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Python: [BREAKING] Moved to a single get_response and run API (#3379)

Browse Source 
* WIP

* big update to new ResponseStream model

* fixed tests and typing

* fixed tests and typing

* fixed tools typevar import

* fix

* mypy fix

* mypy fixes and some cleanup

* fix missing quoted names

* and client

* fix  imports agui

* fix anthropic override

* fix agui

* fix ag ui

* fix import

* fix anthropic types

* fix mypy

* refactoring

* updated typing

* fix 3.11

* fixes

* redid layering of chat clients and agents

* redid layering of chat clients and agents

* Fix lint, type, and test issues after rebase

- Add @overload decorators to AgentProtocol.run() for type compatibility
- Add missing docstring params (middleware, function_invocation_configuration)
- Fix TODO format (TD002) by adding author tags
- Fix broken observability tests from upstream:
  - Replace non-existent use_instrumentation with direct instantiation
  - Replace non-existent use_agent_instrumentation with AgentTelemetryLayer mixin
  - Fix get_streaming_response to use get_response(stream=True)
  - Add AgentInitializationError import
  - Update streaming exception tests to match actual behavior

* Fix AgentExecutionException import error in test_agents.py

- Replace non-existent AgentExecutionException with AgentRunException

* Fix test import and asyncio deprecation issues

- Add 'tests' to pythonpath in ag-ui pyproject.toml for utils_test_ag_ui import
- Replace deprecated asyncio.get_event_loop().run_until_complete with asyncio.run

* Fix azure-ai test failures

- Update _prepare_options patching to use correct class path
- Fix test_to_azure_ai_agent_tools_web_search_missing_connection to clear env vars

* Convert ag-ui utils_test_ag_ui.py to conftest.py

- Move test utilities to conftest.py for proper pytest discovery
- Update all test imports to use conftest instead of utils_test_ag_ui
- Remove old utils_test_ag_ui.py file
- Revert pythonpath change in pyproject.toml

* fix: use relative imports for ag-ui test utilities

* fix agui

* Rename Bare*Client to Raw*Client and BaseChatClient

- Renamed BareChatClient to BaseChatClient (abstract base class)
- Renamed BareOpenAIChatClient to RawOpenAIChatClient
- Renamed BareOpenAIResponsesClient to RawOpenAIResponsesClient
- Renamed BareAzureAIClient to RawAzureAIClient
- Added warning docstrings to Raw* classes about layer ordering
- Updated README in samples/getting_started/agents/custom with layer docs
- Added test for span ordering with function calling

* Fix layer ordering: FunctionInvocationLayer before ChatTelemetryLayer

This ensures each inner LLM call gets its own telemetry span, resulting in
the correct span sequence: chat -> execute_tool -> chat

Updated all production clients and test mocks to use correct ordering:
- ChatMiddlewareLayer (first)
- FunctionInvocationLayer (second)
- ChatTelemetryLayer (third)
- BaseChatClient/Raw...Client (fourth)

* Remove run_stream usage

* Fix conversation_id propagation

* Python: Add BaseAgent implementation for Claude Agent SDK (#3509)

* Added ClaudeAgent implementation

* Updated streaming logic

* Small updates

* Small update

* Fixes

* Small fix

* Naming improvements

* Updated imports

* Addressed comments

* Updated package versions

* Update Claude agent connector layering

* fix test and plugin

* Store function middleware in invocation layer

* Fix telemetry streaming and ag-ui tests

* Remove legacy ag-ui tests folder

* updates

* Remove terminate flag from FunctionInvocationContext, use MiddlewareTermination instead

- Remove terminate attribute from FunctionInvocationContext
- Add result attribute to MiddlewareTermination to carry function results
- FunctionMiddlewarePipeline.execute() now lets MiddlewareTermination propagate
- _auto_invoke_function captures context.result in exception before re-raising
- _try_execute_function_calls catches MiddlewareTermination and sets should_terminate
- Fix handoff middleware to append to chat_client.function_middleware directly
- Update tests to use raise MiddlewareTermination instead of context.terminate
- Add middleware flow documentation in samples/concepts/tools/README.md
- Fix ag-ui to use FunctionMiddlewarePipeline instead of removed create_function_middleware_pipeline

* fix: remove references to removed terminate flag in purview tests, add type ignore

