _reserved_ | orchestration growth | — |
| 48 | `foundry.chat_client` | Foundry chat client | `Microsoft.Agents.AI.Foundry.FoundryChatClient` |
| 49 | `foundry.agent` | Foundry agent | `Microsoft.Agents.AI.Foundry.FoundryAgent` |
| 50 | `foundry.memory` | Foundry memory provider | `Microsoft.Agents.AI.Foundry.FoundryMemoryProvider` |
| 51 | `foundry.evals` | Foundry evaluations | `Microsoft.Agents.AI.Foundry.FoundryEvals` |
| 52 | `foundry.toolbox` | Foundry Toolbox MCP tool | `Microsoft.Agents.AI.Foundry.HostedMcpToolboxAITool` |
| 53 | `foundry_hosting` | Foundry hosting layer | `Microsoft.Agents.AI.Foundry.Hosting.FoundryHostingExtensions.AddFoundryResponses` |
| 54 | `openai` | OpenAI integration | `Microsoft.Agents.AI.OpenAI` |
| 55 | `anthropic` | Anthropic integration | `Microsoft.Agents.AI.Anthropic` |
| 56 | `copilotstudio` | Copilot Studio agent | `Microsoft.Agents.AI.CopilotStudio.CopilotStudioAgent` |
| 57 | `github_copilot` | GitHub Copilot agent | `Microsoft.Agents.AI.GitHub.Copilot.GitHubCopilotAgent` |
| 58 | `azure_cosmos` | Cosmos history / checkpoint store | `Microsoft.Agents.AI.CosmosChatHistoryProvider` |
| 59 | `valkey` | Valkey chat-history provider | `Microsoft.Agents.AI.Valkey.ValkeyChatHistoryProvider` |
| 60 | `mem0` | Mem0 memory provider | `Microsoft.Agents.AI.Mem0.Mem0Provider` |
| 61 | `purview` | Purview integration | `Microsoft.Agents.AI.Purview` |
| 62 | `a2a` | A2A agent | `Microsoft.Agents.AI.A2A.A2AAgent` |
| 63 | `hosting.ag_ui` | AG-UI hosting endpoint | `Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.AGUIEndpointRouteBuilderExtensions.MapAGUIServer` |
| 64 | `devui` | DevUI served | `Microsoft.Agents.AI.DevUI` |
| 65 | `declarative.agent` | Declarative agent definitions | `Microsoft.Agents.AI.PromptAgentFactory.CreateAsync` |
| 66 | `declarative.workflow` | Declarative workflow definitions | `Microsoft.Agents.AI.Workflows.Declarative.DeclarativeWorkflowBuilder.Build` |
| 67 | `durabletask` | Durable task runtime | `Microsoft.Agents.AI.DurableTask` |
| 68 | `azurefunctions` | Azure Functions agent host | `Microsoft.Agents.AI.Hosting.AzureFunctions` |
| 69 | `tools.shell` | Shell tools | `Microsoft.Agents.AI.Tools.Shell.ShellExecutor` |
| 70 | `hyperlight` | Hyperlight CodeAct provider | `Microsoft.Agents.AI.Hyperlight.HyperlightCodeActProvider` |
| 71 | `hosting.agent` | Hosted AF agent wrapper | `Microsoft.Agents.AI.Hosting.AIHostAgent` |
| 72 | `local_codeact` | Local Python CodeAct provider | `Microsoft.Agents.AI.LocalCodeAct.LocalCodeActProvider` |
| 73 | `hosting.a2a` | A2A hosting endpoints | `Microsoft.AspNetCore.Builder.A2AEndpointRouteBuilderExtensions.MapA2AJsonRpc` |
| 74 | `hosting.openai` | OpenAI-compatible hosting endpoints | `Microsoft.AspNetCore.Builder.MicrosoftAgentAIHostingOpenAIEndpointRouteBuilderExtensions.MapOpenAIResponses` |
| 75127 | _reserved_ | future packages | — |
## Opt-out
The dedicated mask-only environment variable is shared by both SDKs:
- `AGENT_FRAMEWORK_FEATURE_MASK_DISABLED=true|1` — drops **only** the feature
mask; the base `agent-framework-<lang>/{version}` User-Agent is still sent.
The dedicated flag lets a privacy-conscious user keep contributing SDK
identity/version (useful for support and compatibility triage) while withholding
the feature-usage signal. Python's existing
`AGENT_FRAMEWORK_USER_AGENT_DISABLED=true|1` also suppresses its entire Agent
Framework User-Agent contribution, mask included. Adding a matching .NET
whole-User-Agent opt-out is outside this design.
## Governance
1. One index per package/feature, **numbered independently per language**, in the
table for that language. New indexes are added by editing this file in a reviewed
PR; indexes are never reused within a `(language, version)`.
2. Each package owns a private `FeatureIndex` declaration containing only its
rows. Core owns the accumulator API and core indexes, but never imports
optional packages. Adding a new optional-package index therefore does not
require a core release once the marker API exists.
3. Adding a feature: apply the [allocation tenet](#allocation-tenet), name the
concrete query/decision owner, add the package-local index and table row, and mark the
stable public entry point where actual use begins.
4. Widening beyond 128-bit or re-partitioning bumps that language's version; old
decoders keep working because the version prefix disambiguates the mapping.
5. A repository validation test gathers all package-local declarations for each
`(language, version)` and asserts exact table parity, complete non-reserved
coverage, `0..127` range, and **no duplicate/overlapping indexes**.
> **No machine-readable registry file ships today.** Nothing consumes one at
> runtime (packages own private declarations). If/when a programmatic decoder is built, this
> table is the contract to export to JSON for it then.
+2 -17
View File
@@ -38,7 +38,7 @@
<PackageVersion Include="Google.GenAI" Version="1.6.0" />
<PackageVersion Include="Mscc.GenerativeAI.Microsoft" Version="2.9.3" />
<!-- Microsoft.Azure.* -->
<PackageVersion Include="Microsoft.Azure.Cosmos" Version="3.54.0" />
<PackageVersion Include="Microsoft.Azure.Cosmos" Version="3.61.0" />
<!-- Newtonsoft.Json -->
<PackageVersion Include="Newtonsoft.Json" Version="13.0.4" />
<!-- System.* -->
@@ -104,6 +104,7 @@
<PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="10.0.0" />
<PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="10.7.0" />
<!-- Vector Stores -->
<PackageVersion Include="CommunityToolkit.VectorData.CosmosNoSql" Version="1.0.0" />
<PackageVersion Include="CommunityToolkit.VectorData.InMemory" Version="1.0.0" />
<PackageVersion Include="CommunityToolkit.VectorData.Qdrant" Version="1.0.0" />
<!-- Agent SDKs -->
@@ -134,22 +135,6 @@
<PackageVersion Include="Microsoft.Agents.ObjectModel.Json" Version="2026.2.4.1" />
<PackageVersion Include="Microsoft.Agents.ObjectModel.PowerFx" Version="2026.2.4.1" />
<PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.8.1" />
<!-- Durable Task -->
<PackageVersion Include="Microsoft.DurableTask.Client" Version="1.18.0" />
<PackageVersion Include="Microsoft.DurableTask.Client.AzureManaged" Version="1.18.0" />
<PackageVersion Include="Microsoft.DurableTask.Worker" Version="1.18.0" />
<PackageVersion Include="Microsoft.DurableTask.Worker.AzureManaged" Version="1.18.0" />
<!-- Azure Functions -->
<PackageVersion Include="Microsoft.Azure.Functions.Worker" Version="2.50.0" />
<PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" Version="1.12.1" />
<PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" Version="1.0.1" />
<PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http" Version="3.3.0" />
<PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" Version="2.1.0" />
<PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Mcp" Version="1.0.0" />
<PackageVersion Include="Microsoft.Azure.Functions.Worker.Sdk" Version="2.0.7" />
<!-- Valkey -->
<!-- Redis -->
<PackageVersion Include="StackExchange.Redis" Version="2.10.1" />
<!-- Valkey -->
<PackageVersion Include="Valkey.Glide" Version="1.1.0" />
<!-- Console UX -->
+8 -49
View File
@@ -15,7 +15,6 @@
<Project Path="samples/01-get-started/03_multi_turn/03_multi_turn.csproj" />
<Project Path="samples/01-get-started/04_memory/04_memory.csproj" />
<Project Path="samples/01-get-started/05_first_workflow/05_first_workflow.csproj" />
<Project Path="samples/01-get-started/06_host_your_agent/06_host_your_agent.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/">
<File Path="samples/02-agents/README.md" />
@@ -31,6 +30,7 @@
<Project Path="samples/02-agents/AgentProviders/custom/Agent_With_CustomImplementation/Agent_With_CustomImplementation.csproj" />
<Project Path="samples/02-agents/AgentProviders/dapr/Agent_With_Dapr/Agent_With_Dapr.csproj" />
<Project Path="samples/02-agents/AgentProviders/github-copilot/Agent_With_GitHubCopilot/Agent_With_GitHubCopilot.csproj" />
<Project Path="samples/02-agents/AgentProviders/github-copilot/Agent_With_GitHubCopilot_BYOK/Agent_With_GitHubCopilot_BYOK.csproj" />
<Project Path="samples/02-agents/AgentProviders/google-gemini/Agent_With_GoogleGemini/Agent_With_GoogleGemini.csproj" />
<Project Path="samples/02-agents/AgentProviders/ollama/Agent_With_Ollama/Agent_With_Ollama.csproj" />
<Project Path="samples/02-agents/AgentProviders/onnx/Agent_With_ONNX/Agent_With_ONNX.csproj" />
@@ -66,28 +66,12 @@
<Project Path="samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj" />
<Project Path="samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools/Agent_Step20_DynamicFunctionTools.csproj" />
