* support skill scripts execution
* fix mixed line endings
* address comments and fix syntax issues
* use few try/except instead of one
* change samples
* validate either script path or script resource is set not both
* fix: separate LLM args from runtime kwargs in skill script execution
* address pr review comments
* address PR review comments
* Update python/packages/core/agent_framework/_skills.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update python/packages/core/agent_framework/_skills.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update python/packages/core/agent_framework/_skills.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* 1. Fixing the caching bug where parameters_schema would re-inspect on every call when the result was None
2. Updating the arguments tool description to be more generic (not CLI-specific)
* fix failing tests
* address pr review comments
* address pr review comments
* allow resource function returning any instead of sting
* address PR review comments
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
The sample was passing raw strings in a list to get_response(), which
expects Message objects. This caused an AttributeError since strings
don't have a 'role' attribute.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Prepare azure-ai-projects 2.0 GA compatibility
Add allow_preview support for internal AIProjectClient creation, keep backward compatibility for renamed SDK model classes, and align Azure AI/core paths and tests for GA validation workflows.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* upgrade to ai-project==2.0.0
* Python: remove azure-ai-projects keyword-guard paths
Assume azure-ai-projects 2.0+ in Azure AI client/provider/responses code paths by removing _supports_keyword_argument gating and related fallback branching.
Also fix pyright typing in FoundryMemoryProvider memory store calls by using ResponseInputItemParam-typed items.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* check fixes
* Python: remove unsupported foundry_features option
Drop foundry_features from Azure AI client and provider surfaces because azure-ai-projects 2.0.0 does not expose that create_version parameter.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: add allow_preview to Foundry memory provider
Propagate allow_preview when FoundryMemoryProvider constructs an AIProjectClient and update tests accordingly.
Also finish wiring allow_preview through AzureAIClient-facing surfaces and related docs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* aligning docstrings
* udpated lock
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update github_copilot package for github-copilot-sdk>=0.1.32 (#4549)
- Update requires-python from >=3.10 to >=3.11
- Remove Python 3.10 classifier
- Update mypy python_version to 3.11
- Update dependency to github-copilot-sdk>=0.1.32
- Fix ToolResult API: use snake_case kwargs (text_result_for_llm,
result_type) instead of camelCase (textResultForLlm, resultType)
- Update test assertions to use attribute access on ToolResult
- Add ToolResult type assertions to tool handler tests
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix tests to use ToolInvocation dataclass instead of plain dict (#4549)
Update test_github_copilot_agent.py to pass ToolInvocation objects to tool
handlers instead of plain dicts, matching the github-copilot-sdk>=0.1.32 API
where ToolInvocation is a dataclass with an .arguments attribute.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add regression tests for ToolInvocation contract (#4549)
Add tests to lock in the new ToolInvocation-based calling convention:
- test_tool_handler_rejects_raw_dict_invocation: verifies passing a raw
dict (old calling convention) raises TypeError/AttributeError
- test_tool_handler_with_empty_arguments: verifies ToolInvocation with
empty arguments works correctly for no-arg tools
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Revert requires-python to >=3.10 to avoid breaking CI (#4549)
The repo CI runs with Python 3.10 (uv sync --all-packages) and all other
packages require >=3.10. Raising this package to >=3.11 would break the
shared install flow. The SDK dependency version constraint (>=0.1.32) will
enforce any Python version requirement from the SDK itself.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix min Python version for github_copilot package to >=3.11
github-copilot-sdk>=0.1.32 requires Python>=3.11, which conflicts
with the package's declared >=3.10 minimum, breaking uv sync.
* Bump py version for GH workflows to 3.11, exclude GHCP sdk from 3.10 items
* Fix uv command
* Fixes
* Update samples
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add propagate_session parameter to as_tool() for session sharing
Add opt-in session propagation in agent-as-tool scenarios. When
propagate_session=True, the parent agent's AgentSession is forwarded
to the sub-agent's run() call, allowing both agents to share session
state (history, metadata, session_id).
- Add propagate_session parameter to BaseAgent.as_tool() (default False)
- Include session in additional_function_arguments so it flows to tools
- Add 3 tests for propagation on/off and shared state verification
- Add sample showing session propagation with observability middleware
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify propagate_session docstring per review feedback
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
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>
* 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>
---------
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>
* feat(python): Add embedding abstractions and OpenAI implementation (Phase 1)
This PR contains two parts:
1. **Overall migration plan** for porting vector stores and embeddings from
Semantic Kernel to Agent Framework (docs/features/vector-stores-and-embeddings/README.md)
covering all 10 phases from core abstractions through connectors and TextSearch.
