* fix(foundry-hosting): root hosted checkpoints under durable home directory
* fix: add None guard for _checkpoint_storage_path in test
* Disable Foundry image test
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
* Allow workflow checkpoint full replayability
Seed the initial run input through the start executor's internal self-edge and record an entry checkpoint (iteration 0) before any executor runs, plus a response-entry checkpoint when responses are delivered, so a run is fully replayable from its checkpoints. Simplify the runner to only checkpoint after each superstep. Drop stale events in apply_checkpoint on restore, and deprecate the unused RunnerContext.reset_for_new_run.
* Fix type
* Add max iteration detailed doc string
* Refine comments
* Python: Fix OpenAIChatCompletionClient passing raw JSON-Schema dict response_format through unwrapped
Raw schema dicts (e.g. {"type": "object", ...}) were forwarded to the
Chat Completions API verbatim, which OpenAI rejects with a 400. The
Responses client already auto-wraps the same input. Mirror its raw-schema
detection (primitive types / schema keywords), wrap into the
{"type": "json_schema", "json_schema": {...}} envelope with
additionalProperties: false injection and title -> name promotion, and
leave already-valid response_format dicts untouched.
Fixes#7197
(cherry picked from commit dce5c3b06328fbde45eb2a9a25638af5b1ec85e3)
* Python: Add live integration coverage for raw JSON-Schema response_format dicts
Adds a response_format_raw_json_schema param to test_integration_options in
both the Chat Completions and Responses client test suites, proving the same
bare schema dict (title set, additionalProperties omitted) round-trips through
both live APIs and yields parsed structured output.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Python: Fix response format dict typing
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
_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.
* 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>
* Python: Add TodoProvider and AgentModeProvider context provider samples
Add two Python samples under samples/02-agents/context_providers/ mirroring the
.NET samples from #7262:
- todo_provider.py: scripted walkthrough of TodoProvider that plans multi-step
work and prints the evolving todo list after each turn.
- agent_mode_provider.py: interactive loop using AgentModeProvider with a /mode
slash command, demonstrating built-in plan/execute and custom modes.
Also index both samples in the context_providers README.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 8725831c-086b-475f-90e6-cdba41d59c33
* Python: Address review comments on AgentModeProvider sample
- Replace the AGENT_MODE_USE_CUSTOM env var with an in-file USE_CUSTOM_MODES
constant for choosing between built-in and custom modes.
- Use plain input() in the interactive loop instead of asyncio.to_thread.
- Update the README prerequisites to reference the in-file toggle.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 8725831c-086b-475f-90e6-cdba41d59c33
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 8725831c-086b-475f-90e6-cdba41d59c33
* fix(python): handle callable class middleware safely in _determine_middleware_type (#6697)
* test(python): type-annotate test middleware lists to pass test-typing checks
* Fix sub-workflow checkpoint restore to preserve sub-workflow state
Add Runner.capture_checkpoint_object/restore_from_checkpoint_object (quiescent-only nested checkpoint) and embed a sub_workflow_checkpoint in WorkflowExecutor.on_checkpoint_save/on_checkpoint_restore so a resumed parent restores each sub-workflow's mid-progress state instead of only replaying pending request-info events. Keeps a backward-compat fallback when sub_workflow_checkpoint is absent.
* Move checkpoint-object construction into the runner context
Add RunnerContext.create_checkpoint_object alongside create_checkpoint (create_checkpoint now delegates to it and persists), so Runner.capture_checkpoint_object builds the snapshot via the context instead of a one-off get_messages peek primitive. In-flight messages are captured non-destructively (per-source lists copied). The checkpoint-less capturing contexts (azurefunctions, durabletask) raise NotImplementedError to match create_checkpoint.
* Remove per-execution bookkeeping from WorkflowExecutor
The sub-workflow is a single shared instance, so per-execution ExecutionContext/request routing never provided real isolation. Delegate request/response tracking to the sub-workflow itself: can_handle accepts targeted propagated responses, _handle_response validates against the sub-workflow's pending requests and forwards responses immediately, and on_checkpoint_save embeds only the sub-workflow checkpoint (on_checkpoint_restore keeps a legacy reader for older checkpoints). Also emit the fresh-message/checkpoint-while-pending warning from FunctionalWorkflow.run to match Workflow.run.