* fix: move _test_utils.py from package to test folder

* fix: call get_final_response() to trigger context provider notification in streaming test

* fix: correct broken links in tools README

* docs: clarify default middleware behavior in summary table

* fix: ensure inner stream result hooks are called when using map()/from_awaitable()

* Fix mypy type errors

* Address PR review comments on observability.py

- Remove TODO comment about unconsumed streams, add explanatory note instead
- Remove redundant _close_span cleanup hook (already called in _finalize_stream)
- Clarify behavior: cleanup hooks run after stream iteration, if stream is not
  consumed the span remains open until garbage collected

* Remove gen_ai.client.operation.duration from span attributes

Duration is a metrics-only attribute per OpenTelemetry semantic conventions.
It should be recorded to the histogram but not set as a span attribute.

* Remove duration from _get_response_attributes, pass directly to _capture_response

Duration is a metrics-only attribute. It's now passed directly to _capture_response
instead of being included in the attributes dict that gets set on the span.

* Remove redundant _close_span cleanup hook in AgentTelemetryLayer

_finalize_stream already calls _close_span() in its finally block,
so adding it as a separate cleanup hook is redundant.

* Use weakref.finalize to close span when stream is garbage collected

If a user creates a streaming response but never consumes it, the cleanup
hooks won't run. Now we register a weak reference finalizer that will close
the span when the stream object is garbage collected, ensuring spans don't
leak in this scenario.

* Fix _get_finalizers_from_stream to use _result_hooks attribute

Renamed function to _get_result_hooks_from_stream and fixed it to
look for the _result_hooks attribute which is the correct name in
ResponseStream class.

* Add missing asyncio import in test_request_info_mixin.py

* Fix leftover merge conflict marker in image_generation sample

* Update integration tests

* Fix integration tests: increase max_iterations from 1 to 2

Tests with tool_choice options require at least 2 iterations:
1. First iteration to get function call and execute the tool
2. Second iteration to get the final text response

With max_iterations=1, streaming tests would return early with only
the function call/result but no final text content.

* Fix duplicate function call error in conversation-based APIs

When using conversation_id (for Responses/Assistants APIs), the server
already has the function call message from the previous response. We
should only send the new function result message, not all messages
including the function call which would cause a duplicate ID error.

Fix: When conversation_id is set, only send the last message (the tool
result) instead of all response.messages.

* Add regression test for conversation_id propagation between tool iterations

Port test from PR #3664 with updates for new streaming API pattern.
Tests that conversation_id is properly updated in options dict during
function invocation loop iterations.

* Fix tool_choice=required to return after tool execution

When tool_choice is 'required', the user's intent is to force exactly one
tool call. After the tool executes, return immediately with the function
call and result - don't continue to call the model again.

This fixes integration tests that were failing with empty text responses
because with tool_choice=required, the model would keep returning function
calls instead of text.

Also adds regression tests for:
- conversation_id propagation between tool iterations (from PR #3664)
- tool_choice=required returns after tool execution

* Document tool_choice behavior in tools README

- Add table explaining tool_choice values (auto, none, required)
- Explain why tool_choice=required returns immediately after tool execution
- Add code example showing the difference between required and auto
- Update flow diagram to show the early return path for tool_choice=required

* Fix tool_choice=None behavior - don't default to 'auto'

Remove the hardcoded default of 'auto' for tool_choice in ChatAgent init.
When tool_choice is not specified (None), it will now not be sent to the
API, allowing the API's default behavior to be used.

Users who want tool_choice='auto' can still explicitly set it either in
default_options or at runtime.

Fixes #3585

* Fix tool_choice=none should not remove tools

In OpenAI Assistants client, tools were not being sent when
tool_choice='none'. This was incorrect - tool_choice='none' means
the model won't call tools, but tools should still be available
in the request (they may be used later in the conversation).

Fixes #3585

* Add test for tool_choice=none preserving tools

Adds a regression test to ensure that when tool_choice='none' is set but
tools are provided, the tools are still sent to the API. This verifies
the fix for #3585.

* Fix tool_choice=none should not remove tools in all clients

Apply the same fix to OpenAI Responses client and Azure AI client:
- OpenAI Responses: Remove else block that popped tool_choice/parallel_tool_calls
- Azure AI: Remove tool_choice != 'none' check when adding tools

When tool_choice='none', the model won't call tools, but tools should
still be sent to the API so they're available for future turns.

