* Fix: Parse oauth_consent_request events in Azure AI client (#3950)
When Azure AI Agent Service returns an oauth_consent_request output item
for OAuth-protected MCP tools, the base OpenAI responses parser drops it
(hits case _ default branch). This causes agent runs to complete silently
with zero content.
Changes:
- Add oauth_consent_request ContentType and Content.from_oauth_consent_request()
factory with consent_link field and user_input_request=True
- Override _parse_response_from_openai and _parse_chunk_from_openai in
RawAzureAIClient to intercept Azure-specific oauth_consent_request items
- Add _emit_oauth_consent helper in AG-UI to emit CustomEvent for frontends
- Add tests proving base parser drops the event and Azure AI override catches it
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* addressed comment
* addressed comments
* addressed comments
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add file_ids and data_sources support to AzureAIAgentClient.get_code_interpreter_tool()
Update the factory method to accept file_ids and data_sources keyword
arguments, matching the underlying azure.ai.agents SDK CodeInterpreterTool
constructor. This enables users to attach uploaded files for code
interpreter analysis.
Fixes#4050
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* addressed comments
* addressed comments
* Add per-message file attachment support for AzureAIAgentClient
Add hosted_file handling in _prepare_messages() to convert
Content.from_hosted_file() into MessageAttachment on ThreadMessageOptions.
This enables per-message file scoping for code interpreter, matching the
underlying Azure AI Agents SDK MessageAttachment pattern.
- Add hosted_file case in _prepare_messages() match statement
- Import MessageAttachment from azure.ai.agents.models
- Add sample for per-message CSV file attachment with code interpreter
- Add employees.csv test data file
- Add 3 unit tests for hosted_file attachment conversion
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: validation, fix assertions, remove MessageAttachment
- Add empty string validation in resolve_file_ids()
- Add test for Content with file_id=None
- Add test for empty string file_ids
- Revert MessageAttachment/hosted_file handling from _prepare_messages()
(moved to separate issue #4352 for proper design)
- Remove per-message file upload sample and employees.csv
- Keep data_sources assertion as-is (dict keyed by asset_identifier)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add OpenTelemetry instrumentation to ClaudeAgent (#4278)
Add inline telemetry to ClaudeAgent.run() so that enable_instrumentation()
emits invoke_agent spans and metrics. Covers both streaming and
non-streaming paths using the same observability helpers as
AgentTelemetryLayer. Adds 5 unit tests for telemetry behavior.
Co-Authored-By: amitmukh <amimukherjee@microsoft.com>
* Address PR review feedback for ClaudeAgent telemetry
- Add justification comment for private observability API imports
- Pass system_instructions to capture_messages for system prompt capture
- Use monkeypatch instead of try/finally for test global state isolation
Co-Authored-By: amitmukh <amitmukh@users.noreply.github.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* Adopt AgentTelemetryLayer instead of inline telemetry
Restructure ClaudeAgent to inherit from AgentTelemetryLayer via a
_ClaudeAgentRunImpl mixin, eliminating duplicated telemetry code and
private API imports.
MRO: ClaudeAgent → AgentTelemetryLayer → _ClaudeAgentRunImpl → BaseAgent
- Remove inline _run_with_telemetry / _run_with_telemetry_stream methods
- Remove private observability helper imports (_capture_messages, etc.)
- Add default_options property mapping system_prompt → instructions
- Net -105 lines by reusing core telemetry layer
Co-Authored-By: amitmukh <amitmukh@users.noreply.github.com>
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix mypy: align _ClaudeAgentRunImpl.run() signature with AgentTelemetryLayer.run()
Remove explicit `options` parameter from mixin's run() signature and
extract it from **kwargs to match AgentTelemetryLayer's signature.
Also align overload return types (ResponseStream, Awaitable) to match.
Co-Authored-By: Claude <noreply@anthropic.com>
* Introduce RawClaudeAgent following framework's RawAgent/Agent pattern
Replace private _ClaudeAgentRunImpl mixin with public RawClaudeAgent
class that contains all core logic (init, run, lifecycle, tools).
ClaudeAgent becomes a thin wrapper that adds AgentTelemetryLayer.
- RawClaudeAgent(BaseAgent): full implementation without telemetry
- ClaudeAgent(AgentTelemetryLayer, RawClaudeAgent): adds OTel tracing
- Export RawClaudeAgent from package __init__.py
Users who want to skip telemetry or provide their own can use
RawClaudeAgent directly.