<Project Path="samples/02-agents/Agents/Agent_Step21_ShellWithEnvironment/Agent_Step21_ShellWithEnvironment.csproj" />
<Project Path="samples/02-agents/Agents/Agent_Step22_AgentMode/Agent_Step22_AgentMode.csproj" />
<Project Path="samples/02-agents/Agents/Agent_Step23_TodoList/Agent_Step23_TodoList.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/DeclarativeAgents/">
<Project Path="samples/02-agents/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/DurableWorkflows/" />
<Folder Name="/Samples/04-hosting/DurableWorkflows/ConsoleApps/">
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/03_ConditionalEdges/03_ConditionalEdges.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/04_WorkflowAndAgents/04_WorkflowAndAgents.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/05_WorkflowEvents/05_WorkflowEvents.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/06_WorkflowSharedState/06_WorkflowSharedState.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/07_SubWorkflows/07_SubWorkflows.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/ConsoleApps/08_WorkflowHITL/08_WorkflowHITL.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/DurableWorkflows/AzureFunctions/">
<Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/03_WorkflowHITL/03_WorkflowHITL.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/04_WorkflowMcpTool/04_WorkflowMcpTool.csproj" />
<Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/05_WorkflowAndAgents/05_WorkflowAndAgents.csproj" />
</Folder>
<Folder Name="/Samples/GettingStarted/">
<File Path="samples/GettingStarted/README.md" />
</Folder>
@@ -202,6 +186,8 @@
<Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
<Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
<Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory.csproj" />
<Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step07_FileMemoryProvider/AgentWithMemory_Step07_FileMemoryProvider.csproj" />
<Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step08_MemoryUsingCosmosNoSql/AgentWithMemory_Step08_MemoryUsingCosmosNoSql.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/AgentProviders/openai/">
<File Path="samples/02-agents/AgentProviders/openai/README.md" />
@@ -341,6 +327,9 @@
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent/HostedChatClientAgent.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent-Dockerfile/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent-Dockerfile/HostedChatClientAgentDocker.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-FoundryAgent/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-FoundryAgent/HostedFoundryAgent.csproj" />
</Folder>
@@ -394,29 +383,6 @@
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-AgentSkills/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-AgentSkills/HostedAgentSkills.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/DurableAgents/" />
<Folder Name="/Samples/04-hosting/DurableAgents/AzureFunctions/">
<File Path="samples/04-hosting/DurableAgents/AzureFunctions/.editorconfig" />
<File Path="samples/04-hosting/DurableAgents/AzureFunctions/README.md" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
<Project Path="samples/04-hosting/DurableAgents/AzureFunctions/08_ReliableStreaming/08_ReliableStreaming.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/DurableAgents/ConsoleApps/">
<File Path="samples/04-hosting/DurableAgents/ConsoleApps/README.md" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/A2A/">
<File Path="samples/02-agents/A2A/README.md" />
<Project Path="samples/02-agents/A2A/A2AAgent_AsFunctionTools/A2AAgent_AsFunctionTools.csproj" />
@@ -626,7 +592,6 @@
<Project Path="src/Microsoft.Agents.AI.CosmosNoSql/Microsoft.Agents.AI.CosmosNoSql.csproj" />
<Project Path="src/Microsoft.Agents.AI.Declarative/Microsoft.Agents.AI.Declarative.csproj" />
<Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
<Project Path="src/Microsoft.Agents.AI.DurableTask/Microsoft.Agents.AI.DurableTask.csproj" />
<Project Path="src/Microsoft.Agents.AI.Foundry.Hosting/Microsoft.Agents.AI.Foundry.Hosting.csproj" />
<Project Path="src/Microsoft.Agents.AI.Foundry/Microsoft.Agents.AI.Foundry.csproj" />
<Project Path="src/Microsoft.Agents.AI.GitHub.Copilot/Microsoft.Agents.AI.GitHub.Copilot.csproj" />
@@ -635,7 +600,6 @@
<Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting.AspNetCore/Microsoft.Agents.AI.Hosting.AspNetCore.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting.AzureFunctions/Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting.OpenAI/Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hosting/Microsoft.Agents.AI.Hosting.csproj" />
<Project Path="src/Microsoft.Agents.AI.Hyperlight/Microsoft.Agents.AI.Hyperlight.csproj" />
@@ -664,10 +628,8 @@
<Project Path="tests/Foundry.Hosting.IntegrationTests.TestContainer/Foundry.Hosting.IntegrationTests.TestContainer.csproj" />
<Project Path="tests/Foundry.Hosting.IntegrationTests/Foundry.Hosting.IntegrationTests.csproj" />
<Project Path="tests/Foundry.IntegrationTests/Foundry.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hyperlight.IntegrationTests/Microsoft.Agents.AI.Hyperlight.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Mem0.IntegrationTests/Microsoft.Agents.AI.Mem0.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Tools.Shell.IntegrationTests/Microsoft.Agents.AI.Tools.Shell.IntegrationTests.csproj" />
@@ -685,14 +647,12 @@
<Project Path="tests/Microsoft.Agents.AI.CosmosNoSql.UnitTests/Microsoft.Agents.AI.CosmosNoSql.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Declarative.UnitTests/Microsoft.Agents.AI.Declarative.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DurableTask.UnitTests/Microsoft.Agents.AI.DurableTask.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Foundry.Hosting.UnitTests/Microsoft.Agents.AI.Foundry.Hosting.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Foundry.UnitTests/Microsoft.Agents.AI.Foundry.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Harness.UnitTests/Microsoft.Agents.AI.Harness.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.UnitTests/Microsoft.Agents.AI.Hosting.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hyperlight.UnitTests/Microsoft.Agents.AI.Hyperlight.UnitTests.csproj" />
@@ -711,4 +671,3 @@
</Folder>
</Solution>
+1 -2
View File
@@ -14,15 +14,14 @@
"src\\Microsoft.Agents.AI.CosmosNoSql\\Microsoft.Agents.AI.CosmosNoSql.csproj",
"src\\Microsoft.Agents.AI.Declarative\\Microsoft.Agents.AI.Declarative.csproj",
"src\\Microsoft.Agents.AI.DevUI\\Microsoft.Agents.AI.DevUI.csproj",
"src\\Microsoft.Agents.AI.DurableTask\\Microsoft.Agents.AI.DurableTask.csproj",
"src\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj",
"src\\Microsoft.Agents.AI.Hosting.A2A\\Microsoft.Agents.AI.Hosting.A2A.csproj",
"src\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj",
"src\\Microsoft.Agents.AI.Hosting.AspNetCore\\Microsoft.Agents.AI.Hosting.AspNetCore.csproj",
"src\\Microsoft.Agents.AI.Hosting.AzureFunctions\\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj",
"src\\Microsoft.Agents.AI.Hosting.OpenAI\\Microsoft.Agents.AI.Hosting.OpenAI.csproj",
"src\\Microsoft.Agents.AI.Hosting\\Microsoft.Agents.AI.Hosting.csproj",
"src\\Microsoft.Agents.AI.LocalCodeAct\\Microsoft.Agents.AI.LocalCodeAct.csproj",
"src\\Microsoft.Agents.AI.Mcp\\Microsoft.Agents.AI.Mcp.csproj",
"src\\Microsoft.Agents.AI.Mem0\\Microsoft.Agents.AI.Mem0.csproj",
"src\\Microsoft.Agents.AI.OpenAI\\Microsoft.Agents.AI.OpenAI.csproj",
+3
View File
@@ -26,6 +26,9 @@
<ItemGroup Condition="'$(InjectSharedDiagnosticIds)' == 'true'">
<Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\DiagnosticIds\*.cs" LinkBase="Shared\DiagnosticIds" />
</ItemGroup>
<ItemGroup Condition="'$(InjectSharedUsage)' == 'true'">
<Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Usage\*.cs" LinkBase="Shared\Usage" />
</ItemGroup>
<ItemGroup Condition="'$(InjectSharedRedaction)' == 'true'">
<Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Redaction\*.cs" LinkBase="Shared\Redaction" />
</ItemGroup>
+3 -3
View File
@@ -26,7 +26,7 @@
When specified, only test projects whose filename matches this pattern are kept.
.PARAMETER TestProjectNameExcludeFilter
Optional wildcard pattern(s) to exclude test projects by name (e.g., *DurableTask.IntegrationTests*).
Optional wildcard pattern(s) to exclude test projects by name (e.g., *Slow.IntegrationTests*).
When specified, test projects whose filename matches any of these patterns are removed.
Applied after TestProjectNameIncludeFilter. Can be a single string or an array of strings.
