* add class-based skills
* address formating issues
* Remove generated filtered-unit.slnx and add to .gitignore
The filtered solution file is generated dynamically by
eng/scripts/New-FilteredSolution.ps1 during CI. Checking it in
risks it becoming stale and out-of-sync with the real solution.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove generated filtered-unit.slnx and add to .gitignore
The filtered solution file is generated dynamically by
eng/scripts/New-FilteredSolution.ps1 during CI. Checking it in
risks it becoming stale and out-of-sync with the real solution.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* consolidate DI samples into one
* fix file encoding
* suppress compatibility warning
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add github actions workflow for verify-samples
* Make workflow run as part of PR (for now)
* Update workflow to remove pr trigger
* Address PR comments
* fix: Remove Timeout from InputWait in StreamingRunEventStream
* fix: Race condition when the workflow executes to halt before TakeEventStream
* test: Make the OffThread Delay test more nimble
* fix: Remove slight window where runStatus could be stale
* Fix GitHubCopilotAgent not calling context provider hooks (#3984)
GitHubCopilotAgent accepted context_providers in its constructor but
never called before_run()/after_run() on them in _run_impl() or
_stream_updates(), silently ignoring all context providers.
Add _run_before_providers() helper to create SessionContext and invoke
before_run on each provider. Both _run_impl() and _stream_updates() now
run the full provider lifecycle: before_run before sending the prompt
(with provider instructions prepended) and after_run after receiving the
response. This follows the same pattern used by A2AAgent.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix GitHubCopilotAgent to invoke context provider before_run/after_run hooks
Fixes#3984
* fix(#3984): address review feedback for context provider integration
- Build prompt from session_context.get_messages(include_input=True) so
provider-injected context_messages are included in both non-streaming
and streaming paths (review comments #1, #2)
- Preserve timeout in opts (use get instead of pop) so providers can
observe it via context.options (review comment #3)
- Eliminate streaming double-buffer: move after_run invocation to a
ResponseStream result_hook (matching Agent class pattern) instead of
maintaining a separate updates list in the generator (review comment #4)
- Improve _run_before_providers docstring
Add tests for:
- Context messages included in prompt (non-streaming + streaming)
- Error path: after_run NOT called when send_and_wait/streaming raises
- Multiple providers: forward before_run, reverse after_run ordering
- BaseHistoryProvider with load_messages=False is skipped
- Streaming after_run response contains aggregated updates
- Streaming with no updates still sets empty response
- Timeout preserved in session context options for providers
Note: _run_before_providers remains on GitHubCopilotAgent for now. A
follow-up PR should extract it to BaseAgent so subclasses can reuse it
without duplicating the provider iteration logic.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #3984: Python: [Bug]: GitHubCopilotAgent Memory Example
* refactor(#3984): promote _run_before_providers to BaseAgent
Move _run_before_providers from GitHubCopilotAgent into BaseAgent,
mirroring the existing _run_after_providers helper. Agent's
_prepare_session_and_messages now delegates to the shared base method,
eliminating the near-duplicate provider iteration logic that could
drift as the provider contract evolves.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #3984: Python: [Bug]: GitHubCopilotAgent Memory Example
* revert: keep _run_before_providers in GitHubCopilotAgent only
Undo the promotion of _run_before_providers to BaseAgent. The method
stays in GitHubCopilotAgent where it is needed, and _agents.py
retains its original inline provider iteration in RawAgent.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: replace deprecated BaseContextProvider/BaseHistoryProvider with ContextProvider/HistoryProvider
Update imports and usages in GitHubCopilotAgent and its tests to use
the new non-deprecated class names from the core package.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: address review feedback - reorder providers before session, wrap streaming after_run in try/except, assert after_run on skipped HistoryProvider
- Move _run_before_providers before _get_or_create_session so provider
contributions can affect session configuration
- Wrap _run_after_providers in try/except in streaming _after_run_hook
to prevent provider errors from replacing successful responses
- Add after_run assertion to test_history_provider_skip_when_load_messages_false
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Add deduplication to `prepend_instructions_to_messages()` to skip
instructions that are already present as leading messages with the
same role and text. This prevents duplicate system messages when
instructions are injected by multiple layers (e.g. Agent + chat client).
Fixes#5049
* Update Foundry Responses as ChatClientAgent
* Migrate obsolete AzureAI integration tests to versioned agent pattern
Replace obsolete CreateAIAgentAsync/GetAIAgentAsync calls with
Agents.CreateAgentVersionAsync() + AsAIAgent(AgentVersion) in all
AzureAI integration tests.
- Rename AIProjectClient* test files to FoundryVersionedAgent*
- Register AIFunction tools in PromptAgentDefinition.Tools for
server-side visibility via AsOpenAIResponseTool()
- Skip structured output tests (AzureAIProjectChatClient clears
ResponseFormat for versioned agents)
- Remove all [Obsolete] attributes and #pragma warning disable CS0618
* Merge FoundryMemory package into AzureAI under Memory/ folder
Move all FoundryMemory source, unit tests, and integration tests into
the Microsoft.Agents.AI.AzureAI package. Change namespace from
Microsoft.Agents.AI.FoundryMemory to Microsoft.Agents.AI.AzureAI.
- Add [Experimental] to FoundryMemoryProviderOptions and Scope
- Rename internal AIProjectClientExtensions to MemoryStoreExtensions
- Update AzureAI .csproj with Compliance.Abstractions, Redaction
- Remove FoundryMemory from solution and release filter
- Update sample to reference AzureAI instead of FoundryMemory
- Delete old Microsoft.Agents.AI.FoundryMemory project and tests
* Add EnsureMemoryStoreCreatedAsync and memory existence checks to integration tests
- Ensure memory store is created before testing memory operations
- Add AZURE_AI_EMBEDDING_DEPLOYMENT_NAME config setting
- Assert memories exist in store via SearchMemoriesAsync before cleanup
- Verify scope isolation with direct memory store queries
* Fix and rename AzureAI unit tests for RAPI vs Versioned clarity
- Rename AsAIAgentAsync_* to AsAIAgent_* (drop Async from method group)
- Add _Rapi_ prefix to non-versioned (Responses API) tests
- Add _Versioned_ prefix to versioned agent tests where needed
- Fix RAPI tests: assert GetService<AIProjectClient>() is null
- Fix Versioned tests: assert IsType<FoundryAgent> and
GetService<AIProjectClient>() returns the client instance
- Fix UserAgent header tests: proper HTTP handler routing
- Fix ChatClient_UsesDefaultConversationIdAsync test setup
- All 153 unit tests pass with 0 failures
* Rename Microsoft.Agents.AI.AzureAI to Microsoft.Agents.AI.Foundry
Rename the project, namespace, folder, and all references from
Microsoft.Agents.AI.AzureAI to Microsoft.Agents.AI.Foundry.
Also rename Workflows.Declarative.AzureAI to .Foundry.
- Rename src, unit test, integration test, and workflow folders
- Update namespaces in all source and test .cs files
- Update ProjectReferences in ~47 sample and test .csproj files
- Update solution files (.slnx, .slnf)
- Update sample using statements
- Update READMEs, SKILL.md, ADRs in docs/
- Disable package validation baseline for renamed packages
- Fix UTF-8 BOM encoding on all affected .cs files
- AzureAI.Persistent left completely unchanged
* Fix format: remove ImplicitUsings, add explicit usings, fix BOM encoding
- Remove ImplicitUsings=enable from Foundry csproj to resolve IDE0005
on shared ReplacingRedactor.cs
- Add explicit System usings to all source files that relied on them
- Sort usings alphabetically per editorconfig rules
- Fix UTF-8 BOM on 12 sample Program.cs files
- Rename Azure AI Foundry Agents to Microsoft Foundry Agents in docs
* Python: Fix broken samples and add missing READMEs
- simple_context_provider: move instructions kwarg into options dict
- suspend_resume_session: use OpenAIChatCompletionClient for in-memory demo
- foundry_chat_client_with_hosted_mcp: move store kwarg into options dict
- Add README.md for context_providers and conversations sample folders
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix additional sample issues in context_providers
- mem0_basic: send preferences query before sleep so Mem0 can learn them,
print result from new session recall
- mem0_sessions: add session for multi-turn conversation in agent-scoped
example, remove user_id from agent-scoped provider (Mem0 API stores
memories without user_id when agent_id is provided), use single message
for storing preferences
- redis_basics: print retrieved context messages instead of raw object
- redis_sessions: add missing load_dotenv() call
- redis_basics/redis_sessions: fix docstrings referencing wrong client type
- azure_redis_conversation: replace duplicate copyright with load_dotenv()
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix broken link in declarative README
openai_responses_agent.py was renamed to openai_agent.py
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Refactor Anthropic model option and provider clients
Rename the Anthropic client model option from model_id to model, add provider-specific Anthropic wrappers for Foundry, Bedrock, and Vertex, and expose them through the Anthropic, Foundry, Amazon, and Google namespaces. Update core option handling, docs, samples, and tests accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix Anthropic skills sample typing
Cast the Anthropic beta client to Any in the skills sample so the pre-commit sample pyright check no longer fails on beta skills and files endpoints that are not exposed by the current SDK stubs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* undo sample mypy
* Retry CI after transient external failures
Retrigger PR validation after an unrelated Copilot review workflow SAML failure and a transient external tau2 git fetch failure in the Windows Python test setup.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback on model option merging
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address Anthropic compatibility review feedback
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* moved all to `model`
* fixes for azure ai search
* Python: standardize remaining sample env var names
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: fix foundry-local pyright compatibility
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updated env vars in cicd
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix RequestInfoEvent lost when resuming workflow from checkpoint
* Fix streaming run double disposal in tests and lockstep republishing before Started event is emitted.
* Fix bug to remove messages after sending to avoid losing messages on send failure.
* Fix declarative test harness
* Fix agent_with_hosted_mcp sample to use AzureOpenAIResponsesClient (#4861)
The agent_with_hosted_mcp sample used AzureOpenAIChatClient with an MCP tool
dict, but the Chat Completions API only supports 'function' and 'custom' tool
types, not 'mcp'. This caused a 400 error at runtime.
Switch the sample to AzureOpenAIResponsesClient which natively supports MCP
tools via the Responses API. Use get_mcp_tool() to construct the tool config.
Changes:
- main.py: Replace AzureOpenAIChatClient with AzureOpenAIResponsesClient
- requirements.txt: Update azure-ai-agentserver-agentframework to 1.0.0b16
and use agent-framework-azure-ai package
- agent.yaml: Use AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME env var
- Add regression test documenting chat client MCP tool passthrough behavior
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix agent_with_hosted_mcp sample to use Responses API client for MCP tools
Fixes#4861
* Remove REPRODUCTION_REPORT.md investigation artifact (#4861)
Remove the reproduction report markdown file from the test directory.
Investigation notes belong in the GitHub issue or PR description,
not as committed files in the source tree. The regression test in
test_openai_chat_client.py already provides automated verification.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add MCP tool API rejection regression test (#4861)
Add test_mcp_tool_dict_causes_api_rejection to verify that MCP tool
dicts passed through to the Chat Completions API result in a clear
ChatClientException rather than being silently dropped. This completes
the regression test coverage requested in code review.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* small fix
* Revert deletion of dotnet local.settings.json files
Restore the two local.settings.json files that were accidentally deleted in this PR.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix _add_text_reasoning_content dropping id during coalescing (#4852)
Preserve the id field (rs_* identifier) when coalescing text_reasoning
Content objects by passing id=self.id or other.id to the Content
constructor. This fixes the encrypted reasoning round-trip where the
missing id prevented _prepare_content_for_openai from including it in
the serialized reasoning item.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix `_add_text_reasoning_content` to preserve `id` during coalescing
Fixes#4852
* Raise AdditionItemMismatch on conflicting text_reasoning ids (#4852)
Detect when both operands have different non-empty ids during
text_reasoning Content coalescing and raise AdditionItemMismatch
instead of silently keeping one. This prevents mis-associating
encrypted_content during round-trips.
Also adds tests for conflicting ids and the neither-has-id edge case.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4852: Python: [Bug]: Content._add_text_reasoning_content drops id during coalescing, breaking encrypted reasoning round-trip
* test fix
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Remove unsupported memory scoping params from samples and docs
Fixes#4353
The `Mem0ContextProvider` and `RedisContextProvider` no longer support
`thread_id` or `scope_to_per_operation_thread_id` parameters. This commit
updates the affected samples and READMEs to use only the currently
supported API (`user_id`, `agent_id`, `application_id`).
Changes:
- mem0_sessions.py: Remove `thread_id` and
`scope_to_per_operation_thread_id` from examples 1 and 2, rewrite to
demonstrate user-scoped and agent-scoped memory patterns
- redis_sessions.py: Update module docstring to remove references to
removed thread scoping params
- mem0/README.md: Update Memory Scoping docs to reflect current API
- redis/README.md: Remove `thread_id` and
`scope_to_per_operation_thread_id` references from docs
* Address Copilot review: rename thread_scope functions, fix docstring
- Rename `example_global_thread_scope` -> `example_global_memory_scope`
- Rename `example_per_operation_thread_scope` -> `example_agent_scoped_memory`
- Update example 2 docstring to mention `application_id` alongside
`user_id` and `agent_id` since it's set in the provider config
- Update module docstring scenario 2 to include `application_id`
* fix: rebase onto main, address giles17 review feedback
- Resolve merge conflicts by rebasing all 4 original files onto current main
- Address giles17's agent review suggestions:
- mem0_basic.py: update comment to remove thread_id from scoping list
- mem0_oss.py: update comment to remove thread_id from scoping list
- redis_sessions.py: rename Example 2 from "Agent-Scoped Memory" to
"Hybrid Vector Search" to accurately describe what it demonstrates
- redis/README.md: update Example 2 description to match renamed example
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
* Add Python A2A agent-as-function-tools sample
Port of the .NET A2AAgent_AsFunctionTools sample to Python.
Resolves a remote A2A agent card, converts each skill to a
FunctionTool via as_tool(), and registers them with a host agent
using AzureOpenAIResponsesClient.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Sanitize A2A skill names before passing to as_tool()
as_tool() only auto-sanitizes when name is omitted. Since we pass
skill.name explicitly, we need to strip special characters ourselves.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add header_provider to MCPStreamableHTTPTool (#4808)
Add a header_provider callback parameter to MCPStreamableHTTPTool that
enables injecting dynamic per-request HTTP headers from runtime kwargs
(originating from FunctionInvocationContext.kwargs set in agent middleware).
The implementation uses contextvars and httpx event hooks to ensure headers
are task-local and safe for concurrent tool calls:
- header_provider receives the runtime kwargs dict and returns headers
- call_tool sets a ContextVar before delegating to MCPTool.call_tool
- An httpx request event hook reads from the ContextVar and injects headers
Example usage:
mcp_tool = MCPStreamableHTTPTool(
name="web-api",
url="https://api.example.com/mcp",
header_provider=lambda kwargs: {
"X-Auth-Token": kwargs.get("auth_token", ""),
},
)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4808: Python: [Bug]: Unable to pass AgentContext to MCPStreamableHTTPTool
* Add test for header_provider via FunctionTool.invoke with FunctionInvocationContext
Addresses PR review comment: exercises the full pipeline from
FunctionInvocationContext.kwargs through FunctionTool.invoke to
MCPStreamableHTTPTool.call_tool and header_provider, rather than
testing call_tool in isolation.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4808: review comment fixes
* Fix streamable MCP transport defaults
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix Azure AI test client mocks
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix MCP runtime kwarg regressions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Stabilize MCP tool runtime kwargs
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use context kwargs in MCP wrappers
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updated mcp samples
* fix link
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix sample bugs: incorrect API params, wrong client types, and invalid options
- typed_options.py: Fix AnthropicClient model->model_id, wrap raw strings in Message objects for get_response(), fix reasoning_effort->reasoning dict, fix budget_tokens minimum (1024), use OpenAIChatClient not FoundryChatClient, remove unused import
- client_reasoning.py: Fix deprecated model_id to model param
- client_with_hosted_mcp.py: Remove invalid store=True kwarg from Agent.run()
- code_defined_skill.py: Fix precision kwarg to use function_invocation_kwargs
- Various other samples: Fix deprecated API usage and incorrect params
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review comments
- client_with_hosted_mcp.py: Fix remaining store=True kwarg on line 68 to use options dict
- client_with_session.py: Change store=True to store=False to match in-memory persistence demo intent
- typed_options.py: Remove non-existent import and model key from docstring example
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* new sample fixes
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Improve CONTRIBUTING.md with dev setup links and docs guidance
- Consolidate Development Scripts into a Development Setup section with
quick links to language-specific dev guides and coding standards
- Add Python build/test/lint commands alongside existing .NET commands
- Add Documentation Contributions section with link checker, writing
guidelines, and style guidance
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use directory note for .NET commands, matching Python style
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Split test commands into unit vs. integration for both Python and .NET
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove Documentation Contributions section
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Foundry Evals integration for Python
Merged and refactored eval module per Eduard's PR review:
- Merge _eval.py + _local_eval.py into single _evaluation.py
- Convert EvalItem from dataclass to regular class
- Rename to_dict() to to_eval_data()
- Convert _AgentEvalData to TypedDict
- Simplify check system: unified async pattern with isawaitable
- Parallelize checks and evaluators with asyncio.gather
- Add all/any mode to tool_called_check
- Fix bool(passed) truthy bug in _coerce_result
- Remove deprecated function_evaluator/async_function_evaluator aliases
- Remove _MinimalAgent, tighten evaluate_agent signature
- Set self.name in __init__ (LocalEvaluator, FoundryEvals)
- Limit FoundryEvals to AsyncOpenAI only
- Type project_client as AIProjectClient
- Remove NotImplementedError continuous eval code
- Add evaluation samples in 02-agents/ and 03-workflows/
- Update all imports and tests (167 passing)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: resolve mypy redundant-cast errors while keeping pyright happy
Use cast(list[Any], x) with type: ignore[redundant-cast] comments to
satisfy both mypy (which considers casting Any redundant) and pyright
strict mode (which needs explicit casts to narrow Unknown types).
Also fix evaluator decorator check_name type annotation to be
explicitly str, resolving mypy str|Any|None mismatch.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: CI failures — pyupgrade, evaluator overloads, sample API, reset attr
- Apply pyupgrade: Sequence from collections.abc, remove forward-ref quotes
- Add @overload signatures to evaluator() for proper @evaluator usage
- Fix evaluate_workflow sample to use WorkflowBuilder(start_executor=) API
- Fix _workflow.py executor.reset() to use getattr pattern for pyright
- Remove unused EvalResults forward-ref string in default_factory lambda
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: skip gRPC-dependent observability test
The test_configure_otel_providers_with_env_file_and_vs_code_port test
triggers gRPC OTLP exporter creation, but the grpc dependency is
optional and not installed by default. Add skipif decorator matching
the pattern used by all other gRPC exporter tests in the same file.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: add nosec B101 for bandit assert check
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* style: align eval samples with repo conventions
- Move module docstrings before imports (after copyright header)
- Add -> None return type to all main() and helper functions
- Fix line-too-long in multiturn sample conversation data
- Add Workflow import for typed return in all_patterns_sample
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback: async fixes, sample bugs, deprecation warnings
- Simplify _ensure_async_result to direct await (async-only clients)
- Replace get_event_loop() with get_running_loop()
- Narrow _fetch_output_items exception handling to specific types
- Add warning log when _filter_tool_evaluators falls back to defaults
- Add DeprecationWarning to options alias in Agent.__init__
- Add DeprecationWarning to evaluate_response()
- Rename raw key to _raw_arguments in convert_message fallback
- Fix evaluate_agent_sample.py: replace evals.select() with FoundryEvals()
- Fix evaluate_multiturn_sample.py: use Message/Content/FunctionTool types
- Fix evaluate_workflow_sample.py: replace evals.select() with FoundryEvals()
- Update test mocks to use AsyncMock for awaited API calls
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add test coverage for review feedback items
- Add num_repetitions=2 positive test verifying 2×items and 4 agent calls
- Add _poll_eval_run tests: timeout, failed, and canceled paths
- Add evaluate_traces tests: validation error, response_ids path, trace_ids path
- Add evaluate_foundry_target happy-path test with target/query verification
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix ruff ISC004 lint error and apply formatter
- Wrap implicit string concatenation in parens in evaluate_multiturn_sample.py
- Apply ruff formatter to 6 other files with minor formatting drift
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove core type changes (extracted to fix/workflow-stale-session branch)
Reverts changes to _agents.py, _agent_executor.py, and _workflow.py
back to upstream/main. These fixes are now in a separate PR.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review round 2: bugs, tests, and architecture
Code fixes:
- Fix _normalize_queries inverted condition (single query now replicates
to match expected_count)
- Fix substring match bug: 'end' in 'backend' matched; use exact set
lookup for executor ID filtering
- Fix used_available_tools sample: tool_definitions→tools param, use
FunctionTool attribute access instead of dict .get()
- Add None-check in _resolve_openai_client for misconfigured project
- Add Returns section to evaluate_workflow docstring
- Cache inspect.signature in @evaluator wrapper (avoid per-item reflection)
Architecture:
- Extract _evaluate_via_responses as module-level helper; evaluate_traces
now calls it directly instead of creating a FoundryEvals instance
- Move Foundry-specific typed-content conversion out of core to_eval_data;
core now returns plain role/content dicts, FoundryEvals applies
AgentEvalConverter in _evaluate_via_dataset
Tests:
- evaluate_response() deprecation warning emission and delegation
- num_repetitions > 1 with expected_output and expected_tool_calls
- Mock output_items.list in test_evaluate_calls_evals_api
- Update to_eval_data assertions for plain-dict format
- Unknown param error now raised at @evaluator decoration time
Skipped (separate PR): executor reset loop, xfail removal, options alias
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix CI: revert test_full_conversation, fix pyright errors
- Revert test_full_conversation.py to upstream/main (the session
preservation test was incorrectly changed to assert clearing)
- Fix pyright reportUnnecessaryComparison on get_openai_client() None
check by adding ignore comment
- Fix pyright reportPrivateUsage: add public EvalItem.split_messages()
method and use it in FoundryEvals._evaluate_via_dataset instead of
accessing private _split_conversation
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review round 3: reliability, test gaps, cleanup
- Add try/except guard for non-numeric score in _coerce_result
- Add poll_interval minimum bound (0.1s) to prevent tight loops
- Add runtime async client check in _resolve_openai_client
- Remove _ensure_async_result wrapper (10 call sites → direct await)
- Better error message when queries provided without agent
- Import-time asserts for evaluator set consistency
- Remove 28 redundant @pytest.mark.asyncio decorators
- Add doc note about _raw_arguments sensitive data
- Tests: tool_called_check mode=any, _normalize_queries branches,
_extract_result_counts paths, _extract_per_evaluator, bare check
via evaluate_agent, output_items assertion, modulo wrapping,
async client check, queries-without-agent error
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix CI: ruff S101 assert, pyright and mypy arg-type errors
- Replace module-level assert with if/raise for evaluator set
consistency checks (ruff S101 disallows bare assert)
- Add type: ignore[arg-type] and pyright: ignore[reportArgumentType]
on OpenAI SDK evals API calls that pass dicts where typed params
are expected (SDK accepts dicts at runtime)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review round 4: bugs, reliability, test fixes
- Fix all_passed ignoring parent result_counts when sub_results present
- Fix _extract_tool_calls: parse string arguments via json.loads before
falling back to None (real LLM responses use string arguments)
- Sanitize _raw_arguments to '[unparseable]' to avoid leaking sensitive
tool-call data to external evaluation services
- Add NOTE comment on to_eval_data message serialization dropping
non-text content (tool calls, results)
- Eliminate double conversation split in _evaluate_via_dataset: build
JSONL dicts directly from split_messages + AgentEvalConverter
- Raise poll_interval floor from 0.1s to 1.0s to prevent rate-limit
exhaustion
- Fix MagicMock(name=...) bug in test: sets display name not .name attr
- Fix mock_output_item.sample: use MagicMock object instead of dict so
_fetch_output_items exercises error/usage/input/output extraction
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review round 5: reliability, docs, test coverage
Code fixes:
- Move import-time RuntimeError checks to unit tests (avoids breaking
imports for all users on developer set-drift mistake)
- _filter_tool_evaluators now raises ValueError when all evaluators
require tools but no items have tools (was silently substituting)
- Add poll_interval upper bound (60s) to prevent single-iteration sleep
- Log exc_info=True in _fetch_output_items for debugging API changes
- Fix evaluate() docstring: remove claim about Responses API optimization
- Validate target dict has 'type' key in evaluate_foundry_target
- Document to_eval_data() limitation: non-text content is omitted
Tests:
- TestEvaluatorSetConsistency: verify _AGENT/_TOOL subsets of _BUILTIN
- TestEvaluateTracesAgentId: agent_id-only path with lookback_hours
- TestFilterToolEvaluatorsRaises: ValueError on all-tool no-items
- TestEvaluateFoundryTargetValidation: target without 'type' key
- Assert items==[] on failed/canceled poll results
- Mock output_items.list in response_ids test for full flow
- TestAllPassedSubResults: result_counts=None + sub_results delegation
and parent failures override sub_results
- TestBuildOverallItemEmpty: empty workflow outputs returns None
Skipped r5-07 (_raw_arguments length hint): marginal debugging value,
could leak content size information.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix error message: evaluate_responses() → evaluate_traces(response_ids=...)