Co-Authored-By: Claude <noreply@anthropic.com>
* Address review nits: trim RawClaudeAgent docstring, fix import paths
- Simplify RawClaudeAgent docstring to a single basic example (not the
primary entry point for most users)
- Use agent_framework.anthropic import path in docstrings instead of
direct agent_framework_claude path
- Add RawClaudeAgent to agent_framework.anthropic lazy re-exports
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Amit Mukherjee <amimukherjee@microsoft.com>
Co-authored-by: amitmukh <amitmukh@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
* Fix#4371: Propagate session to manager agent in StandardMagenticManager
StandardMagenticManager._complete() was calling self._agent.run(messages)
without passing a session. This caused context providers (e.g.
RedisHistoryProvider) configured on the manager agent to silently fail,
as each call created a new ephemeral session with a different session_id.
Changes:
- Create an AgentSession in StandardMagenticManager.__init__()
- Pass session=self._session in _complete() calls to agent.run()
- Persist/restore the session in checkpoint save/restore methods
- Add regression tests for session propagation and checkpoint round-trip
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add type: ignore[reportPrivateUsage] to private attribute assertions in tests
Address PR review feedback: add # type: ignore[reportPrivateUsage] comments
to _session attribute accesses in the new regression tests, matching the
existing convention used elsewhere in test_magentic.py (e.g., lines 401-406).
The @pytest.mark.asyncio decorator is not needed because pyproject.toml
sets asyncio_mode = "auto".
Fixes#4371
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: use getattr for private _session access in tests (#4371)
Replace direct mgr._session access with getattr(mgr, "_session") to avoid
reportPrivateUsage type-checking warnings without needing type: ignore comments.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Address PR review: fix session restore guard and improve test robustness (#4371)
- Use 'is not None' instead of truthiness check for session_payload restore
- Use getattr() for private _session attribute access in tests
- Add backward-compatibility test for on_checkpoint_restore with empty state
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Make non-async tests plain def to avoid pytest-asyncio dependency (#4409)
Tests that never await anything don't need to be async. Using plain def
ensures they always run regardless of pytest-asyncio configuration.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Added shell tool
* Fixed CI error
* Add ShellTool support for OpenAI and Anthropic providers
- Add shell_tool_call, shell_tool_result, and shell_command_output content types
- Add ShellTool class and shell_tool decorator to core
- Add get_hosted_shell_tool() to OpenAI Responses client
- Handle shell_call and shell_call_output parsing in OpenAI (sync and streaming)
- Map ShellTool to Anthropic bash tool API format
- Parse bash_code_execution_tool_result as shell_tool_result in Anthropic
- Add unit tests for all new functionality
- Add sample scripts for hosted and local shell execution
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Addressed comments
* Reverted ruff change
* Fixed tests
* Addressed comments
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix IndexError when reasoning models return no text content (#4384)
In _prepare_message_for_openai(), the text_reasoning case unconditionally
accessed all_messages[-1] to attach reasoning_details. When a reasoning
model (e.g. gpt-5-mini) returns reasoning_details without text content,
all_messages is empty, causing an IndexError.
Guard the access by initializing all_messages with the current args dict
when it is empty, so reasoning_details can be safely attached.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: buffer reasoning details for valid message payloads (#4384)
- Buffer pending reasoning details and attach to the next message with
content/tool_calls, avoiding standalone reasoning-only messages.
- When reasoning is the only content, emit a message with empty content
to satisfy Chat Completions schema requirements.
- Strengthen test assertions to verify text+reasoning co-location and
that all messages with reasoning_details also have content or tool_calls.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix text_reasoning handling: always buffer and tighten tests (#4384)
- Always buffer reasoning into pending_reasoning instead of conditionally
attaching to the previous message via fragile all_messages emptiness check
- Attach buffered reasoning to last message at end-of-loop when no subsequent
content consumed it
- Assert exact content values (content == '' not in ('', None))
- Assert exact list lengths (== 1 not >= 1) for stronger regression guards
- Add test for reasoning before FunctionCallContent
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add regression tests for #3948 - Entry JoinExecutor initializes Workflow.Inputs
Add tests verifying that when workflow.run() is called with a dict or string
input, the Entry node (JoinExecutor with kind: 'Entry') correctly initializes
Workflow.Inputs via _ensure_state_initialized so that:
- Expressions like =inputs.age resolve to the correct value
- Conditions like =Local.age < 13 evaluate based on actual input (not blank/0)
- String inputs populate both inputs.input and System.LastMessage.Text
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Fix D420 and RUF070 lint errors across packages
* Revert _workflow.py yield-inside-context-manager changes
Moving yield inside `with _framework_event_origin()` blocks in the
async generator causes ContextVar token reset failures on Python 3.12
Windows. The token stays un-reset while the generator is suspended,
and async generator finalization in a different contextvars.Context
triggers ValueError, corrupting OpenTelemetry span state and causing
test_span_creation_and_attributes to see leaked spans.