* Drop redundant decode in WorkflowExecutor.on_checkpoint_restore
The storage backend already materializes the full checkpoint on load (FileCheckpointStorage decodes recursively; InMemoryCheckpointStorage deep-copies), so the embedded sub_workflow_checkpoint (and legacy execution_contexts) arrive already decoded - like every other executor's on_checkpoint_restore state. Remove the no-op decode_checkpoint_value calls and the now-unused import.
* Clean up
* Do not allow checkpoint storage in sub workflow
* Address comments
* Fix syntax check
* Add warning
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Python: Fix duplicate function call on approval round-trip (#7267)
`_replace_approval_contents_with_results` deduped restored function calls
against only the message currently being scanned. On an approval round-trip
the hosting layer replays the stored `function_call` item and its
`mcp_approval_request` item as two separate assistant messages, so the
per-message check never fired and the approval request restored a second
copy of the call.
Only one copy received the function result; the orphaned copy was left
unanswered, which the Responses API rejects with
"No tool output found for function call call_<id>".
Collect existing call ids across all messages instead, and add a restored
call to that set so two approval requests for the same call cannot both
expand.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Refactor approval placeholder result handling
Refactor approval handling logic to improve clarity and maintainability.
* Refactor test to support reused call IDs after completion
Updated the test to allow reused call IDs after completion, ensuring that a completed call does not suppress later approval requests with the same ID. Adjusted assertions to reflect the new behavior.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Promote the GitHub Copilot package from release candidate (1.0.0rc4) to released (1.0.0): bump the version, switch the classifier to Production/Stable, update PACKAGE_STATUS.md, and drop the --pre install flag from the package and sample READMEs. Add a github-copilot-1.0.0 CHANGELOG section covering the promotion and the input-attachment forwarding shipped in this release. No core/root bump: this is a standalone package promotion and the core[all] extra references the package without a version pin.
Copilot-Session: f523064c-60b4-4d18-bf95-c16c5fda9126
* Python: Forward GitHub Copilot input attachments as inline blobs
The Python GitHubCopilotAgent built the prompt from message text only, so
DataContent (images/documents) passed on input was silently dropped. The .NET
provider already forwards these as attachments.
Map input data content to the Copilot SDK's inline BlobAttachment (base64,
no temp files) in both the streaming and non-streaming send paths. Data content
without a media type is dropped with a warning instead of silently.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
* Python: Handle non-base64 data URIs and fix attachment docstring
Address PR review feedback:
- Guard _get_data_bytes_as_str against ContentError so a non-base64 data:
URI (which _validate_uri still classifies as type="data") is skipped with a
warning instead of failing the entire Copilot request.
- Correct the docstring: remote URIs and non-base64 data URIs are neither
attached nor added to the prompt (the prompt is built from text content only).
- Add tests for the non-base64 data URI path.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
* Python: Fix flaky attachment test under telemetry
The end-to-end non-base64 data URI test failed in CI because GitHubCopilotAgent's
telemetry layer serializes message content (observability._to_otel_part ->
_get_data_bytes_as_str), which raises ContentError on a non-base64 data: URI
before the attachment code runs. That is an unrelated core-observability
limitation, not attachment behavior.
Use RawGitHubCopilotAgent (no telemetry layer) for that test so it isolates the
provider's send path. The direct helper test still covers the ContentError guard.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: ac85c429-4115-42ef-a18a-f576e3cf03f7
* Python: Support async credentials in `FoundryToolbox`
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Refactor: Use AzureCredentialTypes for credential type annotations in Toolbox classes
* Remove auth_flow method from _ToolboxAuth class
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Prepare the focused alpha release for the progressive A2A adapters from #7258. No other package versions or dependency bounds change.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 003e02dd-dba0-40a5-9ebf-083901aefb57
Bump root and core to 1.12.1, OpenAI to 1.11.0 for new public prompt-cache options, Foundry to 1.10.3, and Gemini and Foundry Hosting to beta 260722 based on CHANGELOG entries. Promote AG-UI from 1.0.0rc9 to stable 1.0.0. No beta cohort bump was applied, and core floors remain unchanged under the strict affected-dependency policy because the connectors do not require a new core API.