@@ -50,8 +50,8 @@
dotnet test --solution (./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net472) --no-build -f net472
.EXAMPLE
# Generate integration tests excluding DurableTask and AzureFunctions
./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net10.0 -TestProjectNameIncludeFilter "*IntegrationTests*" -TestProjectNameExcludeFilter "*DurableTask.IntegrationTests*","*AzureFunctions.IntegrationTests*" -OutputPath filtered-other-integration.slnx
# Generate integration tests while excluding a long-running test project
./dotnet/eng/scripts/New-FilteredSolution.ps1 -Solution dotnet/agent-framework-dotnet.slnx -TargetFramework net10.0 -TestProjectNameIncludeFilter "*IntegrationTests*" -TestProjectNameExcludeFilter "*Slow.IntegrationTests*" -OutputPath filtered-integration.slnx
#>
[CmdletBinding()]
+129
View File
@@ -329,6 +329,80 @@ internal static class AgentsSamples
],
},
new SampleDefinition
{
Name = "Agent_Step20_DynamicFunctionTools",
ProjectPath = "samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
MustContain =
[
"=== Dynamic Function Tools Sample ===",
"=== Non-Streaming Mode ===",
"=== Streaming Mode ===",
"[User]",
"[Agent]",
],
ExpectedOutputDescription =
[
"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.",
],
},
new SampleDefinition
{
Name = "Agent_Step21_ShellWithEnvironment",
ProjectPath = "samples/02-agents/Agents/Agent_Step21_ShellWithEnvironment",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
MustContain =
[
"### Stateless mode",
"### Persistent mode",
"--- Captured environment snapshot ---",
],
ExpectedOutputDescription =
[
"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.",
],
},
new SampleDefinition
{
Name = "Agent_Step22_AgentMode",
ProjectPath = "samples/02-agents/Agents/Agent_Step22_AgentMode",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
SkipReason = "Interactive sample that reads console input in a loop and does not exit on its own.",
},
new SampleDefinition
{
Name = "Agent_Step23_TodoList",
ProjectPath = "samples/02-agents/Agents/Agent_Step23_TodoList",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
MustContain =
[
"User:",
"Agent:",
"--- Current todo list ---",
],
ExpectedOutputDescription =
[
"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.",
],
},
// ── AgentSkills ─────────────────────────────────────────────────────
new SampleDefinition
@@ -436,6 +510,48 @@ internal static class AgentsSamples
SkipReason = "Requires a running Neo4j instance; standalone sample outside the repo's CPM build.",
},
new SampleDefinition
{
Name = "AgentWithMemory_Step07_FileMemoryProvider",
ProjectPath = "samples/02-agents/AgentWithMemory/AgentWithMemory_Step07_FileMemoryProvider",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
MustContain =
[
"Memory files will be written to:",
"=== First conversation ===",
"=== Memory files on disk ===",
"=== Second conversation (new session) ===",
],
ExpectedOutputDescription =
[
"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.",
],
},
new SampleDefinition
{
Name = "AgentWithMemory_Step08_MemoryUsingCosmosNoSql",
ProjectPath = "samples/02-agents/AgentWithMemory/AgentWithMemory_Step08_MemoryUsingCosmosNoSql",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT", "COSMOS_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL", "FOUNDRY_EMBEDDING_MODEL", "COSMOS_DATABASE_NAME"],
MustContain =
[
"First session:",
"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.",
],
},
// ── AgentWithRAG ────────────────────────────────────────────────────
new SampleDefinition
@@ -762,6 +878,19 @@ internal static class AgentsSamples
],
},
new SampleDefinition
{
Name = "Agent_With_GitHubCopilot_BYOK",
ProjectPath = "samples/02-agents/AgentProviders/github-copilot/Agent_With_GitHubCopilot_BYOK",
RequiredEnvironmentVariables = ["BYOK_BASE_URL", "BYOK_API_KEY"],
OptionalEnvironmentVariables = ["BYOK_PROVIDER_TYPE", "BYOK_MODEL_ID"],
ExpectedOutputDescription =
[
"The output should contain a user prompt and a response about the benefits of BYOK.",
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "Agent_With_GoogleGemini",
@@ -92,14 +92,5 @@ internal static class GetStartedSamples
"The output should not contain error messages or stack traces.",
],
},
new SampleDefinition
{
Name = "06_host_your_agent",
ProjectPath = "samples/01-get-started/06_host_your_agent",
RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
SkipReason = "Requires Azure Functions Core Tools runtime and starts a web server.",
},
];
}
+1 -1
View File
@@ -1,6 +1,6 @@
{
"sdk": {
"version": "10.0.301",
"version": "10.0.302",
"rollForward": "minor",
"allowPrerelease": false
},
-6
View File
@@ -1,12 +1,6 @@
<?xml version="1.0" encoding="utf-8"?>
<configuration>
<packageSources>
<clear />
<add key="nuget.org" value="https://api.nuget.org/v3/index.json" />
</packageSources>
<packageSourceMapping>
<packageSource key="nuget.org">
<package pattern="*" />
</packageSource>
</packageSourceMapping>
</configuration>
+3 -3
View File
@@ -1,14 +1,14 @@
<Project>
<PropertyGroup>
<!-- Central version prefix - applies to all nuget packages. -->
<VersionPrefix>1.15.0</VersionPrefix>
<VersionPrefix>1.17.0</VersionPrefix>
<RCNumber>1</RCNumber>
<DateSuffix>260722</DateSuffix>
<DateSuffix>260804</DateSuffix>
<PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).$(DateSuffix).1</PackageVersion>
<PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.$(DateSuffix).1</PackageVersion>
<PackageVersion Condition="'$(IsReleased)' == 'true'">$(VersionPrefix)</PackageVersion>
<GitTag>1.15.0</GitTag>
<GitTag>1.17.0</GitTag>
<Configurations>Debug;Release;Publish</Configurations>
<IsPackable>true</IsPackable>
@@ -1,30 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<!-- The Functions build tools don't like namespaces that start with a number -->
<AssemblyName>HostedAgent</AssemblyName>
<RootNamespace>HostedAgent</RootNamespace>
</PropertyGroup>
<ItemGroup>
<FrameworkReference Include="Microsoft.AspNetCore.App" />
</ItemGroup>
<!-- Azure Functions packages -->
<ItemGroup>
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
@@ -1,41 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to host an AI agent with Azure Functions (DurableAgents).
//
// Prerequisites:
// - Azure Functions Core Tools
// - Foundry project endpoint and credentials
//
// Environment variables:
// FOUNDRY_PROJECT_ENDPOINT
// FOUNDRY_MODEL (defaults to "gpt-5.4-mini")
//
// Run with: func start
// Then call: POST http://localhost:7071/api/agents/HostedAgent/run
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AzureFunctions;
using Microsoft.Azure.Functions.Worker.Builder;
using Microsoft.Extensions.Hosting;
var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
var model = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// Set up an AI agent following the standard Microsoft Agent Framework pattern.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(model: model, instructions: "You are a helpful assistant hosted in Azure Functions.", name: "HostedAgent");
// Configure the function app to host the AI agent.
// This will automatically generate HTTP API endpoints for the agent.
using IHost app = FunctionsApplication
.CreateBuilder(args)
.ConfigureFunctionsWebApplication()
.ConfigureDurableAgents(options => options.AddAIAgent(agent, timeToLive: TimeSpan.FromHours(1)))
.Build();
app.Run();
@@ -0,0 +1,3 @@
# Azure Functions Hosting Sample Has Moved
The Azure Functions hosting tutorial is now maintained as the [single-agent Durable Agent sample](https://github.com/microsoft/agent-framework-durable-extension/tree/main/dotnet/samples/DurableAgents/AzureFunctions/01_SingleAgent).
+1 -1
View File
@@ -228,7 +228,7 @@ dotnet run
`ConversationId` keeps request/response continuity. It is not proof that the caller owns that conversation. In multi-user deployments, authenticate each AG-UI request and authorize conversation access using your application's real boundary, such as the authenticated user, tenant, or workspace.
If your ASP.NET Core host shares session storage across users, pair `MapAGUI` with an isolation strategy such as `UseClaimsBasedSessionIsolation(...)` so the storage key includes a principal-specific dimension instead of relying on the conversation identifier alone.
If your ASP.NET Core host shares session storage across users, pair `MapAGUI` with an isolation strategy such as `UseClaimsBasedAgentIsolation(...)` so the storage key includes a principal-specific dimension instead of relying on the conversation identifier alone.
## Troubleshooting
@@ -11,9 +11,9 @@ builder.Services.AddHttpClient().AddLogging();
builder.Services.AddAGUIServer();
// WARNING: When adding session persistence (e.g., WithInMemorySessionStore), or running in production,
// make sure to also register a SessionIsolationKeyProvider to scope sessions by principal in multi-user
// make sure to also register an AgentIsolationKeyProvider to scope sessions by principal in multi-user
// deployments, e.g.:
// builder.Services.UseClaimsBasedSessionIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
// builder.Services.UseClaimsBasedAgentIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
WebApplication app = builder.Build();
@@ -17,9 +17,9 @@ builder.Services.ConfigureHttpJsonOptions(options =>
builder.Services.AddAGUIServer();
// WARNING: When adding session persistence (e.g., WithInMemorySessionStore), or running in production,
// make sure to also register a SessionIsolationKeyProvider to scope sessions by principal in multi-user
// make sure to also register an AgentIsolationKeyProvider to scope sessions by principal in multi-user
// deployments, e.g.:
// builder.Services.UseClaimsBasedSessionIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
// builder.Services.UseClaimsBasedAgentIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
WebApplication app = builder.Build();
@@ -11,9 +11,9 @@ builder.Services.AddHttpClient().AddLogging();
builder.Services.AddAGUIServer();
// WARNING: When adding session persistence (e.g., WithInMemorySessionStore), or running in production,
// make sure to also register a SessionIsolationKeyProvider to scope sessions by principal in multi-user
// make sure to also register an AgentIsolationKeyProvider to scope sessions by principal in multi-user
// deployments, e.g.:
// builder.Services.UseClaimsBasedSessionIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
// builder.Services.UseClaimsBasedAgentIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
WebApplication app = builder.Build();
@@ -28,9 +28,9 @@ builder.Services.ConfigureHttpJsonOptions(options =>
builder.Services.AddAGUIServer();
// WARNING: When adding session persistence (e.g., WithInMemorySessionStore), or running in production,
// make sure to also register a SessionIsolationKeyProvider to scope sessions by principal in multi-user
// make sure to also register an AgentIsolationKeyProvider to scope sessions by principal in multi-user
// deployments, e.g.:
// builder.Services.UseClaimsBasedSessionIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
// builder.Services.UseClaimsBasedAgentIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
WebApplication app = builder.Build();
@@ -18,9 +18,9 @@ builder.Services.AddAGUIServer();
builder.WebHost.UseUrls("http://localhost:8888");
// WARNING: When adding session persistence (e.g., WithInMemorySessionStore), or running in production,
// make sure to also register a SessionIsolationKeyProvider to scope sessions by principal in multi-user
// make sure to also register an AgentIsolationKeyProvider to scope sessions by principal in multi-user
// deployments, e.g.:
// builder.Services.UseClaimsBasedSessionIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
// builder.Services.UseClaimsBasedAgentIsolation(new() { ClaimType = ClaimTypes.NameIdentifier });
WebApplication app = builder.Build();
@@ -60,6 +60,7 @@ covering basics, function tools, structured output, middleware, MCP, code interp
| Sample | Description |
| --- | --- |
| [GitHub Copilot](./github-copilot/Agent_With_GitHubCopilot/) | Create an AIAgent using GitHub Copilot SDK |
| [GitHub Copilot BYOK](./github-copilot/Agent_With_GitHubCopilot_BYOK/) | Route GitHub Copilot agent requests through your own endpoint (Bring Your Own Key) |
### [Google Gemini](./google-gemini/)
@@ -0,0 +1,20 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<NoWarn>$(NoWarn);GHCP001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="GitHub.Copilot.SDK" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.GitHub.Copilot\Microsoft.Agents.AI.GitHub.Copilot.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,52 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to configure a GitHub Copilot agent with BYOK (Bring Your Own Key),
// routing requests through your own endpoint (OpenAI, Azure OpenAI, Anthropic, or an
// OpenAI-compatible service such as vLLM/LiteLLM/Ollama) instead of the GitHub Copilot backend.