2. **Phase 1 implementation** — core embedding abstractions and OpenAI/Azure OpenAI
embedding clients:
Core types (_types.py):
- EmbeddingGenerationOptions TypedDict (total=False)
- Embedding[EmbeddingT] generic class with model_id, dimensions, created_at
- GeneratedEmbeddings[EmbeddingT, EmbeddingOptionsT] list container with options, usage
- EmbeddingInputT (default str) and EmbeddingT (default list[float]) TypeVars
Protocol + base class (_clients.py):
- SupportsGetEmbeddings protocol — Generic[EmbeddingInputT, EmbeddingT, OptionsContraT]
- BaseEmbeddingClient ABC — Generic[EmbeddingInputT, EmbeddingT, OptionsCoT]
Telemetry (observability.py):
- EmbeddingTelemetryLayer with gen_ai.operation.name = "embeddings"
OpenAI implementation (openai/_embedding_client.py):
- RawOpenAIEmbeddingClient, OpenAIEmbeddingClient, OpenAIEmbeddingOptions
- Uses _ensure_client() factory pattern
Azure OpenAI implementation (azure/_embedding_client.py):
- AzureOpenAIEmbeddingClient following AzureOpenAIChatClient pattern
- Supports API key, Entra ID credentials, env var configuration
Tests:
- 47 unit tests for types, protocol, base class, OpenAI, and Azure clients
- 6 integration tests (gated behind RUN_INTEGRATION_TESTS + credentials)
Samples:
- samples/02-agents/embeddings/openai_embeddings.py
- samples/02-agents/embeddings/azure_openai_embeddings.py
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Add AzureOpenAIEmbeddingClient to azure __init__.pyi stub
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* ci: Add embedding env vars to Python integration tests
Map OPENAI_EMBEDDING_MODEL_ID and AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME
from GitHub vars to the integration test environment.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Handle base64 encoding_format in OpenAI embedding client
When encoding_format='base64' is used, the OpenAI API returns base64-encoded
floats instead of a JSON array. Decode these automatically to list[float]
so the return type stays consistent regardless of encoding format.
Also adds a unit test for base64 decoding and fixes minor docstring/import issues.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Only record INPUT_TOKENS for embedding telemetry
Embeddings have no output/completion tokens. Remove OUTPUT_TOKENS recording
which was double-counting prompt_tokens via the total_tokens fallback.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Resolve mypy variance error and lint warning
Use contravariant/covariant TypeVars for SupportsGetEmbeddings Protocol.
Combine nested if into single statement in telemetry layer.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Make EmbeddingCoT invariant for mypy compatibility
GeneratedEmbeddings is invariant in its type param, so the Protocol
TypeVar cannot be covariant.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Address PR review - empty values guard, service_url for telemetry
- Add early return for empty values in get_embeddings to avoid unnecessary API calls
- Add service_url() method to RawOpenAIEmbeddingClient for proper telemetry endpoint reporting
- Add test for empty values behavior
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix OpenAI chat client compatibility with third-party endpoints and OTel 0.4.14 (#4161)
* Fix system message content sent as list instead of string
Some OpenAI-compatible endpoints (e.g. NVIDIA NIM) reject system messages
when content is a list of content parts. This change flattens system and
developer message content to a plain string in the Chat Completions client.
Fixes https://github.com/microsoft/agent-framework/issues/1407
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix compatibility with opentelemetry-semantic-conventions-ai 0.4.14
Version 0.4.14 removed several LLM_* attributes from SpanAttributes
(LLM_SYSTEM, LLM_REQUEST_MODEL, LLM_RESPONSE_MODEL, LLM_REQUEST_MAX_TOKENS,
LLM_REQUEST_TEMPERATURE, LLM_REQUEST_TOP_P, LLM_TOKEN_TYPE).
Move these to the OtelAttr enum with their well-known gen_ai.* string values
and update all references in observability.py and tests.
Fixes https://github.com/microsoft/agent-framework/issues/4160
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Flatten text-only message content to string for all roles
Extend the system/developer fix to all message roles. Text-only content
lists are now post-processed into plain strings, while multimodal content
(text + images/audio) remains as a list. This fixes compatibility with
OpenAI-like endpoints that cannot deserialize list content (e.g. Foundry
Local's Neutron backend).