* Python: Fix reasoning-paired client tool replay
* Python: Handle middleware-terminated reasoning tool loops
* Python: Replay encrypted reasoning function groups
Key decisions:
- Request encrypted reasoning on client-managed Responses calls while preserving caller include values.
- Store encrypted payloads in Content.protected_data and reconstruct one provider reasoning item per reasoning id.
- Replay active and completed function call/result groups; retain continuation-owned history behavior and the existing orphan-safe MCP path.
Files changed:
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Extend encrypted reasoning preservation to streaming and framework serialization boundaries.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Preserve encrypted reasoning through streaming
Key decisions:
- Capture encrypted reasoning from terminal streamed output items in Content.protected_data.
- Preserve summary and private reasoning as distinct framework contents while reconstructing one provider reasoning item per id.
- Prove replay after Message JSON and workflow checkpoint round trips, including encrypted-only and completed function groups.
Files changed:
- python/packages/core/agent_framework/_types.py
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Extend lossless stateless reasoning replay to hosted MCP call/output groups.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Replay hosted MCP reasoning groups
Key decisions:
- Preserve hosted MCP call/output groups in client-managed history instead of deleting them when reasoning cannot be reconstructed.
- Keep call/result coalescing and orphan-result exclusion intact, while retaining continuation-owned duplicate avoidance.
- Cover completed, active, and multi-call reasoning groups plus the public outgoing request boundary.
Files changed:
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Preserve middleware-terminated and parallel function groups atomically.
- Add preflight rejection for non-replayable reasoning groups in the dedicated validation slice.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Preserve terminated parallel reasoning groups
Key decisions:
- Return ordinary function results when middleware terminates a loop, removing the provider-specific durable marker.
- Preserve every parallel call and available sibling result as one encrypted reasoning group in stateless replay.
- Prove successful and policy-blocked batches through the public two-agent Foundry workflow and outgoing HTTP boundary.
Files changed:
- python/packages/core/agent_framework/_tools.py
- python/packages/core/tests/core/test_function_invocation_logic.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
- python/packages/foundry/tests/foundry/test_foundry_agent.py
Next iteration:
- Add preflight rejection for non-replayable and partially compacted reasoning groups.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Reject unsafe stateless reasoning replay
Key decisions:
- Validate client-managed reasoning groups after compaction and report every affected reasoning and call identifier before transport.
- Permit service-owned continuation and fully excluded atomic groups while rejecting partial compaction projections.
- Surface encrypted-reasoning capability failures without lossy retries.
Files changed:
- python/packages/openai/agent_framework_openai/_chat_client.py
- python/packages/openai/tests/openai/test_openai_chat_client.py
Next iteration:
- Run the resource-specific Foundry proof and finish PR #7233; that live proof remains intentionally local and requires the configured developer resource.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Preserve reasoning metadata in Foundry hosting
* Python: Avoid duplicating reasoning text metadata
* Python: Gate encrypted reasoning for Foundry agents
* Python: Type stateless reasoning integration test
* Python: Narrow Foundry mock call arguments
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Support prompt cache breakpoints for GPT-5.6 models in OpenAI clients
Add request-level prompt_cache_options to OpenAIChatOptions and
OpenAIChatCompletionOptions, and forward a per-part prompt_cache_breakpoint
from Content.additional_properties onto the content blocks each API supports.
Text parts that carry a breakpoint keep typed list content, since the
plain-string form cannot hold one; without a breakpoint the existing string
forms are unchanged.
* Clarify system-message content-shape comment
* Address review: SDK prompt cache types, private helper, add sample
Replace the custom PromptCacheOptions TypedDict with the openai SDK's own
types for each API, which raises the openai floor to 2.45.0 where those
types were introduced. Make the breakpoint helper private to the two chat
clients. Add a prompt caching sample with a README entry, and unquote the
helper's Content annotation so the pyupgrade hook passes.