Also update README to clarify tool_choice=required supports multiple tools.

Fixes #3585

* Keep tool_choice even when tools is None

Move tool_choice processing outside of the 'if tools' block in OpenAI
Responses client so tool_choice is sent to the API even when no tools
are provided.

* Update test to match new parallel_tool_calls behavior

Changed test_prepare_options_removes_parallel_tool_calls_when_no_tools to
test_prepare_options_preserves_parallel_tool_calls_when_no_tools to reflect
that parallel_tool_calls is now preserved even when no tools are present,
consistent with the tool_choice behavior.

* Fix ChatMessage API and Role enum usage after rebase

- Update ChatMessage instantiation to use keyword args (role=, text=, contents=)
- Fix Role enum comparisons to use .value for string comparison
- Add created_at to AgentResponse in error handling
- Fix AgentResponse.from_updates -> from_agent_run_response_updates
- Fix DurableAgentStateMessage.from_chat_message to convert Role enum to string
- Add Role import where needed

* Fix additional ChatMessage API and method name changes

- Fix ChatMessage usage in workflow files (use text= instead of contents= for strings)
- Fix AgentResponse.from_updates -> from_agent_run_response_updates in workflow files
- Fix test files for ChatMessage and Role enum usage

* Fix remaining ChatMessage API usage in test files

* Fix more ChatMessage and Role API changes in source and test files

- Fix ChatMessage in _magentic.py replan method
- Fix Role enum comparison in test assertions
- Fix remaining test files with old ChatMessage syntax

* Fix ChatMessage and Role API changes across packages

- Add Role import where missing
- Fix ChatMessage signature: positional args to keyword args (role=, text=, contents=)
- Fix Role enum comparisons: .role.value instead of .role string
- Fix FinishReason enum usage in ag-ui event converters
- Rename AgentResponse.from_updates to from_agent_run_response_updates in ag-ui

Fixes API compatibility after Types API Review improvements merge

* Fix ChatMessage and Role API changes in github_copilot tests

* Fix ChatMessage and Role API changes in redis and github_copilot packages

- Fix redis provider: Role enum comparison using .value
- Fix redis tests: ChatMessage signature and Role comparisons
- Fix github_copilot tests: ChatMessage signature and Role comparisons
- Update docstring examples in redis chat message store

* Fix ChatMessage and Role API changes in devui package

- Fix executor: ChatMessage signature change
- Fix conversations: Role enum to string conversion in two places
- Fix tests: ChatMessage signatures and Role comparisons

* Fix ChatMessage and Role API changes in a2a and lab packages

- Fix a2a tests: Role comparisons and ChatMessage signatures
- Fix lab tau2 source: Role enum comparison in flip_messages, log_messages, sliding_window
- Fix lab tau2 tests: ChatMessage signatures and Role comparisons

* Remove duplicate test files from ag-ui/tests (tests are in ag_ui_tests)

* Fix ChatMessage and Role API changes across packages

After rebasing on upstream/main which merged PR #3647 (Types API Review
improvements), fix all packages to use the new API:

- ChatMessage: Use keyword args (role=, text=, contents=) instead of
  positional args
- Role: Compare using .value attribute since it's now an enum

Packages fixed:
- ag-ui: Fixed Role value extraction bugs in _message_adapters.py
- anthropic: Fixed ChatMessage and Role comparisons in tests
- azure-ai: Fixed Role comparison in _client.py
- azure-ai-search: Fixed ChatMessage and Role in source/tests
- bedrock: Fixed ChatMessage signatures in tests
- chatkit: Fixed ChatMessage and Role in source/tests
- copilotstudio: Fixed ChatMessage and Role in tests
- declarative: Fixed ChatMessage in _executors_agents.py
- mem0: Fixed ChatMessage and Role in source/tests
- purview: Fixed ChatMessage in source/tests

* Fix mypy errors for ChatMessage and Role API changes

- durabletask: Use str() fallback in role value extraction
- core: Fix ChatMessage in _orchestrator_helpers.py to use keyword args
- core: Add type ignore for _conversation_state.py contents deserialization
- ag-ui: Fix type ignore comments (call-overload instead of arg-type)
- azure-ai-search: Fix get_role_value type hint to accept Any
- lab: Move get_role_value to module level with Any type hint

* Improve CI test timeout configuration

- Increase job timeout from 10 to 15 minutes
- Reduce per-test timeout to 60s (was 900s/300s)
- Add --timeout_method thread for better timeout handling
- Add --timeout-verbose to see which tests are slow
- Reduce retries from 3 to 2 and delay from 10s to 5s

This ensures individual test timeouts are shorter than the job
timeout, providing better visibility when tests hang.