//
// SECURITY NOTE: BYOK uses static credentials (no automatic token refresh) and usage is tracked
// by your provider rather than GitHub. Keep API keys out of source control; load them from
// environment variables or a secret store, as shown here.
using GitHub.Copilot;
using Microsoft.Agents.AI;
string providerType = Environment.GetEnvironmentVariable("BYOK_PROVIDER_TYPE") ?? "openai";
string baseUrl = Environment.GetEnvironmentVariable("BYOK_BASE_URL")
?? throw new InvalidOperationException("The BYOK_BASE_URL environment variable is not set.");
string apiKey = Environment.GetEnvironmentVariable("BYOK_API_KEY")
?? throw new InvalidOperationException("The BYOK_API_KEY environment variable is not set.");
string modelId = Environment.GetEnvironmentVariable("BYOK_MODEL_ID") ?? "gpt-4o";
// Create and start a Copilot client
await using CopilotClient copilotClient = new();
await copilotClient.StartAsync();
// Provider routes the session through a custom endpoint instead of the GitHub Copilot backend.
// Type is "openai", "azure", or "anthropic". WireApi "completions" is the broadly compatible
// choice; use "responses" for providers that support the OpenAI Responses API. BYOK also
// requires Model to be set at the session level.
SessionConfig sessionConfig = new()
{
Model = modelId,
Provider = new ProviderConfig
{
Type = providerType,
WireApi = "completions",
BaseUrl = baseUrl,
ApiKey = apiKey,
ModelId = modelId,
},
};
AIAgent agent = copilotClient.AsAIAgent(sessionConfig, ownsClient: true);
string prompt = "What are the benefits of using your own API keys with an agent framework?";
Console.WriteLine($"User: {prompt}\n");
await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(prompt))
{
Console.Write(update);
}
Console.WriteLine();
@@ -0,0 +1,76 @@
# About BYOK (Bring Your Own Key)
BYOK lets you route model requests through your own API keys and infrastructure instead of the
GitHub Copilot backend — useful for enterprise deployments, custom hosting, or direct billing
arrangements. See [GitHub's BYOK documentation](https://docs.github.com/en/copilot/how-tos/copilot-sdk/auth/byok)
for the full list of supported providers and configuration options.
# Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
- GitHub Copilot CLI installed and available in your PATH (or provide a custom path)
- An OpenAI, Azure OpenAI, Anthropic, or OpenAI-compatible endpoint and API key (e.g. vLLM,
LiteLLM, or Ollama)
## Setting up GitHub Copilot CLI
To use this sample, you need to have the GitHub Copilot CLI installed. You can install it by
following the instructions at:
https://github.com/github/copilot-sdk
## Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `BYOK_PROVIDER_TYPE` | Provider type (`openai`, `azure`, `anthropic`) | `openai` |
| `BYOK_BASE_URL` | Base URL of your provider endpoint | *(required)* |
| `BYOK_API_KEY` | API key for that endpoint | *(required)* |
| `BYOK_MODEL_ID` | Model name to request (e.g. "gpt-4o") | `gpt-4o` |
## Running the Sample
```powershell
dotnet run
```
The sample will:
1. Create a GitHub Copilot client with default options
2. Configure a session with a `Provider` (BYOK) pointing at your own endpoint instead of the
default GitHub Copilot backend
3. Send a message to the agent
4. Stream the response
## Advanced Usage
```csharp
using GitHub.Copilot;
using Microsoft.Agents.AI;
await using CopilotClient copilotClient = new();
await copilotClient.StartAsync();
SessionConfig sessionConfig = new()
{
// BYOK requires Model to also be set at the session level.
Model = "gpt-4o",
Provider = new ProviderConfig
{
Type = "azure", // or "openai", "anthropic"
WireApi = "completions", // or "responses"
BaseUrl = "https://api.example.com/v1",
ApiKey = "your-api-key",
ModelId = "your-model-id", // "deployment-name"
},
};
AIAgent agent = copilotClient.AsAIAgent(sessionConfig, ownsClient: true);
AgentResponse response = await agent.RunAsync("Hello!");
Console.WriteLine(response);
```
> **Note:** BYOK uses static credentials only — dynamic token refresh is not automatic, and
> model availability depends entirely on your provider's offerings. Usage is tracked through
> your provider rather than GitHub.
@@ -38,8 +38,8 @@
<ItemGroup>
<!-- AgentMemory (published) — an unofficial .NET port of the Neo4j Labs agent-memory library + its
Microsoft Agent Framework adapter. -->
<PackageReference Include="AgentMemory" Version="1.2.0" />
<PackageReference Include="AgentMemory.AgentFramework" Version="1.2.0" />
<PackageReference Include="AgentMemory" Version="1.3.0" />
<PackageReference Include="AgentMemory.AgentFramework" Version="1.3.0" />
<!-- Microsoft Agent Framework (matches AgentMemory's target) + the OpenAI/Foundry chat & embedding clients. -->
<PackageReference Include="Microsoft.Agents.AI" Version="1.9.0" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.5.1" />
@@ -0,0 +1,19 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,98 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to give an agent file-based memory using the FileMemoryProvider.
// The FileMemoryProvider exposes a set of tools to the agent (write, read, delete, list, grep and replace)
// that allow it to store memories as individual files in an AgentFileStore.
// Because the files are stored outside of the conversation, the agent can recall them
// in later conversations, even after the original chat history is gone.
//
// The sample also shows how to control the folder that memory files are written to,
// by supplying a state initializer callback that sets the working folder for each session.
#pragma warning disable MAAI001 // AgentFileStore and its implementations are experimental.
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// The id of the user that we are storing memories for.
// It is used below to give each user their own memory folder.
const string UserId = "UID1";
// Create the file store that the FileMemoryProvider will use to persist memory files.
// Here we use a file system backed store rooted at a local folder called "agent-memory",
// but any AgentFileStore implementation can be used, e.g. InMemoryAgentFileStore or a custom
// implementation backed by blob storage.
var memoryRoot = Path.Combine(AppContext.BaseDirectory, "agent-memory");
var fileStore = new FileSystemAgentFileStore(memoryRoot);
// The working folder that memories for this user will be written to, relative to the store root.
// The folder you choose determines the scope and lifetime of the memories:
// - A stable folder, like the per-user one below, gives you durable memories that are shared by
// every session for that user. That is what allows the second conversation further down to
// recall what the user said in the first.
// - A unique folder per session gives you memories that are isolated to a single session, e.g.
// generate one in the state initializer callback below:
// _ => new FileMemoryState { WorkingFolder = Guid.NewGuid().ToString() }
var workingFolder = $"users/{UserId}";
Console.WriteLine($"Memory files will be written to: {Path.Combine(memoryRoot, workingFolder)}");
Console.WriteLine();
// Create the file memory provider.
// The second parameter is a state initializer callback that is invoked whenever the provider
// cannot find existing state in a session, i.e. typically the first time it is used with a new session.
// It allows us to configure the folder that memory files for that session are written to.
// If no callback is supplied, the working folder defaults to the root of the store,
// which means all sessions share a single, flat set of memory files.
using var fileMemoryProvider = new FileMemoryProvider(
fileStore,
_ => new FileMemoryState { WorkingFolder = workingFolder });
// Create the agent and attach the FileMemoryProvider so that the agent gets the file memory tools.
AIAgent agent = new AIProjectClient(
new Uri(endpoint),
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
new DefaultAzureCredential())
.AsAIAgent(new ChatClientAgentOptions
{
ChatOptions = new()
{
ModelId = deploymentName,
Instructions = "You are a helpful travel assistant. Remember what the user tells you about themselves so that you can give better recommendations later."
},
Name = "TravelAssistant",
AIContextProviders = [fileMemoryProvider],
});
// First conversation: tell the agent something worth remembering.
// The agent should use the file_memory_write tool to store it as a file in the working folder.
AgentSession firstSession = await agent.CreateSessionAsync();
Console.WriteLine("=== First conversation ===");
Console.WriteLine(await agent.RunAsync(
"I'm vegetarian and I always travel with my dog. Please remember this for future trips.",
firstSession));
Console.WriteLine();
// Show the memory files that the agent created on disk.
Console.WriteLine("=== Memory files on disk ===");
foreach (var file in Directory.EnumerateFiles(Path.Combine(memoryRoot, workingFolder)))
{
Console.WriteLine(Path.GetFileName(file));
}
Console.WriteLine();
// Second conversation: a brand new session with no chat history from the first conversation.
// The provider surfaces the memory index to the agent, and the agent can read the memory files
// using the file_memory_read tool, so it can still recall the user's preferences.
AgentSession secondSession = await agent.CreateSessionAsync();
Console.WriteLine("=== Second conversation (new session) ===");
Console.WriteLine(await agent.RunAsync(
"Suggest a hotel and a restaurant for my trip to Paris next week.",
secondSession));
@@ -0,0 +1,68 @@
# File Based Memory with FileMemoryProvider
This sample demonstrates how to give an agent file-based memory using the `FileMemoryProvider`.