* Guard the prompt cache options import for older openai versions
The SDK's PromptCacheOptions types only exist in openai 2.45.0 and
later, so each client falls back to a local mirror when the import
fails and the dependency floor stays at 2.25.0. A TYPE_CHECKING-only
import is not enough because the options classes are introspected with
get_type_hints() at runtime. Verified against openai 2.25.0: the
package imports, the fallback resolves, and part-level breakpoints
still work; sending the option itself requires 2.45.0, which the field
docstrings now note.
* Make the old-openai fallback for PromptCacheOptions deliberately empty
Assigning None instead, as suggested in review, trips pyright's
reportInvalidTypeForm on the field annotation (the symbol becomes
type | None after the try/except). An empty TypedDict gives the same
effect for users on older openai versions: any content they put in
prompt_cache_options is flagged by their type checker, since the
option cannot be sent on those versions anyway, while
get_type_hints() on the options classes keeps working at runtime.
* Guard prompt_cache_options at runtime instead of via an empty fallback type
The empty-TypedDict fallback flagged valid `prompt_cache_options` usage under
pyright on every openai version — including this PR's own
`client_prompt_caching.py` sample (`poe check -S`) — because pyright resolves the
try/except symbol to the fallback shape regardless of the installed openai, while
mypy/ty resolve the failed import to `Any` and never warn. So a type-only "warn on
old openai" signal is not achievable cleanly across type checkers.
Restore the faithful fallback (mirrors the SDK's `mode`/`ttl` shape) so the option
type-checks identically on every supported openai version, and add a runtime guard:
setting `prompt_cache_options` on openai < 2.45 now raises a clear
ChatClientInvalidRequestException instead of forwarding an unusable option to the
SDK. This keeps the option non-silent for all users regardless of type checker,
without forcing an openai upgrade. Adds tests covering the guard for both clients.
* Gate system/developer breakpoint shape on a real mapping value
The system/developer branch switched to list-form content whenever
prompt_cache_breakpoint was set to any non-None value, but the option is
only attached when the value is a mapping. A malformed value (e.g. a
string) therefore changed the message shape without adding a breakpoint.
Decide the shape from the built part instead, matching the user-role path.
* Python: preserve Gemini 3 thought_signature across function-call replays
Gemini 3 requires the opaque thought_signature attached to each functionCall
part to be echoed back on every replay of that call, or the request is rejected
with 400 INVALID_ARGUMENT. The signature previously survived only via
raw_representation, so any layer that reconstructs a FunctionCallContent (e.g.
harness tool approval) dropped it and broke the next step of the tool loop.
Capture the signature into additional_properties on parse and replay it when
building the Gemini Part, independent of raw_representation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
* Store Gemini thought_signature as base64 for JSON-safe persistence
Content.additional_properties is serialized via json.dumps(message.to_dict())
by history providers (e.g. RedisHistoryProvider), which fails on raw bytes.
Store the thought_signature as a base64 string on parse and decode it back to
bytes when building the Gemini Part. Also narrow call_id/name in the round-trip
test to satisfy the type checkers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
* Harden Gemini thought_signature decode against corrupted history
Guard the untyped additional_properties value with an isinstance(str) check and
decode with validate=True, degrading gracefully (warn + drop the signature) on
malformed data instead of raising binascii.Error mid tool loop. Matches the
defensive base64 handling already used for data URIs in this file.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
* Carry Gemini thought_signature on reasoning content via protected_data
Represent the signature as a text_reasoning content's protected_data (base64)
immediately preceding the function call, instead of a bespoke additional_properties
key. This uses the framework's first-class opaque-signature field (as Anthropic
does), survives streaming accumulation, and stays intact when the harness
reconstructs the function call. Replay correlates the signature by adjacency.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 33233834-bc6e-4ad2-a6f3-6f1d6e57b1d2
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: make FoundryToolbox.as_skills_provider() disable_caching effective
as_skills_provider() forwarded disable_caching to SkillsProvider, which
ignores it for a caller-supplied SkillsSource, so it was a no-op and the
toolbox re-read skill://index.json on every agent run.
Compose caching in as_skills_provider() instead: wrap the context-independent
_FoundryToolboxSkillsSource in DeduplicatingSkillsSource(CachingSkillsSource(...)).