With 60s timeout and 2 retries, worst case per test is ~180s.

* Fix ChatMessage API usage in docstrings and source

- Fix ChatMessage positional args in docstrings: _serialization.py, _threads.py, _middleware.py
- Fix ChatMessage in tau2 runner.py
- Fix role comparison in _orchestrator_helpers.py to use .value
- Fix role comparison in _group_chat.py docstring example
- Fix role assertions in test_durable_entities.py to use .value

* Revert tool_choice/parallel_tool_calls changes - must be removed when no tools

OpenAI API requires tool_choice and parallel_tool_calls to only be
present when tools are specified. Restored the logic that removes
these options when there are no tools.

- Restored check in _chat_client.py to remove tool_choice and
  parallel_tool_calls when no tools present
- Restored same logic in _responses_client.py
- Reverted test to expect the correct behavior

* fixed issue in tests

* fix: resolve merge conflict markers in ag-ui tests

* fix: restructure ag-ui tests and fix Role/FinishReason to use string types

* fix: streaming function invocation and middleware termination

- Refactor streaming function invocation to use get_final_response() on inner streams
- Fix MiddlewareTermination to accept result parameter for passing results
- Fix _AutoHandoffMiddleware to use MiddlewareTermination instead of context.terminate
- Fix AgentMiddlewareLayer.run() to properly forward function/chat middleware
- Remove duplicate middleware registration in AgentMiddlewareLayer.__init__
- Fix exception handling in _auto_invoke_function to properly capture termination
- Fix mypy errors in core package
- Update tests to use stream=True parameter for unified run API

* fix all tests command

* Refactor integration tests to use pytest fixtures

- Merge testutils.py into conftest.py for azurefunctions integration tests
- Merge dt_testutils.py into conftest.py for durabletask integration tests
- Convert all integration tests to use fixtures instead of direct imports
  (fixes ModuleNotFoundError with --import-mode=importlib)
- Add sample_helper fixture for azurefunctions tests
- Add agent_client_factory and orchestration_helper fixtures for durabletask
- Integration tests now skip with descriptive messages when services unavailable
- Restructure devui tests into tests/devui/ with proper conftest.py
- Add test organization guidelines to CODING_STANDARD.md
- Remove __init__.py from test directories per pytest best practices

* Fix pytest_collection_modifyitems to only skip integration tests

The hook was skipping all tests in the test session, not just
integration tests. Now it only skips items in the integration_tests
directory.

* Fix mem0 tests failing on Python 3.13

Use patch.object on the imported module instead of @patch with string
path to ensure the mock takes effect regardless of import timing.

* fix mem0

* another attempt for mem0

* fix for mem0

* fix mem0

* Increase worker initialization wait time in durabletask tests

Increase from 2 to 8 seconds to allow time for:
- Python startup and module imports
- Azure OpenAI client creation
- Agent registration with DTS worker
- Worker connection to DTS

This helps prevent test failures in CI where the first tests may run
before the worker is fully ready to process requests.

* Fix streaming test to use ResponseStream with finalizer

The _consume_stream method now expects a ResponseStream that can provide
a final AgentResponse via get_final_response(). Update the test to use
ResponseStream with AgentResponse.from_updates as the finalizer.