Partially fixes https://github.com/microsoft/agent-framework/issues/4084
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix streaming text lost when usage data in same chunk
Some providers (e.g. Gemini) include both usage data and text content
in the same streaming chunk. The early return on chunk.usage caused
text and tool call parsing to be skipped entirely. Remove the early
return and process usage alongside text/tool calls.
Fixes https://github.com/microsoft/agent-framework/issues/3434
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix mypy errors in _chat_client.py
Rename shadowed variable 'args' in system/developer branch to 'sys_args'
and rename loop variable 'content' to 'msg_content' to avoid type conflict.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* reorder imports
* fix: Use OtelAttr.REQUEST_MODEL instead of removed SpanAttributes.LLM_REQUEST_MODEL
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: Add score_threshold to vector store plan
Reference SK .NET PR #13501 for score threshold filtering semantics.
Include score_threshold in SearchOptions from Phase 3.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* docs: Add reference to roji's SK .NET MEVD work for SQL connectors
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: Clear env vars in construction tests to avoid CI leakage
Tests for missing API key / model ID now use monkeypatch.delenv to ensure
env vars from the integration test environment don't prevent the expected
ValueError from being raised.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Enhance Azure AI Search citations with document URLs in Foundry V2 (Responses API)
Override _parse_response_from_openai and _parse_chunk_from_openai in
RawAzureAIClient to extract get_urls from azure_ai_search_call_output
items and enrich url_citation annotations with document-specific URLs.
- Non-streaming: first pass collects get_urls, post-processes annotations
- Streaming: captures search output state, enriches url_citation events
(also handles url_citation annotation type not handled by base class)
- Updated V2 sample to demonstrate citation URL extraction
- Added 14 unit tests covering extraction, enrichment, and edge cases
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* refactor: rework search citation enrichment to override _inner_get_response
- Remove all direct openai/pydantic imports from _client.py
- Override _inner_get_response instead of _parse_response_from_openai/_parse_chunk_from_openai
- Use closure-local state for streaming instead of instance-level _streaming_search_get_urls
- Add _build_url_citation_content helper for streaming url_citation handling
- Fix mypy errors by using str(value or '') for Annotation TypedDict fields
- Fix docstring to say 'citation' instead of 'url_citation'
- Update tests to match new approach
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: handle streaming search citations from output_item.done events
The azure_ai_search_call_output item only has populated output data
(including get_urls) in the response.output_item.done event, not in
the response.output_item.added event. Also removed the search_get_urls
guard on url_citation handling so annotations are always produced even
if get_urls haven't been captured yet.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* addressed comments
* refactor: address PR review - eliminate type: ignore[assignment] pattern
Call super()._inner_get_response() independently in each branch instead
of once at the top with union type reassignment. Non-streaming uses
two-arg super() in the closure; streaming uses cast() for type narrowing.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* refactor: remove defensive patterns per PR review
- Replace all getattr() with direct attribute access
- Remove cast() for streaming branch, use type: ignore[assignment]
- Simplify _build_url_citation_content to use dict access directly
- Simplify _extract_azure_search_urls to use item.type/item.output
- Handle empty list output from streaming 'added' events
- Update tests to match actual runtime types (objects, not dicts)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* mypy fix
* small fixes
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add max_function_calls to FunctionInvocationConfiguration (#2329)
Add a new per-request max_function_calls setting to FunctionInvocationConfiguration
that limits the total number of individual function invocations across all iterations
within a single get_response call. This complements max_iterations (which limits LLM
roundtrips) by providing a hard cap on actual tool executions regardless of parallelism.