Add a cache_refresh_interval param, fix the docstring, and add tests covering
cached, disabled, and refresh-interval behavior.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Clarify caller-invariant skill-set wording in as_skills_provider docs
Emphasize that the toolbox advertises the same skill set to every caller (the
per-request call-id governs execution/authorization, not which skills are
listed) rather than leaning on 'ignores SkillsSourceContext'.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Make MCP skills reconnect-safe via session_provider
Cached MCPSkill objects captured the MCP ClientSession at construction, so
after a FoundryToolbox reconnect (which replaces its session) load_skill and
read_skill_resource would fail against the closed session. This regressed once
as_skills_provider() started caching discovery by default.
Add an optional session_provider callable to MCPSkillsSource and MCPSkill
(exactly one of client or session_provider). When supplied, the session is
resolved on every fetch, mirroring how MCPTool resolves self.session live at
call time. _FoundryToolboxSkillsSource now passes a provider that returns the
toolbox's current session, so cached skills always use the live session.
The fixed client= path is unchanged and backward-compatible. Update core tests,
foundry_hosting tests, and core AGENTS.md.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Fix ty error: type captured session_provider as Callable in test
ty could not call the provider narrowed from \object\ (Top callable). Type the
captured value as Callable[[], object] and drop the redundant callable() assert.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Simplify _resolve_mcp_session_provider per review
Address review feedback: replace the dense (client is None) == (session_provider
is None) guard with explicit branches, and drop the cast by binding the narrowed
client to a typed local. Keeps strict 'exactly one' semantics (raises on both and
on neither), matching the codebase convention (e.g. security.py mcp_tool/url).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Add PR #7135 entries to the 1.12.0 changelog
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
* Drop redundant @pytest.mark.asyncio from MCP skills tests
asyncio_mode is 'auto', so the marker is unnecessary. Remove it from the whole
file for consistency with the async-by-default convention. Per review feedback.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 84150ec4-6f7c-4ef8-b9fb-12fa11652773
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Bump Python package versions for 1.12.0 release
Bump packages represented in the 1.12.0 changelog, promote Foundry Hosting, Azure Content Understanding, Gemini, Mistral, Monty, and Tools to beta, and apply the requested beta cohort date stamp. Root and core move to 1.12.0, released and RC packages use their selected increments, alpha packages including Hosting MCP use the 260721 stamp, and core floors are raised only for proven consumers.
Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf
* fix version in readme
* Add Responses conversation ID changes to release notes
Include the breaking Hosting Responses conversation ID helper changes from #7234 in the Python 1.12.0 changelog.
Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf
* Python: Fix PropertySchema.to_json_schema() not recursing into nested schemas
Nested array 'items' and object 'properties' kept the declarative 'kind'
key and empty 'enum' placeholders, producing JSON Schema OpenAI rejects
('schema must have a type key'). Recursively apply the same conversion the
top-level properties loop performs, including the serialized named-list
properties shape and nested required arrays.
Fixes#7198
(cherry picked from commit c156ffd05924fb5a1884625f2fc3d9bdc3e152b1)
* Python: Validate nested properties list before mutating to avoid partial conversion
Review feedback: the list-shaped properties branch popped name/required from
each element and returned on the first unexpected one, leaving earlier
elements half-converted. Validate the whole list first so an unexpected
shape leaves the node fully untouched.
* Python: Type nested-properties normalization for strict Pyright and drop unreachable dict branch
ObjectProperty always stores nested properties as a named list, so the
elif-dict branch in _normalize_nested_schemas was unreachable; remove it
and flatten the list conversion behind an early return. Cast the narrowed
items/props values so strict Pyright no longer reports unknown types.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Python: Emit additionalProperties: false on nested object nodes in PropertySchema.to_json_schema()
OpenAI strict structured outputs require additionalProperties: false on
every object node, but the chat clients only inject it at the schema
root, so declarative schemas with nested objects (e.g. array items)
failed with a schema-validation 400. Route the top-level properties loop
through _normalize_schema_node so all object nodes get the key, and add
a live OpenAI integration test covering the nested array-of-objects
response_format shape.