* Fix MockToolCallingAgent to use new ResponseStream API and update samples

* small updates to run_stream to run

* fix sub workflow

* temp fix for az func test

---------

Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-02-05 21:09:58 +01:00
committed by GitHub
Unverified
parent d1205896a1
commit 3dc59c83b5
372 changed files with 11583 additions and 9465 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/getting_started/agents/anthropic/anthropic_advanced.py
+1 -1
View File
@@ -38,7 +38,7 @@ async def main() -> None:
query = "Can you compare Python decorators with C# attributes?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
if isinstance(content, TextReasoningContent):
print(f"\033[32m{content.text}\033[0m", end="", flush=True)
python/samples/getting_started/agents/anthropic/anthropic_basic.py
+1 -1
View File
@@ -55,7 +55,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland and in Paris?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/anthropic/anthropic_claude_basic.py
+1 -1
View File
@@ -59,7 +59,7 @@ async def streaming_example() -> None:
query = "What's the weather in Paris?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/anthropic/anthropic_foundry.py
+1 -1
View File
@@ -49,7 +49,7 @@ async def main() -> None:
query = "Can you compare Python decorators with C# attributes?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
if isinstance(content, TextReasoningContent):
print(f"\033[32m{content.text}\033[0m", end="", flush=True)
python/samples/getting_started/agents/anthropic/anthropic_skills.py
+1 -1
View File
@@ -53,7 +53,7 @@ async def main() -> None:
print(f"User: {query}")
print("Agent: ", end="", flush=True)
files: list[HostedFileContent] = []
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
match content.type:
case "text":
python/samples/getting_started/agents/azure_ai/azure_ai_basic.py
+1 -1
View File
@@ -68,7 +68,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Tokyo?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/azure_ai/azure_ai_with_agent_as_tool.py
+1 -1
View File
@@ -22,7 +22,7 @@ async def logging_middleware(
context: FunctionInvocationContext,
next: Callable[[FunctionInvocationContext], Awaitable[None]],
) -> None:
"""Middleware that logs tool invocations to show the delegation flow."""
"""MiddlewareTypes that logs tool invocations to show the delegation flow."""
print(f"[Calling tool: {context.function.name}]")
print(f"[Request: {context.arguments}]")
python/samples/getting_started/agents/azure_ai/azure_ai_with_code_interpreter_file_download.py
+2 -2
View File
@@ -11,7 +11,7 @@ from agent_framework import (
Content,
HostedCodeInterpreterTool,
HostedFileContent,
tool,
TextContent,
)
from agent_framework.azure import AzureAIProjectAgentProvider
from azure.identity.aio import AzureCliCredential
@@ -178,7 +178,7 @@ async def streaming_example() -> None:
file_contents_found: list[HostedFileContent] = []
text_chunks: list[str] = []
async for update in agent.run_stream(QUERY):
async for update in agent.run(QUERY, stream=True):
if isinstance(update, AgentResponseUpdate):
for content in update.contents:
if content.type == "text":
python/samples/getting_started/agents/azure_ai/azure_ai_with_code_interpreter_file_generation.py
+1 -1
View File
@@ -78,7 +78,7 @@ async def streaming_example() -> None:
text_chunks: list[str] = []
file_ids_found: list[str] = []
async for update in agent.run_stream(QUERY):
async for update in agent.run(QUERY, stream=True):
if isinstance(update, AgentResponseUpdate):
for content in update.contents:
if content.type == "text":
python/samples/getting_started/agents/azure_ai/azure_ai_with_reasoning.py
+1 -1
View File
@@ -68,7 +68,7 @@ async def streaming_example() -> None:
shown_reasoning_label = False
shown_text_label = False
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
if content.type == "text_reasoning":
if not shown_reasoning_label:
python/samples/getting_started/agents/azure_ai_agent/azure_ai_basic.py
+1 -1
View File
@@ -66,7 +66,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_azure_ai_search.py
+1 -1
View File
@@ -87,7 +87,7 @@ async def main() -> None:
print("Agent: ", end="", flush=True)
# Stream the response and collect citations
citations: list[Annotation] = []
async for chunk in agent.run_stream(user_input):
async for chunk in agent.run(user_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
# Collect citations from Azure AI Search responses
python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_bing_grounding_citations.py
+1 -1
View File
@@ -58,7 +58,7 @@ async def main() -> None:
# Stream the response and collect citations
citations: list[Annotation] = []
async for chunk in agent.run_stream(user_input):