Keep yields outside the context manager blocks to ensure tokens are
reset immediately before the generator suspends.
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: handle thread.message.completed event in Assistants API streaming
Previously, `thread.message.completed` events fell through to the
catch-all `else` branch and yielded empty `ChatResponseUpdate` objects,
silently discarding fully-resolved annotation data (file citations,
file paths, and their character-offset regions).
This commit adds a dedicated handler for `thread.message.completed`
that:
- Walks the completed ThreadMessage.content array
- Extracts text blocks with their fully-resolved annotations
- Maps FileCitationAnnotation and FilePathAnnotation to the
framework's Annotation type with proper TextSpanRegion data
- Yields a ChatResponseUpdate containing the complete text and
annotations
Fixes#4322
* test: add tests for thread.message.completed annotation handling
Tests cover:
- File citation annotation extraction
- File path annotation extraction
- Multiple annotations on a single text block
- Text-only messages (no annotations)
- Non-text blocks are skipped
- Mixed content blocks (text + image)
- Conversation ID propagation
* fix: address Copilot review - add quote field and log unrecognized annotations
- Include `quote` from `annotation.file_citation.quote` in
`additional_properties` for FileCitationAnnotation, preserving the
exact cited text snippet from the source file
- Add `else` clause to log unrecognized annotation types at debug level,
consistent with the pattern in `_responses_client.py`
- Add `import logging` and module-level logger
* test: add coverage for quote field and unrecognized annotation logging
- test_message_completed_with_file_citation_quote: verifies quote is
included in additional_properties
- test_message_completed_with_file_citation_no_quote: verifies quote
is omitted when None
- test_message_completed_unrecognized_annotation_logged: verifies
unknown annotation types are logged at debug level and skipped
* fix: address reviewer nits — logger name convention + annotation type string
Per @giles17's review:
- Use logging.getLogger('agent_framework.openai') to match module convention
- Simplify debug message to use annotation.type instead of type().__name__
* refactor: move message.completed tests into consolidated test file
Per @giles17's review: moved all tests from test_assistants_message_completed.py
into test_openai_assistants_client.py and deleted the standalone file.
* fix: resolve mypy no-redef and ruff RET504 lint errors
- Remove duplicate type annotation for 'ann' variable (no-redef)
- Return directly from fixture instead of unnecessary assignment (RET504)
* fix: rename annotation variable in completed block to fix mypy type conflict
The 'annotation' loop variable in thread.message.completed has type
FileCitationAnnotation | FilePathAnnotation, which conflicts with the
delta block's 'annotation' of type FileCitationDeltaAnnotation |
FilePathDeltaAnnotation. Renamed to 'completed_annotation' to avoid
mypy 'Incompatible types in assignment' error.
* fix: remove quote field from FileCitationAnnotation handling
---------
Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
* fix(python): use AgentResponse.value instead of model_validate_json in HITL sample
Since the agent is configured with response_format=GuessOutput, the
AgentResponse already provides .value with the parsed Pydantic model.
Using .value is more idiomatic and avoids redundant JSON parsing.
Fixes#4396
* fix: add safety guard for AgentResponse.value being None
Address Copilot review feedback: .value is optional and may be None
if response_format isn't propagated through the streaming path.
Add an explicit None check with a clear error message.
* Fix walrus operator precedence for model_id in AzureOpenAIResponsesClient (#4299)
Add parentheses around the walrus assignment so model_id receives the
actual string value instead of the boolean result of
`kwargs.pop(...) and not deployment_name`.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: replace walrus with explicit None check, add edge-case tests (#4299)
- Replace walrus operator with explicit assignment and 'is not None'
check to avoid boolean-coercion pitfalls (empty string now correctly
surfaces as ValueError instead of silently falling back)
- Add test: deployment_name takes precedence over model_id kwarg
- Add test: model_id='' raises ValueError
- Add test: model_id=None falls back to env var
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add explicit validation for empty model_id in AzureOpenAIResponsesClient
Reject empty or whitespace-only model_id with ValueError instead of
silently passing an empty deployment name downstream. This ensures the
test_init_model_id_kwarg_empty_string test correctly validates behavior
defined in production code rather than relying on downstream validation.