Verified live against the Responses API: the previous emission fails
with "In context=('properties', 'issues', 'items'),
'additionalProperties' is required to be supplied and to be false";
the new emission returns valid structured output.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Python: fix header_provider headers not reaching streamable HTTP requests
MCPStreamableHTTPTool.call_tool stores header_provider output in a
ContextVar, but the streamable HTTP transport sends requests from tasks
spawned at connect time, whose contexts never observe values set later.
The request hook therefore always read an empty dict on real connections
and the per-call headers (e.g. Authorization) were silently dropped.
Keep the ContextVar for in-context reads and add an instance-level
snapshot of the active call's headers that the request hook falls back
to across tasks.
* Python: serialize header_provider tool calls to prevent cross-call header mixing
Parallel tool invocations run concurrently per function-invocation batch,
so two call_tool invocations on the same MCPStreamableHTTPTool could
overwrite each other's active-header snapshot while requests were still
in flight, attaching the wrong per-call credentials. Hold a per-instance
lock for the duration of a header-bearing call, add a regression test
that fails without the lock, and normalize captured header casing in the
transport-task test.
* Python: forward GitHubCopilotOptions verbatim to create_session
Refactor the GitHub Copilot agent to forward the full options dict to the
Copilot SDK's create_session/resume_session instead of hand-mapping a fixed
subset. GitHubCopilotOptions stays as the curated, typed surface, but any
other create_session parameter (reasoning_effort, context_tier,
enable_citations, ...) is now passed through verbatim. Unknown keys surface
as TypeError from the SDK instead of being silently dropped.
De-duplicates the near-identical _create_session/_resume_session bodies into
a shared _build_session_kwargs helper.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f9d016d5-4d8c-43f0-a8fb-f3cf3d1ad7eb
* Python: address review feedback on GHCP options passthrough
- Strip agent-internal/client-level keys (on_pre_tool_use, on_function_approval,
timeout, cli_path, log_level, base_directory) from the forwarded kwargs so they
cannot leak into create_session/resume_session and raise TypeError.
- Source caller tools from the merged options layer so tools supplied via
default_options are honored instead of silently dropped.
- Honor a caller-supplied native 'hooks' dict in _build_session_hooks (composing
with the on_pre_tool_use shortcut) instead of unconditionally overwriting it.
- Validate mock create_session/resume_session calls against the real SDK
signatures in tests so invalid kwargs surface as TypeError, and add regression
tests for the passthrough contract.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f9d016d5-4d8c-43f0-a8fb-f3cf3d1ad7eb
* Python: avoid redundant re-read of model in _build_session_kwargs
model is popped from default_options into settings at init, so a per-run
model already lands in the merged kwargs. Keep that value when present and
only fall back to the resolved setting otherwise, instead of re-reading opts.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Copilot-Session: f9d016d5-4d8c-43f0-a8fb-f3cf3d1ad7eb
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add agent-framework-azure-cosmos-memory context provider (draft)
Introduces CosmosMemoryContextProvider, a ContextProvider that wraps the azure-cosmos-agent-memory toolkit to give agents long-term, Cosmos DB-backed memory (fact/procedural recall + user summaries). Includes package scaffolding, unit tests (mocked client), live Azure integration tests (marked), samples, README, and AGENTS.md.
Draft: uv.lock is intentionally left unchanged. This package depends on azure-cosmos-agent-memory (requires Python >=3.11), which is unsatisfiable against the workspace's current >=3.10 floor, so adding it to the shared lock requires a workspace decision (raise floor to 3.11 or exclude from workspace). Test coverage to be expanded.
* ci: exclude azure-cosmos-memory from uv workspace resolution
The package depends on azure-cosmos-agent-memory which requires Python
>=3.11 and a prompty pre-release (>=2.0.0a9). Both are unsatisfiable
against the workspace's >=3.10 floor and pre-release policy, causing
uv sync to fail in every Python CI job. Exclude the package from the
shared workspace so it is resolved and tested as a standalone package.