async for chunk in agent.run(user_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter_file_generation.py
+1 -5
View File
@@ -4,7 +4,6 @@ import asyncio
import os
from agent_framework import (
AgentResponseUpdate,
HostedCodeInterpreterTool,
HostedFileContent,
)
@@ -60,10 +59,7 @@ async def main() -> None:
# Collect file_ids from the response
file_ids: list[str] = []
async for chunk in agent.run_stream(query):
if not isinstance(chunk, AgentResponseUpdate):
continue
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
if content.type == "text":
print(content.text, end="", flush=True)
python/samples/getting_started/agents/azure_openai/azure_assistants_basic.py
+1 -1
View File
@@ -58,7 +58,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/azure_openai/azure_assistants_with_code_interpreter.py
+1 -1
View File
@@ -55,7 +55,7 @@ async def main() -> None:
print(f"User: {query}")
print("Agent: ", end="", flush=True)
generated_code = ""
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
code_interpreter_chunk = get_code_interpreter_chunk(chunk)
python/samples/getting_started/agents/azure_openai/azure_chat_client_basic.py
+1 -1
View File
@@ -60,7 +60,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/azure_openai/azure_responses_client_basic.py
+1 -1
View File
@@ -58,7 +58,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/azure_openai/azure_responses_client_with_hosted_mcp.py
+4 -4
View File
@@ -30,10 +30,10 @@ async def handle_approvals_without_thread(query: str, agent: "AgentProtocol"):
f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
f" with arguments: {user_input_needed.function_call.arguments}"
)
new_inputs.append(ChatMessage("assistant", [user_input_needed]))
new_inputs.append(ChatMessage(role="assistant", contents=[user_input_needed]))
user_approval = input("Approve function call? (y/n): ")
new_inputs.append(
ChatMessage("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
ChatMessage(role="user", contents=[user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
)
result = await agent.run(new_inputs)
@@ -71,8 +71,8 @@ async def handle_approvals_with_thread_streaming(query: str, agent: "AgentProtoc
new_input_added = True
while new_input_added:
new_input_added = False
new_input.append(ChatMessage("user", [query]))
async for update in agent.run_stream(new_input, thread=thread, store=True):
new_input.append(ChatMessage(role="user", text=query))
async for update in agent.run(new_input, thread=thread, options={"store": True}, stream=True):
if update.user_input_requests:
for user_input_needed in update.user_input_requests:
print(
python/samples/getting_started/agents/copilotstudio/copilotstudio_basic.py
+1 -1
View File
@@ -39,7 +39,7 @@ async def streaming_example() -> None:
query = "What is the capital of Spain?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/custom/README.md
+48 -5
View File
@@ -7,20 +7,63 @@ This folder contains examples demonstrating how to implement custom agents and c
| File | Description |
|------|-------------|
| [`custom_agent.py`](custom_agent.py) | Shows how to create custom agents by extending the `BaseAgent` class. Demonstrates the `EchoAgent` implementation with both streaming and non-streaming responses, proper thread management, and message history handling. |
| [`custom_chat_client.py`](custom_chat_client.py) | Demonstrates how to create custom chat clients by extending the `BaseChatClient` class. Shows the `EchoingChatClient` implementation and how to integrate it with `ChatAgent` using the `create_agent()` method. |
| [`custom_chat_client.py`](../../chat_client/custom_chat_client.py) | Demonstrates how to create custom chat clients by extending the `BaseChatClient` class. Shows a `EchoingChatClient` implementation and how to integrate it with `ChatAgent` using the `as_agent()` method. |
## Key Takeaways
### Custom Agents
- Custom agents give you complete control over the agent's behavior
- You must implement both `run()` (for complete responses) and `run_stream()` (for streaming responses)
- You must implement both `run()` for both the `stream=True` and `stream=False` cases
- Use `self._normalize_messages()` to handle different input message formats
- Use `self._notify_thread_of_new_messages()` to properly manage conversation history
### Custom Chat Clients
- Custom chat clients allow you to integrate any backend service or create new LLM providers
- You must implement both `_inner_get_response()` and `_inner_get_streaming_response()`
- You must implement `_inner_get_response()` with a stream parameter to handle both streaming and non-streaming responses
- Custom chat clients can be used with `ChatAgent` to leverage all agent framework features
- Use the `create_agent()` method to easily create agents from your custom chat clients