The `FileMemoryProvider` is an `AIContextProvider` that exposes a set of memory tools to the agent, allowing the agent to decide what to remember and when to recall it. Each memory is stored as an individual file in an `AgentFileStore`, so memories survive beyond the lifetime of a single conversation.
## Concepts
- **`FileMemoryProvider`**: An `AIContextProvider` that adds the following tools to the agent:
| Tool | Description |
|---|---|
| `file_memory_write` | Write a memory file with a name, content and optional description. |
| `file_memory_read` | Read the content of a memory file by name. |
| `file_memory_delete` | Delete a memory file by name. |
| `file_memory_ls` | List all memory files with their descriptions. |
| `file_memory_grep` | Search memory file contents using a regular expression. |
| `file_memory_replace` | Replace occurrences of a substring within a memory file. |
| `file_memory_replace_lines` | Replace whole lines within a memory file. |
The provider also maintains a `memories.md` index file, which it injects into the conversation so the agent knows which memories are available without having to list them first.
- **`AgentFileStore`**: The pluggable storage abstraction used by the provider. This sample uses `FileSystemAgentFileStore` to store memories on the local disk, but `InMemoryAgentFileStore` or a custom implementation (e.g. backed by blob storage) can be used instead.
- **`FileMemoryState`**: The per-session state of the provider. Its `WorkingFolder` property determines the folder, relative to the store root, that memory files are written to.
## Configuring the memory folder
By default, all sessions share the root folder of the store, which means every session reads and writes the same flat set of memory files.
To scope memories, e.g. per user, per tenant or per session, pass a state initializer callback to the `FileMemoryProvider` constructor. The callback receives the `AgentSession` and is invoked whenever the provider cannot find existing state in that session, i.e. typically the first time the provider is used with a new session:
```csharp
using var fileMemoryProvider = new FileMemoryProvider(
fileStore,
session => new FileMemoryState { WorkingFolder = $"users/{userId}" });
```
In this sample, memories are written to `agent-memory/users/UID1` under the application's base directory. Because the folder is derived from a fixed user id rather than the session, a new session for the same user picks up the memories written by earlier sessions.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- A Microsoft Foundry project with a chat model deployment
- Run `az login` to authenticate with `DefaultAzureCredential`
## Configuration
Set the following environment variables:
| Variable | Description | Default |
|---|---|---|
| `FOUNDRY_PROJECT_ENDPOINT` | Your Foundry project endpoint | *(required)* |
| `FOUNDRY_MODEL` | Chat model deployment name | `gpt-5.4-mini` |
## Running the Sample
```bash
dotnet run
```
## How it Works
1. A `FileSystemAgentFileStore` is created, rooted at a local `agent-memory` folder.
2. A `FileMemoryProvider` is created over that store, with a state initializer that puts the memories for the current user in their own working folder.
3. The provider is attached to the agent via `ChatClientAgentOptions.AIContextProviders`, which gives the agent the `file_memory_*` tools and instructions for using them.
4. In the first conversation, the user shares some preferences and the agent calls `file_memory_write` to store them as a file in the working folder. The sample then lists the files that were created on disk.
5. In the second conversation, a brand new session is created with no chat history from the first conversation. The provider injects the memory index into the conversation, and the agent calls `file_memory_read` to recall the stored preferences when making its recommendations.
@@ -0,0 +1,22 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.Identity" />
<PackageReference Include="CommunityToolkit.VectorData.CosmosNoSql" />
<PackageReference Include="Microsoft.Azure.Cosmos" />
<PackageReference Include="Newtonsoft.Json" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,92 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to persist chat history in Azure Cosmos DB for NoSQL using the ChatHistoryMemoryProvider.
// The agent can then use chat history from prior conversations to inform responses in new conversations.
using System.Text.Json;
using Azure.AI.Projects;
using Azure.Identity;
using CommunityToolkit.VectorData.CosmosNoSql;
using Microsoft.Agents.AI;
using Microsoft.Azure.Cosmos;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.VectorData;
var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
var embeddingDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_EMBEDDING_MODEL") ?? "text-embedding-3-large";
var embeddingDimensions = 3072;
if (Environment.GetEnvironmentVariable("FOUNDRY_EMBEDDING_DIMENSIONS") is string embeddingDimensionsValue &&
(!int.TryParse(embeddingDimensionsValue, out embeddingDimensions) || embeddingDimensions <= 0))
{
throw new InvalidOperationException("FOUNDRY_EMBEDDING_DIMENSIONS must be a positive integer.");
}
var cosmosEndpoint = Environment.GetEnvironmentVariable("COSMOS_ENDPOINT") ?? throw new InvalidOperationException("COSMOS_ENDPOINT is not set.");
var cosmosDatabaseName = Environment.GetEnvironmentVariable("COSMOS_DATABASE_NAME") ?? "agent-memory";
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
DefaultAzureCredential credential = new();
AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
using CosmosClient cosmosClient = new(
cosmosEndpoint,
credential,
new CosmosClientOptions
{
UseSystemTextJsonSerializerWithOptions = JsonSerializerOptions.Default,
});
DatabaseResponse databaseResponse = await cosmosClient.CreateDatabaseIfNotExistsAsync(cosmosDatabaseName);
VectorStore vectorStore = new CosmosNoSqlVectorStore(
databaseResponse.Database,
new CosmosNoSqlVectorStoreOptions
{
JsonSerializerOptions = JsonSerializerOptions.Default,
EmbeddingGenerator = aiProjectClient
.GetProjectOpenAIClient()
.GetEmbeddingClient(embeddingDeploymentName)
.AsIEmbeddingGenerator(),
});
var userId = $"sample-{Guid.NewGuid():N}";
// Create the agent and add the ChatHistoryMemoryProvider to store chat messages in Cosmos DB.
AIAgent agent = aiProjectClient
.AsAIAgent(new ChatClientAgentOptions
{
ChatOptions = new() { ModelId = deploymentName, Instructions = "You are good at telling jokes." },
Name = "Joker",
AIContextProviders = [new ChatHistoryMemoryProvider(
vectorStore,
collectionName: "chathistory",
vectorDimensions: embeddingDimensions,
// Callback to configure the initial state of the ChatHistoryMemoryProvider.
// The ChatHistoryMemoryProvider stores its state in the AgentSession and this callback
// will be called whenever the ChatHistoryMemoryProvider cannot find existing state in the session,
// typically the first time it is used with a new session.
_ => new ChatHistoryMemoryProvider.State(
// Configure the scope values under which chat messages will be stored.
// In this case, we are using a per-run user ID and a unique session ID for each new session.
storageScope: new() { UserId = userId, SessionId = Guid.NewGuid().ToString("N") },
// Configure the scope which would be used to search for relevant prior messages.
// In this case, we are searching for any messages for the user across all sessions.
searchScope: new() { UserId = userId }))]
});
// Start a new session for the agent conversation.
AgentSession session = await agent.CreateSessionAsync();
// Run the agent with the session that stores conversation history in Cosmos DB.
Console.WriteLine("First session:");
Console.WriteLine(await agent.RunAsync("I like jokes about Pirates. Tell me a joke about a pirate.", session));
// Start a second session. Since we configured the search scope to be across all sessions for the user,
// the agent should remember that the user likes pirate jokes.
AgentSession session2 = await agent.CreateSessionAsync();
// Run the agent with the second session.
Console.WriteLine("Second session (recalling prior chat history from Cosmos DB):");
Console.WriteLine(await agent.RunAsync("Tell me a joke that I might like.", session2));
@@ -0,0 +1,41 @@
# Agent with Memory Using Azure Cosmos DB for NoSQL
This sample uses `ChatHistoryMemoryProvider` with `CosmosNoSqlVectorStore` to persist chat history in Azure Cosmos DB for NoSQL and recall relevant messages in a new agent session.
## Features Demonstrated
- Authenticating to Microsoft Foundry and Azure Cosmos DB with `DefaultAzureCredential`
- Storing chat messages in an Azure Cosmos DB vector store
- Creating the configured database and chat-history container when they do not exist
- Recalling relevant chat history across agent sessions
## Prerequisites
1. [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
2. A Microsoft Foundry project with:
- A chat model deployment (the default is `gpt-5.4-mini`)
- A `text-embedding-3-large` deployment with 3,072 dimensions
3. An Azure Cosmos DB for NoSQL account with [vector search enabled](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search)
4. An Azure identity that can create the configured database and container and read and write items
5. Azure CLI authentication (`az login`)
## Configuration
Set the following environment variables:
| Variable | Description | Default |
|---|---|---|
| `FOUNDRY_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | *(required)* |
| `COSMOS_ENDPOINT` | Azure Cosmos DB account endpoint | *(required)* |
| `FOUNDRY_MODEL` | Chat model deployment name | `gpt-5.4-mini` |
| `FOUNDRY_EMBEDDING_MODEL` | Embedding model deployment name | `text-embedding-3-large` |
| `FOUNDRY_EMBEDDING_DIMENSIONS` | Number of dimensions produced by the embedding deployment | `3072` |
| `COSMOS_DATABASE_NAME` | Database used to store agent memory | `agent-memory` |
## Run the Sample
```bash
dotnet run
```
The first session stores the user's preference for pirate jokes. The second session uses a different `AgentSession` but the same per-run user search scope, allowing the agent to retrieve that preference from Azure Cosmos DB without recalling data from earlier sample runs.