The referenced function doesn't exist; the correct API is
evaluate_traces(response_ids=...) from the azure-ai package.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove dead to_eval_data() method, fix docstring claims
- Remove to_eval_data() from EvalItem (dead code after r4-05 JSONL refactor)
- Migrate 15 tests from to_eval_data() to split_messages()
- Update sample to use split_messages() + Message properties
- Remove unimplemented Responses API optimization docstring claim
- Update split_messages() docstring to not reference removed method
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Reduce default eval timeout from 600s to 180s (3 minutes)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove dead _evaluate_via_responses method from FoundryEvals
The method was never called — evaluate() uses _evaluate_via_dataset,
and evaluate_traces() calls _evaluate_via_responses_impl directly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Revert unrelated formatting changes to get-started samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix pyright: remove phantom FoundryMemoryProvider import, apply ruff format
- Remove import of non-existent _foundry_memory_provider module
(incorrectly kept during rebase conflict resolution)
- Apply ruff formatter to test_local_eval.py and get-started samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix eval samples: use FoundryChatClient for Agent()
The upstream provider-leading client refactor (#4818) made client=
a required parameter on Agent(). Update the three getting-started
eval samples to use FoundryChatClient with FOUNDRY_PROJECT_ENDPOINT,
matching the standard pattern from 01-get-started samples.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify self-reflection sample using FoundryEvals
Replace ~80 lines of manual OpenAI evals API code (create_eval,
run_eval, manual polling, raw JSONL params) with FoundryEvals:
- evaluate_groundedness() uses FoundryEvals.evaluate() with EvalItem
- Remove create_openai_client(), create_eval(), run_eval() functions
- Remove openai SDK type imports (DataSourceConfigCustom, etc.)
- run_self_reflection_batch creates FoundryEvals instance once,
reuses it for all iterations across all prompts
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update eval samples to FoundryChatClient and FOUNDRY_PROJECT_ENDPOINT
- Migrate all foundry_evals samples from AzureOpenAIResponsesClient to FoundryChatClient
- Update env var from AZURE_AI_PROJECT_ENDPOINT to FOUNDRY_PROJECT_ENDPOINT
- Use AzureCliCredential consistently across all samples
- Fix README.md: correct function names (evaluate_dataset -> FoundryEvals.evaluate, evaluate_responses -> evaluate_traces)
- Update self_reflection .env.example and README.md
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix lint errors in eval samples (E501, ASYNC240, formatting)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove evaluate_all_patterns_sample.py (redundant with focused samples)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix async credential mismatch: use azure.identity.aio for async AIProjectClient
AIProjectClient from azure.ai.projects.aio requires an async credential.
Switch all foundry_evals samples from azure.identity.AzureCliCredential
to azure.identity.aio.AzureCliCredential. Also pass project_client to
FoundryChatClient instead of duplicating endpoint+credential.
Close credential in self_reflection sample to avoid resource leak.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Revert test_observability.py to upstream/main (not our test)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address moonbox3 review: sphinx docstrings, pagination, isinstance check
- Convert all Example:: / Typical usage:: code blocks to .. code-block:: python
format matching codebase convention (both _evaluation.py and _foundry_evals.py)
- Add async pagination in _fetch_output_items via async for (handles large result sets)
- Replace hasattr(__aenter__) with isinstance(client, AsyncOpenAI) in _resolve_openai_client
- Move AsyncOpenAI import from TYPE_CHECKING to runtime (needed for isinstance)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix test failures and address remaining moonbox3 review comments
- Fix tests: use MagicMock(spec=AsyncOpenAI) for project_client mocks
(isinstance check now requires proper type, not duck-typing)
- Fix tests: replace mock_page.__iter__ with _AsyncPage helper for async for
- Fix evaluate_response: auto-extract queries from response messages when
query is not provided (previously always raised ValueError)
- Add debug logging when skipping internal _-prefixed executor IDs
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address Tao's PR review comments on Foundry Evals
- T1: Add comment explaining builtin.* pass-through in _resolve_evaluator
- T2: Add comment referencing OpenAI evals API for testing_criteria dict
- T3: Document Mustache-style {{item.*}} template placeholders
- T4: Document poll loop 60s sleep upper bound rationale
- T5: Narrow run type to RunRetrieveResponse, use typed field access
instead of vars()/getattr dance in _extract_result_counts and
_extract_per_evaluator; use run.error and run.report_url directly
- T6: Clarify openai_client docstring re: Azure Foundry endpoint
- T8: Remove misleading empty expected_tool_calls from sample
- Update tests to match real SDK PerTestingCriteriaResult shape
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove unnecessary Any union from run type annotations
RunRetrieveResponse is the correct type — no backward compat needed
for a brand new feature.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Accept FoundryChatClient instead of raw AsyncOpenAI
FoundryEvals now takes client: FoundryChatClient as its primary
parameter instead of openai_client: AsyncOpenAI. The builtin.*
evaluators require a Foundry endpoint, so the type should reflect that.
- FoundryEvals.__init__: client: FoundryChatClient replaces openai_client
- evaluate_traces / evaluate_foundry_target: same change
- _resolve_openai_client: extracts .client from FoundryChatClient
- project_client fallback retained for standalone functions
- All samples updated to construct FoundryChatClient and pass as client=
- Tests updated (openai_client= → client=)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove implicit 60s upper bound on poll interval
If a developer sets a higher poll_interval, respect it. Only clamp
to remaining time and enforce a 1s minimum for rate-limit protection.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove 1s floor on poll interval — let the developer control it
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update python/samples/05-end-to-end/evaluation/foundry_evals/.env.example
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Update python/samples/02-agents/evaluation/evaluate_agent.py
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Address eavanvalkenburg review (round 2) on Python eval PR
- Rename model_deployment -> model across FoundryEvals and all samples
- Make model param optional, resolves from client.model
- Convert EvalResults from dataclass to regular class
- Remove deprecated evaluate_response() function
- Refactor splitters: BUILT_IN_SPLITTERS dict + standalone functions
- Change per_turn_items from classmethod to staticmethod
- Simplify EvalCheck type alias to use Awaitable[CheckResult]
- Remove errored property from EvalResults
- Remove default value from Evaluator protocol eval_name
- Rename assert_passed -> raise_for_status, add EvalNotPassedError
- Type agent param as SupportsAgentRun | None
- Fix Arguments docstring
- Update __init__.py exports
- Update all tests and samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Move FoundryEvals to foundry package, split tool eval sample
- Move _foundry_evals.py from azure-ai to foundry package
- Move test_foundry_evals.py to foundry/tests/
- Update lazy re-exports in agent_framework.foundry namespace
- Update .pyi type stubs
- All samples now import from agent_framework.foundry
- Split tool-call evaluation into evaluate_tool_calls_sample.py
- Fix all_passed to check errored count from result_counts
- Fix raise_for_status to include errored item details
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Auto-create FoundryChatClient from env vars when no client provided
FoundryEvals() now works zero-config when FOUNDRY_PROJECT_ENDPOINT and
FOUNDRY_MODEL environment variables are set. Auto-creates a FoundryChatClient
under the hood, matching the established env var pattern.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix pyright errors: remove dead _normalize_queries, suppress EvalAPIError check
- Remove unused _normalize_queries function and its tests
- Add pyright ignore for EvalAPIError None check (defensive guard)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Support multimodal image content in eval pipeline
Add image (data/uri) content handling to AgentEvalConverter.convert_message()
so that Content.from_data() and Content.from_uri() image payloads are
preserved as input_image parts in the Foundry evaluator format.
- Handle Content type='data' and type='uri' → emit input_image parts
- Add 6 unit tests for image content through convert_message/convert_messages
- Add integration test verifying images flow through EvalItem → JSONL path
- Add evaluate_multimodal.py sample demonstrating local image eval
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address remaining review comments
- Fix project_client docstring to say async-only (not sync/async)
- Add builtin evaluator name validation warning in _resolve_evaluator
- Replace getattr with typed attribute access in _poll_eval_run,
_extract_result_counts, _extract_per_evaluator, _fetch_output_items
- Remove cast import from _foundry_evals (no longer needed)
- Tighten _coerce_result: honour explicit 'passed' when both 'score'
and 'passed' are present; remove performative cast
- Fix self_reflection sample: add env file existence check
- Fix traces sample: correct Pattern 2 section label
- Update all Foundry eval samples to FoundryChatClient + FOUNDRY_MODEL
(remove AIProjectClient + AZURE_AI_MODEL_DEPLOYMENT_NAME pattern)
- Add eval_name and OpenAI client docs to FoundryEvals docstring
- Update test mocks to match typed SDK objects (_MockResultCounts)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix ruff lint errors (E501, SIM108, SIM102)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix pyright errors: type-narrow dict to dict[str, Any], add ignore comments
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Replace ConversationSplitter type alias with Protocol
ConversationSplitter is now a runtime-checkable Protocol with a named
'conversation' parameter, making the expected signature self-documenting.
ConversationSplit enum members gain a __call__ method so they satisfy
the protocol directly -- ConversationSplit.LAST_TURN(conversation) works.
This simplifies _split_conversation from an isinstance dispatch to a
single split(conversation) call.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Standardize on AZURE_AI_MODEL_DEPLOYMENT_NAME and fix Unicode in samples
- Replace FOUNDRY_MODEL with AZURE_AI_MODEL_DEPLOYMENT_NAME in all
eval samples to match repo convention
- Replace Unicode symbols with ASCII equivalents in all eval sample
print statements to avoid cp1252 encoding errors on Windows
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update python/samples/03-workflows/evaluation/evaluate_workflow.py
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Rename ADR 0020 to 0023 (foundry evals integration)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: alliscode <bentho@microsoft.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Add API breaking change validation for RC packages
Enable .NET Package Validation for release candidate packages to detect
API breaking changes in CI. This follows the same pattern used by
Semantic Kernel, centralized through nuget-package.props.
Changes:
- Enable EnablePackageValidation for IsReleaseCandidate packages
- Update PackageValidationBaselineVersion to 1.0.0-rc4 (latest published)
- Generate CompatibilitySuppressions.xml for existing known API changes
in 5 packages (AI, AzureAI, OpenAI, Workflows, Workflows.Declarative.AzureAI)
- Opt out Workflows.Declarative.Mcp (not yet published to NuGet)
- Add breaking changes guidance to CONTRIBUTING.md
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback
- Remove unnecessary empty PackageValidationBaselineVersion override
in Workflows.Declarative.Mcp.csproj (EnablePackageValidation=false
is sufficient)
- Tighten CONTRIBUTING.md wording to clarify opt-out possibility
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Enable package validation for GA packages (no VersionSuffix)
Expand the EnablePackageValidation condition to also cover future GA
packages that have no VersionSuffix, not just RC packages.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix EnablePackageValidation GA condition to check PackageVersion
The previous condition VersionSuffix=='' matched all packages (preview
included) since VersionSuffix defaults to empty. Now uses two separate
conditions: one for RC, one for true GA (PackageVersion == VersionPrefix).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add IsGeneralAvailable flag for package validation
Replace fragile PackageVersion condition with explicit IsGeneralAvailable
property, following the same per-project self-declaration pattern as
IsReleaseCandidate.
- Directory.Build.props: Add IsGeneralAvailable=false default
- nuget-package.props: EnablePackageValidation on RC OR GA
- CONTRIBUTING.md: Update docs to mention both flags
When packages go GA, they set IsGeneralAvailable=true in their .csproj.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Rename IsGeneralAvailable to IsGenerallyAvailable
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* add inline skills
* Fix IDE1006 and IDE0004 formatting errors in test files
- Add 'Async' suffix to async test methods in FilteringAgentSkillsSourceTests,
DeduplicatingAgentSkillsSourceTests, and AgentInMemorySkillsSourceTests
- Use pragma to suppress false-positive IDE0004 on casts needed for overload
disambiguation in AgentInlineSkillTests and AgentInlineSkillResourceTests
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* address issues
* address comments
* make inline skills script and resource model classes internal
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Stage
* Add FoundryAgentClient, model param, chatClientFactory, and RAPI samples
- Add model parameter to FoundryAgentClient simple constructor
- Add chatClientFactory parameter to both constructors
- Switch to OpenAI.GetProjectResponsesClientForModel for direct Responses API usage
- Add FoundryAgents-RAPI samples (Step01 Basics, Step02 Multiturn, Step03 FunctionTools)
- Add solution folder entry for FoundryAgents-RAPI samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add auto-discovery constructor and simplify RAPI samples
- Add FoundryAgentClient constructor that reads AZURE_AI_PROJECT_ENDPOINT and
AZURE_AI_MODEL_DEPLOYMENT_NAME from environment variables with DefaultAzureCredential
- Simplify RAPI samples to use auto-discovery (no env var or credential code)
- Remove Azure.Identity direct references from sample csproj files
- Update READMEs to document environment variable requirements
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add remaining RAPI samples (Step04-Step12)
- Step04: Function tools with human-in-the-loop approvals
- Step05: Structured output with typed responses
- Step06: Persisted conversations with session serialization
- Step07: Observability with OpenTelemetry
- Step08: Dependency injection with hosted service
- Step10: Image multi-modality
- Step11: Agent as function tool (agent composition)
- Step12: Middleware (PII, guardrails, function logging, HITL approval)
- Update solution file and folder README with all new samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add all RAPI samples (Step09-Step23) and switch to AzureCliCredential
- Step09: MCP client as tools (GitHub server via stdio)
- Step13: Plugins with dependency injection
- Step14: Code Interpreter tool
- Step15: Computer Use tool with screenshot simulation
- Step16: File Search with vector stores
- Step17: OpenAPI tools (REST Countries API)
- Step18: Bing Custom Search
- Step19: SharePoint grounding
- Step20: Microsoft Fabric
- Step21: Web Search with citations
- Step22: Memory Search with multi-turn conversations
- Step23: Local MCP via HTTP (Microsoft Learn)
- Switch all samples (Step04-Step12) to use AzureCliCredential with env vars
- Update solution file and README with all 23 samples
- All 23 samples build successfully, tested Step05/06/11/13/21
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Switch Step01-03 samples to AzureCliCredential for consistency
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify connection ID format in SharePoint and Fabric READMEs
Document that SHAREPOINT_PROJECT_CONNECTION_ID and FABRIC_PROJECT_CONNECTION_ID
should use the connection name (e.g., 'SharepointTestTool'), not the full ARM
resource URI.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Normalize env vars, fix structured output, update READMEs with connection ID formats
- Normalize AZURE_FOUNDRY_PROJECT_* env vars to AZURE_AI_PROJECT_ENDPOINT / AZURE_AI_MODEL_DEPLOYMENT_NAME across all samples (Steps 18-22 READMEs + Steps 19-20 Program.cs)
- Fix RAPI Step05 StructuredOutput to use full constructor with ResponseFormat for streaming JSON
- Update Deep Research sample to use AzureCliCredential
- Enrich Bing Grounding README with full ARM resource URI format
- Fix Bing Custom Search README env var mismatch (BING_CUSTOM_SEARCH_* -> AZURE_AI_CUSTOM_SEARCH_*)
- Add finding instructions for connection ID and instance name in Bing Custom Search READMEs
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Refactor memory samples and switch to DefaultAzureCredential
- Refactor RAPI Step22 MemorySearch: extract store setup to EnsureMemoryStoreAsync local function
- Refactor non-RAPI Step22 MemorySearch: same pattern with explicit memory lifecycle
- Set UpdateDelay=0 on MemoryUpdateOptions and MemorySearchPreviewTool for faster ingestion
- Use WaitForMemoriesUpdateAsync with 500ms polling interval
- Switch Step19 SharePoint, Step20 Fabric, Step22 MemorySearch (both) to DefaultAzureCredential
- Remove SearchOptions from MemorySearchPreviewTool (causes unknown parameter error)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Switch all RAPI samples to DefaultAzureCredential and format
- Replace AzureCliCredential with DefaultAzureCredential across all 20 RAPI samples
- Run dotnet format on all RAPI and non-RAPI Foundry samples
- AzureAI unit tests: 341 passed (net10.0 + net472)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Rename to Microsoft Foundry, add metadata, rename RAPI folder
- Replace 'Azure AI Foundry' / 'Azure Foundry' with 'Microsoft Foundry' in all docs, comments, and XML docs
- Update FoundryAgentClient metadata provider name to 'microsoft.foundry'
- Rename FoundryAgents-RAPI folder to FoundryResponseAgents
- Rewrite FoundryResponseAgents README with comparison table vs Foundry Agents
- Update slnx and parent README with new folder references
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: simplify sample comments and fix DeepResearch credential
- Remove 'no server-side agent' and 'Responses API directly' phrasing from comments
- Simplify to 'Create a FoundryAgentClient' per review feedback
- Switch Agent_Step15_DeepResearch to DefaultAzureCredential
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Restore full DefaultAzureCredential warning comment in DeepResearch sample
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add ADR 0020: Foundry agent type naming convention
Proposes naming options for a new MAF type wrapping versioned
Foundry agents (Prompt, ContainerApp, Hosted, Workflow) to
distinguish from the existing FoundryResponsesAgent (RAPI path).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify FoundryResponsesAgent samples with env-var constructors and rename folders
- Add env-var constructors to FoundryResponsesAgent (simple + options-based)
- Fix Constructor 1 model optionality (no longer throws on missing AZURE_AI_MODEL_DEPLOYMENT_NAME)
- Add ApplyModelDeploymentFallback helper for options-based constructor
- Update all 23 FoundryResponseAgents samples to remove Environment.GetEnvironmentVariable boilerplate
- Condense 6 simple samples to one-liner constructor calls
- Add XML doc remarks about auto-resolved parameters on all constructors
- Rename FoundryAgents -> FoundryVersionedAgents (server-side, versioned)
- Rename FoundryResponseAgents -> FoundryAgents (now the default path forward)
- Update .slnx and README cross-references for new folder names
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add FoundryAITool factory, rename RAPI folders, and clean up references
- Create FoundryAITool static factory class with 17 methods wrapping AgentTool.Create* and ResponseTool.Create* into AITool returns
- Rename 23 FoundryAgentsRAPI_* subfolders to FoundryAgents_* (drop RAPI prefix)
- Rename .csproj files and update .slnx references accordingly
- Update 12 samples (6 FoundryAgents + 6 FoundryVersionedAgents) to use FoundryAITool
- Replace all FoundryResponsesAgent references with FoundryAgent in comments and READMEs
- Update sample READMEs to reference FoundryAITool methods
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Rename FoundryVersionedAgents subfolders from FoundryAgents_* to FoundryVersionedAgents_*
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add FoundryVersionedAgent class and refactor extension method internals
- Create FoundryVersionedAgent with private ctor and async static factory methods
(CreateAIAgentAsync/GetAIAgentAsync) with env-var and explicit endpoint tiers
- Extract shared internal helpers from AzureAIProjectChatClientExtensions:
CreateChatClientAgent, CreateAgentVersionFromOptionsAsync,
CreateAgentVersionWithProtocolAsync (tools overload),
CreateChatClientAgentOptions, GetAgentRecordByNameAsync, ThrowIfInvalidAgentName
- Extension methods now delegate to shared internal helpers
- All 49 existing samples continue to build successfully
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add CreateConversationSessionAsync, DeleteAIAgentAsync, auto-resolve model, simplify samples
- Add CreateConversationSessionAsync to FoundryAgent and FoundryVersionedAgent
(returns ChatClientAgentSession, creates server-side conversation + session in one call)
- Add DeleteAIAgentAsync static method to FoundryVersionedAgent
- Make model parameter optional in env-var factory overloads (auto-resolves from
AZURE_AI_MODEL_DEPLOYMENT_NAME)
- Update all FoundryVersionedAgents samples to use DeleteAIAgentAsync
- Remove deploymentName env var from samples where only used for model parameter
- Use CreateConversationSessionAsync in Step02_MultiturnConversation
- Use explicit types instead of var for agent/session variables
- All 49 samples build successfully
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove manual AIProjectClient construction from FoundryVersionedAgents samples
- Replace manual AIProjectClient construction with GetService<AIProjectClient>()
from the FoundryVersionedAgent in all dual-option and tool-specific samples
- Remove AZURE_AI_PROJECT_ENDPOINT env var reads from updated samples
- Remove Azure.Identity usings where no longer needed
- Only Step01.1, Step01.2, Eval_Step01 retain manual construction (pedagogical samples)
- All 49 samples build successfully
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Replace aiProjectClient extension calls with FoundryVersionedAgent factories in all samples
- Replace aiProjectClient.CreateAIAgentAsync with FoundryVersionedAgent.CreateAIAgentAsync
in Option 2 (Native SDK) paths across Steps 14-21
- Replace aiProjectClient.Agents.DeleteAgentAsync with FoundryVersionedAgent.DeleteAIAgentAsync
- Remove unused AIProjectClient variables and using directives
- Only Step01.1, Step01.2, Eval_Step01 retain direct AIProjectClient usage (pedagogical)
- Step16, Step22 use GetService<AIProjectClient>() for file/memory operations
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove unused using directives from Step01.2, Step09, Eval_Step02
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update ADR 0020 with accepted decision: Option 6
- Add Option 6 detailing FoundryAgent, FoundryVersionedAgent, FoundryAITool,
env-var auto-discovery, and self-contained factory patterns
- Mark decision as accepted with rationale
- Update current state and metadata sections
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update Step01 basics samples to use FoundryVersionedAgent factories
- Step01.1: Replace manual AIProjectClient/AsAIAgent with FoundryVersionedAgent.CreateAIAgentAsync/GetAIAgentAsync/DeleteAIAgentAsync
- Step01.2: Replace manual AIProjectClient with FoundryVersionedAgent.CreateAIAgentAsync/DeleteAIAgentAsync
- Remove env var boilerplate and Azure.Identity dependency
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add DeleteAIAgentVersionAsync to FoundryVersionedAgent
- DeleteAIAgentAsync: deletes the agent and all its versions (existing)
- DeleteAIAgentVersionAsync: deletes only the specific version associated with the agent instance
- Internally delegates to Agents.DeleteAgentAsync vs Agents.DeleteAgentVersionAsync
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix cleanup comments: DeleteAIAgentAsync deletes the agent and all its versions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update all FoundryVersionedAgents READMEs for FoundryVersionedAgent and auto-discovery
- Rewrite main README with FoundryVersionedAgent usage, auto-discovery table, code example
- Fix sample table links from FoundryAgents_Step* to FoundryVersionedAgents_Step*
- Add FoundryAITool references in tool-specific sample descriptions
- Update individual READMEs: fix stale paths, add auto-discovery note after env var blocks
- Update tool references: AgentTool/ResponseTool -> FoundryAITool
- Update parent 02-agents/README.md with FoundryVersionedAgent description
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Revert unrelated AGUI and Hosting.OpenAI formatting changes to main
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove env-var auto-discovery, add AsAIAgent, mark extensions Obsolete
- Remove 2 env-var constructors from FoundryAgent (keep explicit endpoint ctors)
- Remove 5 env-var factory methods from FoundryVersionedAgent (keep explicit ones)
- Add 3 AsAIAgent static methods to FoundryVersionedAgent (AgentVersion/AgentRecord/AgentReference)
- Mark all 8 AIProjectClient extension methods as [Obsolete] pointing to FoundryVersionedAgent
- Remove ApplyModelDeploymentFallback, env var constants, Azure.Identity usings from source
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update all samples to use explicit endpoint, credential, and model parameters
- Add explicit Environment.GetEnvironmentVariable reads for AZURE_AI_PROJECT_ENDPOINT
and AZURE_AI_MODEL_DEPLOYMENT_NAME to all 48 sample files
- Pass new Uri(endpoint), new DefaultAzureCredential(), deploymentName to
FoundryAgent constructors and FoundryVersionedAgent factory methods
- Add using Azure.Identity where missing
- Matches repo-wide pattern used by other non-Foundry samples
- All 49 samples build successfully
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Migrate remaining samples and source from obsoleted extension methods
- Migrate AgentProviders, AgentWithRAG, AgentWithMemory, HostedWorkflow samples to FoundryVersionedAgent
- Migrate AzureAgentProvider.cs to FoundryVersionedAgent.AsAIAgent
- Migrate AzureAIProjectChatClientTests.cs to FoundryVersionedAgent.GetAIAgentAsync
- Remove pragma suppressions from migrated files
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add unit tests for FoundryAgent and FoundryVersionedAgent
- FoundryAgentTests.cs: 14 tests covering constructors, validation,
properties, metadata, GetService, chat client factory, user-agent header
- FoundryVersionedAgentTests.cs: 31 tests covering CreateAIAgentAsync,
GetAIAgentAsync, AsAIAgent (3 overloads), DeleteAIAgentAsync,
DeleteAIAgentVersionAsync, validation, invalid names, metadata, GetService
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Finalize Foundry agent migration
Align FoundryAgent and FoundryVersionedAgent samples, docs, and tests with the explicit configuration model, clean up stale README guidance, and fix AzureAI unit test validation/build issues.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply formatter cleanup after validation
Capture the dotnet format follow-up changes produced during branch validation so the committed state matches the successfully built and tested branch.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add integration tests for FoundryAgent and FoundryVersionedAgent
Mark old AIProjectClient extension-method integration tests as obsolete and add new integration test suites for both FoundryAgent (Responses API) and FoundryVersionedAgent (versioned agents). All 71 non-skipped tests pass against the live Foundry service.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update ADR 0020 with test coverage details
Add integration test coverage note to the Current State section of ADR 0020.
* Simplify Foundry agents and validate moved samples
* Rename FoundryAgent integration tests to ResponsesAgent
The test classes exercise the non-versioned Responses path via
AIProjectClient.AsAIAgent(), not the removed FoundryAgent wrapper type.
Rename files and class names to reflect the actual test surface.
* Update documentation for ChatClientAgent usage
Added example usage of ChatClientAgent with JokerAgent.
* Refactor ChatClientAgent instantiation for clarity
* Revise agent type naming and usage examples
Updated documentation to reflect changes in agent creation methods and added examples for using `ChatClientAgent`.
* Fix Azure SDK namespace migration after rebase
Update Azure.AI.Projects.OpenAI references to Azure.AI.Projects.Agents
and Azure.AI.Extensions.OpenAI to match Azure.AI.Projects 2.0.0-beta.2.