- Use the `as_agent()` method to easily create agents from your custom chat clients
Both approaches allow you to extend the framework for your specific use cases while maintaining compatibility with the broader Agent Framework ecosystem.
Both approaches allow you to extend the framework for your specific use cases while maintaining compatibility with the broader Agent Framework ecosystem.
## Understanding Raw Client Classes
The framework provides `Raw...Client` classes (e.g., `RawOpenAIChatClient`, `RawOpenAIResponsesClient`, `RawAzureAIClient`) that are intermediate implementations without middleware, telemetry, or function invocation support.
### Warning: Raw Clients Should Not Normally Be Used Directly
**The `Raw...Client` classes should not normally be used directly.** They do not include the middleware, telemetry, or function invocation support that you most likely need. If you do use them, you should carefully consider which additional layers to apply.
### Layer Ordering
There is a defined ordering for applying layers that you should follow:
1. **ChatMiddlewareLayer** - Should be applied **first** because it also prepares function middleware
2. **FunctionInvocationLayer** - Handles tool/function calling loop
3. **ChatTelemetryLayer** - Must be **inside** the function calling loop for correct per-call telemetry
4. **Raw...Client** - The base implementation (e.g., `RawOpenAIChatClient`)
Example of correct layer composition:
```python
class MyCustomClient(
ChatMiddlewareLayer[TOptions],
FunctionInvocationLayer[TOptions],
ChatTelemetryLayer[TOptions],
RawOpenAIChatClient[TOptions], # or BaseChatClient for custom implementations
Generic[TOptions],
):
"""Custom client with all layers correctly applied."""
pass
```
### Use Fully-Featured Clients Instead
For most use cases, use the fully-featured public client classes which already have all layers correctly composed:
- `OpenAIChatClient` - OpenAI Chat completions with all layers
- `OpenAIResponsesClient` - OpenAI Responses API with all layers
- `AzureOpenAIChatClient` - Azure OpenAI Chat with all layers
- `AzureOpenAIResponsesClient` - Azure OpenAI Responses with all layers
- `AzureAIClient` - Azure AI Project with all layers
These clients handle the layer composition correctly and provide the full feature set out of the box.
python/samples/getting_started/agents/custom/custom_agent.py
+37 -29
View File
@@ -11,6 +11,8 @@ from agent_framework import (
BaseAgent,
ChatMessage,
Content,
Role,
TextContent,
)
"""
@@ -25,7 +27,7 @@ class EchoAgent(BaseAgent):
"""A simple custom agent that echoes user messages with a prefix.
This demonstrates how to create a fully custom agent by extending BaseAgent
and implementing the required run() and run_stream() methods.
and implementing the required run() method with stream support.
"""
echo_prefix: str = "Echo: "
@@ -53,30 +55,45 @@ class EchoAgent(BaseAgent):
**kwargs,
)
async def run(
def run(
self,
messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
*,
stream: bool = False,
thread: AgentThread | None = None,
**kwargs: Any,
) -> "AsyncIterable[AgentResponseUpdate] | asyncio.Future[AgentResponse]":
"""Execute the agent and return a response.
Args:
messages: The message(s) to process.
stream: If True, return an async iterable of updates. If False, return an awaitable response.
thread: The conversation thread (optional).
**kwargs: Additional keyword arguments.
Returns:
When stream=False: An awaitable AgentResponse containing the agent's reply.
When stream=True: An async iterable of AgentResponseUpdate objects.
"""
if stream:
return self._run_stream(messages=messages, thread=thread, **kwargs)
return self._run(messages=messages, thread=thread, **kwargs)
async def _run(
self,
messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
*,
thread: AgentThread | None = None,
**kwargs: Any,
) -> AgentResponse:
"""Execute the agent and return a complete response.
Args:
messages: The message(s) to process.
thread: The conversation thread (optional).
**kwargs: Additional keyword arguments.
Returns:
An AgentResponse containing the agent's reply.
"""
"""Non-streaming implementation."""
# Normalize input messages to a list
normalized_messages = self._normalize_messages(messages)
if not normalized_messages:
response_message = ChatMessage(
"assistant",
[Content.from_text(text="Hello! I'm a custom echo agent. Send me a message and I'll echo it back.")],
role=Role.ASSISTANT,
contents=[Content.from_text(text="Hello! I'm a custom echo agent. Send me a message and I'll echo it back.")],
)
else:
# For simplicity, echo the last user message
@@ -86,7 +103,7 @@ class EchoAgent(BaseAgent):
else:
echo_text = f"{self.echo_prefix}[Non-text message received]"
response_message = ChatMessage("assistant", [Content.from_text(text=echo_text)])