Addresses PR review feedback for #4299.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify model_id handling using walrus operator
Addresses review comment on PR #4310.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Restore explicit model_id validation to fix test failures (#4299)
The walrus operator refactor silently dropped the empty-string validation,
causing test_init_model_id_kwarg_empty_string to fail. Restore the explicit
None check and ValueError raise for empty model_id.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Revert "Restore explicit model_id validation to fix test failures (#4299)"
This reverts commit 1d2965fff6.
* Revert to walrus operator fix per review feedback
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix .NET conversation memory in DevUI (#3484)
* formatting fixes
* fix memory regression in python devui , fix for #4123
* Fix for #3983: Added _get_event_type() helper that safely accesses event type on both objects (.type) and dicts (.get("type")). Replaced all 4 bare event.type accesses in _executor.py (lines 267, 477, 499, 523).
Root cause: PR #3690 changed event.__class__.__name__ == "RequestInfoEvent" (safe) to event.type == "request_info" (crashes on dicts), but _execute_workflow still yields raw dicts on error paths.
Test: test_workflow_error_yields_dict_event_without_crash — mocks a workflow that raises, verifies execute_entity consumes the dict error events without crashing.
* format fixes
* lint fixes
* Python: Fix Executor handler type checking with __future__ annotations (#3898)
Use typing.get_type_hints() in _validate_handler_signature to resolve
string annotations from `from __future__ import annotations`. This
mirrors the fix applied to FunctionExecutor in #2308.
When __future__ annotations are enabled, type annotations are stored as
strings. The handler decorator was passing these strings directly to
validate_workflow_context_annotation, which uses typing.get_origin and
returns None for strings, causing a ValueError.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #3898: improve error handling and test coverage
- Wrap typing.get_type_hints() in try/except to provide a descriptive
ValueError mentioning the handler name when annotations cannot be resolved
- Strengthen bare context test to assert output_types and workflow_output_types
- Add test for @handler(input=..., output=...) with future annotations
covering the skip_message_annotation branch
- Add test for union-type context annotations with future annotations
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Narrow exception catch and add test for unresolvable annotations (#3898)
- Narrow except clause from bare Exception to (NameError, AttributeError,
TypeError) to avoid masking unexpected errors.
- Add test_handler_unresolvable_annotation_raises to verify that a handler
with a forward-reference to a non-existent type raises ValueError with
the expected message.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix#3898: fall back to raw annotations when get_type_hints fails
When typing.get_type_hints(func) raises NameError (unresolvable forward
ref), AttributeError, RecursionError, or any other exception, fall back
to the raw parameter annotations instead of raising a ValueError.
This matches the suggestion from @moonbox3 on PR #4317.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix test to match new fallback behavior when get_type_hints fails (#3898)
The code now falls back to raw string annotations instead of raising
'Failed to resolve type annotations'. A ValueError is still raised when
the raw string ctx annotation is not a valid WorkflowContext type, so
update the test to match on ValueError without checking the message.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pyupgrade: remove unnecessary string annotation quote
* Add noqa for intentionally undefined name in annotation test
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix _merge_options dropping dict-defined tools (#4303)
_merge_options used getattr(tool, 'name', None) to de-duplicate tools,
which returns None for dict-style tool definitions. This caused all
override dict tools to be treated as duplicates of each other and of any
base dict tools, silently dropping them.
Add _get_tool_name() helper that extracts the name from both object-style
tools (via .name attribute) and dict-style tools (via tool['function']['name']).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: fix None dedup bug and add comprehensive tests (#4303)
- Exclude None from existing_names set so nameless/malformed tools are
not silently deduplicated against each other
- Add test for cross-type dedup (dict tool + object tool with same name)
- Add test verifying nameless tools are preserved (not falsely deduped)
- Add unit tests for _get_tool_name edge cases: missing function key,
non-dict function value, missing name, no name attribute, non-dict
inputs, and valid dict/object tools
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix OpenAIResponsesClient mishandling single-tool inputs (#4304)
Use normalize_tools() in _prepare_tools_for_openai to wrap single tools
(FunctionTool or dict) in a list before iteration, consistent with the
chat client implementation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #4304
- Use precise type annotation matching normalize_tools/OpenAIChatClient signature
instead of collapsed Sequence[Any] | Any | None
- Move emptiness guard after normalize_tools() call so single falsy tool
objects are not silently swallowed
- Import ToolTypes for the type annotation
- Expand test_prepare_tools_for_openai_single_function_tool assertions to
verify parameters, strict, and parameter schema fields
- Add test_prepare_tools_for_openai_none to verify None input returns []
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
WorkflowAgent._run_impl() and _run_stream_impl() did not set
session_context._response before calling _run_after_providers().