- Add max_function_calls field to FunctionInvocationConfiguration (default: None/unlimited)
- Track cumulative function call count in both streaming and non-streaming tool loops
- Force tool_choice='none' when the limit is reached
- Add validation in normalize_function_invocation_configuration
- Improve docstrings for FunctionInvocationConfiguration, FunctionTool, and @tool
to clarify semantics of max_iterations vs max_function_calls vs max_invocations
- Add tests for parallel calls, single calls, unlimited mode, and config validation
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add sample for controlling total tool executions
Showcases all three mechanisms for limiting tool executions:
1. max_iterations — caps LLM roundtrips
2. max_function_calls — caps total individual function invocations per request
3. max_invocations — lifetime cap on a specific tool instance
Plus a combined scenario demonstrating defense in depth.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Suppress ruff E305/fmt in hosting sample to preserve XML doc tags
The XML snippet tags (# <create_agent> / # </create_agent>) are used for
docs extraction and must stay adjacent to the code they wrap. Both ruff
check (E305) and ruff format add blank lines after the function definition,
pushing the closing tag away. Suppress with ruff: noqa: E305 and fmt: off.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add per-agent tool wrapping scenario to control_total_tool_executions sample
Show that wrapping the same callable with @tool multiple times creates
independent FunctionTool instances with separate invocation counters,
enabling per-agent max_invocations budgets for shared functions.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify max_function_calls is a best-effort limit
The limit is checked after each batch of parallel calls completes, so the
current batch always runs to completion even if it overshoots the limit.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: fix docstring reference, clarify best-effort in sample
- Fix malformed Sphinx :attr: role in FunctionTool docstring — use plain
backtick reference instead
- Update sample to say 'best-effort cap' instead of 'hard cap' for
max_function_calls, noting it's checked between iterations
- Parametrize pattern is correct (fixture override, matching existing tests)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* clarify max_invocations limits
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix Workflow.as_agent() streaming regression in ag-ui
* Address PR feedback
* workflows wip
* wip
* wip
* Workflow AG-UI demo
* Fixes for handoff workflow demo
* Fixes to workflows support in AG-UI
* Fixes
* Add headers to some demo files
* Fix comment
* Fixes for store
* Make _input_schema lazy-loaded
* fix mypy
* revert session change to handoff only for now
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* fix: strip function_call and text_reasoning from cross-agent workflow handoff
When a reasoning model (e.g. gpt-5-mini) runs as Agent 1 in a workflow, its
response includes text_reasoning items (with server-scoped IDs like rs_XXXX)
and function_call items. Forwarding these to Agent 2 in a fresh conversation
caused API errors because the reasoning/call IDs are scoped to the original
stored response context.
Changes:
- Strip 'function_call', 'text_reasoning', 'function_approval_request', and
'function_approval_response' from handoff messages in _agent_executor.py
- Keep 'function_result' so the actual tool output content is preserved for
the next agent's context
- Update unit tests to reflect that function_result messages survive handoff
(messages grow from 2→3: user, tool(result), assistant(summary))
- Fix incorrect test assertions in test_function_invocation_stop_clears_*
that assumed the client layer updates session.service_session_id
- Also fixed _extract_function_calls to search all messages with call_id
deduplication, and the error-limit stop path to submit function_call_output
items before halting (via tool_choice=none cleanup call)
Relates to: https://github.com/microsoft/agent-framework/issues/4047
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: reasoning model workflow handoff and history serialization
Fixes multiple related issues when using reasoning models (gpt-5-mini,
gpt-5.2) in multi-agent workflows that chain agents via from_response
or replay full conversation history via AgentExecutorRequest.
## Reasoning items always emitted on output_item.added
When a reasoning model produces encrypted or hidden reasoning (no
visible text), the Responses API still fires a reasoning output item
without any reasoning_text.delta events. Previously no text_reasoning
Content was emitted in that case, making it invisible to downstream
logic. Both the non-streaming (_parse_response_from_openai) and
streaming (output_item.added) paths now always emit at least one
text_reasoning Content — with empty text if no content is available —
so co-occurrence detection and serialization guards work reliably.
## Reasoning items only serialized when paired with a function_call
The Responses API only accepts reasoning items in input when they
directly preceded a function_call in the original response. Sending a
reasoning item that preceded a text response (no tool call) causes:
"reasoning was provided without its required following item"
_prepare_message_for_openai now checks has_function_call per message
and skips text_reasoning serialization when there is no accompanying
function_call.
## summary field is an array, not an object
The reasoning item summary field sent to the Responses API must be an
array of objects ([{"type": "summary_text", "text": ...}]), not a
single object. Fixed _prepare_content_for_openai accordingly.
## service_session_id cleared when explicit history is provided
When a workflow coordinator replays a full conversation (including
function calls from a previous agent run) back to an executor via
AgentExecutorRequest or from_response, the executor's session still
held a service_session_id (previous_response_id) from the prior run.
The API then received the same function-call items twice — once from
previous_response_id (server-stored) and once from the explicit input —
causing: "Duplicate item found with id fc_...".
AgentExecutor.run (when should_respond=True) and from_response now
reset self._session.service_session_id = None before running so that
explicit input is the sole source of conversation context.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* small improvements in text reasoning
* refactor: add reset_service_session to AgentExecutorRequest for explicit history replay
Replace the implicit 'always clear service_session_id when should_respond=True'
with an explicit opt-in field on AgentExecutorRequest.