- Replace deprecated namespace across samples, tests, and src
- Fix renamed types: OpenAPIFunctionDefinition -> OpenApiFunctionDefinition,
BingCustomSearchToolParameters -> BingCustomSearchToolOptions,
BrowserAutomationToolParameters -> BrowserAutomationToolOptions
- Fix API changes: AgentRecord.Versions -> GetLatestVersion(),
ResponsesClient constructor, FunctionApprovalRequestContent ->
ToolApprovalRequestContent
- Apply dotnet format
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address merge markers
* Replace obsolete GetAIAgentAsync with AsAIAgent in samples
Switch Agent_Step07_AsMcpTool and A2AServer to use the non-obsolete
PersistentAgentsClient.AsAIAgent(PersistentAgent) extension instead
of the deprecated GetAIAgentAsync, fixing CS0618 build errors.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix broken markdown links in Responses sample READMEs
Replace stale ChatClientAgents_Step* folder references with the
correct Agent_Step* names across all Responses sample READMEs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix format errors and address PR review comments
- Fix charset and remove unused using in AzureAIProjectResponsesChatClient
- Fix doc comment tags (code -> c) in FoundryAITool
- Fix stray period in LocalMCP sample comment
- Fix grammar in FoundryMemoryProvider xmldoc
- Fix AIProjectClientAgentRunStreamingConversationTests base class
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply dotnet format fixes to PR-changed files
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix build errors from format pass and apply naming conventions
- Fix static call to CreateSessionAsync in Step02 samples and extension tests
- Use expression-bodied lambda in FoundryMemoryProvider (RCS1021)
- Apply PascalCase naming to const fields in ResponsesAgentExtensionCreateTests (IDE1006)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Introduce FoundryAgent sealed type and update AsAIAgent extensions
- Add FoundryAgent sealed class wrapping ChatClientAgent with:
- Public ctors: (projectEndpoint, credential, model, instructions) and (agentEndpoint, credential)
- Internal ctor: (AIProjectClient, ChatClientAgent) for extension use
- CreateConversationSessionAsync() for server-side conversations
- GetService<ChatClientAgent>() and GetService<AIProjectClient>()
- MEAI user-agent policy on internally-created AIProjectClient
- Change all AsAIAgent extension return types from ChatClientAgent to FoundryAgent
- Update all samples and tests to use FoundryAgent type
- Add 16 FoundryAgentTests covering ctors, GetService, UserAgent, RunAsync
- Fix pre-existing Agent_Step12_Plugins build error
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Collapse sample folders and add FoundryAgent_Step01 sample
- Move all Responses/* samples up to AgentsWithFoundry/ (flat structure)
- Remove entire Versioned/ folder (26 samples)
- Add FoundryAgent_Step01 sample showing direct FoundryAgent ctor usage
- Update slnx to reflect flat folder structure
- Fix csproj ProjectReference paths for new depth
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update READMEs for flat AgentsWithFoundry structure
- Rewrite AgentsWithFoundry/README.md with FoundryAgent quick start
- Fix cd commands and paths in 11 sample READMEs
- Update 02-agents/README.md to single Foundry link
- Update AGENTS.md tree to flat structure
- Fix AgentWithMemory cross-reference
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix FoundryAgent_Step01 sample with full create/run/delete lifecycle
Show the complete server-side agent lifecycle: create version with
native SDK, wrap as FoundryAgent via AsAIAgent, run, then delete.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Revert RAPI samples to use AIAgent instead of FoundryAgent
RAPI samples should not reference FoundryAgent directly. Restored
original sample code with only ChatClientAgent -> AIAgent type change
to accommodate the AsAIAgent return type.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Convert versioned-pattern samples to pure RAPI
Step09, Step13, Step17, Step22 were using CreateAgentVersionAsync +
PromptAgentDefinition which is the versioned pattern. Converted to
use AsAIAgent(model, instructions, tools) which is the RAPI path.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix format issues from Docker CI check
- FoundryAgent_Step01: CRLF -> LF
- Agent_Step09: missing final newline
- Agent_Step11_Middleware: add internal modifier, final newline
- Agent_Step02: remove redundant cast (IDE0004)
- Agent_Step08: simplify name (IDE0001)
- FoundryAgentTests: s_ prefix, Async suffix naming conventions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Switch Step09 MCP sample to Microsoft Learn HTTP endpoint
Replace npx stdio GitHub MCP server with the public Microsoft Learn
MCP endpoint (https://learn.microsoft.com/api/mcp) using HTTP transport.
No external tooling required to run.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix missing final newline in Step09 MCP sample
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: use DelegatingAIAgent, clean up Step01 sample
- FoundryAgent now inherits DelegatingAIAgent instead of AIAgent,
removing manual delegation boilerplate (westey-m feedback)
- Simplified Agent_Step01_Basics to single agent creation path,
moved composable IChatClient approach to README (westey-m feedback)
- Fixed FoundryAgentTests param name assertion
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update sample using Project specialized type instead
* Address PR review feedback: DefaultAzureCredential warnings, sample simplifications, format fixes
- Add DefaultAzureCredential production warning comments to ~25 samples
- Simplify Anthropic and OpenAI Step01 samples to single agent
- Convert Step11 Middleware regex patterns to [GeneratedRegex]
- Remove unnecessary cleanup comment from Step06
- Fix Step09 README MCP transport description
- Enhance FoundryAgent xmldoc with non-persistent agent comparison
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Split Step02, simplify RAG Step04, sharpen Step23 differentiation
- Split Step02 into 02.1 (simple multi-turn via sessions) and 02.2 (server-side conversations via CreateConversationSessionAsync)
- RAG Step04: replace HostedFileSearchTool + MEAI wrapping with native OpenAI FileSearchTool
- Step23: clarify DelegatingAIFunction wrapping pattern vs Step09 basic MCP
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix Hosted MCP sample: use ResponseTool.CreateMcpTool and move tool to PromptAgentDefinition
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix broken README link after Step02 split
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address Sergey round 3 feedback: branding, README nav, sample rename
- Replace 'Azure AI Foundry' with 'Microsoft Foundry' in ADR 0020
- Fix 3 READMEs: 'ChatClientAgents' → 'AgentsWithFoundry' sample directory
- Rename FoundryAgent_Step01 → Agent_Step00_FoundryAgentLifecycle for naming consistency
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Changes
* Fix ChatClientAgent streaming responses missing MessageId
Generate fallback MessageId in ChatClientAgent.RunCoreStreamingAsync when
the underlying LLM provider does not set ChatResponseUpdate.MessageId.
Without a MessageId the AGUI converter's null==null check silently drops
all text content, causing CopilotKit Zod validation errors.
Changes:
- ChatClientAgent: generate msg_{Guid} fallback via ??= in streaming loop
- AgentResponseExtensions: sync wrapper MessageId back to RawRepresentation
in AsChatResponseUpdate() so downstream consumers see the value
- Add unit tests for both fixes and AGUI streaming MessageId scenarios
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR #4615 review comments
- Fix MessageId seeding: use first-seen provider MessageId (or generate
fallback) and apply consistently to all chunks in the stream, preventing
message splitting when providers set MessageId only on the first chunk
- Add test for mixed MessageId scenario (first chunk only)
- Fix skipped TextStreaming test: assert Empty (not NotEmpty) to match
actual null==null behavior
- Fix skipped ToolCalls test: assert empty ParentMessageId to match
actual empty-string passthrough behavior
* Handle empty MessageId in AsChatResponseUpdate sync
Treat empty/whitespace MessageId the same as null when syncing from
the AgentResponseUpdate wrapper back to RawRepresentation. Providers
that return empty string MessageId (e.g. tool call responses) now get
the wrapper value recovered correctly.
Add test for empty string MessageId recovery scenario.
* Move MessageId fallback generation to AGUI layer
Move fallback MessageId generation from ChatClientAgent to
AsAGUIEventStreamAsync, addressing the architectural concern that
MessageId is nullable in the AIAgent abstraction and the requirement
for non-null values is specific to the AGUI protocol.
The AGUI layer now generates a fallback MessageId for null or
empty/whitespace values, covering all agent types (not just
ChatClientAgent) including external implementations.
Changes:
- Revert MessageId generation from ChatClientAgent.RunCoreStreamingAsync
- Add fallback MessageId generation in AsAGUIEventStreamAsync for
null/empty MessageId values (handles both null and whitespace)
- Unskip and update AGUI tests to verify fallback generation
- Update ChatClientAgent tests to reflect passthrough behavior
* Revert AsChatResponseUpdate MessageId sync-back
Remove the MessageId sync-back logic from AsChatResponseUpdate() as it
is no longer needed. With fallback generation moved to the AGUI layer,
the abstraction layer should not mutate the RawRepresentation object.
Revert to the original passthrough behavior for AsChatResponseUpdate()
and update tests accordingly.
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix broken samples for GitHub Copilot, declarative, and Responses API
- Add missing on_permission_request handler to github_copilot_basic and
github_copilot_with_session samples (required by copilot SDK)
- Increase timeout for remote MCP query in github_copilot_with_mcp sample
- Soften session isolation claim in github_copilot_with_session sample
- Fix inline_yaml sample: pass project_endpoint via client_kwargs instead
of relying on YAML connection block (AzureAIClient expects
project_endpoint, not endpoint)
- Handle raw JSON schemas in Responses client _convert_response_format
so declarative outputSchema works with the Responses API
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Improve raw JSON schema detection heuristic and add tests
- Broaden raw schema detection to handle anyOf, oneOf, allOf, $ref, $defs
keywords and JSON Schema primitive types, not just 'properties'
- Apply same raw schema handling to azure-ai _shared.py for consistency
- Add unit tests for both openai and azure-ai response_format conversion
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* feat: Implement return-to-previous routing in handoff workflow
- Also obsoletes HandoffsWorkflowBuilder => HandoffWorkflowBuilder (no "s")
* refactor: Remove instance-shared current agent tracking in handoffs
Because the tracker was instance-shared between the start and end executors, it would be shared between all sessions, resulting in incorrect behaviour.
The corect way to do this is to keep the data in a shared executor scope, which is per-session.
* fix: Fix test logic for Handoff to correctly use checkpointing for multiturn
* Move ag_ui_workflow_handoff demo to 05-end-to-end (#4895)
Move the AG-UI workflow handoff demo from python/samples/demos/ to
python/samples/05-end-to-end/ to follow the current folder structure
convention. Update README paths accordingly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix review feedback: remove build artifacts, fix README paths (#4895)
- Add .gitignore to frontend/ to exclude *.tsbuildinfo, vite.config.js,
and vite.config.d.ts build artifacts from version control
- Remove the 4 tracked build artifact files from the tree
- Fix step 2 cd path in README to be relative after 'cd python'
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Clarify working directory context in README Step 2 (#4895)
Step 2 uses a python/-relative path (samples/...) which assumes the
user is still in the python/ directory from Step 1. Add a brief note
making this explicit.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use actual message role when creating ChatMessage
Replace hard-coded ChatRole.User with a ChatRole constructed from the message's Role. The change ensures ToChatMessage and FunctionMessage use the original role (new ChatRole(this.Role)) for both text and contents branches, fixing incorrect role assignment when constructing ChatMessage instances.
* Update changes
* Fix formatting in ToChatMessage tests
---------
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
* .NET: Add integration test for OpenAPI tools with AsAIAgent(agentVersion)
Validates end-to-end flow creating a Foundry agent with an OpenAPI tool
definition via native Azure.AI.Projects SDK types and wrapping it with
AsAIAgent(agentVersion). The test confirms the server-side OpenAPI
function is invoked correctly through RunAsync.
Addresses #4883
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: RetryFact, PascalCase naming, stronger tool assertion
- Use RetryFact with Skip for manual testing (flaky due to external API)
- Fix agentName -> AgentName to match PascalCase convention in file
- Strengthen tool invocation assertion: require >= 3 Eurozone countries
- Add comment explaining server-side OpenAPI tools don't surface as
FunctionCallContent in the MEAI abstraction
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add AsIChatClientWithStoredOutputDisabled for ProjectResponsesClient
Add extension method on ProjectResponsesClient in Microsoft.Agents.AI.AzureAI
package (Azure.AI.Extensions.OpenAI namespace) mirroring the existing extension
on ResponsesClient in the OpenAI package. This enables Azure AI consumers to
disable server-side response storage without depending on the OpenAI package.
- New ProjectResponsesClientExtensions class with AsIChatClientWithStoredOutputDisabled
- Optional deploymentName parameter (model is no longer required)
- Updated OpenAI counterpart doc to remove 'Required' wording for model param
- Added unit tests covering null guard, inner client accessibility,
StoredOutputEnabled=false, and reasoning encrypted content inclusion/exclusion
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Preserve existing RawRepresentationFactory when disabling stored output
Address PR review feedback: wrap/chain the existing factory instead of
replacing it, so upstream configuration (e.g., deploymentName/model defaults
from AsIChatClient) is preserved.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* add adr suggesting a new design to support a multi-source architecture for agent skills
* add deciders
* move the adr to the decisions folder
* remove unnecessary section
* describe adding a custom skill source
* update
* address comments
* add constructor overloads to inline skill resource and script
* consider ai-function as an alternative for skill script and skill resource model classes
* update decision outcome section and sync adr with latest changes in the code
* Add ADR to decide consitency of Chat History Persistence
* Add example
* Update ADR with review results
* Remove unecessary clarification
* Rename ADR to no 22
* Support MCP sampling tools capability (#4625)
Forward systemPrompt, tools, and toolChoice from MCP sampling requests
to the chat client's get_response() call. Also advertise the
sampling.tools capability to MCP servers when a client is configured.
- Pass SamplingCapability with tools support to ClientSession
- Convert systemPrompt to instructions in options
- Convert MCP Tool objects to FunctionTool instances for options
- Map MCP ToolChoice.mode to tool_choice in options
- Add tests for all new behaviors and update existing sampling tests
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix#4625: Support MCP sampling tool with proper typing and structured content
- Fix mypy error by typing sampling callback options as ChatOptions[None]
instead of dict[str, Any], and importing ChatOptions from _types
- Handle structuredContent from CallToolResult in _parse_tool_result_from_mcp,
serializing it as JSON text Content when present
- Add tests for structuredContent parsing (with and without regular content)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix lint: add author to TODO comment
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4625: remove default=str, add edge-case tests
- Remove default=str from json.dumps for structuredContent to fail fast
on non-JSON-serializable values instead of silently converting
- Add test for non-JSON-serializable structuredContent (TypeError)
- Add tests for empty systemPrompt ('') and empty tools list ([]) edge
cases in sampling callback
- Expand TODO comment noting list[Content] return type constraint for
future result_type support
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Sanitize sampling callback error to avoid leaking internals (#4625)
Log exception details at DEBUG level instead of including them in the
ErrorData message returned to the MCP server, which may be untrusted.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4625: move params to options, restore error info
- Remove stale TODO comment about response_format (ChatOptions already has it)
- Restore {ex} in sampling callback error message for useful debugging info
- Set structuredContent as additional_property on Content for structured access
- Move temperature, max_tokens, stop into options dict (not top-level kwargs)
- Only set temperature when provided (not all models support it)
- Add tests for generation params in options and temperature omission
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix MCP sampling callback and structured content error handling (#4625)
- Guard max_tokens like temperature: only set when not None, so options
can properly evaluate to None when all params are absent
- Wrap json.dumps of structuredContent in try/except to fall back to
str() for non-serializable values instead of propagating TypeError
- Extract test_connect_sampling_capabilities_with_client into its own
test function so pytest can discover it independently
- Add test for max_tokens=None omission from options
- Update structured content non-serializable test to expect fallback
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4625: review comment fixes
* Fix MCP and Azure validation regressions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Include reasoning messages in MESSAGES_SNAPSHOT (#4843)
FlowState now tracks reasoning messages emitted during a run.
_emit_text_reasoning() persists reasoning (including encrypted_value)
into flow.reasoning_messages, and _build_messages_snapshot() appends
them to the final MESSAGES_SNAPSHOT event.
Changes:
- Add reasoning_messages field to FlowState
- Update _emit_text_reasoning() to accept optional flow parameter
- Include reasoning_messages in _build_messages_snapshot()
- Add 'reasoning' to ALLOWED_AGUI_ROLES so normalize_agui_role()
preserves the role through snapshot round-trips
- Skip reasoning messages in agui_messages_to_agent_framework() since
they are UI-only state and should not be forwarded to LLM providers
- Add regression tests for snapshot emission, encrypted value
preservation, and multi-turn round-trip with reasoning
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Include reasoning messages in MESSAGES_SNAPSHOT events
Fixes#4843
* Fix PR review feedback for reasoning persistence (#4843)
- Accumulate reasoning text per message_id (append deltas) instead of
storing only the current chunk, matching flow.accumulated_text pattern
- Use camelCase encryptedValue in snapshot JSON to match AG-UI protocol
conventions (toolCallId, encryptedValue)
- Normalize snake_case encrypted_value to encryptedValue in
agui_messages_to_snapshot_format for input compatibility
- Update normalize_agui_role docstring to include reasoning role
- Add tests for incremental reasoning accumulation and key normalization
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4843: Python: agent-framework-ag-ui: include reasoning messages in MESSAGES_SNAPSHOT
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix streaming path to deliver mcp_server_tool_result content (#4814)
Remove premature mcp_server_tool_result emission from the
response.output_item.added/mcp_call handler — at that point the MCP
server has not yet responded and output is always None.
Add a handler for response.mcp_call.completed that emits
mcp_server_tool_result with the actual tool output, matching the
non-streaming path behavior.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix streaming path to deliver mcp_server_tool_result content (#4814)
Stop eagerly emitting mcp_server_tool_result on response.output_item.added
(when output is always None). Instead, handle response.output_item.done for
mcp_call items, which carries the full McpCall with populated output.
This matches the non-streaming path which guards with 'if item.output is not
None' before emitting the result.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix test docstring to match actual implementation event name
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: call_id fallback and raw_representation consistency (#4814)
- Add call_id fallback in response.output_item.done mcp_call handler to
match the output_item.added handler pattern
- Use done_item instead of event for raw_representation to keep
consistent with other output_item branches and non-streaming path
- Add test for call_id fallback when id attribute is missing
- Add raw_representation assertions to existing done handler tests
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: call_id fallback for non-streaming path and test coverage (#4814)
- Apply defensive call_id fallback (getattr with id/call_id/empty) to
non-streaming mcp_call path for consistency with streaming path
- Add raw_representation assertion to call_id fallback test
- Add test for empty-string fallback when neither id nor call_id exist
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix A2AAgent dropping message content from in-progress TaskStatusUpdateEvents (#4783)
_updates_from_task() returned [] for working-state tasks when
background=False, silently discarding all intermediate message content
from task.status.message. Now extracts and yields message parts from
in-progress status updates during streaming.
Also fixed MockA2AClient.send_message to yield all queued responses
(enabling multi-event streaming tests) and added text parameter to
add_in_progress_task_response for tests that need status messages.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix: gate intermediate status updates behind emit_intermediate flag and add missing test coverage
- Add emit_intermediate parameter to _updates_from_task and _map_a2a_stream
- Thread stream flag from run() so only streaming callers see intermediate updates
- Add IN_PROGRESS_TASK_STATES guard to emit_intermediate condition
- Add role parameter to test helper add_in_progress_task_response
- Add clarifying comment on MockA2AClient.send_message batch semantics
- Add tests for user role mapping, background precedence, non-streaming behavior,
terminal task with no artifacts, and empty parts edge case
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix: HandoffAgentExecutor does not output any reponse when non-streaming
* fix: Ensure Workflow outputs persisted in chat history when hosted AsAgent
* fix: Remove duplicate history entry creation and ad test
* test: Add streaming tests for AsAgent to smoke tests
* feat: Add output configurability to Handoffs
* refactor: [BREAKING] Config => ExecutorConfig
Make the Config name less likely to collide with other classes by renaming to ExecutorConfig. Makes Configured and related classes internal as they do not need to be part of the public surface.
* fix: Make RouteBuilder explicit in SourceGen to avoid conflicts
* Handle external input request and response conversion for workflow as agent scenario
* Remove unnecessary test comment
* Fix PR comments
* Updated to fix edge cases, and add more tests.
* Update pending requests to use typed properties instead of relying on StateBag. replying to PR feedback.
* Fixed external response de-dup and updated possible brittle test.
* Address PR comments on sending turn token for normal messages and handle contentId collision by source agent
* Remove unnecessary serialization element and address pr comment on intercepted outgoing requests
* Updated MEAI changes for UserInput request and response abstractions.
* Expose workflow as MCP Tool
* Expose workflow as MCP Tool
* Cleanup
* PR feedback fixes
* update changelog to include PR numner
* Improvements to error handling.
* Adding a sample project demonstrating how to setup Agents and Workflows together.
* Ensure duplicate agent registrations are properly handled.
The `sessionId`, an optional parameter when starting a new session when
running a workflow is an arbitrary string. This allows consumers to
support whatever ids are needed by other systems, but can result in
errors when an OS special or forbidden character is included.
The fix is to escape the paths, in a 1:1 manner. We rely on
EncodeDataString to do this.
* Also modifies the index file to make it easier to determine what the
name of the file on disk is for a given `sessionId`.
* Persist messages during the Function Call Loop
* Revert version reset
* Fix bugs and improve sample
* Fix formatting issues
* Also updating conversation id during run
* Update based on ADR feedback
Azure.AI.Agents.Persistent 1.2.0-beta.10 now targets ME.AI 10.4.0+,
resolving the compatibility issue that required disabling this package.
- Remove IsPackable=false from the csproj
- Re-enable all 6 integration test classes (IntegrationDisabled → Integration)
- Remove outdated compatibility warning from README.md
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix PydanticSchemaGenerationError with PEP 563 annotations in @tool
_resolve_input_model used raw param.annotation from inspect.signature(),
which returns string annotations when 'from __future__ import annotations'
is active (PEP 563). This caused Pydantic's create_model to fail for
complex types like Optional[int] or FunctionInvocationContext.
Use typing.get_type_hints() to resolve annotations to actual types before
passing them to create_model, matching the approach already used by
_discover_injected_parameters.
Fixes#4809
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Remove reproduction report and unused test imports
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix(tests): strengthen PEP 563 regression tests per review feedback (#4809)
- Verify type correctness in schema assertions (not just key presence)
- Fix ctx annotation to FunctionInvocationContext | None for type consistency
- Add test for Optional[CustomType] pattern (original bug trigger)
- Add test for get_type_hints() fallback with unresolvable forward refs
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Address review feedback for #4809: Python: [Bug]: PydanticSchemaGenerationError in FunctionInvocationContext
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Bump HostedAgents samples to AgentFramework beta.11 and pass credential to UseFoundryTools
Update all 8 HostedAgents samples:
- Azure.AI.AgentServer.AgentFramework -> 1.0.0-beta.11
- Microsoft.Agents.AI.OpenAI -> 1.0.0-rc4
- Microsoft.Agents.AI/AzureAI/Workflows -> 1.0.0-rc4
- Azure.AI.Projects -> 2.0.0-beta.1
- Fix Workflow.AsAgent() -> AsAIAgent() in FoundryMultiAgent
- Pass credential to UseFoundryTools in AgentWithTools (resolves#56802)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove AgentWithTools sample (UseFoundryTools no longer supported)
Remove the AgentWithTools hosted agent sample as the UseFoundryTools
backend is no longer supported. Updated HostedAgents README and solution
file to remove all references.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix AgentWithHostedMCP: downgrade Azure.AI.OpenAI to 2.8.0-beta.1 for rc4 compatibility
Azure.AI.OpenAI 2.9.0-beta.1 has breaking changes (GetResponsesClient no
longer accepts deployment name, ResponsesClient.Model removed) that are
incompatible with Microsoft.Agents.AI.OpenAI rc4. Pin to 2.8.0-beta.1 and
use GetResponsesClient(deploymentName).AsAIAgent() pattern.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix source generator bug that silently drops base class handler registrations for protocol-only partial executors
* Fixed xml comments and variable naming.
* Fix workflow samples broken due to routing change
* Add ADR-0020: Foundry Evals integration design
Captures the design for integrating Azure AI Foundry Evaluations with
agent-framework. Key decisions:
- EvalItem with conversation (list[Message]) as single source of truth
- query/response derived from configurable conversation split strategies
- Tools as list[FunctionTool] (including auto-extracted MCP tools)
- FoundryEvals provider with auto-detection of evaluator capabilities
- LocalEvaluator with @function_evaluator decorator for local checks
- Consistent Python/C# APIs: evaluate_agent, evaluate_workflow
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Mark ADR 0020 Foundry Evals as accepted
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: alliscode <bentho@microsoft.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: avoid duplicate agent response telemetry
* Python: conditionally suppress duplicate agent telemetry
* Simplify telemetry ownership tracking
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Aggregate token usage from inner chat spans on invoke_agent span
The invoke_agent span now carries the aggregated input/output token
counts from all inner chat completion spans that occur during an agent
run. Previously, when inner ChatTelemetryLayer spans captured usage,
the outer AgentTelemetryLayer skipped setting usage entirely to avoid
duplication. Now a new INNER_ACCUMULATED_USAGE context variable tracks
cumulative usage across all inner completions, and the agent span
always reports the total.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Deprecate Azure AI v1 (Persistent Agents API) helper methods
Add DeprecationWarning to v1 classes and functions that have been
superseded by the v2 (Projects/Responses) API:
- AzureAIAgentsProvider -> use AzureAIProjectAgentProvider
- AzureAIAgentClient -> use AzureAIClient
- AzureAIAgentOptions -> use AzureAIProjectAgentOptions
- to_azure_ai_agent_tools() -> use to_azure_ai_tools()
- from_azure_ai_agent_tools() -> use from_azure_ai_tools()
- AzureAIAgentClient static tool factory methods -> use AzureAIClient equivalents
All v1 components still function but emit warnings to guide migration.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add deprecation warnings to AzureAIAgentsProvider methods
Mark create_agent(), get_agent(), and as_agent() as deprecated
individually, pointing to AzureAIProjectAgentProvider equivalents.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Emit tool call events in GitHubCopilotAgent streaming
_stream_updates now yields FunctionCallContent for TOOL_EXECUTION_START
and FunctionResultContent for TOOL_EXECUTION_COMPLETE events from the
Copilot SDK session. This enables DevUI and other consumers to display
tool calls during streaming agent execution. Previously only ASSISTANT_MESSAGE_DELTA, SESSION_IDLE, and SESSION_ERROR
were handled — tool execution events were silently dropped.