* ci: fix code-quality failures for azure-cosmos-memory
- Strip trailing whitespace from package files (pre-commit trailing-whitespace hook)
- Exclude the package README from markdown-code-lint: the package is excluded
from the uv workspace, so its README snippets import a module that is not
installed in the workspace env and Pyright cannot resolve it
* Exclude azure-cosmos-memory README from markdown-code-lint task
* Address PR review comments on cosmos-memory context provider
- Wire credential into Cosmos and AI Foundry clients; let toolkit own
DefaultAzureCredential when none supplied (remove dead import).
- Honor auto_extract=False by zeroing extraction/summary cadence thresholds.
- Skip whitespace-only conversation turns and store stripped content.
- Show confidence 0.0 and coerce confidence to float in _format_memories.
- Register both 'integration' and 'azure' pytest markers accurately.
- Fix duplicated install block in README.
- Update and extend unit tests for new credential wiring and fixes.
* Include azure-cosmos-memory in the uv workspace
Follow the github_copilot pattern for a package with a Python 3.11-only
dependency: lower requires-python to >=3.10 and gate azure-cosmos-agent-memory
behind a python_version >= '3.11' marker. Add a direct, gated prompty
pre-release dependency so the workspace's if-necessary-or-explicit prerelease
policy permits the toolkit's transitive prompty requirement. Guard the test
modules with pytest.importorskip so the 3.10 CI leg skips cleanly. Remove the
workspace exclude and the markdown-code-lint exclude, and regenerate uv.lock.
* Address review feedback on cosmos-memory provider
Rename provider parameters to match Agent Framework conventions:
foundry_endpoint (was ai_foundry_endpoint) and embedding_model/chat_model
(were *_deployment_name). Move DEFAULT_* to module-level constants, type
memory_types as a Literal, use DEFAULT_CONTEXT_PROMPT as the default value,
and add ProcessorConfig/CosmosMemorySettings TypedDicts. Resolve connection
settings via agent_framework load_settings with required-field validation,
replacing the manual getenv/raise blocks. Scope user_id/thread_id to the
provider state and drop the unpreventable first-turn warning.
Rewrite the samples around Agent (not raw SessionContext), provider-scoped
state, and session-id threading; use PEP 723 inline dependencies instead of a
samples dependency group; use a plain input() loop; remove the dead custom
processor stub. Update README/AGENTS for the renamed parameters and env vars.
Add a samples ruff per-file-ignores entry now that the package is linted in CI.
* Add emulator-backed vector search integration test
Bump azure-cosmos-agent-memory to >=0.2.0b2 (adds the embeddings/chat client
injection seam) and add tests/test_emulator.py: an integration (not azure)
suite that exercises real Cosmos vector search with a quantizedFlat index
against a local Cosmos DB emulator, using deterministic in-memory fakes for
embeddings and chat so no Azure AI Foundry account or LLM is required.
To run on a stock emulator the fixture strips the toolkit's full-text index
(the provider only does pure vector search) and requests provisioned autoscale
throughput instead of serverless. The suite skips cleanly when no emulator is
reachable.
* Fix CI typing and package checks for azure-cosmos-memory
The package recently joined the uv workspace, so its source and tests are now covered by the Test Typing Checks and Package Checks gates for the first time.
tests: rename stale constructor kwargs to the current provider API (foundry_endpoint/embedding_model/chat_model); use a typed _STUB_AGENT for the unused agent param so pyright/pyrefly/ty/zuban all accept it; make processor_config values ints; assert non-None memory_client in the emulator tests.
source: relax reportUnknown*/reportOptional* for this package only (the toolkit ships no py.typed; mirrors the hosting-telegram precedent); decouple the conditional toolkit import from the annotation type; use settings.get(); fix memory_types list invariance; drop a redundant None guard; read role via getattr.
* Apply pyupgrade: single-arg AsyncGenerator in test_integration
* Make Cosmos memory extraction drain transparently on provider exit
The provider now drains in-flight background memory extraction in __aexit__, so applications no longer need to call flush() in their own control flow; the client's close() would otherwise cancel pending extraction tasks. flush() is hardened against clients that expose no usable background-task registry.
sample: interactive_chat reads input via asyncio.to_thread so the event loop stays free and background extraction runs during the session; removes the manual flush now that the provider drains on exit.
tests: add explicit transparent-extraction integration tests (emulator: after_run schedules extraction and __aexit__ drains it; live Azure: a fact is extracted and recalled in a later session with no manual flush). Emulator tests reuse a single fixed database to avoid exhausting the emulator's partition budget across runs.