The old approach used should_respond=True as a proxy for 'full history replay',
but that conflates two distinct intents:
- Orchestrations group chat sends should_respond=True with an empty/single-message
list (not a full replay) — unnecessarily clearing service_session_id.
- HITL / feedback coordinators send the full prior conversation and truly need
a fresh service session ID to avoid duplicate-item API errors.
Changes:
- Add AgentExecutorRequest.reset_service_session: bool = False
- AgentExecutor.run only clears service_session_id when this flag is True
- AgentExecutor.from_response unchanged (always clears; always full conversation)
- Set reset_service_session=True in all full-history-replay call sites:
agents_with_HITL.py, azure_chat_agents_tool_calls_with_feedback.py,
autogen-migration round-robin coordinator, tau2 runner
- Update _FullHistoryReplayCoordinator test helper to pass the flag
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* comment update
* fixes from feedback
* fix test
* reverted changes to agent executor
* fix: remove reset_service_session from tau2 runner
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* two other reverts
* fix sample
---------
Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: improve .env precedence and observability samples
- Switch load_settings to explicit precedence: overrides -> explicit .env -> environment -> defaults\n- Raise when env_file_path is provided but missing\n- Update settings docs and tests for new behavior\n- Refresh observability samples and README guidance for env loading options\n\nCloses #3864\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fixed some imports
* Fix load_settings CI regressions
Allow explicit env_file_path values that exist but are not regular files (for example /dev/null) by checking path existence before dotenv parsing, and restore a dict accumulator with typed return cast to satisfy mypy.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Avoid implicit dotenv in observability
Only load dotenv in observability helpers when env_file_path is explicitly provided, and remove test os.devnull workarounds that are no longer necessary.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix streaming branch in weather override middleware sample
The streaming branch of weather_override_middleware only prefixed the
original weather data via a transform hook instead of replacing the
content with the 'perfect weather' override like the non-streaming
branch does. Replace with a new ResponseStream that yields the override
content as ChatResponseUpdate chunks.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fixed exception handling middleware sample
* Fixed runtime context delegation middleware example
* Fixed multimodal input examples
* Small update
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* feat: Inject OpenTelemetry trace context into MCP requests and update documentation
* Update python/samples/getting_started/observability/README.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update python/packages/core/tests/core/test_mcp.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* refactor: move opentelemetry import to module level
OpenTelemetry is a hard dependency of agent-framework-core (per
pyproject.toml), so the try/except ImportError guard was dead code.
Move the import to the top of the file to fail fast on missing
dependencies instead of silently hiding installation issues.
---------
Co-authored-by: Pete Roden <Pete.Roden@microsoft.com>
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* PR2: Wire context provider pipeline and update all internal consumers
- Replace AgentThread with AgentSession across all packages
- Replace ContextProvider with BaseContextProvider across all packages
- Replace context_provider param with context_providers (Sequence)
- Replace thread= with session= in run() signatures
- Replace get_new_thread() with create_session()
- Add get_session(service_session_id) to agent interface
- DurableAgentThread -> DurableAgentSession
- Remove _notify_thread_of_new_messages from WorkflowAgent
- Wire before_run/after_run context provider pipeline in RawAgent
- Auto-inject InMemoryHistoryProvider when no providers configured
* fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files
* refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider)
* fix: update remaining ag-ui references (client docstring, getting_started sample)
* fix: make get_session service_session_id keyword-only to avoid confusion with session_id
* refactor: rename _RunContext.thread_messages to session_messages
* refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists
* rename: remove _new_ prefix from test files
* refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider)
* fix: read full history from session state directly instead of reaching into provider internals
* fix: update stale .pyi stubs, sample imports, and README references for new provider types
* fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples
* refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider
* refactor: UserInfoMemory stores state in session.state instead of instance attributes
* feat: add Pydantic BaseModel support to session state serialization
Pydantic models stored in session.state are now automatically serialized
via model_dump() and restored via model_validate() during to_dict()/from_dict()
round-trips. Models are auto-registered on first serialization; use
register_state_type() for cold-start deserialization.
Also export register_state_type as a public API.
* fix mem0
* Update sample README links and descriptions for session terminology
- Replace 'thread' with 'session' in sample descriptions across all READMEs
- Update file links for renamed samples (mem0_sessions, redis_sessions, etc.)