Signed-off-by: James Sturtevant <jsturtevant@gmail.com>
* Add some tests
Signed-off-by: James Sturtevant <jsturtevant@gmail.com>
* Respond to feedback
Signed-off-by: James Sturtevant <jsturtevant@gmail.com>
* Fix TOOL_EXECUTION_COMPLETE to use correct SDK types
- Read result text from session_events.Result.content (not ToolResult.text_result_for_llm)
- Read failure state from event.data.success/error (not result_obj.result_type/error)
- Handle ErrorClass.message and plain string errors
- Update tests to use session_events.Result and ErrorClass
- Add tests for string errors, success-with-error, and COMPLETE missing fields
Signed-off-by: James Sturtevant <jsturtevant@gmail.com>
---------
Signed-off-by: James Sturtevant <jsturtevant@gmail.com>
* [BREAKING] Refactor middleware layering and raw clients
Reorder chat client layers so function invocation wraps chat middleware, and chat middleware stays outside telemetry while still running for each inner model call. Add middleware pipeline caching, refresh docs and samples, and split Anthropic into raw and public clients to match the standard layering model.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Tighten typing ignores in ancillary modules
Add targeted typing ignores in workflow visualization and lab modules so pyright stays clean alongside the middleware refactor work.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix categorize_middleware to unpack tuple/Sequence and use relative MRO assertions
- Broaden isinstance check in categorize_middleware from list to Sequence
so tuples and other Sequence types are properly unpacked instead of
being appended as a single item.
- Replace fragile hardcoded MRO index assertions in anthropic test with
relative ordering via mro.index().
- Add regression tests for categorize_middleware with tuple, list, and
None inputs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix middleware string decomposition, add middleware param to FunctionInvocationLayer, and add tests (#4710)
- Guard categorize_middleware Sequence check against str/bytes to prevent
character-by-character decomposition of accidentally passed strings
- Add explicit middleware parameter to FunctionInvocationLayer.get_response
and merge it into client_kwargs before categorization, fixing the
inconsistency where only OpenAIChatClient supported this parameter
- Add assertions that RawAnthropicClient does not inherit convenience layers
- Add chat middleware cache test with non-empty base middleware
- Add tests for single unwrapped middleware item and string input
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Apply pre-commit auto-fixes
* Address review feedback for #4710: review comment fixes
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <copilot@github.com>
* Emit TOOL_CALL_RESULT events on approval resume (#4589)
When a tool call is approved via the interrupt/resume flow,
_resolve_approval_responses executes the tool and injects the result
into the messages array, but no TOOL_CALL_RESULT SSE event was yielded
to the client.
Changes:
- _resolve_approval_responses now returns the list of resolved
function_result Content objects instead of None
- run_agent_stream yields ToolCallResultEvent for each resolved
approval result after RunStartedEvent is emitted
- Add ToolCallResultEvent to ag_ui.core imports in _agent_run.py
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* fix(ag-ui): address PR review feedback for #4589
1. _resolve_approval_responses now returns only approved results (not
rejections) so TOOL_CALL_RESULT events are emitted only for executed
tools. Rejection results are still written into message history.
2. Emit resolved TOOL_CALL_RESULT events in the no-updates fallback
RUN_STARTED path so approval results are never lost.
3. Rewrite tests to use real FunctionTool with func and
approval_mode='always_require' via StubAgent default_options,
verifying actual tool execution output in TOOL_CALL_RESULT content.
Added test for rejection not emitting TOOL_CALL_RESULT.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix#4589: clean up approval resolution and add missing tests
- Extract duplicated TOOL_CALL_RESULT emission block into
_make_approval_tool_result_events helper to prevent drift
- Remove dead rejection_results construction in _resolve_approval_responses;
_replace_approval_contents_with_results already handles rejections inline
- Pass only approved_results (not all_results) to clarify the contract
- Add mixed approve/reject test validating the core splitting logic
- Add zero-updates test covering the no-updates fallback emission path
- Add direct unit test for _resolve_approval_responses return value
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Fix import sorting lint error in test_approval_result_event.py
Add blank line between first-party and third-party import groups
to satisfy ruff I001 rule.
Fixes#4589
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Emit AG-UI events for MCP tool calls, results, and text reasoning
Fixes#4213 — `_emit_content()` in the AG-UI layer only handled `text`,
`function_call`, `function_result`, `function_approval_request`, `usage`,
and `oauth_consent_request` content types. Foundry MCP content types
(`mcp_server_tool_call`, `mcp_server_tool_result`) and `text_reasoning`
fell through unhandled, producing no SSE events for AG-UI consumers.
Added three new handler functions wired into `_emit_content()`:
- `_emit_mcp_tool_call`: emits TOOL_CALL_START + TOOL_CALL_ARGS and
tracks in FlowState for MESSAGES_SNAPSHOT inclusion
- `_emit_mcp_tool_result`: emits TOOL_CALL_END + TOOL_CALL_RESULT with
full FlowState cleanup mirroring `_emit_tool_result`
- `_emit_text_reasoning`: emits the protocol-defined reasoning event
sequence (ReasoningStart → MessageStart → MessageContent → MessageEnd
→ ReasoningEnd) with ReasoningEncryptedValueEvent for protected_data
* Add HTTP round-trip tests for MCP tool and reasoning SSE events
Exercises the full POST → SSE bytes → parse → validate pipeline for
mcp_server_tool_call, mcp_server_tool_result, text_reasoning, and
ReasoningEncryptedValueEvent content through FastAPI TestClient.
* Fix _emit_mcp_tool_result missing predictive_handler support (#4213)
- Add predictive_handler parameter to _emit_mcp_tool_result and mirror
the apply_pending_updates + StateSnapshotEvent block from _emit_tool_result
- Forward predictive_handler from _emit_content to _emit_mcp_tool_result
- Add assertion for stored arguments in MCP tool call test
- Add test for predictive handler state snapshot after MCP tool result
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Refactor MCP tool emit functions and add missing tests (#4213)
- Extract _emit_tool_result_common shared helper to eliminate duplication
between _emit_tool_result and _emit_mcp_tool_result
- Remove server_name prefix from tool_call_name in _emit_mcp_tool_call;
display_name now equals tool_name directly
- Add test for tool_name fallback to 'mcp_tool' when tool_name is None
- Add test for output=None fallback to empty string in _emit_mcp_tool_result
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4213: review comment fixes
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add script to ping on stale issues/PRs
* Add script to ping on stale issues/PRs
* Fix stale issue/PR ping script review comments
- Rename TEAM_NAME env var to TEAM_SLUG for clarity
- Add actionable error messages for 403/404 team lookup failures
- Add contents:read permission for actions/checkout
- Use github.event.inputs context with fallback for scheduled runs
- Pin PyGithub to 2.6.0 for reproducible builds
- Fetch comments once in should_ping() to reduce API calls
- Make ping() retry loop idempotent (track comment/label state)
- Validate DAYS_THRESHOLD with helpful error for non-numeric input
- Fix timezone bug: use astimezone() instead of replace(tzinfo=)
- Add comprehensive unit tests (29 tests)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Support detail field in OpenAI image_url payload (#4616)
Include the optional 'detail' field from Content.additional_properties
when building image_url payloads for the OpenAI Chat API, matching the
existing pattern used for 'filename' in document file payloads.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Remove reproduction report
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify detail extraction from additional_properties (#4616)
- Remove unnecessary hasattr check; additional_properties is always
initialized as a dict on Content instances.
- Use 'is not None' instead of truthy check to be more precise.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Remove detail allowlist in chat client to align with responses client
Replace the strict allowlist check ('low', 'high', 'auto') with an
isinstance(detail, str) check so that any valid string detail value is
passed through to OpenAI. This aligns the chat client behavior with the
responses client, which passes detail through unconditionally.
Also add test coverage for:
- Future/unknown string detail values being passed through
- Data URI images (covering the 'data' branch of the match)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix zero-argument MCP tool schema missing 'properties' key (#4540)
MCP servers for zero-argument tools (e.g. matlab-mcp-core-server's
detect_matlab_toolboxes) declare inputSchema as {"type": "object"}
without a "properties" key. OpenAI's API requires "properties" to
be present on object schemas, causing a 400 invalid_request_error.
Normalize inputSchema at MCP ingestion in load_tools() to inject an
empty "properties": {} when it is missing from object-type schemas.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4540: improve test robustness and add defensive guard
- Look up loaded functions by name instead of index to avoid brittle
ordering assumptions
- Add negative-path test cases: non-object schema (type: string) and
empty schema ({}) to verify guard clause skips them correctly
- Assert original inputSchema dicts are not mutated by load_tools()
- Add defensive guard for tool.inputSchema being None
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4540: Python: [Bug]: Local stdio MCP works for calculator but fails for official matlab-mcp-core-server on LM Studio /v1/responses
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix A2AAgent to invoke context providers before and after run
A2AAgent.run() bypassed the context provider lifecycle (before_run/after_run)
that BaseAgent defines as a contract for all agents. This caused A2AAgent to
violate the semantic definition of BaseAgent, resulting in inconsistency with
other agent implementations.
The fix follows the same pattern used by WorkflowAgent:
- Create SessionContext and run before_run on all context providers before
processing the A2A stream
- Collect response updates and run after_run on all context providers after
the stream is fully consumed
- Auto-create a session when context providers are configured but no session
is explicitly passed
Fixes#4754
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Remove reproduction report from repository
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #4754
- Validate messages when no continuation_token: raise ValueError if
normalized_messages is empty, preventing IndexError on messages[-1]
- Import BaseContextProvider/SessionContext from public agent_framework
package instead of internal agent_framework._sessions module
- Add test for ValueError on run(None) without continuation_token
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Improve test coverage for empty-messages guard in A2AAgent.run (#4754)
- Parameterize test to cover both messages=None and messages=[] inputs
- Add test verifying run(None, continuation_token=...) does not raise
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix invoke_agent span to aggregate token usage across LLM calls (#4062)
The FunctionInvocationLayer._get_response() loop was overwriting the
response on each iteration, so usage_details only reflected the last
chat completion call. Now tracks aggregated_usage across all iterations
using add_usage_details() and sets it on the returned response.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Remove reproduction report artifact
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Apply pre-commit auto-fixes
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix source generator bug that silently drops base class handler registrations for protocol-only partial executors
* Fixed xml comments and variable naming.
* Fix FileAgentSkillsProvider accepting SkillsInstructionPrompt without {0} placeholder (#4638)
BuildSkillsInstructionPrompt validated only format-string syntax via
string.Format(template, ""), which silently accepted templates without a
{0} placeholder. The generated skills list was then dropped from the final
instructions.
Tighten validation to format with a sentinel string and verify it appears
in the output, rejecting templates that do not reference argument 0 with
an ArgumentException.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix netstandard2.0 compat and simplify prompt template validation (#4638)
- Replace string.Contains(string, StringComparison) with IndexOf for
netstandard2.0/net472 compatibility
- Remove sentinel round-trip check; validate {0} directly on the raw
template string using IndexOf
- Add positive test verifying custom SkillsInstructionPrompt with {0}
is accepted and applied to output
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updated automation tasks and commands, with alias for the time being
* Restore aggregate test exclusions
Preserve the legacy all-tests scope for test --all by excluding lab and devui from the default aggregate sweep, while still allowing explicit package selection. Also ignore hidden/generated test directories such as .mypy_cache during aggregate discovery.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updated versions in pre-commit
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Re-read env vars in configure_otel_providers and enable_instrumentation (#4119)
Fix ENABLE_SENSITIVE_DATA and VS_CODE_EXTENSION_PORT env vars being ignored
when load_dotenv() runs after module import. The module-level
OBSERVABILITY_SETTINGS singleton cached env state at import time, and
configure_otel_providers() / enable_instrumentation() never re-read from
os.environ when parameters were None.
Both functions now construct a fresh ObservabilitySettings() to pick up
current env vars when explicit parameters are not provided, matching the
existing behavior of the env_file_path branch.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #4119: avoid throwaway ObservabilitySettings
- Add _read_bool_env/_read_int_env helpers to read env vars without
constructing a full ObservabilitySettings (which calls create_resource())
- Replace ObservabilitySettings() in enable_instrumentation() and
configure_otel_providers() else-branch with direct env reads
- Add enable_console_exporters parameter to configure_otel_providers()
for override parity with enable_sensitive_data and vs_code_extension_port
- Propagate _resource and _executed_setup in the non-env_file_path branch
- Make existing tests hermetic (clear VS_CODE_EXTENSION_PORT and
ENABLE_CONSOLE_EXPORTERS env vars)
- Add tests: enable_console_exporters env refresh, explicit param overrides
for both enable_instrumentation() and configure_otel_providers()
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address remaining review feedback for #4119
- Refresh enable_console_exporters in enable_instrumentation() for
consistency with configure_otel_providers(), so env var changes
after import are picked up by both public API functions
- Make test_configure_otel_providers_reads_env_vs_code_port hermetic
by clearing ENABLE_CONSOLE_EXPORTERS from the environment
- Add test_enable_instrumentation_reads_env_console_exporters to
cover the new refresh behavior
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove unconditional enable_console_exporters overwrite from enable_instrumentation() (#4119)
enable_instrumentation() is documented as not configuring exporters, so
managing enable_console_exporters there was a leaky abstraction. The
unconditional _read_bool_env call silently reset the value to False when
ENABLE_CONSOLE_EXPORTERS was absent from env, clobbering any value
previously set by configure_otel_providers(enable_console_exporters=True).
- Remove the unconditional overwrite line from enable_instrumentation()
- Replace test_enable_instrumentation_reads_env_console_exporters with
test_enable_instrumentation_does_not_touch_console_exporters
- Add regression test: enable_instrumentation() does not clobber a
previously configured enable_console_exporters value
- Add test: explicit enable_sensitive_data param still leaves
enable_console_exporters untouched
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add two hosted agent samples using the foundry agent
* Refactor formatting and improve readability in main.py
* Add agent-framework dependency to requirements and update copyright notice in main.py files
* Refactor agent imports and update credential handling in hosted agent samples
* Update agent framework dependency in requirements for hosted agents
* chore: update Python version to 3.14 and improve Dockerfile for hosted agents
* feat: add hosted agent samples for Azure AI with local tools and multi-agent workflows
* fix: update Azure AI client import and refactor agent initialization in hotel agent sample
* feat: add hosted agent samples for Seattle hotel search and writer-reviewer workflow
* fix: correct agent name in YAML configuration for local tools agent
* Fix Content serialization in DurableAgentStateUnknownContent (#4719)
DurableAgentStateUnknownContent.from_unknown_content() stored raw Content
objects without converting them to dicts, causing json.dumps to fail in
Azure Durable Functions' entity state serialization. This affected content
types not explicitly handled (e.g., mcp_server_tool_call/result).
The fix converts Content objects to dicts via to_dict() when storing in
DurableAgentStateUnknownContent, and restores them via Content.from_dict()
in to_ai_content().
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add to_json and from_json methods to Content class (#4719)
Add to_json() and from_json() methods to the Content class to match the
serialization interface provided by SerializationMixin on other model classes.
Also fix pre-existing pyright type errors in durabletask's
DurableAgentStateUnknownContent.to_ai_content().
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: add type guard, remove to_json, add fallback, and tests
- Remove Content.to_json() per reviewer request (comment 3)
- Add type guard in Content.from_json() for non-dict JSON (comments 1, 4)
- Wrap json.JSONDecodeError as ValueError for consistent exception contract
- Add try/except fallback in to_ai_content() for invalid Content dicts (comment 5)
- Add test_content_to_dict_exclude_none and test_content_to_dict_exclude_fields (comment 2)
- Add test_unknown_content_to_ai_content_fallback_on_invalid_type_dict (comment 5)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Address review feedback for #4719: review comment fixes
* Remove Content.from_json, move logic to consuming code (#4719)
Remove the from_json convenience method from Content class per review
feedback. This is the same trivial json.loads + from_dict wrapper as
to_json which was already removed. Consumers should call json.loads
and Content.from_dict directly.
Update tests to use Content.from_dict(json.loads(...)) pattern and
remove from_json-specific error handling tests (those errors are
already covered by json.loads and Content.from_dict).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix race condition issue in FanInEdge while processing messages.
* refactored to limit the code segment under lock.
* Remove extra materialization of the result.
* Added comment to clarify future changes if process message is made async.
* Fix flow.interrupts overwrite when multiple tools need approval (#4590)
Change flow.interrupts assignment to append so that all interrupt entries
accumulate when multiple tools require approval in a single turn.
Both _run_common.py and _agent_run.py used assignment (=) which caused
each new interrupt to overwrite the previous one. Switching to append()
ensures RUN_FINISHED.interrupt contains all pending approvals.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add test for streaming path with multiple confirm_changes interrupts (#4590)
Add integration test exercising run_agent_stream with multiple predictive
tool calls requiring confirmation. Verifies that flow.interrupts.append()
correctly accumulates all interrupt entries and they appear in the
RUN_FINISHED event.
Also confirms FlowState already declares interrupts field with
default_factory=list, addressing the AttributeError concern from review.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: [Feature Branch] Add basic durable workflow support (#3648)
* Add basic durable workflow support.
* PR feedback fixes
* Add conditional edge sample.
* PR feedback fixes.
* Minor cleanup.
* Minor cleanup
* Minor formatting improvements.
* Improve comments/documentation on the execution flow.
* .NET: [Feature Branch] Add Azure Functions hosting support for durable workflows (#3935)
* Adding azure functions workflow support.
* - PR feedback fixes.
- Add example to demonstrate complex Object as payload.
* rename instanceId to runId.
* Use custom ITaskOrchestrator to run orchestrator function.
* .NET: [Feature Branch] Adding support for events & shared state in durable workflows (#4020)
* Adding support for events & shared state in durable workflows.
* PR feedback fixes
* PR feedback fixes.
* Add YieldOutputAsync calls to 05_WorkflowEvents sample executors
The integration test asserts that WorkflowOutputEvent is found in the
stream, but the sample executors only used AddEventAsync for custom
events and never called YieldOutputAsync. Since WorkflowOutputEvent is
only emitted via explicit YieldOutputAsync calls, the assertion would
fail. Added YieldOutputAsync to each executor to match the test
expectation and demonstrate the API in the sample.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix deserialization to use shared serializer options.
* PR feedback updates.
* Sample cleanup
* PR feedback fixes
* Addressing PR review feedback for DurableStreamingWorkflowRun
- Use -1 instead of 0 for taskId in TaskFailedException when task ID is not relevant.
- Add [NotNullWhen(true)] to TryParseWorkflowResult out parameter following .NET TryXXX conventions.
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: [Feature Branch] Add nested sub-workflow support for durable workflows (#4190)
* .NET: [Feature Branch] Add nested sub-workflow support for durable workflows
* fix readme path
* Switch Orchestration output from string to DurableWorkflowResult.
* PR feedback fixes
* Minor cleanup based on PR feedback.
* .NET: [Feature Branch] Add Human In the Loop support for durable workflows (#4358)
* Add Azure Functions HITL workflow sample
Add 06_WorkflowHITL Azure Functions sample demonstrating Human-in-the-Loop
workflow support with HTTP endpoints for status checking and approval responses.
The sample includes:
- ExpenseReimbursement workflow with RequestPort for manager approval
- Custom HTTP endpoint to check workflow status and pending approvals
- Custom HTTP endpoint to send approval responses via RaiseEventAsync
- demo.http file with step-by-step interaction examples
* PR feedback fixes
* Minor comment cleanup
* Minor comment clReverted the `!context.IsReplaying` guards on `PendingEvents.Add`/`RemoveAll` and `SetCustomStatus` in `ExecuteRequestPortAsync`. The guards broke fan-out scenarios where parallel RequestPorts need to be discoverable after replay. `SetCustomStatus` is idempotent metadata that doesn't affect replay determinism.eanup
* fix for PR feedback
* PR feedback updates
* Improvements to samples
* Improvements to README
* Update samples to use parallel request ports.
* Unit tests
* Introduce local variables to improve readability of Workflows.Workflows access patter
* Use GitHub-style callouts and add PowerShell command variants in HITL sample README
* Add changelog entries for durable workflow support (#4436)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Bump Microsoft.DurableTask.Worker to 1.19.1 to fix version downgrade
Microsoft.Azure.Functions.Worker.Extensions.DurableTask 1.13.1 requires
Microsoft.DurableTask.Worker >= 1.19.1 via its transitive dependency on
Microsoft.DurableTask.Worker.Grpc 1.19.1.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix broken markdown links in durable workflow sample READMEs
- Create Workflow/README.md with environment setup docs
- Fix ../README.md -> ../../README.md in ConsoleApps 01, 02, 03, 08
- Fix SubWorkflows relative path (3 levels -> 4 levels up)
- Fix dead Durable Task Scheduler URL
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix build errors from main merge: Throw conflict, ExecuteAsync rename, GetNewSessionAsync rename
- Remove InjectSharedThrow from DurableTask csproj (uses Workflows' internal Throw via InternalsVisibleTo)
- Update ExecuteAsync -> ExecuteCoreAsync with WorkflowTelemetryContext.Disabled
- Update GetNewSessionAsync -> CreateSessionAsync
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Move durable workflow samples to 04-hosting/DurableWorkflows
Aligns with main branch sample reorganization where durable samples
live under 04-hosting/ (alongside DurableAgents/).
- Move samples/Durable/Workflow/ -> samples/04-hosting/DurableWorkflows/
- Add Directory.Build.props matching DurableAgents pattern
- Update slnx project paths
- Update integration test sample paths
- Update README cd paths and cross-references
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix build errors: remove duplicate base class members, update renamed APIs
- Remove duplicate OutputLog, WriteInputAsync, CreateTestTimeoutCts, etc. from
ConsoleAppSamplesValidation (already in SamplesValidationBase)
- Update AddFanInEdge -> AddFanInBarrierEdge in workflow samples
- Update GetNewSessionAsync -> CreateSessionAsync in workflow samples
- Update SourceId -> ExecutorId (obsolete) in workflow samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix dotnet format issues: add UTF-8 BOM and remove unused using
- Add UTF-8 BOM to 20 .cs files across DurableTask, AzureFunctions,
unit tests, and workflow samples
- Remove unnecessary using directive in 07_SubWorkflows/Executors.cs
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix typo PaymentProcesser -> PaymentProcessor and garbled arrows in README
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix GetExecutorName to handle agent names with underscores
Split on last underscore instead of first, and validate that the
suffix is a 32-char hex string (sanitized GUID) before stripping it.
This prevents truncation of agent names like 'my_agent' when the
executor ID is 'my_agent_<guid>'.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Align DurableTask.Client.AzureManaged to 1.19.1
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Bump DurableTask and Azure Functions extension package versions
- DurableTask.* packages: 1.19.1 -> 1.22.0
- Functions.Worker.Extensions.DurableTask: 1.13.1 -> 1.16.0
- Functions.Worker.Extensions.DurableTask.AzureManaged: 1.0.1 -> 1.5.0 (telemetry bug fix)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Bump DurableTask SDK packages to 1.22.0
- DurableTask.Client: 1.19.1 -> 1.22.0
- DurableTask.Client.AzureManaged: 1.19.1 -> 1.22.0
- DurableTask.Worker: 1.19.1 -> 1.22.0
- DurableTask.Worker.AzureManaged: 1.19.1 -> 1.22.0
- Azure Functions extensions kept at original versions (1.13.1/1.0.1) due to
host-side DurableTask.Core 3.7.0 incompatibility with newer extensions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update Microsoft.Azure.Functions.Worker.Extensions.DurableTask to "1.16.0"
* Add the local.settings.json files to the sample which were previously ignored. This aligns with our other samples.
* Increase timeout for tests as CI has them failing transiently.
* increaset timeout value for azure functions integration tests.
* Add YieldsOutput(string) to workflow shared state sample executors
ValidateOrder and EnrichOrder call YieldOutputAsync with string messages,
but only their TOutput (OrderDetails) was in the allowed yield types.
This caused TargetInvocationException in the WorkflowSharedState sample
validation integration test.
* Downgrade the durable packages to 1.18.0
* Downgrading Worker.Extensions.DurableTask to 1.12.1
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix _deduplicate_messages catch-all branch dropping valid repeated messages (#4682)
Remove the catch-all dedup branch that used (role, hash(content_str)) as a
dedup key. This incorrectly treated any two messages with the same role and
identical content as duplicates, dropping valid repeated messages (e.g., a
user saying 'yes' to confirm two separate things).
The tool-specific dedup branches (tool results by call_id, assistant tool
calls by call_id tuple) remain unchanged as they correctly identify true
protocol-level duplicates.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: consecutive-duplicate detection for non-tool messages (#4682)
- Replace blanket dedup removal with consecutive-duplicate detection:
only skip a message if the immediately preceding message has the same
role and content, preserving protection against upstream replays while
allowing identical messages at different conversation points.
- Strengthen test assertions to verify message identity and order, not
just list length.
- Add tests for consecutive duplicate skipping, non-consecutive
preservation, and messages with contents=None.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Use message_id for deduplication instead of content hashing
Deduplicate general messages by message_id when available, replacing
the consecutive-duplicate content check. Two messages with the same id
are definitively the same message (upstream replay), while identical
content with distinct ids (e.g. repeated "yes" confirmations) is
preserved. Messages without a message_id are always kept.
* Fix message_id dedup: truthy check, content-hash fallback, log safety
- Use truthy check (`if msg.message_id`) instead of `is not None` so
empty-string IDs fall through to content-hash dedup rather than
collapsing unrelated messages.
- Add content-hash fallback for messages without message_id, preventing
false negatives from integrations that don't set IDs.
- Remove raw message_id from log format string (addresses log-injection
surface with control characters).
- Add tests for empty-string message_id edge cases.