@@ -10,6 +10,8 @@ These samples show how to create an agent with the Agent Framework that uses Mem
|[Memory with Microsoft Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories.|
|[Bounded Chat History with Overflow](./AgentWithMemory_Step05_BoundedChatHistory/)|This sample demonstrates how to create a bounded chat history provider that overflows older messages to a vector store and recalls them as memories.|
|[Memory Using AgentMemory](./AgentWithMemory_Step06_MemoryUsingAgentMemory/)|This sample demonstrates a retail shopping assistant built with [`AgentMemory`](https://www.nuget.org/packages/AgentMemory), an unofficial .NET port of the Neo4j Labs graph-memory provider, to learn customer preferences and recommend products via graph traversal.|
|[File Based Memory](./AgentWithMemory_Step07_FileMemoryProvider/)|This sample demonstrates how to use the `FileMemoryProvider` to give an agent tools for storing and recalling memories as files, and how to configure the folder that those memory files are written to.|
|[Memory with Azure Cosmos DB for NoSQL](./AgentWithMemory_Step08_MemoryUsingCosmosNoSql/)|This sample demonstrates how to persist and retrieve chat history across sessions with Azure Cosmos DB for NoSQL.|
> **See also**: [Memory Search with Foundry Agents](../AgentProviders/foundry/Agent_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Microsoft Foundry agents.
@@ -0,0 +1,16 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.Identity" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,153 @@
// Copyright (c) Microsoft. All rights reserved.
// Agent Mode — Switch an agent's operating mode at runtime with AgentModeProvider
//
// This sample shows how to use the AgentModeProvider, an AIContextProvider that tracks the
// agent's current operating "mode" in the session state and exposes tools (mode_get / mode_set)
// so the agent can query and switch modes as its work progresses. The mode is folded into the
// instructions sent to the model on every turn, so different modes can drive different behavior.
//
// The sample demonstrates two things:
// 1. The built-in default modes ("plan" and "execute") that ship with the provider.
// 2. How to customize the available modes via AgentModeProviderOptions.
//
// It runs a simple interactive loop. In addition to chatting with the agent, you can switch the
// agent's mode yourself using a slash command:
// /mode — show the current mode
// /mode <name> — switch to the named mode
// /help — list the available commands and modes
// /exit — quit
//
// When you switch modes with /mode, the provider injects a notification on the next turn so the
// agent clearly sees the change and adjusts its behavior accordingly.
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
var model = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// Set AGENT_MODE_USE_CUSTOM=true to run the sample with the custom modes defined below instead of
// the provider's built-in "plan" / "execute" defaults.
bool useCustomModes = string.Equals(Environment.GetEnvironmentVariable("AGENT_MODE_USE_CUSTOM"), "true", StringComparison.OrdinalIgnoreCase);
// <create_mode_provider>
AgentModeProvider modeProvider;
string[] availableModes;
if (useCustomModes)
{
// Customize the set of modes by supplying AgentModeProviderOptions. Each mode has a name and a
// block of instructions describing how the agent should behave while operating in that mode.
// DefaultMode selects the mode new sessions start in (defaults to the first mode when omitted).
modeProvider = new AgentModeProvider(new AgentModeProviderOptions
{
DefaultMode = "concise",
Modes =
[
new AgentModeProviderOptions.AgentMode(
"concise",
"Answer in a single short sentence. Do not elaborate unless the user explicitly asks for more detail."),
new AgentModeProviderOptions.AgentMode(
"detailed",
"Answer thoroughly. Explain your reasoning, provide examples, and cover relevant edge cases."),
],
});
availableModes = ["concise", "detailed"];
}
else
{
// Use the provider's built-in modes: "plan" (interactive planning) and "execute" (autonomous
// execution). No options are required.
modeProvider = new AgentModeProvider();
availableModes = ["plan", "execute"];
}
// </create_mode_provider>
// Create the agent and attach the mode provider as an AIContextProvider.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(new ChatClientAgentOptions
{
Name = "ModeAwareAssistant",
ChatOptions = new ChatOptions
{
ModelId = model,
Instructions = "You are a helpful assistant. Follow the process and behavior required by your current operating mode.",
},
AIContextProviders = [modeProvider],
});
using var providerToDispose = modeProvider;
AgentSession session = await agent.CreateSessionAsync();
Console.WriteLine("Agent Mode sample. Type a message to chat, or use a slash command.");
Console.WriteLine($"Available modes: {string.Join(", ", availableModes)}");
Console.WriteLine($"Current mode: {await modeProvider.GetModeAsync(session)}");
PrintHelp(availableModes);
Console.WriteLine();
while (true)
{
Console.Write("> ");
string? input = Console.ReadLine()?.Trim();
// Treat empty input or end-of-stream (Ctrl+D / Ctrl+Z) as a request to exit.
if (string.IsNullOrWhiteSpace(input) || input.Equals("/exit", StringComparison.OrdinalIgnoreCase))
{
break;
}
if (input.Equals("/help", StringComparison.OrdinalIgnoreCase))
{
PrintHelp(availableModes);
continue;
}
// Handle the /mode slash command: "/mode" shows the current mode, "/mode <name>" switches to it.
if (input.Equals("/mode", StringComparison.OrdinalIgnoreCase) || input.StartsWith("/mode ", StringComparison.OrdinalIgnoreCase))
{
string[] parts = input.Split(' ', 2, StringSplitOptions.RemoveEmptyEntries | StringSplitOptions.TrimEntries);
if (parts.Length < 2)
{
Console.WriteLine($"Current mode: {await modeProvider.GetModeAsync(session)}");
continue;
}
try
{
await modeProvider.SetModeAsync(session, parts[1]);
Console.WriteLine($"Switched to \"{parts[1]}\" mode.");
}
catch (ArgumentException ex)
{
// SetModeAsync throws when the requested mode is not one of the configured modes.
Console.WriteLine(ex.Message);
}
continue;
}
// Anything else is a message for the agent. The mode provider injects the current mode (and any
// pending mode-change notification) into the context for this turn.
Console.WriteLine(await agent.RunAsync(input, session));
// Print the mode after the turn: the agent may have switched it itself via the mode_set tool as
// its work progressed, so this reflects any change the agent made during the turn.
Console.WriteLine($"Current mode: {await modeProvider.GetModeAsync(session)}");
}
static void PrintHelp(string[] availableModes)
{
Console.WriteLine("Commands:");
Console.WriteLine(" /mode Show the current mode");
Console.WriteLine($" /mode <name> Switch mode ({string.Join(" | ", availableModes)})");
Console.WriteLine(" /help Show this help");
Console.WriteLine(" /exit Quit");
}
@@ -0,0 +1,62 @@
# Agent Mode
This sample demonstrates how to use the `AgentModeProvider` to track and switch an agent's
operating **mode** at runtime, and drive different agent behavior depending on the active mode.
The `AgentModeProvider` is an `AIContextProvider` that stores the current mode in the session
state and injects it into the instructions sent to the model on every turn. It also exposes
`mode_get` and `mode_set` tools so the agent can query and switch modes on its own as its work
progresses.
## What it demonstrates
- Attaching an `AgentModeProvider` to an agent via `ChatClientAgentOptions.AIContextProviders`.
- The provider's **built-in** modes: `plan` (interactive planning) and `execute` (autonomous execution).
- **Customizing** the available modes with `AgentModeProviderOptions` (set the
`AGENT_MODE_USE_CUSTOM` environment variable to `true` to switch to a simple `concise` /
`detailed` mode set).
- Reading and changing the mode from application code with `GetModeAsync` / `SetModeAsync`.
- A simple interactive input loop that lets the user switch mode with a slash command. When the
mode changes this way, the provider injects a notification on the next turn so the agent adjusts
its behavior.
## Commands
| Command | Description |
|---|---|
| `/mode` | Show the current mode |
| `/mode <name>` | Switch to the named mode |
| `/help` | List the available commands and modes |
| `/exit` | Quit (an empty line also exits) |
Any other input is sent to the agent as a message.
## Prerequisites
- .NET 10 SDK or later
- Microsoft Foundry project endpoint and model configured
- Azure CLI installed and authenticated (run `az login`)
- User has the required role to invoke models in the Foundry project
## Running the sample
Set the required environment variables:
```powershell
$env:FOUNDRY_PROJECT_ENDPOINT="https://your-project-endpoint"
$env:FOUNDRY_MODEL="gpt-5.4-mini" # Optional, defaults to gpt-5.4-mini
```
Run the sample:
```powershell
dotnet run
```
To try the custom modes instead of the built-in `plan` / `execute` modes, set the
`AGENT_MODE_USE_CUSTOM` environment variable to `true` and re-run:
```powershell
$env:AGENT_MODE_USE_CUSTOM="true"
dotnet run
```
@@ -0,0 +1,16 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.Identity" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
@@ -0,0 +1,83 @@
// Copyright (c) Microsoft. All rights reserved.
// Todo List — Track work items across turns with TodoProvider
//
// This sample shows how to use the TodoProvider, an AIContextProvider that gives an agent a set of
// tools for managing a todo list (todos_add, todos_complete, todos_remove, todos_get_remaining,
// todos_get_all) along with instructions on how to use them. The todo list is stored in the
// session state and persists across turns, so the agent can plan multi-step work, track progress,
// and adjust the list as the conversation evolves.
//
// This is a scripted, non-interactive walkthrough: it sends a sequence of messages to the agent
// and, after each turn, prints the agent's reply followed by the current todo list (read directly
// from the provider via GetAllTodosAsync). This lets you watch the todo state evolve as the agent
// adds, completes, and removes items.