This caused InMemoryHistoryProvider.after_run() to see context.response
as None, so response messages were never stored in the session.
On subsequent runs, the workflow only received prior user inputs without
assistant responses, breaking multi-turn conversations.
Fix: Set session_context._response to the workflow result before running
after_run providers, matching the behavior of the regular Agent class.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
During Assistants API streaming, TextDeltaBlock.text.annotations was
ignored when creating Content objects. This caused raw placeholder
strings like 【4:0†source】 to pass through to downstream consumers
(including AG-UI) instead of being resolved to citation metadata.
Map FileCitationDeltaAnnotation and FilePathDeltaAnnotation from
delta_block.text.annotations to Annotation objects on the Content,
consistent with the existing patterns in _responses_client.py and
_chat_client.py.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix(python): preserve workflow run kwargs on response continuation (#4293)
When continuing a paused workflow with run(responses=...), the existing
run kwargs stored in state were unconditionally overwritten with an empty
dict. This caused subsequent agent invocations to lose the original run
context (e.g., custom_data, user tokens).
Now kwargs are only overwritten when:
- New kwargs are explicitly provided (override), or
- State was just cleared for a fresh run (initialize to {})
On continuation without new kwargs, existing kwargs are preserved.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #4293
- Use consistent get_state(key, {}) default pattern in _agent_executor.py
and _workflow_executor.py instead of get_state(key) or {} to safely
handle missing WORKFLOW_RUN_KWARGS_KEY
- Add test for empty-value kwargs on continuation (custom_data={}) to
verify the is-not-None boundary between overwrite and preserve
- Add test for reset_context=True with no kwargs to exercise the elif
branch that initializes WORKFLOW_RUN_KWARGS_KEY to {}
- Add len assertion to override test for consistency
- Document kwargs-collapsing behavior at the public API call site
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Strip reserved kwargs in AgentExecutor to prevent collision (#4295)
workflow.run(session=...) passed 'session' through to agent.run() via
**run_kwargs while AgentExecutor also passes session=self._session
explicitly, causing TypeError: got multiple values for keyword argument.
_prepare_agent_run_args now strips reserved params (session, stream,
messages) from run_kwargs and logs a warning when they are present.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #4295
- Use _RESERVED_RUN_PARAMS constant in stripping loop instead of
hardcoded tuple to maintain single source of truth
- Trim frozenset to only stripped keys (session, stream, messages);
options and additional_function_arguments have separate merge logic
- Fix caplog type annotation to use TYPE_CHECKING pattern
- Assert options return value in reserved-kwarg stripping test
- Add test for multiple reserved kwargs supplied simultaneously
- Add integration test for messages= kwarg via workflow.run()
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
HandoffBuilder.participants() accepted SupportsAgentRun by API contract,
but build() failed at runtime because _prepare_agent_with_handoffs()
requires Agent instances for cloning, tool injection, and middleware.
Fix: Update all public type hints, docstrings, and validation in
HandoffBuilder and HandoffAgentExecutor to require Agent explicitly.
The isinstance check is now performed early in participants() with a
clear error message explaining why Agent is required.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix AgentResponse.value being None when streaming workflow (#3970)
The streaming path in BaseAgent.run() used the raw 'options' parameter
(passed by the caller) to bind response_format into the outer stream's
finalizer. When response_format was set in default_options rather than
runtime options, it was missing from the finalizer and value was None.
Fix: Use the merged chat_options from the run context (via ctx_holder),
matching the non-streaming path which already uses ctx['chat_options'].