- Update existing tests to reflect content-hash dedup behavior.
Fixes#4682
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Filter empty AIContent from durable agent state responses
Prevent opaque AIContent objects (e.g., with only RawRepresentation set)
from being stored in durable entity state, where they serialize to empty
JSON payloads. Base AIContent instances are kept only if they have
Annotations or AdditionalProperties.
Fixes https://github.com/microsoft/agent-framework/issues/4481
* Update CHANGELOG.md and fix linter violation
* Python: clean up kwargs across agents, chat clients, tools, and sessions (#3642)
Audit and refactor public **kwargs usage across core agents, chat clients,
tools, sessions, and provider packages per the migration strategy codified
in CODING_STANDARD.md.
Key changes:
- Add explicit runtime buckets: function_invocation_kwargs and client_kwargs
on RawAgent.run() and chat client get_response() layers.
- Refactor FunctionTool to prefer explicit ctx: FunctionInvocationContext
injection; legacy **kwargs tools still work via _forward_runtime_kwargs.
- Refactor Agent.as_tool() to use direct JSON schema, always-streaming
wrapper, approval_mode parameter, and UserInputRequiredException
propagation (integrates PR #4568 behavior).
- Remove implicit session bleeding into FunctionInvocationContext; tools
that need a session must receive it via function_invocation_kwargs.
- Lower chat-client layers after FunctionInvocationLayer accept only
compatibility **kwargs (client_kwargs flattened, function_invocation_kwargs
ignored).
- Add layered docstring composition from Raw... implementations via
_docstrings.py helper.
- Clean up provider constructors to use explicit additional_properties.
- Deprecation warnings on legacy direct kwargs paths.
- Update samples, tests, and typing across all 23 packages.
Resolves#3642
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* clarified docstring
* feedback fixes
* Add unit tests for _docstrings.py build/apply helpers
Tests cover: no docstring source, no extra kwargs, appending to existing
Keyword Args section, inserting after Args, inserting in plain docstrings,
multiline descriptions, ordering, and apply_layered_docstring.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add test for propagate_session TypeError on non-AgentSession values
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add tests for multi-content and empty UserInputRequiredException propagation
Cover the branching logic in _try_execute_function_calls for:
- Multiple user_input_request items in a single exception (extra_user_input_contents path)
- Empty contents list (fallback function_result path)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add tests for DurableAIAgent.get_session forwarding service_session_id
Verifies get_session correctly forwards service_session_id and session_id
to the executor's get_new_session, replacing the removed kwargs test.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Simplify ag-ui test stub to read session from client_kwargs only
Remove dual-mode detection (client_kwargs vs raw kwargs fallback) from
the test mock. Session is now read exclusively from client_kwargs,
matching the settled public calling convention.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updated create and get sessions in durable
* fixed docstrings
* fix test
* updated session handling
* updated from main
* updated tests
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: A2AAgent defaults name/description from AgentCard
When an AgentCard is provided but name/description are not explicitly
set, A2AAgent now falls back to agent_card.name and agent_card.description.
This avoids redundant duplication when constructing A2AAgent instances,
especially in GroupChat orchestrations where name and description are
essential for routing decisions.
Explicit values still take precedence over card values.
Fixes#4630
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use 'is None' checks instead of truthiness for name/description fallback
Ensures explicitly provided empty strings are not overridden by
agent_card values. Adds test for the empty string edge case.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* feat(python): allow @tool functions to return rich content (images, audio)
Add support for tool functions to return Content objects that the model can perceive natively. Closes#4272
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Anthropic logging + mypy fix
* Address PR review: fix MCP ordering, fold helper into from_function_result, fix Chat client
- Preserve original content order in MCP tool results instead of text-first
- Move _build_function_result logic into Content.from_function_result()
- Chat Completions: inject user message for rich items (API only supports string tool content)
- Update tests for ordering and new from_function_result behavior
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use native Responses API multi-part output, warn+omit for Chat client
- Responses client: put rich items directly in function_call_output's
output field as list (native API support) instead of user message injection
- Chat client: warn and omit rich items (API doesn't support multi-part
tool results), matching Ollama/Bedrock pattern
- Unify test image: use sample_image.jpg across all integration tests
- Add Azure OpenAI Responses integration test
- Assert model describes house image to verify perception
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix lint: remove print statement, wrap long line
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback: bug fixes, single-pass MCP, unit tests
- Add isinstance guard in from_function_result for non-Content lists
- Fix Anthropic empty tool_content fallback to string result
- Fix Content(type='text', text=None) edge case in parse_result
- Rewrite MCP _parse_tool_result_from_mcp as single-pass (no index counters)
- Add Anthropic unit tests: data image, uri image, unsupported media, all-unsupported
- Add OpenAI Chat unit test: rich items warning and omission
- Add OpenAI Responses unit tests: function_result with/without items
- Add test_types tests: only-rich-items list, non-Content list fallback
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix pyright errors: add type ignore comments for Any list iteration
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix mypy/pyright: ensure ToolExecutionException receives str
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix lint: remove duplicate test_prepare_options_excludes_conversation_id
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* refactor: unify all tool results into Content items
* addressed copilot comments
* pyright fix
* small fix
* comments
* fix: address Copilot review - warnings, blob safety, dedup
- Add warning logs when rich content is dropped in Claude agent and
MCP server handlers (matching Chat/Bedrock/Ollama pattern)
- Defensive blob URI construction: wrap plain base64 in data: prefix
- Simplify Chat client _prepare_content_for_openai to use content.result
- Simplify Responses client text-only path, remove redundant nesting
- Add test for plain base64 blob without data: prefix
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix token double-counting in compaction and address review comments
- Exclude items from _serialize_content() to prevent double-counting
tokens when items mirrors result in function_result content
- Add rich content warning in GitHub Copilot agent tool handler
- Replace raw Content debug log with concise item count/type summary
- Update stale test comments about FunctionTool.invoke return type
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Bedrock's Converse API only accepts "auto", "any", or "tool" as valid
toolChoice keys. The previous code mapped tool_choice="none" to
{"none": {}}, which causes a botocore.exceptions.ParamValidationError.
When tool_choice="none" (set by FunctionInvocationLayer after exhausting
max iterations), the fix now omits toolConfig entirely so the model
won't attempt tool calls.
Added tests for tool_choice="none", "auto", and "required" modes.
Fixes#4529
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
* Use deepcopy for state snapshot to detect nested mutations (#4500)
Replace dict() shallow copy with copy.deepcopy() when snapshotting
workflow state before activity execution. The shallow copy shared
references to nested objects (dicts, lists), so in-place mutations by
executors were reflected in both the snapshot and live state, producing
an empty diff and preventing state updates from propagating to
downstream activities.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix state snapshot to use deepcopy so nested mutations are detected in durable workflow activities
Fixes#4500
* Address PR review: remove report, extract testable helpers (#4500)
- Delete REPRODUCTION_REPORT.md (debugging artifact with local paths
and raw LLM output)
- Extract _create_state_snapshot() and _compute_state_updates() as
module-level helpers in _app.py so tests exercise the production
code path
- Update TestStateSnapshotDiff to import and use production helpers
instead of reimplementing snapshot/diff logic locally
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Add regression tests proving shallow copy bug and deep copy isolation (#4500)
Add two additional tests to TestStateSnapshotDiff:
- test_shallow_copy_would_miss_nested_mutations: reproduces the original
bug by demonstrating that dict() (shallow copy) misses nested mutations
- test_create_state_snapshot_isolates_nested_objects: verifies the
production _create_state_snapshot helper creates a true deep copy
These tests ensure a regression back to shallow copy would be caught.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add integration test exercising full activity code path (#4500)
Address PR review comment: add test_executor_activity_detects_nested_state_mutations
that captures the actual executor_activity function from _setup_executor_activity
and verifies it detects in-place nested mutations. This test would fail if
_app.py line 314 regressed from _create_state_snapshot() back to dict().
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review feedback for #4518: review comment fixes
* Address PR review feedback for state snapshot diff
- Inline _compute_state_updates logic at call site to reuse precomputed
original_keys/current_keys sets, avoiding redundant set allocations
- Fix test docstring to describe behavioral regression instead of
hard-coding a specific line number
- Use SOURCE_ORCHESTRATOR constant in integration test instead of
literal string
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* fix: remove unused _compute_state_updates from _app.py (#4518)
The function was inlined per review comment, making the module-level
helper unused and triggering a pyright reportUnusedFunction error.
Move the helper into the test file where it is still needed for unit
testing the diffing logic.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
The DevUI /v1/responses endpoint accepts function_approval_response content
without verifying that the request_id corresponds to a real pending approval
request issued by the server. This allows forged approval responses to
execute arbitrary tools with attacker-controlled arguments, bypassing
approval_mode='always_require'.
Changes:
- Track outgoing approval requests in a server-side registry
(_pending_approvals) keyed by request_id
- Validate incoming approval responses against this registry; reject
any response whose request_id was not issued by the server
- Use server-stored function_call data (tool name, arguments, call_id)
instead of client-supplied data when constructing the approval response
- Consume request_ids on use (pop from registry) to prevent replay attacks
Tests:
- 8 new tests covering forged rejection, server-data enforcement,
anti-replay, multiple independent approvals, and edge cases
Co-authored-by: REDMOND\tusharmudi <tusharmudi@microsoft.com>
* Validate approval responses against server-side pending request registry
* improvements
* pin GHCP sdk version to non-breaking for now
* Pin CHCP sdk to LKG.
* really fix GHCP sdk pkg version
* Fix HITL approval validation security gaps and memory leak
- Validate rejected approval responses against pending_approvals registry,
not just approved ones. Fabricated rejections without a prior request are
now stripped from messages before reaching the LLM.
- Bound _pending_approvals with OrderedDict + LRU eviction (max 10k) to
prevent unbounded memory growth from abandoned approval requests.
- Skip registration when function_call.name is None/empty; log warning
when content.id or function_call is missing at registration time.
- Document pending_approvals parameter in run_agent_stream docstring.
- Add test for fabricated rejection attack scenario.
- Assert pending approval entry is preserved after function name mismatch.
- Pre-populate pending_approvals in rejection test for correct validation.
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>
* Extract function_approval_response from workflow messages (#4546)
_extract_responses_from_messages now handles function_approval_response
content in addition to function_result content. Previously, approval
responses sent via the messages field were silently dropped because the
function only checked for content.type == "function_result".
The approval response is keyed by content.id and includes the approved
status, id, and serialized function_call — consistent with how
_coerce_content identifies approval response payloads.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Fix#4546: Update docstring and add integration tests for message-based approvals
- Update _extract_responses_from_messages docstring to reflect that it
now handles function_approval_response content in addition to
function_result content.
- Add integration tests for run_workflow_stream across two turns with
approval responses provided via messages (function_approvals) rather
than resume.interrupts, covering both approved and denied scenarios.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review feedback for #4546
- Use safer 'not .get("interrupt")' assertion instead of 'not in'
to handle Pydantic v2 model_dump() including keys with None values
- Add unit test for mixed function_result and function_approval_response
in the same message to TestExtractResponsesFromMessages
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Implement annotation-based context compaction
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Handle missing compaction attributes in BaseChatClient
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix CI typing and bandit issues
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Optimize incremental compaction annotation pass
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* refinement
* Python: add ToolResultCompactionStrategy and CompactionProvider
Add ToolResultCompactionStrategy that collapses older tool-call groups
into short summary messages (e.g. [Tool calls: get_weather]) while
keeping the most recent groups verbatim. This mirrors the .NET
ToolResultCompactionStrategy from PR #4533.
Add CompactionProvider as a context-provider that auto-applies compaction
before each agent turn and stores compacted history in session state
after each turn.
Includes tests and samples for both features.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* refinement and alignment with dotnet PR
* updated tool result compaction
* updated tool result compaction
* Python: add ToolResultCompactionStrategy, CompactionProvider, and skip_excluded
- ToolResultCompactionStrategy collapses older tool-call groups into
[Tool results: func_name: result] summaries with bidirectional tracing
(same pattern as SummarizationStrategy).
- CompactionProvider as BaseContextProvider with separate before_strategy
and after_strategy parameters. before_strategy compacts loaded context;
after_strategy compacts stored history via history_source_id.
- InMemoryHistoryProvider gains skip_excluded flag to filter out messages
marked as excluded by compaction strategies.
- Tests, samples, and exports updated.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fixed checks
* fix mypy
* Fix: ensure summary messages from both strategies get full compaction annotations
SummarizationStrategy was not calling annotate_message_groups after
inserting its summary message, so the summary lacked core group
annotations (id, kind, index, has_reasoning, _excluded). Added the
missing call. ToolResultCompactionStrategy already had it.
Added tests verifying both strategies produce fully annotated summaries.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* updated propagation
* fix mypy
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* 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>
* Suppress IL2026/IL3050 with targeted pragmas on affected methods
Add #pragma warning disable/restore for IL2026 and IL3050 only around
the specific methods where dotnet format incorrectly adds
[RequiresUnreferencedCode] and [RequiresDynamicCode] attributes despite
proper interceptors configuration in the csproj.
See https://github.com/dotnet/sdk/issues/51136
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Upgrade to .NET SDK 10.0.200 and remove IL2026/IL3050 workarounds
Bump global.json to SDK 10.0.200 which fixes the dotnet format bug
that incorrectly added [RequiresUnreferencedCode] and
[RequiresDynamicCode] attributes (https://github.com/dotnet/sdk/issues/51136).
Remove all #pragma warning disable IL2026/IL3050 workarounds from
source files and the --exclude-diagnostics flag from the CI format
workflow.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix `executor_completed` event with non-copyable raw_representation in mixed workflows
Fixes#4455
* fix(#4455): use class-level sets for deepcopy field exclusion
- SerializationMixin.__deepcopy__: check type(self).DEFAULT_EXCLUDE
instead of hardcoding 'raw_representation'
- Content.__deepcopy__: add _SHALLOW_COPY_FIELDS class variable and
check against it instead of hardcoding
- Fix tautological assertion in test (was always True)
- Add second excluded field to test to verify DEFAULT_EXCLUDE is
respected generically
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Decouple __deepcopy__ from DEFAULT_EXCLUDE in SerializationMixin (#4455)
Introduce _SHALLOW_COPY_FIELDS class variable in SerializationMixin to
separate deep-copy semantics from serialization semantics. Previously,
__deepcopy__ used DEFAULT_EXCLUDE to decide which fields to shallow-copy,
conflating 'not serialized' with 'not safe to deep-copy'. A field added
to DEFAULT_EXCLUDE purely for serialization (e.g. additional_properties)
would be silently shared between original and copy.
- Add _SHALLOW_COPY_FIELDS (default {'raw_representation'}) to
SerializationMixin, matching the pattern already used by Content
- Update __deepcopy__ to read from _SHALLOW_COPY_FIELDS instead of
DEFAULT_EXCLUDE
- Add test verifying DEFAULT_EXCLUDE fields are deep-copied unless
also in _SHALLOW_COPY_FIELDS
- Add test for Content._SHALLOW_COPY_FIELDS identity preservation
- Add test for ChatResponse deep-copying additional_properties
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add test for _SHALLOW_COPY_FIELDS and DEFAULT_EXCLUDE independence
Add test_deepcopy_shallow_copy_fields_override_default_exclude to verify
that a field in both DEFAULT_EXCLUDE and _SHALLOW_COPY_FIELDS is
shallow-copied (controlled by _SHALLOW_COPY_FIELDS), while a field in
DEFAULT_EXCLUDE only is still deep-copied. This addresses review comment
#11 ensuring the two class variables control independent concerns.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove unnecessary local variable in __deepcopy__
Inline cls._SHALLOW_COPY_FIELDS directly in the loop check instead of
assigning to a local variable first, per review feedback.
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>
* fix: prevent pickle deserialization of untrusted HITL input
Add strip_pickle_markers() to sanitize HTTP input before it reaches
pickle.loads() via the checkpoint decoding path. Applied as a 3-layer
defence-in-depth:
1. _app.py: sanitize req.get_json() at the HTTP boundary
2. _workflow.py: sanitize in _deserialize_hitl_response() before decode
3. _serialization.py: sanitize in reconstruct_to_type() as final guard
Any dict containing __pickled__ or __type__ markers from untrusted
sources is replaced with None, blocking arbitrary code execution via
crafted payloads to POST /workflow/respond/{instanceId}/{requestId}.
Includes 12 new unit tests covering the sanitizer and end-to-end
attack prevention.
* refactor: address review concerns for pickle fix
1. Remove deserialize_value() fallback in _deserialize_hitl_response
untrusted HITL data now returns as-is when no type hint is available,
never flowing into pickle.loads().
2. Move strip_pickle_markers() out of reconstruct_to_type() the function
is general-purpose again; untrusted-data callers are responsible for
sanitizing first (documented with NOTE comment).
3. Define _PICKLE_MARKER/_TYPE_MARKER as local constants with import-time
assertions against core's values decouples from private names while
failing loudly if core ever changes them.
4. Update tests to reflect new responsibility boundaries.
* fix: simplify warning message and fix ruff RUF001 lint
* fix: suppress pyright reportPrivateUsage on core marker imports
* Lower marker-strip log from warning to debug to avoid log flooding
* Replace assert with RuntimeError for marker sync checks (ruff S101)
* Fix pyright and ruff CI errors in security fix
- Use cast() for dict/list comprehensions in strip_pickle_markers (pyright)
- type: ignore for narrowed dict return in _workflow.py (pyright)
- Simplify marker imports: use core constants directly, remove local copies
- Remove duplicate pyright ignore comment
* Remove duplicate end-to-end test in TestStripPickleMarkers
* Suppress mypy redundant-cast on list cast needed by pyright
* Initial plan
* Fix broken Strands Agents documentation links in ADR 0001
Replace 5 broken strandsagents.com URLs (returning 404) with stable
GitHub source code links in docs/decisions/0001-agent-run-response.md.
The Strands Agents docs site restructured from /api-reference/python/
to /api/python/, breaking the old links.
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
* Update Strands Agents links to use official documentation site
Replace GitHub source links with official strandsagents.com/docs/api/python/
documentation URLs in docs/decisions/0001-agent-run-response.md.
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
* Update Strands Agents links to use specific documentation URLs
- Streaming: strandsagents.com/docs/user-guide/concepts/streaming/
- Structured output: strandsagents.com/docs/user-guide/concepts/agents/structured-output/
- AgentResult/stop_reason: strandsagents.com/docs/api/python/strands.agent.agent_result/#agentresult
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
* Deduplicate Strands AgentResult link in stop-reason row
Replaced the duplicate hyperlink on `stop_reason` with inline code,
keeping a single AgentResult link to the same URL.
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com>
* Skip flaky CodeInterpreter integration tests in CI
The CreateAgent_CreatesAgentWithCodeInterpreter tests fail intermittently
because the Azure AI Code Interpreter service sometimes fails to read/execute
uploaded Python files. This causes all 4 integration test jobs to fail
consistently across both platforms (ubuntu/windows) and TFMs (net10.0/net472).
Mark both test variants with Skip to match the convention used by other
flaky tests in the suite (e.g., AzureAIAgentsPersistentStructuredOutputRunTests).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Skip flaky CodeInterpreter integration tests in CI
The CreateAgent_CreatesAgentWithCodeInterpreter tests fail intermittently
because the Azure AI Code Interpreter service sometimes fails to read/execute
uploaded Python files. This causes all 4 integration test jobs to fail
consistently across both platforms (ubuntu/windows) and TFMs (net10.0/net472).
Mark both test variants with Skip to match the convention used by other
flaky tests in the suite (e.g., AzureAIAgentsPersistentStructuredOutputRunTests).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add A2A server sample and fix client streaming bug
Add a pure Python A2A server sample so testing the A2A client no longer
requires running the .NET server. The server uses the a2a-sdk's
A2AStarletteApplication with uvicorn and supports three agent types
(invoice, policy, logistics) backed by AzureOpenAIResponsesClient.
New files:
- a2a_server.py: Main server entry point with CLI args
- agent_executor.py: Bridges a2a-sdk AgentExecutor to Agent Framework
- agent_definitions.py: Agent and AgentCard factory definitions
- invoice_data.py: Mock invoice data and query tool functions
- a2a_server.http: REST Client requests for testing
Also fixes a streaming bug in agent_with_a2a.py where async with was
used on ResponseStream which does not support the async context manager
protocol. Changed to async for to match all other samples.
Closes#4045
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR review: handle CancelledError and fix end_date filtering
- Re-raise asyncio.CancelledError before the broad exception handler
so cooperative cancellation is not swallowed.
- Make end_date filter inclusive of the full day by comparing with
< end + timedelta(days=1) instead of <= midnight.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+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>
* Add multi-turn streaming sample and rename multi-turn samples
- Rename 03_multi_turn.py to 03a_multi_turn.py
- Add 03b_multi_turn_streaming.py showing streaming with session history
- The new sample demonstrates calling get_final_response() after
iterating the stream to persist conversation history
- Update READMEs to reflect the new file names
Closes#4447
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Auto-finalize ResponseStream on iteration completion
When a ResponseStream is fully consumed via async iteration,
automatically trigger finalization (finalizer + result hooks).
This ensures session history is persisted in streaming multi-turn
conversations without requiring an explicit get_final_response() call.
- Add auto-finalize call in __anext__ on StopAsyncIteration
- Guard inner stream finalization to prevent double-execution
- Re-check _finalized after iteration in get_final_response()
- Add tests for auto-finalization and streaming session history
- Revert sample file renames from previous commit
Closes#4447
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* README fix
* Fix SIM102 lint: combine nested if statements
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Exclude conversation_id from chat completions options (#4315)
When a session with service_session_id is passed to an agent using the
Chat Completions client, conversation_id leaked through _prepare_options()
into AsyncCompletions.create(), causing an 'unexpected keyword argument'
error. The Responses client already excluded conversation_id but the Chat
Completions client did not.
Added conversation_id to the exclusion set in _prepare_options().
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Apply pre-commit auto-fixes
* Remove reproduction report artifact
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix#4305: Handle dict chat_options in _update_conversation_id
_update_conversation_id assumed chat_options had attribute access, but
ChatOptions is a TypedDict (dict). When a dict was passed, setting
.conversation_id raised AttributeError. Now checks isinstance(dict) and
uses key access for dicts, falling back to attribute access for objects.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address PR feedback: use Mapping ABC and add missing tests (#4305)
- Use collections.abc.Mapping instead of dict for isinstance check in
_update_conversation_id, making it more robust for non-dict mapping types.
- Add test for object-style chat_options with optional options dict parameter.
- Add test verifying existing conversation_id gets overwritten (idempotent).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove unnecessary Mapping check in _update_conversation_id (#4305)
chat_options is always a dict, so the isinstance(chat_opts, Mapping)
check and the else branch for attribute-style access are dead code.
Simplify to direct dict key assignment and remove object-style tests.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
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>
* Initial plan
* Update HostedAgents samples to Azure.AI.AgentServer.AgentFramework 1.0.0-beta.9 and MEAI 10.3.0
Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>
* Fix HostedAgents samples for Microsoft.Agents.AI 1.0.0-rc2 API changes
- Rename CreateAIAgent -> AsAIAgent (AgentThreadAndHITL, AgentWithHostedMCP, AgentWithTextSearchRag)
- Rename AsAgent -> AsAIAgent (AgentsInWorkflows)
- Replace AIContextProviderFactory with AIContextProviders and simplified TextSearchProvider ctor (AgentWithTextSearchRag)
- Update Microsoft.Agents.AI.OpenAI to 1.0.0-rc2 (AgentThreadAndHITL, AgentWithTextSearchRag, AgentWithTools)
- Update Microsoft.Agents.AI.Workflows to 1.0.0-rc2 (AgentsInWorkflows)
- Add Microsoft.Agents.AI 1.0.0-rc2 reference (AgentWithHostedMCP)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update HostedAgents samples for beta.9 API changes and add missing projects to slnx
- Use DefaultAzureCredential consistently across all samples
- Add AgentThreadAndHITL, AgentWithLocalTools, AgentWithTools to slnx
- Apply dotnet format
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove unnecessary Microsoft.Agents.AI.* package references (transitive from AgentFramework)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add DefaultAzureCredential production warning comments to all HostedAgents samples
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update HostedAgents READMEs to reflect DefaultAzureCredential usage
Replace AzureCliCredential references with DefaultAzureCredential in all
HostedAgents README files to match the actual sample code.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Replace Microsoft.Extensions.AI.OpenAI with Microsoft.Agents.AI.OpenAI and remove AsIChatClient()
Swap package references from Microsoft.Extensions.AI.OpenAI to
Microsoft.Agents.AI.OpenAI across all 6 HostedAgents samples. This enables
using the AsAIAgent() extension directly on ChatClient/ResponsesClient
(from OpenAI.Chat/OpenAI.Responses namespaces), removing the intermediate
AsIChatClient() call in 3 samples where it was unnecessary.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Use explicit types and AsAIAgent() extensions across all HostedAgents samples
Replace var with explicit types for clarity in all 6 samples. Replace
new ChatClientAgent() constructor calls with chatClient.AsAIAgent()
extension method in AgentWithLocalTools and AgentsInWorkflows.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix filter combine logic for ChatHistoryMemoryProvider
* Replace var with explicit types in filter building code and test
Address PR review nit: use explicit types instead of var for better
readability in the filter-building logic and the new combined filter
compilation test.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix style issues
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Improve ag-ui tests and coverage
* fix tests paths
* Fixes
* Improve AG-UI test robustness and correctness
- Map toolName → tool_call_name in SSE helpers for TOOL_CALL_START events
- Fail loudly on malformed SSE JSON in parse_sse_response() instead of silently dropping
- Detect duplicate TOOL_CALL_START/TOOL_CALL_END in assert_tool_calls_balanced()
- Remove fragile source line reference from test docstring
- Add found guard in test_client_tool_sets_additional_properties to prevent vacuous pass
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix as_agent() not defaulting name/description from client properties
AzureAIClient.as_agent() and AzureAIAgentClient.as_agent() now fall back
to self.agent_name and self.agent_description when name/description are
not explicitly passed. This ensures Agent.name is populated for
telemetry spans without requiring callers to repeat the name.