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
var model = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// <create_todo_provider>
// Create the TodoProvider and attach it to the agent as an AIContextProvider. The provider
// contributes the todo-management tools and instructions to every agent invocation.
using var todoProvider = new TodoProvider();
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(new ChatClientAgentOptions
{
Name = "PlanningAssistant",
ChatOptions = new ChatOptions
{
ModelId = model,
Instructions = "You are a helpful planning assistant. Use your todo list to plan and track multi-step work.",
},
AIContextProviders = [todoProvider],
});
// </create_todo_provider>
AgentSession session = await agent.CreateSessionAsync();
// A scripted set of turns that exercises the provider end-to-end: the agent should add todos for a
// multi-step request, mark items complete as progress is reported, and adjust the list on a change
// of plan.
string[] userMessages =
[
"I'm organizing a small team offsite. Can you help me plan it? Break the work into a todo list.",
"I've booked the venue and sent out the invites. Please update the list.",
"Actually, let's skip catering and instead plan a group hike. Update the plan accordingly.",
];
foreach (string userMessage in userMessages)
{
Console.WriteLine($"User: {userMessage}");
Console.WriteLine($"Agent: {await agent.RunAsync(userMessage, session)}");
// Read the current todo list straight from the provider and print it so the state is visible.
await PrintTodoListAsync(todoProvider, session);
Console.WriteLine();
}
static async Task PrintTodoListAsync(TodoProvider todoProvider, AgentSession session)
{
IReadOnlyList<TodoItem> todos = await todoProvider.GetAllTodosAsync(session);
Console.WriteLine("--- Current todo list ---");
if (todos.Count == 0)
{
Console.WriteLine(" (empty)");
return;
}
foreach (TodoItem todo in todos)
{
string status = todo.IsComplete ? "x" : " ";
Console.WriteLine($" [{status}] {todo.Id}. {todo.Title}");
}
}
@@ -0,0 +1,47 @@
# Todo List
This sample demonstrates how to use the `TodoProvider` to let an agent plan and track multi-step
work using a todo list that persists across turns within a session.
The `TodoProvider` is an `AIContextProvider` that contributes todo-management tools and instructions
to the agent, and stores the todo list in the session state. The provider exposes the following
tools to the agent:
- `todos_add` — add one or more todo items (title + optional description).
- `todos_complete` — mark one or more items complete, with a reason.
- `todos_remove` — remove one or more items by ID.
- `todos_get_remaining` — retrieve the incomplete items.
- `todos_get_all` — retrieve all items (complete and incomplete).
## What it demonstrates
- Attaching a `TodoProvider` to an agent via `ChatClientAgentOptions.AIContextProviders`.
- The agent breaking a complex request into trackable todo items, marking items complete as
progress is reported, and adjusting the list when the plan changes.
- Reading the todo list from application code with `TodoProvider.GetAllTodosAsync`.
This is a **scripted, non-interactive** walkthrough: it sends a fixed sequence of messages and,
after each turn, prints the agent's reply followed by the current todo list so you can watch the
state evolve.
## Prerequisites
- .NET 10 SDK or later
- Microsoft Foundry project endpoint and model configured
- Azure CLI installed and authenticated (run `az login`)
- User has the required role to invoke models in the Foundry project
## Running the sample
Set the required environment variables:
```powershell
$env:FOUNDRY_PROJECT_ENDPOINT="https://your-project-endpoint"
$env:FOUNDRY_MODEL="gpt-5.4-mini" # Optional, defaults to gpt-5.4-mini
```
Run the sample:
```powershell
dotnet run
```
@@ -47,6 +47,9 @@ Before you begin, ensure you have the following prerequisites:
|[Using compaction pipeline with an agent](./Agent_Step18_CompactionPipeline/)|This sample demonstrates how to use a compaction pipeline to efficiently limit the size of the conversation history for an agent.|
|[In-function-loop checkpointing](./Agent_Step19_InFunctionLoopCheckpointing/)|This sample demonstrates how to persist chat history after each service call during a tool-calling loop, enabling crash recovery and mid-run observability.|
|[Dynamic function tools](./Agent_Step20_DynamicFunctionTools/)|This sample demonstrates how to dynamically expand the set of function tools available to an agent during a function-calling loop using the ambient FunctionInvocationContext.|
|[Shell tool with environment-aware system prompt](./Agent_Step21_ShellWithEnvironment/)|This sample demonstrates how to use the shell tool together with the ShellEnvironmentProvider to run commands in stateless and persistent modes, injecting environment-aware instructions so the agent emits commands in the right shell idiom.|
|[Switching agent operating mode](./Agent_Step22_AgentMode/)|This sample demonstrates how to use the AgentModeProvider to track and switch an agent's operating mode at runtime, including the built-in plan/execute modes and custom modes, with a simple input loop that switches mode using a slash command.|
|[Tracking work with a todo list](./Agent_Step23_TodoList/)|This sample demonstrates how to use the TodoProvider to let an agent plan and track multi-step work using a todo list that persists across turns, printing the evolving todo list after each turn.|
## Running the samples from the console
@@ -81,7 +81,12 @@ public static class Program
}
Console.WriteLine($"Number of checkpoints created: {checkpoints.Count}");
// Rehydrate a new workflow instance from a saved checkpoint and continue execution
// <rehydrate_workflow>
// A rehydrated workflow must preserve the topology and executor identities of the workflow that
// created the checkpoint. This executor-only workflow rebuilds identically because its executors
// use fixed ids. Agent-based workflows must recreate each local agent with the same
// ChatClientAgentOptions.Id (and, if set, the same Name), otherwise the executor ids no longer
// match the checkpoint and resume fails.
var newWorkflow = WorkflowFactory.BuildWorkflow();
const int CheckpointIndex = 5;
Console.WriteLine($"\n\nHydrating a new workflow instance from the {CheckpointIndex + 1}th checkpoint.");
@@ -89,6 +94,7 @@ public static class Program
await using StreamingRun newCheckpointedRun =
await InProcessExecution.ResumeStreamingAsync(newWorkflow, savedCheckpoint, checkpointManager);
// </rehydrate_workflow>
await foreach (WorkflowEvent evt in newCheckpointedRun.WatchStreamAsync())
{
@@ -10,50 +10,81 @@ using Microsoft.Extensions.AI;
/// <param name="chatClient">The <see cref="IChatClient"/> to use as the agent backend.</param>
internal sealed class AgentRegistry(IChatClient chatClient)
{
// <stable_agent_identity>
// Give each agent a stable, unique Id so its workflow executor identity stays the same when the
// workflow is reconstructed (for example per request or dependency-injection scope), which keeps
// checkpoints resumable. If an agent also has a Name, keep that stable too, since the executor
// identity includes it. Use a fixed logical role here, not a conversation, request, or user id.
internal const string IntakeAgentName = "Assistant";
public AIAgent IntakeAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
public AIAgent IntakeAgent { get; } = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Id = "intake-agent",
Name = IntakeAgentName,
ChatOptions = new()
{
Instructions =
"""
You receive a user request and are responsible for routing to the correct initial expert agent.
""",
IntakeAgentName
);
},
});
// </stable_agent_identity>
internal const string LiquidityAnalysisAgentName = "Liquidity Analysis";
public AIAgent LiquidityAnalysisAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
public AIAgent LiquidityAnalysisAgent { get; } = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Id = "liquidity-analysis-agent",
Name = LiquidityAnalysisAgentName,
ChatOptions = new()
{
Instructions =
"""
You are responsible for Liquidity Analysis.
""",
LiquidityAnalysisAgentName
);
},
});
internal const string TaxAnalysisAgentName = "Tax Analysis";
public AIAgent TaxAnalysisAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Tax Analysis.
public AIAgent TaxAnalysisAgent { get; } = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Id = "tax-analysis-agent",
Name = TaxAnalysisAgentName,
ChatOptions = new()
{
Instructions =
"""
You are responsible for Tax Analysis.
""",
TaxAnalysisAgentName
);
},
});
internal const string ForeignExchangeAgentName = "Foreign Exchange Analysis";
public AIAgent ForeignExchangeAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Foreign Exchange Analysis.
public AIAgent ForeignExchangeAgent { get; } = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Id = "foreign-exchange-agent",
Name = ForeignExchangeAgentName,
ChatOptions = new()
{
Instructions =
"""
You are responsible for Foreign Exchange Analysis.
""",
ForeignExchangeAgentName
);
},
});
internal const string EquityAgentName = "Equity Analysis";
public AIAgent EquityAgent { get; } = chatClient.AsAIAgent(
instructions:
"""
You are responsible for Equity Analysis.
public AIAgent EquityAgent { get; } = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Id = "equity-analysis-agent",
Name = EquityAgentName,
ChatOptions = new()
{
Instructions =
"""
You are responsible for Equity Analysis.
""",
EquityAgentName
);
},
});
public IEnumerable<AIAgent> Experts => [this.LiquidityAnalysisAgent, this.TaxAnalysisAgent, this.ForeignExchangeAgent, this.EquityAgent];
@@ -59,6 +59,10 @@ static async Task RunWorkflowAsync(Workflow workflow)
}
await run.TrySendMessageAsync(userInput);
// Agents are wrapped as executors that cache incoming messages and only run when they receive a TurnToken,
// so the turn must be triggered explicitly after sending the user input.
await run.TrySendMessageAsync(new TurnToken(emitEvents: true));
string? speakingAgent = null;
await foreach (WorkflowEvent evt in run.WatchStreamAsync(cts.Token))
{
@@ -1,10 +0,0 @@
# .editorconfig
[*.cs]
# See https://github.com/Azure/azure-functions-durable-extension/issues/3173
dotnet_diagnostic.DURABLE0001.severity = none
dotnet_diagnostic.DURABLE0002.severity = none
dotnet_diagnostic.DURABLE0003.severity = none
dotnet_diagnostic.DURABLE0004.severity = none
dotnet_diagnostic.DURABLE0005.severity = none
dotnet_diagnostic.DURABLE0006.severity = none
@@ -1,42 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
<OutputType>Exe</OutputType>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<!-- The Functions build tools don't like namespaces that start with a number -->
<AssemblyName>SingleAgent</AssemblyName>
<RootNamespace>SingleAgent</RootNamespace>
</PropertyGroup>
<ItemGroup>
<FrameworkReference Include="Microsoft.AspNetCore.App" />
</ItemGroup>
<!-- Azure Functions packages -->
<ItemGroup>
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
<!--
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
</ItemGroup>
-->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,42 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
#pragma warning disable IDE0002 // Simplify Member Access
using Azure;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AzureFunctions;
using Microsoft.Azure.Functions.Worker.Builder;
using Microsoft.Extensions.Hosting;
using OpenAI.Chat;
// Get the Azure OpenAI endpoint and deployment name from environment variables.