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #3970: safer ctx access, add test coverage
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference
Add embedding client implementations to existing provider packages:
- OllamaEmbeddingClient: Text embeddings via Ollama's embed API
- BedrockEmbeddingClient: Text embeddings via Amazon Titan on Bedrock
- AzureAIInferenceEmbeddingClient: Text and image embeddings via Azure AI
Inference, supporting Content | str input with separate model IDs for
text (AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID) and image
(AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID) endpoints
Additional changes:
- Rename EmbeddingCoT -> EmbeddingT, EmbeddingOptionsCoT -> EmbeddingOptionsT
- Add otel_provider_name passthrough to all embedding clients
- Register integration pytest marker in all packages
- Add lazy-loading namespace exports for Ollama and Bedrock embeddings
- Add image embedding sample using Cohere-embed-v3-english
- Add azure-ai-inference dependency to azure-ai package
Part of #1188
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix mypy duplicate name and ruff lint issues
- Rename second 'vector' variable to 'img_vector' in image embedding loop
- Combine nested with statements in tests
- Remove unused result assignments in tests
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updates from feedback
* Fix CI failures in embedding usage handling
- Fix Azure AI embedding mypy issues by normalizing vectors to list[float],
safely accumulating optional usage token fields, and filtering None entries
before constructing GeneratedEmbeddings
- Avoid Bandit false positive by initializing usage details as an empty dict
- Update OpenAI embedding tests to assert canonical usage keys
(input_token_count/total_token_count)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* small updates and improvements in the azure AISearch provider
* Fix mypy errors and embedding function test
- Use separate variable for embeddings result to avoid mypy type reassignment error
- Fix test_vectorized_query_with_embedding_function: use real async function
instead of AsyncMock which falsely matches SupportsGetEmbeddings protocol
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fixes from feedback
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix thread corruption when max_iterations exhausted (#1366)
When the function invocation loop exhausts max_iterations while the model
keeps requesting tools, the failsafe code path (calling the model with
tool_choice='none' and prepending fcc_messages) was unreachable because
'if response is not None: return response' short-circuited before it.
The fix removes the premature return so the failsafe always runs after
loop exhaustion, making a final model call with tool_choice='none' to
produce a clean text answer and prepending accumulated fcc_messages from
prior iterations. This matches the existing pattern used by the error
threshold and max_function_calls paths.
Also unskips test_max_iterations_limit and test_streaming_max_iterations_limit
which were previously skipped with 'needs investigation in unified API'.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add fix report for issue #1366
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix ruff formatting in _tools.py and test_issue_1366_thread_corruption.py
Apply ruff format to fix multi-line string concatenation and function call
formatting issues flagged by the linter.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add quality review for issue #1366 fix
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove temporary investigation docs.
* Address PR review: explicit enabled check in log condition, clarify mock behavior in test
- Add explicit function_invocation_configuration['enabled'] check to the
'Maximum iterations reached' log condition in both non-streaming and
streaming paths, making intent clearer when function invocation is disabled.
- Add comment in test_thread_safe_after_max_iterations_with_agent explaining
that the failsafe response (tool_choice='none') is provided automatically
by the mock client, not from run_responses.
* Blend fix and tests into project without issue-specific callouts
- Remove issue #1366 references from _tools.py comments
- Move regression tests from standalone test_issue_1366_thread_corruption.py
into test_function_invocation_logic.py alongside existing max_iterations tests
- Clean up test docstrings to describe behavior generically
- Delete the standalone issue-specific test file
---------
Co-authored-by: alliscode <bentho@microsoft.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: prevent doubled tool_call arguments in MESSAGES_SNAPSHOT
When streaming with client-side tools, some providers send a full-
arguments replay after the streaming deltas complete. The `_emit_tool_call`
function unconditionally appends every arguments delta to the internal
`flow.tool_calls_by_id` tracking dictionary via `+=`. When the replay
contains the exact same complete arguments string that was already
accumulated from prior deltas, the arguments get doubled (e.g.,
`{"todoText":"buy groceries"}{"todoText":"buy groceries"}`).
This causes `MESSAGES_SNAPSHOT` events to contain invalid doubled JSON in
`tool_calls[].function.arguments`, breaking any client or middleware that
relies on snapshots for state reconstruction.
The fix adds a guard (mirroring the existing duplicate guard in
`_emit_text`) that detects when the incoming delta exactly equals the
already-accumulated arguments string, indicating a full-arguments replay
rather than an incremental delta. In this case the append is skipped,
preventing the doubling.
The `ToolCallArgsEvent` deltas are still emitted correctly for real-time
streaming — only the internal snapshot accumulator is guarded.
Fixes#4194
* fix: move duplicate check before event emission + add test
Address Copilot review feedback:
1. Move duplicate full-arguments replay detection BEFORE emitting
ToolCallArgsEvent, for consistency with _emit_text() which returns
early without emitting any events on replay detection.
2. Add test_emit_tool_call_skips_duplicate_full_arguments_replay() to
verify the duplicate detection behavior for tool call arguments,
matching the existing test pattern for text content.
Tao ChenandGitHub
Victor DibiaandGitHub
Rishabh ChawlaandGitHub
Vincent KocandGitHub
L. Elaine DazzioandGitHub