Fixes#4471
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Address review: use is None checks instead of truthiness
Switch from name or self.agent_name to explicit is None checks so
that callers can intentionally pass empty strings without them being
replaced by client defaults. Added edge-case tests for empty strings.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update docstrings to document name/description defaulting behavior
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix Python pyright package scoping and typing remediation
Implements issue #4407 by removing the root pyright include, adding package-level pyright includes, and resolving pyright/mypy typing issues across Python packages. Also cleans unnecessary casts and applies line-level, rule-specific ignores where external libraries are too dynamic.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Reduce pyright cost in handoff cloning
Simplify cloned_options construction in HandoffAgentExecutor to avoid expensive TypedDict narrowing/inference in _handoff.py, which was causing pyright to spend a long time in orchestrations.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix types
* Fix lint and type-check regressions
Resolve current Python package check failures across lint, pyright, and mypy after recent code changes, including purview/declarative pyright issues and multiple ruff simplification findings.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fixed hooks
* Stabilize package tests and test tasks
Resolve cross-package non-integration test failures, simplify streaming type flow, harden locale/culture handling, and standardize package test poe tasks to exclude integration tests where applicable.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* lots of small fixes
* Fix current Python test regressions
Address current failing unit tests in azure-ai, bedrock, and azure-cosmos while keeping Bedrock parsing logic inline (no new static helper methods).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* small fixes
* small fixes
* removed pydantic from json
* final updates
* fix core
* fix tests
* fix obser
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Upgrade to XUnit 3 and Microsoft Testing Platform (#4176)
* Fix copilot studio integration tests failure (#4209)
* Fix anthropic integration tests and skip reason (#4211)
* Remove accidental add of code coverage for integration tests (#4219)
* Add solution filtered parallel test run (#4226)
* Fix build paths (#4228)
* Fix coverage settings path and trait filter (#4229)
* Add project name filter to solution (#4231)
* Increase Integration Test Parallelism (#4241)
* Increase integration tests threads to 4x (#4242)
* Separate build and test into parallel jobs (#4243)
* Filter src by framework for tests build (#4244)
* Separate build and test into parallel jobs
* Filter source projects by framework for tests build
* Pre-build samples via tests to avoid timeouts (#4245)
* Separate build from run for console sample validation (#4251)
* Address PR comments (#4255)
* Merge and move scripts (#4308)
* .NET: Add Microsoft Fabric sample #3674 (#4230)
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
* Python: Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference (#4207)
* 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>
* [Purview] Mark responses as responses and fix epoch bug for python long overflow (#4225)
* .NET: Support InvokeMcpTool for declarative workflows (#4204)
* Initial implementation of InvokeMcpTool in declarative workflow
* Cleaned up sample implementation
* Updated sample comments.
* Added missing executor routing attribute
* Fix PR comments.
* Updated based on PR comments.
* Updated based on PR comments.
* Removed unnecessary using statement.
* Update Python package versions to rc2 (#4258)
- Bump core and azure-ai to 1.0.0rc2
- Bump preview packages to 1.0.0b260225
- Update dependencies to >=1.0.0rc2
- Add CHANGELOG entries for changes since rc1
- Update uv.lock
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* .NET: Fixing issue where OpenTelemetry span is never exported in .NET in-process workflow execution (#4196)
* 1. Add reproduction test for issue #4155: workflow.run Activity never stopped in streaming OffThread path
The WorkflowRunActivity_IsStopped_Streaming_OffThread test demonstrates that
the workflow.run OpenTelemetry Activity created in StreamingRunEventStream.RunLoopAsync
is started but never stopped when using the OffThread/Default streaming execution.
The background run loop keeps running after event consumption completes, so the
using Activity? declaration never disposes until explicit StopAsync() is called.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2. Fix workflow.run Activity never stopped in streaming OffThread execution (#4155)
The workflow.run OpenTelemetry Activity in StreamingRunEventStream.RunLoopAsync
was scoped to the method lifetime via 'using'. Since the run loop only exits on
cancellation, the Activity was never stopped/exported until explicit disposal.
Fix: Remove 'using' and explicitly dispose the Activity when the workflow reaches
Idle status (all supersteps complete). A safety-net disposal in the finally block
handles cancellation and error paths.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add root-level workflow.session activity spanning run loop lifetime\n\nImplements two-level telemetry hierarchy per PR feedback from lokitoth:\n- workflow.session: spans the entire run loop / stream lifetime\n- workflow_invoke: per input-to-halt cycle, nested within the session\n\nThis ensures the session activity stays open across multiple turns,\nwhile individual run activities are created and disposed per cycle.\n\nAlso fixes linkedSource CancellationTokenSource disposal leak in\nStreamingRunEventStream (added using declaration)."
* Address Copilot review: fix Activity/CTS disposal, rename activity, add error tag\n\n1. LockstepRunEventStream: Remove 'using' from Activity in async iterator\n and manually dispose in finally block (fixes#4155 pattern). Also dispose\n linkedSource CTS in finally to prevent leak.\n2. Tags.cs: Add ErrorMessage (\"error.message\") tag for runtime errors,\n distinct from BuildErrorMessage (\"build.error.message\").\n3. ActivityNames: Rename WorkflowRun from \"workflow_invoke\" to \"workflow.run\"\n for cross-language consistency.\n4. WorkflowTelemetryContext: Fix XML doc to say \"outer/parent span\" instead\n of \"root-level span\".\n5. ObservabilityTests: Assert WorkflowSession absence when DisableWorkflowRun\n is true.\n6. WorkflowRunActivityStopTests: Fix streaming test race by disposing\n StreamingRun before asserting activities are stopped.\n7. StreamingRunEventStream/LockstepRunEventStream: Use Tags.ErrorMessage\n instead of Tags.BuildErrorMessage for runtime error events."
* Review fixes: revert workflow_invoke rename, use 'using' for linkedSource, move SessionStarted earlier\n\n- Revert ActivityNames.WorkflowRun back to \"workflow_invoke\" (OTEL semantic convention contract)\n- Use 'using' declaration for linkedSource CTS in LockstepRunEventStream (no timing sensitivity)\n- Move SessionStarted event before WaitForInputAsync in StreamingRunEventStream to match Lockstep behavior"
* Improve naming and comments in WorkflowRunActivityStopTests"
* Prevent session Activity.Current leak in lockstep mode, add nesting test
Save and restore Activity.Current in LockstepRunEventStream.Start() so the
session activity doesn't leak into caller code via AsyncLocal. Re-establish
Activity.Current = sessionActivity before creating the run activity in
TakeEventStreamAsync to preserve parent-child nesting.
Add test verifying app activities after RunAsync are not parented under the
session, and that the workflow_invoke activity nests under the session."
* Fix stale XML doc: WorkflowRun -> WorkflowInvoke in ObservabilityTests
---------
Co-authored-by: alliscode <bentho@microsoft.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python / .NET Samples - Restructure and Improve Samples (Feature Branc… (#4092)
* Python: .NET Samples - Restructure and Improve Samples (Feature Branch) (#4091)
* Moved by agent (#4094)
* Fix readme links
* .NET Samples - Create `04-hosting` learning path step (#4098)
* Agent move
* Agent reorderd
* Remove A2A section from README
Removed A2A section from the Getting Started README.
* Agent fixed links
* Fix broken sample links in durable-agents README (#4101)
* Initial plan
* Fix broken internal links in documentation
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
* Revert template link changes; keep only durable-agents README fix
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
* .NET Samples - Create `03-workflows` learning path step (#4102)
* Fix solution project path
* Python: Fix broken markdown links to repo resources (outside /docs) (#4105)
* Initial plan
* Fix broken markdown links to repo resources
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
* Update README to rename .NET Workflows Samples section
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
* .NET Samples - Create `02-agents` learning path step (#4107)
* .NET: Fix broken relative link in GroupChatToolApproval README (#4108)
* Initial plan
* Fix broken link in GroupChatToolApproval README
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
* Update labeler configuration for workflow samples
* .NET - Reorder Agents samples to start from Step01 instead of Step04 (#4110)
* Fix solution
* Resolve new sample paths
* Move new AgentSkills and AgentWithMemory_Step04 samples
* Fix link
* Fix readme path
* fix: update stale dotnet/samples/Durable path reference in AGENTS.md
Co-authored-by: crickman <66376200+crickman@users.noreply.github.com>
* Moved new sample
* Update solution
* Resolve merge (new sample)
* Sync to new sample - FoundryAgents_Step21_BingCustomSearch
* Updated README
* .NET Samples - Configuration Naming Update (#4149)
* .NET: Restore AzureFunctions index parity with ConsoleApps under DurableAgents samples (#4221)
* Clean-up `05_host_your_agent`
* Config setting consistency
* Refine samples
* AGENTS.md
* Move new samples
* Re-order samples
* Move new project and fixup solution
* Fixup model config
* Fix up new UT project
---------
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
* Python: Fix Bedrock embedding test stub missing meta attribute (#4287)
* Fix Bedrock embedding test stub missing meta attribute
* Increase test coverage so gate passes
* Python: (ag-ui): fix approval payloads being re-processed on subsequent conversation turns (#4232)
* Fix ag-ui tool call issue
* Safe json fix
* Python: Update workflow orchestration samples to use AzureOpenAIResponsesClient (#4285)
* Update workflow orchestration samples to use AzureOpenAIResponsesClient
* Fix broken link
* Move scripts to scripts folder
---------
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Rishabh Chawla <rishabhchawla1995@gmail.com>
Co-authored-by: Peter Ibekwe <109177538+peibekwe@users.noreply.github.com>
Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
Co-authored-by: Ben Thomas <ben.thomas@microsoft.com>
Co-authored-by: alliscode <bentho@microsoft.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Fix encoding (#4309)
* Disable Parallelization for WorkflowRunActivityStopTests (#4313)
* Revert parallel disable (#4324)
* .NET: Disable flakey Workflow Observability tests (#4416)
* Disable flakey OffThread test
* Disable additional OffThread test
* Disable a further test
* Disable all observability tests
---------
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Rishabh Chawla <rishabhchawla1995@gmail.com>
Co-authored-by: Peter Ibekwe <109177538+peibekwe@users.noreply.github.com>
Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
Co-authored-by: Ben Thomas <ben.thomas@microsoft.com>
Co-authored-by: alliscode <bentho@microsoft.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
* Add foundry extension samples for python and dotnet
* Align foundry extension samples with existing hosted agent patterns
- Fix Python multiagent indentation bug (from_agent_framework ran in both modes)
- Remove hardcoded personal endpoint from appsettings.Development.json
- Rename .NET folders/projects to PascalCase (FoundryMultiAgent, FoundrySingleAgent)
- Upgrade .NET multiagent from net9.0 to net10.0
- Add ManagePackageVersionsCentrally=false and analyzer blocks to .csproj files
- Replace wildcard package versions with fixed versions
- Use alpine Docker images and standard build pattern
- Align agent.yaml structure (template nesting, displayName, resources, authors)
- Convert .NET multiagent from namespace/class to top-level statements
- Add run-requests.http for multiagent sample
- Fix Python requirements.txt (remove dev deps, add agent-framework)
- Add proper copyright headers
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Align foundry samples: fix builds, upgrade AgentServer to beta.8
- Fix TargetFrameworks (plural) to override inherited net472 from Directory.Build.props
- Upgrade Azure.AI.AgentServer.AgentFramework to 1.0.0-beta.8 (latest)
- Bump OpenTelemetry packages to 1.12.0 (required by beta.8)
- Fix Roslynator/format errors (imports ordering, BOM, sealed record, target-typed new)
- Verified with docker dotnet format (matching CI pipeline)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Refactor hosted samples to use AIProjectClient.CreateAIAgentAsync
Replace PersistentAgentsClient and manual AzureOpenAIClient setup with
AIProjectClient.CreateAIAgentAsync() from Microsoft.Agents.AI.AzureAI.
- FoundryMultiAgent: Remove Azure.AI.Agents.Persistent, use CreateAIAgentAsync
for Writer and Reviewer agents with cleanup in finally block
- FoundrySingleAgent: Remove manual GetConnection/AzureOpenAIClient chain,
use CreateAIAgentAsync with hotel search tool
- Update csproj: add Microsoft.Agents.AI.AzureAI, remove unused packages
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update READMEs to reflect AIProjectClient.CreateAIAgentAsync usage
- Reference Microsoft.Agents.AI.AzureAI and Microsoft.Agents.AI.Workflows packages
- Add Azure AI Developer role requirement for agents/write data action
- Replace PersistentAgentsClient references
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Add HostedAgents READMEs and Foundry samples to solution
- Create dotnet/samples/05-end-to-end/HostedAgents/README.md with sample index
- Create python/samples/05-end-to-end/hosted_agents/README.md with sample index
- Add FoundryMultiAgent and FoundrySingleAgent to agent-framework-dotnet.slnx
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix Python linting: reorder imports before load_dotenv, remove trailing whitespace
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Update uv.lock to match latest package versions
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Fix trailing whitespace in foundry_single_agent agent.yaml
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Exclude dotnet.microsoft.com from link checker
This domain intermittently times out in CI, causing flaky markdown
link check failures unrelated to PR changes.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Align env vars to AZURE_AI_PROJECT_ENDPOINT and default model to gpt-4o-mini
Addresses PR review feedback:
- Rename PROJECT_ENDPOINT to AZURE_AI_PROJECT_ENDPOINT across all
Foundry samples (dotnet + python) to match existing samples
- Change default model from gpt-4.1-mini to gpt-4o-mini consistently
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Skip flaky test CreatesWorkflowEndToEndActivities_WithCorrectName_DefaultAsync
Tracked in #4398
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Remove Python foundry samples from PR scope
Python hosted agent samples need further alignment with the azure-ai
package conventions. Removing from this PR to ship .NET samples first.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Narrow linkspector exclusion to dotnet.microsoft.com/download only
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Leo Yao <leoyao@Leos-MacBook-Pro.local>
Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
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>
* bug fix for duplicate output on GitHubCopilotAgent
* Add Test code for bug fix of duplicate output on GitHubCopilotAgenttT
* update Test code for bug fix of duplicate output on GitHubCopilotAgenttT
* update Test for duplicate output of GitHubCopilotAgent
---------
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
2026-03-04 21:40:34 +00:00
1953 changed files with 123754 additions and 58882 deletions
# Probe the highest allowed dependency versions, then open issues/PRs from the passing updates.
name:Python - Dependency Range Validation
on:
workflow_dispatch:
permissions:
contents:write
issues:write
pull-requests:write
env:
UV_CACHE_DIR:/tmp/.uv-cache
jobs:
dependency-range-validation:
name:Dependency Range Validation
runs-on:ubuntu-latest
env:
# For now only run 3.13, if we do encounter situations where there are mismatches between packages and python versions (other then 3.10 and 3.14 which are known to not be able to install everything)
# then we will have to reevaluate.
UV_PYTHON:"3.13"
GH_TOKEN:${{ secrets.GITHUB_TOKEN }}
steps:
- uses:actions/checkout@v6
with:
fetch-depth:0
- name:Set up python and install the project
uses:./.github/actions/python-setup
with:
python-version:${{ env.UV_PYTHON }}
os:${{ runner.os }}
env:
UV_CACHE_DIR:/tmp/.uv-cache
- name:Run dependency range validation
id:validate_ranges
# Keep workflow running so we can still publish diagnostics from this run.
continue-on-error:true
run:uv run poe validate-dependency-bounds-project --mode upper --package "*"
working-directory:./python
- name:Upload dependency range report
# Always publish the report so failures are inspectable even when validation fails.
// Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
var agent = new OpenAIClient(
new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
.GetResponsesClient("gpt-4o-mini")
.AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(model: deploymentName, name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
```
@@ -163,15 +160,43 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
### Python
- [Getting Started with Agents](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
- [Getting Started](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
- [End-to-End](./dotnet/samples/05-end-to-end): full applications and demos
## Troubleshooting
### Authentication
| Problem | Cause | Fix |
|---------|-------|-----|
| Authentication errors when using Azure credentials | Not signed in to Azure CLI | Run `az login` before starting your app |
| API key errors | Wrong or missing API key | Verify the key and ensure it's for the correct resource/provider |
> **Tip:** `DefaultAzureCredential` is convenient for development but in production, consider using a specific credential (e.g., `ManagedIdentityCredential`) to avoid latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
### Environment Variables
The samples typically read configuration from environment variables. Common required variables:
This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../python/samples/02-agents/declarative/).
This folder contains sample agent definitions that can be run using the declarative agent support, for python see the [declarative agent python sample folder](../../python/samples/02-agents/declarative/).
@@ -64,7 +64,7 @@ Approaches observed from the compared SDKs:
| AutoGen | **Approach 1** Separates messages into Agent-Agent (maps to Primary) and Internal (maps to Secondary) and these are returned as separate properties on the agent response object. See [types of messages](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/messages.html#types-of-messages) and [Response](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.Response) | **Approach 2** Returns a stream of internal events and the last item is a Response object. See [ChatAgent.on_messages_stream](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.ChatAgent.on_messages_stream) |
| OpenAI Agent SDK | **Approach 1** Separates new_items (Primary+Secondary) from final output (Primary) as separate properties on the [RunResult](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L39) | **Approach 1** Similar to non-streaming, has a way of streaming updates via a method on the response object which includes all data, and then a separate final output property on the response object which is populated only when the run is complete. See [RunResultStreaming](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L136) |
| Google ADK | **Approach 2** [Emits events](https://google.github.io/adk-docs/runtime/#step-by-step-breakdown) with [FinalResponse](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L232) true (Primary) / false (Secondary) and callers have to filter out those with false to get just the final response message | **Approach 2** Similar to non-streaming except [events](https://google.github.io/adk-docs/runtime/#streaming-vs-non-streaming-output-partialtrue) are emitted with [Partial](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L133) true to indicate that they are streaming messages. A final non partial event is also emitted. |
| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.stream_async) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/docs/api/python/strands.agent.agent_result/) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/docs/api/python/strands.agent.agent/) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
| LangGraph | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
| Agno | **Combination of various approaches** Returns a [RunResponse](https://docs.agno.com/reference/agents/run-response) object with text content, messages (essentially chat history including inputs and instructions), reasoning and thinking text properties. Secondary events could potentially be extracted from messages. | **Approach 2** Returns [RunResponseEvent](https://docs.agno.com/reference/agents/run-response#runresponseevent-types-and-attributes) objects including tool call, memory update, etc, information, where the [RunResponseCompletedEvent](https://docs.agno.com/reference/agents/run-response#runresponsecompletedevent) has similar properties to RunResponse|
| A2A | **Approach 3** Returns a [Task or Message](https://a2aproject.github.io/A2A/latest/specification/#71-messagesend) where the message is the final result (Primary) and task is a reference to a long running process. | **Approach 2** Returns a [stream](https://a2aproject.github.io/A2A/latest/specification/#72-messagestream) that contains task updates (Secondary) and a final message (Primary) |
@@ -496,7 +496,7 @@ We need to decide what AIContent types, each agent response type will be mapped
|-|-|
| AutoGen | **Approach 1** Supports [configuring an agent](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/agents.html#structured-output) at agent creation. |
| Google ADK | **Approach 1** Both [input and output schemas can be specified for LLM Agents](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key) at construction time. This option is specific to this agent type and other agent types do not necessarily support |
| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.structured_output) |
| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/docs/api/python/strands.agent.agent/) |
| LangGraph | **Approach 1** Supports [configuring an agent](https://langchain-ai.github.io/langgraph/agents/agents/?h=structured#6-configure-structured-output) at agent construction time, and a [structured response](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) can be retrieved as a special property on the agent response |
| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/input-output/structured-output/agent) at agent construction time |
| A2A | **Informal Approach 2** Doesn't formally support schema negotiation, but [hints can be provided via metadata](https://a2a-protocol.org/latest/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation time |
@@ -508,7 +508,7 @@ We need to decide what AIContent types, each agent response type will be mapped
|-|-|
| AutoGen | Supports a [stop reason](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.TaskResult.stop_reason) which is a freeform text string |
| Google ADK | [No equivalent present](https://github.com/google/adk-python/blob/main/src/google/adk/events/event.py) |
| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/latest/documentation/docs/api-reference/python/types/event_loop/#strands.types.event_loop.StopReason) property on the [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) class with options that are tied closely to LLM operations. |
| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/docs/api/python/strands.types.event_loop/) property on the [AgentResult](https://strandsagents.com/docs/api/python/strands.agent.agent_result/) class with options that are tied closely to LLM operations. |
| LangGraph | No equivalent present, output contains only [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
| A2A | No equivalent present, response only contains a [message](https://a2a-protocol.org/latest/specification/#64-message-object) or [task](https://a2a-protocol.org/latest/specification/#61-task-object). |
@@ -1240,3 +1240,10 @@ class AttributionAwareStrategy(CompactionStrategy):
- [ADR-0016: Unifying Context Management with ContextPlugin](0016-python-context-middleware.md) — Parent ADR that established `ContextProvider`, `HistoryProvider`, and `AgentSession` architecture.
- [Context Compaction Limitations Analysis](https://gist.github.com/victordibia/ec3f3baf97345f7e47da025cf55b999f) — Detailed analysis of why current architecture cannot support in-run compaction, with attempted solutions and their failure modes. Option 4 in this ADR corresponds to "Option A: Middleware Access to Mutable Message Source" from that analysis; Options 1-3 correspond to "Option B: Tool Loop Hook", adapted here to a `BaseChatClient` hook instead of `FunctionInvocationConfiguration`.
### Implementation Rollout Note
Implementation is split into two phases:
1. **Phase 1 (PR 1):** runtime compaction foundation in `agent_framework/_compaction.py`, in-run integration, and extensive core tests, plus in-run compaction samples (`basics`, `advanced`, `custom`).
2. **Phase 2 (PR 2):** history/storage compaction (`upsert`-based full replacement), provider support, storage tests, and storage-focused sample (`storage`).
# Foundry agent surface stays centered on `ChatClientAgent`
## Context
The Microsoft Foundry integration exposes two distinct usage patterns:
1. Direct Responses usage, where callers provide model, instructions, and tools at runtime.
2. Server-side versioned agents, where callers create and manage `AgentVersion` resources through `AIProjectClient.Agents`.
We briefly explored adding public wrapper types such as `FoundryAgent`, `FoundryVersionedAgent`, and `FoundryResponsesChatClient` to make those paths feel more specialized. That direction created extra public types, duplicated existing `ChatClientAgent` behavior, and pushed samples toward compatibility helpers instead of the native Azure SDK flow.
## Decision
Keep the public surface centered on `ChatClientAgent`.
- Direct Responses scenarios use `AIProjectClient.AsAIAgent(...)`.
- Server-side versioned scenarios use native `AIProjectClient.Agents` APIs to create or retrieve agent resources, then wrap `AgentRecord` or `AgentVersion` with `AIProjectClient.AsAIAgent(...)`.
- Compatibility helpers such as `AIProjectClient.CreateAIAgentAsync(...)` and `AIProjectClient.GetAIAgentAsync(...)` remain only as obsolete migration shims.
- Public wrapper types `FoundryAgent`, `FoundryVersionedAgent`, `FoundryResponsesChatClient`, and `FoundryResponsesChatClientAgent` are not part of the chosen direction.
## Why
-`ChatClientAgent` is already the framework abstraction used everywhere else.
-`AIProjectClient` is the native Azure SDK entry point for versioned agent lifecycle operations.
- A single agent abstraction avoids parallel type hierarchies for the same backend.
- Samples become clearer when they show either:
- direct Responses construction via `AIProjectClient.AsAIAgent(...)`, or
- native Foundry resource management via `AIProjectClient.Agents`.
## Consequences
### Direct Responses path
Use the convenience overloads on `AIProjectClient`:
-`FoundryAgents/` samples show the direct Responses path with `AIProjectClient.AsAIAgent(...)`.
-`FoundryVersionedAgents/` samples should show native `AIProjectClient.Agents` create/get/delete flows plus `AsAIAgent(...)`.
### Compatibility APIs
Obsolete helper extensions remain only to ease migration of existing code. New samples and new guidance should not be written against them.
## Rejected direction
Do not introduce or preserve separate public wrapper types whose main purpose is to forward to `ChatClientAgent` while carrying Foundry-specific naming.
That approach:
- duplicates lifecycle concepts already present on `AIProjectClient`,
- fragments the public API,
- complicates samples and docs,
- and makes migration harder by encouraging wrapper-specific affordances.
The Agent Framework needs a skills system that lets agents discover and use domain-specific knowledge, reference documents, and executable scripts. Skills can originate from different sources — filesystem directories (SKILL.md files), inline C# code, or reusable class libraries — and the framework must support all three uniformly while allowing extensibility, composition, and filtering.
## Decision Drivers
- Skills must be definable from multiple sources: filesystem, inline code, reusable classes, etc
- Common abstractions are needed so the provider and builder work uniformly regardless of skill origin
- File-based scripts must support user-defined executors, enabling custom runtimes and languages; code/class-based scripts execute in-process as C# delegates
- Skills must be filterable so consumers can include or exclude specific skills based on defined criteria
- Multiple skill sources must be composable into a single provider
- It must be possible to add custom skill sources (e.g., databases, REST APIs, package registries) by implementing a common abstraction
## Architecture
### Model-Facing Tools
Skills are presented to the model as up to three tools that progressively disclose skill content. The system prompt lists available skill names and descriptions; the model then calls these tools on demand:
- **`load_skill(skillName)`** — returns the full skill body (instructions, listed resources, listed scripts)
- **`read_skill_resource(skillName, resourceName)`** — reads a supplementary resource (file-based or code-defined) associated with a skill
- **`run_skill_script(skillName, scriptName, arguments?)`** — executes a script associated with a skill; only registered when at least one skill contains scripts
Each tool delegates to the corresponding method on the resolved `AgentSkill` — calling `Resource.ReadAsync()` or `Script.RunAsync()` respectively.