- Fix Threads section → Sessions section in main samples/README.md
- Update tools, middleware, workflows, durabletask, azure_functions READMEs
- Update architecture diagrams in concepts/tools/README.md
- Update migration guides (autogen, semantic-kernel)
* Fix broken Redis README link to renamed sample
* Fix Mem0 OSS client search: pass scoping params as direct kwargs
AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs,
while AsyncMemoryClient (Platform) expects them in a filters dict.
Adds tests for both client types.
Port of fix from #3844 to new Mem0ContextProvider.
* Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic
- Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase
- Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages
- Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests
- Add tests/workflow/__init__.py for relative import support
- Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep)
* Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection
Chat clients that store history server-side by default (OpenAI Responses API,
Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this
during auto-injection and skips InMemoryHistoryProvider unless the user
explicitly sets store=False.
* Fix broken markdown links in azure_ai and redis READMEs
* Fix getting-started samples to use session API instead of removed thread/ContextProvider API
* updates to workflow as agent
* fix group chat import
* Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread
- Fix: Propagate conversation_id from ChatResponse back to session.service_session_id
in both streaming and non-streaming paths in _agents.py
- Rename AgentThreadException → AgentSessionException
- Remove stale AGUIThread from ag_ui lazy-loader
- Rename use_service_thread → use_service_session in ag-ui package
- Rename test functions from *_thread_* to *_session_*
- Rename sample files from *_thread* to *_session*
- Update docstrings and comments: thread → session
- Update _mcp.py kwargs filter: add 'session' alongside 'thread'
- Fix ContinuationToken docstring example: thread=thread → session=session
- Fix _clients.py docstring: 'Agent threads' → 'Agent sessions'
* Fix broken markdown links after thread→session file renames
* fix azure ai test
* restructure: Python samples into progressive 01-05 layout
- 01-get-started/: 6 numbered steps (hello agent → hosting)
- 02-agents/: all agent concept samples (tools, middleware, providers, etc.)
- 03-workflows/: ALL existing workflow samples preserved as-is
- 04-hosting/: azure-functions, durabletask, a2a
- 05-end-to-end/: demos, evaluation, hosted agents
- Old files moved to _to_delete/ for review
- Added AGENTS.md with structure documentation
- autogen-migration/ and semantic-kernel-migration/ preserved at root
* fix: switch to AzureOpenAI Foundry, fix CI failures
- Switch all 01-get-started samples to AzureOpenAIResponsesClient with
Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT +
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential)
- Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes
- Fix test paths in packages/ that referenced old getting_started/ dirs:
durabletask conftest + streaming test, azurefunctions conftest,
devui conftest + capture_messages + openai_sdk_integration
- Fix workflow_as_agent_human_in_the_loop.py import (sibling import)
- Update hosting READMEs and tool comment paths
- Replace root README.md with new structure overview
- Update AGENTS.md to document Azure OpenAI Foundry as default provider
* cleanup: remove _to_delete folder, copy resource files to active dirs
All files in _to_delete/ were either:
- Exact duplicates of files in the new structure (240 files)
- Same file with only comment path updates (100 files)
- One import-fix diff (workflow_as_agent_human_in_the_loop.py)
- One superseded minimal_sample.py
Resource files (sample.pdf, countries.json, employees.pdf, weather.json)
copied to 02-agents/sample_assets/ and 02-agents/resources/ since active
samples reference them.
* fix: address PR review comments, centralize resources, remove root duplicates
- Fix type annotation in 04_memory.py (string union -> proper types)
- Fix old sample paths in observability files
- Fix grammar/spelling in observability samples
- Move sample_assets/ and resources/ to shared/ folder
- Remove 8 duplicate observability files from 02-agents root
- Update resource path references in multimodal_input and provider samples
* fix: update broken links from old getting_started paths to new structure
- Update relative paths in READMEs: getting_started/ → 01-get-started/,
02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/
- Fix absolute GitHub URLs in package READMEs
- Fix broken link in ollama package README
* fix: convert absolute GitHub URLs to relative paths for link checker
Absolute URLs to python/samples/ on main branch 404 until PR merges.
Converted to relative paths that linkspector can verify locally.
* fix: update link for handoff sample moved to orchestrations/
* fix: update chatkit-integration README path from demos/ to 05-end-to-end/
* fix: update broken links in orchestrations README to match flat directory structure
Dmytro StrukandGitHub
Vincent KocandGitHub