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME")
?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
: new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
// Set up an AI agent following the standard Microsoft Agent Framework pattern.
const string JokerName = "Joker";
const string JokerInstructions = "You are good at telling jokes.";
AIAgent agent = client.GetChatClient(deploymentName).AsAIAgent(JokerInstructions, JokerName);
// Configure the function app to host the AI agent.
// This will automatically generate HTTP API endpoints for the agent.
using IHost app = FunctionsApplication
.CreateBuilder(args)
.ConfigureFunctionsWebApplication()
.ConfigureDurableAgents(options => options.AddAIAgent(agent, timeToLive: TimeSpan.FromHours(1)))
.Build();
app.Run();
@@ -1,89 +0,0 @@
# Single Agent Sample
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that hosts a single AI agent and provides direct HTTP API access for interactive conversations.
## Key Concepts Demonstrated
- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
- Registering agents with the Function app and running them using HTTP.
- Conversation management (via session IDs) for isolated interactions.
## Environment Setup
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
## Running the Sample
With the environment setup and function app running, you can test the sample by sending an HTTP request to the agent endpoint.
You can use the `demo.http` file to send a message to the agent, or a command line tool like `curl` as shown below:
Bash (Linux/macOS/WSL):
```bash
curl -X POST http://localhost:7071/api/agents/Joker/run \
-H "Content-Type: text/plain" \
-d "Tell me a joke about a pirate."
```
PowerShell:
```powershell
Invoke-RestMethod -Method Post `
-Uri http://localhost:7071/api/agents/Joker/run `
-ContentType text/plain `
-Body "Tell me a joke about a pirate."
```
You can also send JSON requests:
```bash
curl -X POST http://localhost:7071/api/agents/Joker/run \
-H "Content-Type: application/json" \
-H "Accept: application/json" \
-d '{"message": "Tell me a joke about a pirate."}'
```
To continue a conversation, include the `thread_id` in the query string or JSON body:
```bash
curl -X POST "http://localhost:7071/api/agents/Joker/run?thread_id=your-thread-id" \
-H "Content-Type: application/json" \
-H "Accept: application/json" \
-d '{"message": "Tell me another one."}'
```
The response from the agent will be displayed in the terminal where you ran `func start`. The expected `text/plain` output will look something like:
```text
Why don't pirates ever learn the alphabet? Because they always get stuck at "C"!
```
The expected `application/json` output will look something like:
```json
{
"status": 200,
"thread_id": "ee6e47a0-f24b-40b1-ade8-16fcebb9eb40",
"response": {
"Messages": [
{
"AuthorName": "Joker",
"CreatedAt": "2025-11-11T12:00:00.0000000Z",
"Role": "assistant",
"Contents": [
{
"Type": "text",
"Text": "Why don't pirates ever learn the alphabet? Because they always get stuck at 'C'!"
}
]
}
],
"Usage": {
"InputTokenCount": 78,
"OutputTokenCount": 36,
"TotalTokenCount": 114
}
}
}
```
@@ -1,8 +0,0 @@
# Default endpoint address for local testing
@authority=http://localhost:7071
### Prompt the agent
POST {{authority}}/api/agents/Joker/run
Content-Type: text/plain
Tell me a joke about a pirate.
@@ -1,20 +0,0 @@
{
"version": "2.0",
"logging": {
"logLevel": {
"Microsoft.Agents.AI.DurableTask": "Information",
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
"DurableTask": "Information",
"Microsoft.DurableTask": "Information"
}
},
"extensions": {
"durableTask": {
"hubName": "default",
"storageProvider": {
"type": "AzureManaged",
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
}
}
}
}
@@ -1,42 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
<OutputType>Exe</OutputType>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<!-- The Functions build tools don't like namespaces that start with a number -->
<AssemblyName>AgentOrchestration_Chaining</AssemblyName>
<RootNamespace>AgentOrchestration_Chaining</RootNamespace>
</PropertyGroup>
<ItemGroup>
<FrameworkReference Include="Microsoft.AspNetCore.App" />
</ItemGroup>
<!-- Azure Functions packages -->
<ItemGroup>
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
<!--
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
</ItemGroup>
-->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,92 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Net;
using System.Text.Json;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.DurableTask;
using Microsoft.Azure.Functions.Worker;
using Microsoft.Azure.Functions.Worker.Http;
using Microsoft.DurableTask;
using Microsoft.DurableTask.Client;
namespace AgentOrchestration_Chaining;
public static class FunctionTriggers
{
public sealed record TextResponse(string Text);
[Function(nameof(RunOrchestrationAsync))]
public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
{
DurableAIAgent writer = context.GetAgent("WriterAgent");
AgentSession writerSession = await writer.CreateSessionAsync();
AgentResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
message: "Write a concise inspirational sentence about learning.",
session: writerSession);
AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
session: writerSession);
return refined.Result.Text;
}
// POST /singleagent/run
[Function(nameof(StartOrchestrationAsync))]
public static async Task<HttpResponseData> StartOrchestrationAsync(
[HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "singleagent/run")] HttpRequestData req,
[DurableClient] DurableTaskClient client)
{
string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
orchestratorName: nameof(RunOrchestrationAsync));
HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
await response.WriteAsJsonAsync(new
{
message = "Single-agent orchestration started.",
instanceId,
statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
});
return response;
}
// GET /singleagent/status/{instanceId}
[Function(nameof(GetOrchestrationStatusAsync))]
public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
[HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "singleagent/status/{instanceId}")] HttpRequestData req,
string instanceId,
[DurableClient] DurableTaskClient client)
{
OrchestrationMetadata? status = await client.GetInstanceAsync(
instanceId,
getInputsAndOutputs: true,
req.FunctionContext.CancellationToken);
if (status is null)
{
HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
return notFound;
}
HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
await response.WriteAsJsonAsync(new
{
instanceId = status.InstanceId,
runtimeStatus = status.RuntimeStatus.ToString(),
input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
failureDetails = status.FailureDetails
});
return response;
}
private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
{
// NOTE: This can be made more robust by considering the value of
// request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
return $"{authority}/api/singleagent/status/{instanceId}";
}
}
@@ -1,45 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
#pragma warning disable IDE0002 // Simplify Member Access
using Azure;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AzureFunctions;
using Microsoft.Azure.Functions.Worker.Builder;
using Microsoft.Extensions.Hosting;
using OpenAI.Chat;
// Get the Azure OpenAI endpoint and deployment name from environment variables.
string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME")
?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
: new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
// Single agent used by the orchestration to demonstrate sequential calls on the same session.
const string WriterName = "WriterAgent";
const string WriterInstructions =
"""
You refine short pieces of text. When given an initial sentence you enhance it;
when given an improved sentence you polish it further.
""";
AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterInstructions, WriterName);
using IHost app = FunctionsApplication
.CreateBuilder(args)
.ConfigureFunctionsWebApplication()
.ConfigureDurableAgents(options => options.AddAIAgent(writerAgent))
.Build();
app.Run();
@@ -1,59 +0,0 @@
# Single Agent Orchestration Sample
This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that orchestrates sequential calls to a single AI agent using the same session for context continuity.
## Key Concepts Demonstrated
- Orchestrating multiple interactions with the same agent in a deterministic order
- Using the same `AgentSession` across multiple calls to maintain conversational context
- Durable orchestration with automatic checkpointing and resumption from failures
- HTTP API integration for starting and monitoring orchestrations
## Environment Setup
See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
## Running the Sample
With the environment setup and function app running, you can test the sample by sending an HTTP request to start the orchestration.
You can use the `demo.http` file to start the orchestration, or a command line tool like `curl` as shown below:
Bash (Linux/macOS/WSL):
```bash
curl -X POST http://localhost:7071/api/singleagent/run
```
PowerShell:
```powershell
Invoke-RestMethod -Method Post -Uri http://localhost:7071/api/singleagent/run
```
The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
```json
{
"message": "Single-agent orchestration started.",
"instanceId": "86313f1d45fb42eeb50b1852626bf3ff",
"statusQueryGetUri": "http://localhost:7071/api/singleagent/status/86313f1d45fb42eeb50b1852626bf3ff"
}
```
The orchestration will proceed to run the WriterAgent twice in sequence:
1. First, it writes an inspirational sentence about learning
2. Then, it refines the initial output using the same conversation thread
Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
```json
{
"failureDetails": null,
"input": null,
"instanceId": "86313f1d45fb42eeb50b1852626bf3ff",
"output": "Learning serves as the key, opening doors to boundless opportunities and a brighter future.",
"runtimeStatus": "Completed"
}
```
@@ -1,3 +0,0 @@
### Start the single-agent orchestration
POST http://localhost:7071/api/singleagent/run
@@ -1,20 +0,0 @@
{
"version": "2.0",
"logging": {
"logLevel": {
"Microsoft.Agents.AI.DurableTask": "Information",
"Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
"DurableTask": "Information",
"Microsoft.DurableTask": "Information"
}
},
"extensions": {
"durableTask": {
"hubName": "default",
"storageProvider": {
"type": "AzureManaged",
"connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
}
}
}
}
@@ -1,42 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<AzureFunctionsVersion>v4</AzureFunctionsVersion>
<OutputType>Exe</OutputType>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
<!-- The Functions build tools don't like namespaces that start with a number -->
<AssemblyName>AgentOrchestration_Concurrency</AssemblyName>
<RootNamespace>AgentOrchestration_Concurrency</RootNamespace>
</PropertyGroup>
<ItemGroup>
<FrameworkReference Include="Microsoft.AspNetCore.App" />
</ItemGroup>
<!-- Azure Functions packages -->
<ItemGroup>
<PackageReference Include="Microsoft.Azure.Functions.Worker" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
<PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
<!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
<!--
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
</ItemGroup>
-->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>

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