If skills have no scripts defined, the `run_skill_script` tool is **not advertised** to the model and instructions related to script execution are **not included** in the default skills instructions.
### Abstract Base Types
The architecture defines four abstract base types that all skill variants implement:
1.**File-Based Skills** — discovered from `SKILL.md` files on the filesystem. Resources and scripts are files in subdirectories.
2.**Programmatic Skills** — defined in C# code. These are further divided into:
- **Inline Skills** — built at runtime via the `AgentInlineSkill` class and its fluent API. Ideal for quick, agent-specific skill definitions.
- **Class-Based Skills** — defined as reusable C# classes that subclass `AgentClassSkill`. Ideal for packaging skills as shared libraries or NuGet packages.
Both programmatic skill types use `AgentInlineSkillResource` and `AgentInlineSkillScript` for their resources and scripts. They are typically served by `AgentInMemorySkillsSource`, which accepts any `AgentSkill` and is not limited to programmatic skills.
### File-Based Skills
File-based skills are authored as `SKILL.md` files on disk. Resources and scripts are discovered from corresponding subfolders within the skill directory.
**`AgentFileSkill`** — A filesystem-based skill discovered from a directory containing a `SKILL.md` file. Parsed from YAML frontmatter; content is the raw markdown body. Resources and scripts are discovered from files in corresponding subfolders:
**`AgentFileSkillScript`** — A file-based skill script that represents a script file on disk. Delegates execution to an external `AgentFileSkillScriptRunner` callback (e.g., runs Python/shell via `Process.Start`). Throws `NotSupportedException` if no executor is configured:
The executor can be provided at the **provider level** via `AgentSkillsProviderBuilder.UseFileScriptRunner(executor)` and optionally overridden for a **particular file skill** or for a **set of skills** at the file skill source level, giving fine-grained control over how different scripts are executed.
**`AgentFileSkillsSource`** — A skill source that discovers skills from filesystem directories containing `SKILL.md` files. Recursively scans directories (max 2 levels), validates frontmatter, and enforces path traversal and symlink security checks:
**`AgentFileSkillsSourceOptions`** — Configuration options for `AgentFileSkillsSource`. Allows customizing the allowed file extensions for resources and scripts without adding constructor parameters:
Programmatic skills are defined in C# code rather than discovered from the filesystem. There are two kinds: **inline** and **class-based**. Both use `AgentInlineSkillResource` and `AgentInlineSkillScript` for resources and scripts, and are held by a single `AgentInMemorySkillsSource`.
**`AgentInMemorySkillsSource`** — A general-purpose skill source that holds any `AgentSkill` instances in memory. Although commonly used for programmatic skills (`AgentInlineSkill` and `AgentClassSkill`), it accepts any `AgentSkill` subclass and is not restricted to code-defined skills:
Inline skills are built at runtime via the `AgentInlineSkill` class and its fluent API. They are ideal for quick, agent-specific skill definitions where a full class hierarchy would be overkill.
**`AgentInlineSkill`** — A skill defined entirely in code. Resources can be static values or functions; scripts are always functions. Constructed with name, description, and instructions, then extended with resources and scripts:
**`AgentInlineSkillResource`** — A skill resource backed by a delegate. The delegate is invoked via an `AIFunction` each time `ReadAsync` is called, producing a dynamic (computed) value:
Class-based skills are designed for packaging skills as reusable libraries. Users subclass `AgentClassSkill` and override properties. Unlike inline skills, class-based skills are self-contained, can live in shared libraries or NuGet packages, and are well-suited for dependency injection.
**`AgentClassSkill`** — An abstract base class for defining skills as reusable C# classes that bundle all skill components (frontmatter, instructions, resources, scripts) together. Designed for packaging skills as distributable libraries:
```csharp
publicabstractclassAgentClassSkill:AgentSkill
{
publicabstractstringInstructions{get;}
// Content is auto-synthesized from Frontmatter + Instructions + Resources + Scripts
The following subsections present alternative approaches for handling filtering, caching, and deduplication of skills across multiple sources.
### Via Composition
In this approach, the `AgentSkillsProvider` accepts a **single**`AgentSkillsSource`. Multiple sources are composed externally via an aggregate source, and cross-cutting concerns like filtering, caching, and deduplication are implemented as **source decorators** — subclasses of `DelegatingAgentSkillsSource` that intercept `GetSkillsAsync()`.
**`FilteringAgentSkillsSource`** — A decorator that applies filter logic before returning results. The decorator pattern keeps filtering orthogonal to source implementations and allows composing multiple filters:
**`CachingAgentSkillsSource`** — A decorator that caches skills after the first load, keeping the provider stateless and giving consumers control over caching granularity per source. For example, file-based skills (expensive to discover) can be cached while code-defined skills remain uncached:
**Deduplication** is similarly implemented as a decorator (`DeduplicatingAgentSkillsSource`) that deduplicates by name (case-insensitive, first-one-wins) and logs a warning for skipped duplicates.
**Example** — Combining file-based and code-defined sources with filtering and caching:
- Clean single-responsibility: the provider serves skills, sources provide them.
- Caching, filtering, and deduplication are composable as source decorators — each concern is a separate, testable wrapper.
**Cons:**
- DI is less flexible: multiple `AgentSkillsSource` implementations registered in the container cannot be auto-injected into the provider. The consumer must manually compose them via an aggregate source.
- Increased public API surface: requires additional public classes (aggregate source, caching decorators, filtering decorators) that consumers need to learn and use.
### Via AgentSkillsProvider
In this approach, the `AgentSkillsProvider` accepts **`IEnumerable<AgentSkillsSource>`** and handles aggregation, filtering, caching, and deduplication internally.
The provider aggregates skills from all registered sources, deduplicates by name (case-insensitive, first-one-wins), caches the result after the first load, and optionally applies filtering via a predicate on `AgentSkillsProviderOptions`. Duplicate skill names are logged as warnings.
**Example** — Registering multiple sources directly with the provider:
```csharp
// Conceptual example — in practice, use AgentSkillsProviderBuilder
- DI-friendly: register multiple `AgentSkillsSource` implementations in the container, and they are all auto-injected into `AgentSkillsProvider` via `IEnumerable<AgentSkillsSource>`.
- Smaller public API surface: no need for aggregate source, caching decorators, or filtering decorator classes — these concerns are handled internally by the provider.
**Cons:**
- The provider takes on multiple responsibilities — aggregation, caching, deduplication, and filtering.
- Less granular caching control: caching is all-or-nothing across sources rather than per-source as with decorators.
- Less extensible: new behaviors (e.g., ordering, TTL expiration) require modifying the provider rather than adding a decorator.
### Builder Pattern
**`AgentSkillsProviderBuilder`** provides a fluent API for composing skills from multiple sources. The builder centralizes configuration — script executors, approval callbacks, prompt templates, and filtering — so consumers don't need to know the underlying source types.
The builder internally decides how to wire up the object graph: it creates the appropriate source instances, applies caching and filtering, and returns a fully configured `AgentSkillsProvider`. This keeps the setup code concise while still allowing fine-grained control when needed.
**Example** — Using the builder to combine multiple source types with configuration:
- **Explicit skill context at execution time.** `RunAsync` receives the owning `AgentSkill`, so any script can access skill metadata or resources during execution without requiring construction-time wiring.
- **Self-contained abstraction.** A dedicated type communicates clearly that scripts are a skills-framework concept, separate from general-purpose AI functions.
- **Easier extensibility for custom script types.** Third-party implementations can subclass `AgentSkillScript` and access the owning skill in `RunAsync` without special setup.
**Cons:**
- **Wrapper overhead.** `AgentInlineSkillScript` is a thin pass-through around `AIFunction` — it adds a class, a constructor, and an indirection layer for no behavioral difference.
- **Parallel abstraction.** `AgentSkillScript` and `AIFunction` serve overlapping purposes (named callable with arguments), creating two parallel hierarchies for the same concept.
- **Friction for consumers.** Users who already have `AIFunction` instances must wrap them in `AgentInlineSkillScript` to use them as scripts, adding ceremony.
### Option B — Reuse `AIFunction` directly
Scripts are represented as `AIFunction` (from `Microsoft.Extensions.AI`). `AgentSkill.Scripts` returns
`IReadOnlyList<AIFunction>?`. `AgentInlineSkillScript` is eliminated entirely — callers use
`AIFunctionFactory.Create(delegate, name: ...)` or pass `AIFunction` instances directly.
`AgentFileSkillScript` becomes an `AIFunction` subclass that captures its owning `AgentFileSkill` via
an internal back-reference set during construction.
```csharp
// AgentSkill exposes scripts as AIFunction directly:
- **Fewer types.** Eliminates `AgentSkillScript` and `AgentInlineSkillScript`, reducing the public API surface by two classes.
- **Seamless interop.** Any `AIFunction` — whether from `AIFunctionFactory`, a custom subclass, or an external library — can be used as a skill script with zero wrapping.
- **Consistent with `Microsoft.Extensions.AI` ecosystem.** Scripts share the same type as tool functions used by `IChatClient` and `FunctionInvokingChatClient`, reducing conceptual overhead for developers already familiar with the ecosystem.
**Cons:**
- **No owning-skill context in invocation signature.** `AIFunction.InvokeAsync` does not accept an `AgentSkill` parameter, so `AgentFileSkillScript` must capture its owning skill via an internal setter during construction. This adds a construction-order dependency: the skill must set the back-reference on its scripts.
- **Custom script types lose automatic skill access.** Third-party `AIFunction` subclasses that need the owning skill must implement their own mechanism (e.g., constructor injection, closure capture) instead of receiving it as a method parameter.
- **Semantic overloading.** `AIFunction` now means both "a tool the model can call" and "a script within a skill", which could blur the distinction for framework users.
## Resource Representation: `AgentSkillResource` vs `AIFunction`
Two approaches were considered for representing skill resources (supplementary content such as references, assets, or dynamic data):
### Option A — Custom `AgentSkillResource` abstract base class (original design)
Resources are modeled as a custom `AgentSkillResource` abstract class with `Name`, `Description`, and
- **Clear semantic distinction.** A dedicated `AgentSkillResource` type distinguishes resources (data providers) from scripts (executable actions), making the API self-documenting.
- **Purpose-built API.** `ReadAsync` communicates intent better than `InvokeAsync` for a data-access operation.
**Cons:**
- **Wrapper overhead.** `AgentInlineSkillResource` wraps `AIFunction` internally for delegate/function cases — adding a class and indirection for no behavioral difference.
- **Parallel abstraction.** `AgentSkillResource` and `AIFunction` serve overlapping purposes (named callable that returns data), creating two parallel hierarchies.
- **Friction for consumers.** Users who already have `AIFunction` instances must wrap them in `AgentInlineSkillResource`, adding ceremony.
### Option B — Reuse `AIFunction` directly
Resources are represented as `AIFunction`. `AgentSkill.Resources` returns `IReadOnlyList<AIFunction>?`.
`AgentInlineSkillResource` becomes an `AIFunction` subclass (retained as a convenience for the static-value
pattern: `new AgentInlineSkillResource("data", "name")`). `AgentFileSkillResource` becomes an `AIFunction`
subclass that reads file content.
```csharp
// AgentSkill exposes resources as AIFunction directly:
- **Fewer base types.** Eliminates the `AgentSkillResource` abstract class, reducing the public API surface.
- **Seamless interop.** Any `AIFunction` can be used as a skill resource with zero wrapping.
**Cons:**
- **Loss of semantic distinction.** Resources and scripts are now both `AIFunction`, which could make it less obvious which list a function belongs to when reading code.
- **Static values require a wrapper.** Unlike the original `ReadAsync` which could return a stored value directly, `AIFunction.InvokeAsync` implies invocation. `AgentInlineSkillResource` is retained as a convenience subclass to handle the static-value case, so this is not eliminated — just moved to a different class.
## Decision Outcome
### 1. Keep `AgentSkillResource` and `AgentSkillScript` (Option A for both sections)
We are staying with the custom `AgentSkillResource` and `AgentSkillScript` model classes instead of reusing `AIFunction`:
- **Resources have no parameters.** If a consumer provides an `AIFunction` with parameters, those parameters will never be advertised to the LLM, and the resulting call will fail.
- **Approval breaks for `AIFunction`-based representations.** When a resource or script represented by an `AIFunction` is configured with approval, the second approval invocation will not work correctly.
- **Injecting the owning skill into an `AIFunction`-based script is problematic.** Constructor injection would introduce a circular reference between the skill and the script. An internal property setter is possible but adds coupling.
### 2. Make all agent skill classes internal
All agent-skill-related classes are made `internal` to minimize the public API surface while the feature matures. We can reconsider and promote types to `public` later based on community signal.
This leaves two public entry points:
- **`AgentSkillsProvider`** — use directly when all skills come from a single source and filtering is not needed.
- **`AgentSkillsProviderBuilder`** — use when mixing skill types or when filtering support is required.
### 3. Caching at provider level
Caching of tools and instructions is implemented inside `AgentSkillsProvider` rather than as an external decorator. Recreating tools and instructions on every provider call is wasteful, and a caching decorator sitting outside the provider would not have the information needed to cache them effectively.
The `agent-framework-core` package currently bundles OpenAI and Azure OpenAI client implementations along with their dependencies (`openai`, `azure-identity`, `azure-ai-projects`, `packaging`). This makes core heavier than necessary for users who don't use OpenAI, and it conflates the core abstractions with a specific provider implementation. Additionally, the current class naming (`OpenAIResponsesClient`, `OpenAIChatClient`) is based on the underlying OpenAI API names rather than what users actually want to do, making discoverability harder for newcomers.
## Decision Drivers
- **Lightweight core**: Core should only contain abstractions, middleware infrastructure, and telemetry — no provider-specific code or dependencies.
- **Discoverability-first**: Import namespaces should guide users to the right client. `from agent_framework.openai import ...` should surface all OpenAI-related clients; `from agent_framework.azure import ...` should surface Foundry, Azure AI, and other Azure-specific classes.
- **Provider-leading naming**: The primary client name should reflect the provider, not the underlying API. The Responses API is now the recommended default for OpenAI, so its client should be called `OpenAIChatClient` (not `OpenAIResponsesClient`).
- **Clean separation of concerns**: Azure-specific deprecated wrappers belong in the azure-ai package, not in the OpenAI package.
## Considered Options
- **Keep OpenAI in core**: Simpler but keeps core heavy; doesn't help discoverability.
- **Extract OpenAI with Azure wrappers in the OpenAI package**: Keeps Azure OpenAI wrappers alongside OpenAI code, but pollutes the OpenAI package with Azure concerns.
- **Extract OpenAI, place Azure wrappers in azure-ai**: Clean separation; the OpenAI package has zero Azure dependencies; deprecated Azure wrappers live in a single file in azure-ai for easy future deletion.
## Decision Outcome
Chosen option: "Extract OpenAI, place Azure wrappers in azure-ai", because it achieves the lightest core, cleanest OpenAI package, and the most maintainable deprecation path.
Key changes:
1.**New `agent-framework-openai` package** with dependencies on `agent-framework-core`, `openai`, and `packaging` only.
2.**Class renames**: `OpenAIResponsesClient` → `OpenAIChatClient` (Responses API), `OpenAIChatClient` → `OpenAIChatCompletionClient` (Chat Completions API). Old names remain as deprecated aliases.
3.**Deprecated classes**: `OpenAIAssistantsClient`, all `AzureOpenAI*Client` classes, `AzureAIClient`, `AzureAIAgentClient`, and `AzureAIProjectAgentProvider` are marked deprecated.
4.**New `FoundryChatClient`** in azure-ai for Azure AI Foundry Responses API access, built on `RawFoundryChatClient(RawOpenAIChatClient)`.
5.**All deprecated `AzureOpenAI*` classes** consolidated into a single file (`_deprecated_azure_openai.py`) in the azure-ai package for clean future deletion.
6.**Core's `agent_framework.openai` and `agent_framework.azure` namespaces** become lazy-loading gateways, preserving backward-compatible import paths while removing hard dependencies.
7.**Unified `model` parameter** replaces `model_id` (OpenAI), `deployment_name` (Azure OpenAI), and `model_deployment_name` (Azure AI) across all client constructors. The term `model` is intentionally generic: it naturally maps to an OpenAI model name *and* to an Azure OpenAI deployment name, making it straightforward to use `OpenAIChatClient` with either OpenAI or Azure OpenAI backends (via `AsyncAzureOpenAI`). Environment variables are similarly unified (e.g., `OPENAI_MODEL` instead of separate `OPENAI_CHAT_MODEL_ID` / `OPENAI_CHAT_COMPLETION_MODEL_ID`).
8.**`FoundryAgent`** replaces the pattern of `Agent(client=AzureAIClient(...))` for connecting to pre-configured agents in Azure AI Foundry (PromptAgents and HostedAgents). The underlying `RawFoundryAgentChatClient` is an implementation detail — most users interact only with `FoundryAgent`. `AzureAIAgentClient` is separately deprecated as it refers to the V1 Agents Service API. See below for design rationale.
### Foundry Agent Design: `FoundryAgentClient` vs `FoundryAgent`
The existing `AzureAIClient` combines two concerns: CRUD lifecycle management (creating/deleting agents on the service) and runtime communication (sending messages via the Responses API). The new design removes CRUD entirely — users connect to agents that already exist in Foundry.
**Two approaches were considered:**
**Option A — `FoundryAgentClient` only (public ChatClient):**
Users compose `Agent(client=FoundryAgentClient(...), tools=[...])`. This follows the universal `Agent(client=X)` pattern used by every other provider. However, a "client" that wraps a named remote agent (with `agent_name` as a constructor param) is semantically odd — clients typically wrap a model endpoint, not a specific agent.
**Option B — `FoundryAgent` (Agent subclass) + private `_FoundryAgentChatClient` and public `RawFoundryAgentChatClient`:**
Users write `FoundryAgent(agent_name="my-agent", ...)` for the common case. Internally, `FoundryAgent` creates a `_FoundryAgentChatClient` and passes it to the standard `Agent` base class. For advanced customization, users pass `client_type=RawFoundryAgentChatClient` (or a custom subclass) to control the client middleware layers. The `Agent(client=RawFoundryAgentChatClient(...))` composition pattern still works for users who prefer it.
**Chosen option: Option B**, because:
- The common case (`FoundryAgent(...)`) is a single object with no boilerplate.
-`client_type=` gives full control over client middleware without parameter duplication — the agent forwards connection params to the client internally.
-`RawFoundryAgent(RawAgent)` and `FoundryAgent(Agent)` mirror the established `RawAgent`/`Agent` pattern.
- Runtime validation (only `FunctionTool` allowed) lives in `RawFoundryAgentChatClient._prepare_options`, ensuring it applies regardless of how the client is used — through `FoundryAgent`, `Agent(client=...)`, or any custom composition.
**Public classes:**
-`RawFoundryAgentChatClient(RawOpenAIChatClient)` — Responses API client that injects agent reference and validates tools. Extension point for custom client middleware.
-`RawFoundryAgent(RawAgent)` — Agent without agent-level middleware/telemetry.
-`FoundryAgent(AgentTelemetryLayer, AgentMiddlewareLayer, RawFoundryAgent)` — Recommended production agent.
**Internal (private):**
-`_FoundryAgentChatClient` — Full client with function invocation, chat middleware, and telemetry layers. Created automatically by `FoundryAgent`; users customize via `client_type=RawFoundryAgentChatClient` or a custom subclass.
**Deprecated:**
-`AzureAIClient` — replaced by `FoundryAgent` (which uses `FoundryAgentClient` internally).
-`AzureAIAgentClient` — refers to V1 Agents Service API, no direct replacement.
-`AzureAIProjectAgentProvider` — replaced by `FoundryAgent`.
When using `ChatClientAgent` with tools, the `FunctionInvokingChatClient` (FIC) loops multiple times — service call → tool execution → service call → … — before producing a final response. There are two points of discrepancy between how chat history is stored by the framework's `ChatHistoryProvider` and how the underlying AI service stores chat history (e.g., OpenAI Responses with `store=true`):
1.**Persistence timing**: The AI service persists messages after *each* service call within the FIC loop. The `ChatHistoryProvider` currently persists messages only once, at the *end* of the full agent run (after all FIC loop iterations complete).
2.**Trailing `FunctionResultContent` storage**: When tool calling is terminated mid-loop (e.g., via `FunctionInvokingChatClient` termination filters), the final response from the agent may contain `FunctionResultContent` that was never sent to a subsequent service call. The AI service never stores this trailing `FunctionResultContent`, but the `ChatHistoryProvider` currently stores all response content, including the trailing `FunctionResultContent`.
These discrepancies mean that a `ChatHistoryProvider`-managed conversation and a service-managed conversation can diverge in content and structure, even when processing the same interactions.
### Practical Impact: Resuming After Tool-Call Termination
Today, users of `AIAgent` get different behaviors depending on whether chat history is stored service-side or in a `ChatHistoryProvider`. This creates concrete challenges — for example, when the function call loop is terminated and the user wants to resume the conversation in a subsequent run. With service-stored history, the trailing `FunctionResultContent` is never persisted, so the last stored message is the `FunctionCallContent` from the service. With `ChatHistoryProvider`-stored history, the trailing `FunctionResultContent`*is* persisted. The user cannot know whether the last `FunctionResultContent` is in the chat history or not without inspecting the storage mechanism, making it difficult to write resumption logic that works correctly regardless of the storage backend.
### Relationship Between the Two Discrepancies
The persistence timing and `FunctionResultContent` trimming behaviors are interrelated:
- **Per-service-call persistence**: When messages are persisted after each individual service call, trailing `FunctionResultContent` trimming is unnecessary. If tool calling is terminated, the `FunctionResultContent` from the terminated call was never sent to a subsequent service call, so it is never persisted. The per-service-call approach naturally matches the service's behavior.
- **Per-run persistence**: When messages are batched and persisted at the end of the full run, trailing `FunctionResultContent` trimming becomes necessary to match the service's behavior. Without trimming, the stored history contains `FunctionResultContent` that the service would never have stored.
## Decision Drivers
- **A. Consistency**: The default behavior of `ChatHistoryProvider` should produce stored history that closely matches what the underlying AI service would store, minimizing surprise when switching between framework-managed and service-managed chat history.
- **B. Atomicity**: A run that fails mid-way through a multi-step tool-calling loop should not leave chat history in a partially-updated state, unless the user explicitly opts into that behavior.
- **C. Recoverability**: For long-running tool-calling loops, it should be possible to recover intermediate progress if the process is interrupted, rather than losing all work from the current run.
- **D. Simplicity**: The default behavior should be easy to understand and predict for most users, without requiring knowledge of the FIC loop internals.
- **E. Flexibility**: Regardless of the chosen default, users should be able to opt into the alternative behavior.
## Considered Options
- Option 1: Per-run persistence with opt-in FRC (FunctionResultContent) trimming
### Option 1: Per-run persistence with opt-in FRC trimming
Keep the current default behavior of persisting chat history only at the end of the full agent run. Add `FunctionResultContent` trimming as an opt-in behavior to improve consistency with service storage.
- Good, because runs are atomic — chat history is only updated when the full run succeeds, satisfying driver B.
- Good, because the mental model is simple: one run = one history update, satisfying driver D.
- Good, because trimming trailing `FunctionResultContent` improves consistency with service storage, partially satisfying driver A.
- Bad, because the default persistence timing still differs from the service's behavior (per-run vs. per-service-call), only partially satisfying driver A.
- Bad, because if the process crashes mid-loop, all intermediate progress from the current run is lost, not satisfying driver C.
- Bad, because this option alone does not provide a way for users to opt into per-service-call persistence, not satisfying driver E.
Introduce an optional RequirePerServiceCallChatHistoryPersistence setting to persist chat history after each individual service call within the FIC loop, matching the AI service's behavior. Trailing `FunctionResultContent` trimming is unnecessary with this approach (it is naturally handled).
- Good, because the stored history matches the service's behavior when opting in for both timing and content, fully satisfying driver A.
- Good, because intermediate progress is preserved if the process is interrupted, satisfying driver C.
- Good, because no separate `FunctionResultContent` trimming logic is needed, reducing complexity.
- Bad, because chat history may be left in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), not satisfying driver B. A subsequent run cannot proceed without manually providing the missing `FunctionResultContent`.
- Bad, because the mental model is more complex: a single run may produce multiple history updates, partially failing driver D.
- Neutral, because users can opt out to per-run persistence if they prefer atomicity, satisfying driver E.
## Decision Outcome
Chosen option: **Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)**. The existing per-run persistence behavior is retained as-is, requiring no changes from users. Per-service-call persistence is available as an opt-in feature via the `RequirePerServiceCallChatHistoryPersistence` setting. This satisfies drivers B (atomicity) and D (simplicity) for the common case, while fully satisfying driver A (consistency) for users who opt into simulated service-stored behavior. Users who need per-service-call persistence for recoverability (driver C) can enable it explicitly.
### Configuration Matrix
The behavior depends on the combination of `UseProvidedChatClientAsIs` and `RequirePerServiceCallChatHistoryPersistence`:
| `false` (default) | `false` (default) | **Per-run persistence.** Messages are persisted at the end of the full agent run via the `ChatHistoryProvider`. |
| `false` | `true` | **Per-service-call persistence (simulated).** A `PerServiceCallChatHistoryPersistingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. A sentinel `ConversationId` causes FIC to treat the conversation as service-managed. |
| `true` | `false` | **Per-run persistence.** No middleware is injected because the user has provided a custom chat client stack. Messages are persisted at the end of the run. |
| `true` | `true` | **User responsibility.** The system checks whether the custom chat client stack includes a `PerServiceCallChatHistoryPersistingChatClient`. If not, a warning is emitted — the user is expected to have added their own per-service-call persistence mechanism. End-of-run persistence is skipped. |
### Consequences
- Good, because per-run persistence is atomic by default — chat history is only updated when the full run succeeds, satisfying driver B.
- Good, because the default mental model is simple: one run = one history update, satisfying driver D.