* Add custom extraction-prompt seam and sample to cosmos-memory provider
Adds a prompts_dir option to CosmosMemoryContextProvider that points the Agent Memory Toolkit pipeline at a caller-supplied directory of Prompty templates, so callers can override extract_memories.prompty to control what the extraction LLM produces. The toolkit exposes no public prompts-directory seam, so the provider contains the one internal touch (swapping the pipeline's template loader after the store connects); applies to both provider-built and supplied clients.
sample: interactive_chat_custom_extraction.py - the interactive chat wired with a custom coding-assistant extraction rubric. It derives a complete prompts directory at runtime (copies the bundled templates and augments extract_memories.prompty) so it stays schema-compatible with the installed toolkit.
tests: unit tests assert the provider redirects the pipeline loader only when prompts_dir is set; an emulator integration test proves end to end that a unique marker in a custom extract_memories.prompty reaches the extraction LLM call.
* docs: document prompts_dir custom-extraction seam in cosmos-memory README
Replaces the stale, non-functional CustomMemoryProcessor snippet with the working prompts_dir approach, lists the new interactive_chat_custom_extraction.py sample, and corrects the interactive-sample feature list.
* Address review: rename _new_session, drop defensive toolkit import guard
Sample (comment): rename _new_thread to _new_session in both interactive samples (a new session is the new thread).
Provider (comment): replace the _memory_toolkit_available flag + __init__ ImportError guard with a plain guarded import that re-raises a clear ImportError, matching the github_copilot package's pattern for its 3.11-only SDK. Kept requires-python >=3.10 (bumping this one workspace member to 3.11 would force the entire uv workspace lock floor to 3.11). Tests now run importorskip before importing the package, mirroring github_copilot.
* Pass cadence via cadence_thresholds instead of mutating os.environ
* Mark package alpha and drop private naming in samples
* Require Python 3.11 and inject user summary as untrusted context
* CI: exclude azure-cosmos-memory from uv sync on Python 3.10
* Re-trigger CI (flaky external link check)
* Require chat/embedding models instead of silent defaults
* Fix pyright: narrow resolved chat/embedding models to str
---------
Co-authored-by: Theo van Kraay <thvankra@microsoft.com>
TokenBudgetComposedStrategy estimates tokens by feeding a JSON-serialized
message to the tokenizer, but _serialize_message() used ensure_ascii=True.
That escapes non-ASCII text into \uXXXX sequences, so CJK and other
non-Latin content is token-counted as the escape sequences rather than the
characters the model actually sees, inflating the estimate (~1.6x for mixed
Japanese, more for pure CJK) and skewing compaction/token-budget decisions.
Serialize with ensure_ascii=False, matching the ensure_ascii=False already
used elsewhere in this module. Only affects token estimation; the serialized
string is never stored or transmitted.
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Python: preserve explicit null arguments in auto function calling
FunctionTool.invoke dumped validated arguments with model_dump(exclude_none=True),
which strips any argument the model set to null. A required nullable parameter
(e.g. unit: Literal["C","F"] | None) that the model deliberately sets to null was
therefore dropped, and the function failed to invoke on the missing argument.
Use exclude_unset instead: keep the arguments the model actually provided (null
included) and omit only the ones it left out, so the function's own defaults still
apply. Because the input model is generated from the function signature, its field
defaults match the signature defaults, so omitted optionals are unchanged.
Fixes#5934
Signed-off-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
* Python: extend null-arg fix to the auto function-calling path
The earlier change fixed FunctionTool.invoke, but _auto_invoke_function
(the path a model-emitted function_call actually takes) still ran
model_dump(exclude_none=True), so an explicit null for a required
nullable argument was still dropped and the call failed with a missing
argument. Switch it to exclude_unset to match invoke, and add a
regression test that drives _auto_invoke_function with an explicit null.
---------
Signed-off-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
pratik wayase