- Good, because users who opt into `RequirePerServiceCallChatHistoryPersistence` get stored history that matches the service's behavior for both timing and content, fully satisfying driver A.
- Good, because per-service-call persistence preserves intermediate progress if the process is interrupted, satisfying driver C when opted in.
- Good, because no separate `FunctionResultContent` trimming logic is needed when per-service-call persistence is active — it is naturally handled.
- Good, because conflict detection (configurable via `ThrowOnChatHistoryProviderConflict`, `WarnOnChatHistoryProviderConflict`, `ClearOnChatHistoryProviderConflict`) prevents misconfiguration when a service returns a `ConversationId` alongside a configured `ChatHistoryProvider`.
- Bad, because per-service-call persistence (when opted in) may leave chat history in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), requiring manual recovery in rare cases.
- Neutral, because users who want per-service-call consistency can opt in via `RequirePerServiceCallChatHistoryPersistence = true`, satisfying driver E.
- Neutral, because increased write frequency from per-service-call persistence may impact performance for some storage backends; this can be mitigated with a caching decorator.
### Implementation Notes
#### Conversation ID Consistency
When `RequirePerServiceCallChatHistoryPersistence` is enabled, the `PerServiceCallChatHistoryPersistingChatClient`
decorator also updates `session.ConversationId` after each service call. This handles two scenarios:
1.**Framework-managed chat history** — the decorator sets a sentinel `ConversationId` on the response
so that `FunctionInvokingChatClient` treats the conversation as service-managed (clearing accumulated
history between iterations and not injecting duplicate `FunctionCallContent` during approval processing).
2.**Service-stored chat history** — when the service returns a real `ConversationId`, the decorator
updates `session.ConversationId` immediately after each service call, rather than deferring the update
to the end of the run. This ensures intermediate ConversationId changes are captured even if the
process is interrupted mid-loop.
For some service-stored scenarios (e.g., the Conversations API with the Responses API), there is only
one thread with one ID, so every service call returns the same ConversationId and this per-call update
makes no practical difference. Enabling `RequirePerServiceCallChatHistoryPersistence` ensures consistent
per-service-call behavior across all service types regardless of how they manage ConversationIds.
# Agent Evaluation Architecture with Azure AI Foundry Integration
## Context and Problem Statement
Azure AI Foundry provides a rich evaluation service for AI agents — built-in evaluators for agent behavior (task adherence, intent resolution), tool usage (tool call accuracy, tool selection), quality (coherence, fluency, relevance), and safety (violence, self-harm, prohibited actions). Results are viewable in the Foundry portal with dashboards and comparison views.
However, using Foundry Evals with an agent-framework agent today requires significant manual effort. Developers must:
1. Transform agent-framework's `Message`/`Content` types into the OpenAI-style agent message schema that Foundry evaluators expect
2. Map tool definitions from agent-framework's `FunctionTool` format to evaluator-compatible schemas
3. Manually wire up the correct Foundry data source type (`azure_ai_traces`, `jsonl`, `azure_ai_target_completions`, etc.) depending on their scenario
4. Handle App Insights trace ID queries, response ID collection, and eval polling
Additionally, evaluation is a concern that extends beyond any single provider. Developers may want to use local evaluators (LLM-as-judge, regex, keyword matching), third-party evaluation libraries, or multiple providers in combination. The architecture must support this without creating a Foundry-specific lock-in at the API level.
### Functional Requirements for Agent Evaluation
- **Single agents and workflows.** Evaluate both individual agent responses and multi-agent workflow results, with per-agent breakdown to pinpoint underperformance.
- **One-shot and multi-turn conversations.** Capture full conversation trajectories — including tool calls and results — not just final query/response pairs.
- **Conversation factoring.** Support splitting conversations into query/response in multiple ways (last turn, full trajectory, per-turn) because different factorings measure different things.
- **Multiple providers, mix and match.** Run Foundry LLM-as-judge evaluators alongside fast local checks and custom evaluators on the same data, without restructuring code.
- **Third-party extensibility.** Any evaluation library can participate by implementing the `Evaluator` protocol (Python) or `IAgentEvaluator` interface (.NET). No predetermined list of supported libraries — the protocol is intentionally simple (`evaluate(items) → results`) so that wrappers for libraries like DeepEval, RAGAS, or Promptfoo are straightforward to write.
- **Bring your own evaluator.** Creating a custom evaluator should be as simple as writing a function.
- **Evaluate without re-running.** Evaluate existing responses from logs or previous runs without invoking the agent again.
## Decision Drivers
- **Zero-friction evaluation**: Developers should go from "I have an agent" to "I have eval results" with minimal code.
- **Provider-agnostic API**: Core evaluation capabilities must not be tied to any specific provider. Provider configuration should be separate from the evaluation call.
- **Lowest concept count**: Introduce the fewest possible new types, abstractions, and APIs for developers to learn.
- **Leverage existing knowledge**: The framework already knows which agents exist, what tools they have, and what conversations occurred. Evals should use this automatically rather than requiring the developer to re-specify it.
- **Foundry-native results**: When using Foundry, results should be viewable in the Foundry portal with dashboards and comparison views.
- **Progressive disclosure**: Simple scenarios should be near-zero code. Advanced scenarios should build on the same primitives.
- **Cross-language parity**: Design must be implementable in both Python and .NET.
## Considered Options
1.**Provider-specific functions** — Build Foundry-specific helper functions (`evaluate_agent()`, etc.) directly in the Azure package. All eval functions take Foundry connection parameters.
2.**Evaluator protocol with shared orchestration** — Define a provider-agnostic `Evaluator` protocol in the base agent library (`agent_framework` in Python, `Microsoft.Agents.AI` in .NET). Orchestration functions live alongside it. Providers implement the protocol.
3.**Full eval framework** — Build comprehensive eval infrastructure including custom evaluator definitions, scoring profiles, and reporting inside agent-framework.
## Decision Outcome
Proposed option: "Evaluator protocol with shared orchestration", because it delivers the low-friction developer experience, supports multiple providers without API changes, and keeps the concept count low.
### Usage Examples
#### Evaluate an agent
The agent is invoked once per query by default. For statistically meaningful evaluation, provide multiple diverse queries. For measuring **consistency** (does the same query produce reliable results?), use `num_repetitions` to run each query N times independently:
Each `AgentResponse` already contains the conversation (query + response), so the evaluator extracts query/response from the conversation. When you pass `responses` without `queries`, the conversation is the source of truth.
#### Evaluate with conversation split strategies
By default, evaluators see only the last turn (final user message → final assistant response). For multi-turn conversations, you can control how the conversation is factored for evaluation:
`@evaluator` uses **parameter name injection** — the function's parameter names determine what data it receives from the `EvalItem`. Supported names: `query`, `response`, `expected`, `expected_tool_calls`, `conversation`, `tools`, `context`. Any combination is valid.
query:str# property — derived from conversation split
response:str# property — derived from conversation split
```
`conversation` is the single source of truth. `query` and `response` are derived properties — splitting the conversation at the last user message (default) and extracting text from each side. Changing the `split_strategy` consistently changes all derived values.
`tools` provides typed `FunctionTool` objects — including MCP tools, which are automatically extracted after agent runs.
### Internal: AgentEvalConverter
Internal class that converts agent-framework types to `EvalItem`. Used by `evaluate_agent()` and `evaluate_workflow()` — not part of the public API:
| Agent Framework | Eval Format |
|---|---|
| `Content.function_call` | `tool_call` in OpenAI chat format |
| `Content.function_result` | `tool_result` in OpenAI chat format |
results.report_url# str | None: portal link (Foundry)
results.assert_passed()# raises AssertionError with details
```
### Core: Orchestration Functions
Provider-agnostic functions that extract data and delegate to evaluators:
| Function | What it does |
|---|---|
| `evaluate_agent()` | Runs agent against test queries (or evaluates pre-existing `responses=`), converts to `EvalItem`s, passes to evaluator. Accepts optional `expected_output=` for ground-truth comparison, `expected_tool_calls=` for tool-correctness evaluation, and `num_repetitions=` for consistency measurement |
| `evaluate_workflow()` | Extracts per-agent data from `WorkflowRunResult`, evaluates each agent and overall output. Per-agent breakdown in `sub_results`. Also accepts `num_repetitions=` |
### Core: Conversation Split Strategies
Multi-turn conversations must be split into query (input) and response (output) halves for evaluation. How you split determines *what you're evaluating*:
**Last-turn split** — split at the last user message. Everything up to and including it is the query context; the agent's subsequent actions are the response:
This evaluates: "Given all the context so far, did the agent answer the latest question well?" Best for response quality at a specific point in the conversation.
**Full-conversation split** — the first user message is the query; everything after is the response:
This evaluates: "Given the original request, did the entire conversation trajectory serve the user?" Best for task completion and overall conversation quality.
**Per-turn split** — produces N eval items from an N-turn conversation. Each turn is evaluated with its cumulative context:
This evaluates each response independently. Best for fine-grained analysis and pinpointing where a conversation goes wrong.
These factorings produce different scores for the same conversation. The framework ships all three as built-in strategies, defaulting to last-turn. Developers can also provide a custom splitter — a function (Python) or `IConversationSplitter` implementation (.NET) — and override the strategy at the call site or per evaluator.
### Azure AI: FoundryEvals
`Evaluator` implementation backed by Azure AI Foundry:
| `evaluate_traces()` | Evaluate from stored response IDs or OTel traces |
| `evaluate_foundry_target()` | Evaluate a Foundry-registered agent or deployment |
### Core: LocalEvaluator and Function Evaluators
`LocalEvaluator` implements the `Evaluator` protocol for fast, API-free evaluation. It runs check functions locally — useful for inner-loop development, CI smoke tests, and combining with cloud-based evaluators.
Built-in checks:
-`keyword_check(*keywords)` — response must contain specified keywords
-`tool_called_check(*tool_names)` — agent must have called specified tools
-`tool_calls_present` — all `expected_tool_calls` names appear in conversation (unordered, extras OK)
-`tool_call_args_match` — expected tool calls match on name + arguments (subset match on args)
Custom function evaluators use `@evaluator` to wrap plain Python functions. The function's **parameter names** determine what data it receives from the `EvalItem`:
```python
fromagent_frameworkimportevaluator,LocalEvaluator
# Tier 1: Simple check — just query + response
@evaluator
defis_concise(response:str)->bool:
returnlen(response.split())<500
# Tier 2: Ground truth — compare against expected output
Return types: `bool`, `float` (≥0.5 = pass), `dict` with `score` or `passed` key, or `CheckResult`.
Async functions are handled automatically — `@evaluator` detects `async def` and produces the right wrapper.
### Example: GAIA Benchmark
[GAIA](https://huggingface.co/gaia-benchmark) tests real-world multi-step tasks with known expected answers. Each task has a question and a ground-truth answer, with optional file attachments. The framework accommodates GAIA's knobs (difficulty levels, file inputs, multi-step tool use) through the existing `EvalItem` fields:
- Azure-AI re-exports core types for convenience (Python)
## Known Limitations
1.**Tool evaluators require query + agent**: Tool evaluators need tool definition schemas. When using these evaluators with `evaluate_agent(responses=...)`, provide `queries=` and pass an agent with tool definitions.
2.**`model_deployment` always required**: Could potentially be inferred from the Foundry project configuration.
## Open Questions
1.**Red teaming non-registered agents**: Requires Foundry API support for callback-based flows.
2.**Datasets with expected outputs**: A dataset abstraction for pre-populating `expected_output` values across eval runs is a natural next step but not yet designed.
3.**Multi-modal evaluation**: The `conversation` field on `EvalItem` already stores full `Message`/`Content` (Python) and `ChatMessage` (.NET) objects, which can represent multi-modal content (images, audio, structured data). Evaluators that accept the full `EvalItem` or `conversation` parameter can access this content today. However, the convenience shortcuts — `query`/`response` string projections and the `FunctionEvaluator` string overloads — are text-only. Multi-modal-aware evaluators should use the full-item path (`Func<EvalItem, CheckResult>` in .NET, `conversation: list` parameter in Python).
## .NET Implementation Design
### Key Difference: MEAI Ecosystem
Unlike Python, the .NET ecosystem already has `Microsoft.Extensions.AI.Evaluation` (v10.3.0) providing:
-`IEvaluator` — per-item evaluation of `(messages, chatResponse) → EvaluationResult`
The .NET integration uses MEAI's `IEvaluator` directly — no new evaluator interface. Our contribution is the **orchestration layer**: extension methods that run agents, extract data, call `IEvaluator` per item, and aggregate results.
All evaluators implement MEAI's `IEvaluator`. The orchestration layer doesn't need to know which kind — it calls `EvaluateAsync(messages, chatResponse)` per item on all of them. `FoundryEvals` handles batching internally (buffers items, submits once, returns per-item results).
### .NET Core Types
**No new evaluator interface.** Use MEAI's `IEvaluator` directly.
**`AgentEvaluationResults`** — The only new type. Aggregates per-item MEAI `EvaluationResult`s across a batch of queries:
```csharp
publicclassAgentEvaluationResults
{
publicstringProvider{get;init;}
publicstring?ReportUrl{get;init;}
// Per-item — standard MEAI EvaluationResult, unchanged
**`LocalEvaluator`** — Runs lambda checks locally, returns `BooleanMetric` per check:
```csharp
varlocal=newLocalEvaluator(
FunctionEvaluator.Create("is_concise",
(stringresponse)=>response.Split().Length<500),
EvalChecks.KeywordCheck("weather"),
EvalChecks.ToolCalledCheck("get_weather"));
```
**MEAI evaluators** — Used directly, no adapter needed:
```csharp
varquality=newCompositeEvaluator(
newRelevanceEvaluator(),
newCoherenceEvaluator());
```
**`FoundryEvals`** — Implements `IEvaluator` but batches internally. On first call, buffers the item. On the last item (or when explicitly flushed), submits the batch to Foundry and distributes per-item results:
`EvalItem` is a lightweight record used only by `FunctionEvaluator` and `LocalEvaluator` to pass context to check functions. It is not part of the `IEvaluator` interface:
dotnet test tests/Microsoft.Agents.AI.<Package>.UnitTests
dotnet test --project tests/Microsoft.Agents.AI.<Package>.UnitTests
dotnet format src/Microsoft.Agents.AI.<Package>
# Run a single test
dotnet test --filter "FullyQualifiedName~Namespace.TestClassName.TestMethodName"
# Replace the filter values with the appropriate assembly, namespace, class, and method names for the test you want to run and use * as a wildcard elsewhere, e.g. "/*/*/HttpClientTests/GetAsync_ReturnsSuccessStatusCode"
# Use `--ignore-exit-code 8` to avoid failing the build when no tests are found for some projects
dotnet test --filter-query "/<assemblyFilter>/<namespaceFilter>/<classFilter>/<methodFilter>" --ignore-exit-code 8
# Run unit tests only
dotnet test --filter FullyQualifiedName\~UnitTests
# Use `--ignore-exit-code 8` to avoid failing the build when no tests are found for integration test projects
dotnet test --filter-query "/*UnitTests*/*/*/*" --ignore-exit-code 8
```
Use `--tl:off` when building to avoid flickering when running commands in the agent.
@@ -56,7 +59,7 @@ Example: Running tests for a single project using .NET 10.
```bash
# From dotnet/ directory
dotnet test ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0
dotnet test --project ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0
```
Example: Running a single test in a specific project using .NET 10.
@@ -64,7 +67,7 @@ Provide the full namespace, class name, and method name for the test you want to
```bash
# From dotnet/ directory
dotnet test ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0 --filter"FullyQualifiedName~Microsoft.Agents.AI.Abstractions.UnitTests.AgentRunOptionsTests.CloningConstructorCopiesProperties"
dotnet test --project ./tests/Microsoft.Agents.AI.Abstractions.UnitTests -f net10.0 --filter-query "/*/Microsoft.Agents.AI.Abstractions.UnitTests/AgentRunOptionsTests/CloningConstructorCopiesProperties"
```
### Multi-target framework tip
@@ -83,3 +86,45 @@ Just remember to run `dotnet restore` after pulling changes, making changes to p
Unit tests target both .NET Framework as well as .NET Core. When running on Linux, only the .NET Core tests can be run, as .NET Framework is not supported on Linux.
To run only the .NET Core tests, use the `-f net10.0` option with `dotnet test`.
### Microsoft Testing Platform (MTP)
Tests use the [Microsoft Testing Platform](https://learn.microsoft.com/dotnet/core/testing/unit-testing-platform-intro) via xUnit v3. Key differences from the legacy VSTest runner:
- **`dotnet test` requires `--project`** to specify a test project directly (positional arguments are no longer supported).
- **Test output** uses the MTP format (e.g., `[✓112/x0/↓0]` progress and `Test run summary: Passed!`).
- **TRX reports** use `--report-xunit-trx` instead of `--logger trx`.
- **Code coverage** uses `Microsoft.Testing.Extensions.CodeCoverage` with `--coverage --coverage-output-format cobertura`.
- **Running a test project directly** is supported via `dotnet run --project <test-project>`. This bypasses the `dotnet test` infrastructure and runs the test executable directly with the MTP command line.
- **Running tests across the solution** with a filter may cause some projects to match zero tests, which MTP treats as a failure (exit code 8). Use `--ignore-exit-code 8` to suppress this:
```bash
# Run all unit tests across the solution, ignoring projects with no matching tests
dotnet test --solution ./agent-framework-dotnet.slnx --no-build -f net10.0 --ignore-exit-code 8
```
- **Running tests with `--solution` for a specific TFM** requires all projects in the solution to support that TFM. Not all projects target every framework (e.g., some are `net10.0`-only). Use `./dotnet/eng/scripts/New-FilteredSolution.ps1` to generate a filtered solution:
```powershell
# Generate a filtered solution for net472 and run tests
description:How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
---
# verify-samples Tool
The `verify-samples` project (`dotnet/eng/verify-samples/`) is an automated tool that runs sample projects and verifies their output using deterministic checks and AI-powered verification.
## Running verify-samples
```bash
cd dotnet
# Run all samples across all categories
dotnet run --project eng/verify-samples -- --log results.log --csv results.csv
# Run a specific category
dotnet run --project eng/verify-samples -- --category 02-agents --log results.log
# Run specific samples by name
dotnet run --project eng/verify-samples -- Agent_Step02_StructuredOutput Agent_Step09_AsFunctionTool
# Control parallelism (default 8)
dotnet run --project eng/verify-samples -- --parallel 8 --log results.log
-`AZURE_OPENAI_ENDPOINT` — for the AI verification agent
-`AZURE_OPENAI_DEPLOYMENT_NAME` (optional, defaults to `gpt-5-mini`)
Individual samples require their own env vars (e.g., `AZURE_AI_PROJECT_ENDPOINT`). The tool automatically checks and skips samples with missing env vars.
### Output Files
-`--log results.log` — detailed per-sample log with stdout/stderr, AI reasoning, and a summary
-`--csv results.csv` — tabular summary with Sample, ProjectPath, Status, FailedChecks, and Failures columns
-`--md results.md` — Markdown summary with results table and collapsible failure details (suitable for GitHub PR comments)
## Sample Categories
Definitions are in the `dotnet/eng/verify-samples/` directory:
// Skip this sample with a reason (for structural issues only)
SkipReason=null,// or "Requires external service X."
// Deterministic checks: substrings that must appear in stdout
MustContain=["=== Section Header ==="],
// Substrings that must NOT appear in stdout
MustNotContain=[],
// If true, only MustContain checks are used (no AI verification)
IsDeterministic=false,
// AI verification: natural-language descriptions of expected output
// Each entry describes one aspect to verify independently
ExpectedOutputDescription=
[
"The output should show structured person information with Name, Age, and Occupation fields.",
"The output should not contain error messages or stack traces.",
],
// Stdin inputs to feed to the sample (for interactive samples)
Inputs=["Y","Y","Y"],
// Delay between stdin inputs in ms (default 2000, increase for LLM calls between inputs)
InputDelayMs=3000,
}
```
## How to Add a New Sample Definition
1.**Check the sample's Program.cs** to understand:
- What environment variables it reads (look for `GetEnvironmentVariable`)
- Whether it needs stdin input (look for `Console.ReadLine`, `Application.GetInput`)
- Whether it has an external loop (look for `EXIT` patterns in YAML workflows)
- What output it produces (section headers, markers, expected behavior)
- Whether it exits on its own or runs as a server
2.**Choose the right verification strategy:**
- **Deterministic** (`IsDeterministic = true`): Use `MustContain` for samples with fixed output strings. No AI verification.
- **AI-verified** (default): Use `ExpectedOutputDescription` with semantic descriptions. Write expectations that are flexible enough for non-deterministic LLM output.
- **Both**: Use `MustContain` for fixed markers AND `ExpectedOutputDescription` for LLM-generated content.
3.**Set `SkipReason` only for structural issues:**
@@ -29,13 +29,14 @@ using types like `IChatClient`, `FunctionInvokingChatClient`, `AITool`, `AIFunct
## Key Conventions
- **Encoding**: All new files must be saved with UTF-8 encoding with BOM (Byte Order Mark). This is required for `dotnet format` to work correctly.
- **Command output capture**: When running `dotnet build`, `dotnet test`, `dotnet format`, or similar commands, redirect output to a temp file first (e.g., `dotnet build --tl:off 2>&1 | Out-File $env:TEMP\build.log`), then analyze the file as needed. This avoids re-running expensive commands when the initial analysis misses something.
- **Encoding**: All new files must be saved with UTF-8 encoding with BOM (Byte Order Mark). This is required for `dotnet format` to work correctly. When using PowerShell `Set-Content`, always pass `-Encoding UTF8BOM` to preserve the BOM (e.g., `Set-Content $file $content -NoNewline -Encoding UTF8BOM`).
- **Copyright header**: `// Copyright (c) Microsoft. All rights reserved.` at top of all `.cs` files
- **XML docs**: Required for all public methods and classes
- **Async**: Use `Async` suffix for methods returning `Task`/`ValueTask`
- **Private classes**: Should be `sealed` unless subclassed
- **Config**: Read from environment variables with `UPPER_SNAKE_CASE` naming
- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking
- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking; test methods returning `Task`/`ValueTask` must use the `Async` suffix.
">> Use deserialized session with previously created memories",
">> Read memories using memory component",
"MEMORY - User Name:",
"MEMORY - User Age:",
">> Use new session with previously created memories",
],
ExpectedOutputDescription=
[
"In the 'Use session with blank memory' section, the agent should respond to the user's messages. It may ask for the user's name or age if not yet known.",
"In the 'Use deserialized session with previously created memories' section, the agent should correctly recall that the user's name is Ruaidhrí and age is 20.",
"The 'MEMORY - User Name:' line should show 'Ruaidhrí' (or a close transliteration).",
"The 'MEMORY - User Age:' line should show '20'.",
"In the 'Use new session with previously created memories' section, the agent should know the user's name and age from the transferred memory.",
"The output should not contain error messages or stack traces.",
ExpectedOutputDescription=["The output should show a customer support workflow processing a laptop issue, with agent responses providing troubleshooting or support."],
ExpectedOutputDescription=["The output should show a declarative workflow executing generated code, processing a math question and producing a result."],
ExpectedOutputDescription=["The output should show a workflow calling function tools (e.g. a menu plugin) to answer a question about restaurant specials."],
ExpectedOutputDescription=["The output should show a workflow invoking a function tool (e.g. a menu plugin) to answer a question about the soup of the day."],
Inputs=["Search for .NET tutorials on Microsoft Learn"],
InputDelayMs=3000,
ExpectedOutputDescription=["The output should show a workflow using MCP tools to search Microsoft Learn documentation and provide a summary of results."],
ExpectedOutputDescription=["The output should show a student-teacher workflow where a student asks a math question and a teacher provides the answer."],
ExpectedOutputDescription=["The output should show a workflow using an MCP tool with approval to search Microsoft Learn, followed by an exit from the input loop."],
@@ -121,7 +109,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
// Track approval ID to original call ID mapping
_=newDictionary<string,string>();
#pragmawarningdisableMEAI001// Type is for evaluation purposes only and is subject to change or removal in future updates. Suppress this diagnostic to proceed.
@@ -5,8 +5,8 @@ This sample demonstrates how to create an AIAgent using Anthropic Claude models
The sample supports three deployment scenarios:
1.**Anthropic Public API** - Direct connection to Anthropic's public API
2.**Azure Foundry with API Key** - Anthropic models deployed through Azure Foundry using API key authentication
3.**Azure Foundry with Azure CLI** - Anthropic models deployed through Azure Foundry using Azure CLI credentials
2.**Microsoft Foundry with API Key** - Anthropic models deployed through Microsoft Foundry using API key authentication
3.**Microsoft Foundry with Azure CLI** - Anthropic models deployed through Microsoft Foundry using Azure CLI credentials
## Prerequisites
@@ -25,29 +25,29 @@ $env:ANTHROPIC_API_KEY="your-anthropic-api-key" # Replace with your Anthropic A
$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"# Optional, defaults to claude-haiku-4-5
```
### For Azure Foundry with API Key
### For Microsoft Foundry with API Key
-Azure Foundry service endpoint and deployment configured
-Microsoft Foundry service endpoint and deployment configured
- Anthropic API key
Set the following environment variables:
```powershell
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"# Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"# Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_API_KEY="your-anthropic-api-key"# Replace with your Anthropic API key
$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"# Optional, defaults to claude-haiku-4-5
```
### For Azure Foundry with Azure CLI
### For Microsoft Foundry with Azure CLI
-Azure Foundry service endpoint and deployment configured
-Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
Set the following environment variables:
```powershell
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"# Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"# Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
$env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"# Optional, defaults to claude-haiku-4-5
```
**Note**: When using Azure Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
**Note**: When using Microsoft Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
Before you begin, ensure you have the following prerequisites:
- .NET 10 SDK or later
-Azure Foundry service endpoint and deployment configured
-Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
Set the following environment variables:
```powershell
$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"# Replace with your Azure Foundry resource endpoint
$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"# Replace with your Microsoft Foundry resource endpoint
$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"# Optional, defaults to gpt-4o-mini
```
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