response_message = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(text=echo_text)])
# Notify the thread of new messages if provided
if thread is not None:
@@ -94,23 +111,14 @@ class EchoAgent(BaseAgent):
return AgentResponse(messages=[response_message])
async def run_stream(
async def _run_stream(
self,
messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
*,
thread: AgentThread | None = None,
**kwargs: Any,
) -> AsyncIterable[AgentResponseUpdate]:
"""Execute the agent and yield streaming response updates.
Args:
messages: The message(s) to process.
thread: The conversation thread (optional).
**kwargs: Additional keyword arguments.
Yields:
AgentResponseUpdate objects containing chunks of the response.
"""
"""Streaming implementation."""
# Normalize input messages to a list
normalized_messages = self._normalize_messages(messages)
@@ -132,7 +140,7 @@ class EchoAgent(BaseAgent):
yield AgentResponseUpdate(
contents=[Content.from_text(text=chunk_text)],
role="assistant",
role=Role.ASSISTANT,
)
# Small delay to simulate streaming
@@ -140,7 +148,7 @@ class EchoAgent(BaseAgent):
# Notify the thread of the complete response if provided
if thread is not None:
complete_response = ChatMessage("assistant", [Content.from_text(text=response_text)])
complete_response = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(text=response_text)])
await self._notify_thread_of_new_messages(thread, normalized_messages, complete_response)
@@ -167,7 +175,7 @@ async def main() -> None:
query2 = "This is a streaming test"
print(f"\nUser: {query2}")
print("Agent: ", end="", flush=True)
async for chunk in echo_agent.run_stream(query2):
async for chunk in echo_agent.run(query2, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
python/samples/getting_started/agents/github_copilot/github_copilot_basic.py
+1 -1
View File
@@ -61,7 +61,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Tokyo?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/ollama/ollama_agent_basic.py
+1 -1
View File
@@ -54,7 +54,7 @@ async def streaming_example() -> None:
query = "What time is it in San Francisco? Use a tool call"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/ollama/ollama_agent_reasoning.py
+2 -9
View File
@@ -2,7 +2,6 @@
import asyncio
from agent_framework import TextReasoningContent
from agent_framework.ollama import OllamaChatClient
"""
@@ -18,7 +17,7 @@ https://ollama.com/
"""
async def reasoning_example() -> None:
async def main() -> None:
print("=== Response Reasoning Example ===")
agent = OllamaChatClient().as_agent(
@@ -30,16 +29,10 @@ async def reasoning_example() -> None:
print(f"User: {query}")
# Enable Reasoning on per request level
result = await agent.run(query)
reasoning = "".join((c.text or "") for c in result.messages[-1].contents if isinstance(c, TextReasoningContent))
reasoning = "".join((c.text or "") for c in result.messages[-1].contents if c.type == "text_reasoning")
print(f"Reasoning: {reasoning}")
print(f"Answer: {result}\n")
async def main() -> None:
print("=== Basic Ollama Chat Client Agent Reasoning ===")
await reasoning_example()
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/ollama/ollama_chat_client.py
+1 -1
View File
@@ -33,7 +33,7 @@ async def main() -> None:
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_time):
async for chunk in client.get_response(message, tools=get_time, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/agents/ollama/ollama_with_openai_chat_client.py
+1 -1
View File
@@ -68,7 +68,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/openai/openai_assistants_basic.py
+1 -1
View File
@@ -72,7 +72,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/openai/openai_assistants_with_code_interpreter.py
+1 -1
View File
@@ -60,7 +60,7 @@ async def main() -> None:
print(f"User: {query}")
print("Agent: ", end="", flush=True)
generated_code = ""
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
code_interpreter_chunk = get_code_interpreter_chunk(chunk)
python/samples/getting_started/agents/openai/openai_assistants_with_file_search.py
+7 -5
View File
@@ -3,7 +3,7 @@
import asyncio
import os
from agent_framework import HostedFileSearchTool, HostedVectorStoreContent
from agent_framework import Content, HostedFileSearchTool
from agent_framework.openai import OpenAIAssistantProvider
from openai import AsyncOpenAI
@@ -15,7 +15,7 @@ for document-based question answering and information retrieval.
"""
async def create_vector_store(client: AsyncOpenAI) -> tuple[str, HostedVectorStoreContent]:
async def create_vector_store(client: AsyncOpenAI) -> tuple[str, Content]:
"""Create a vector store with sample documents."""
file = await client.files.create(
file=("todays_weather.txt", b"The weather today is sunny with a high of 75F."), purpose="user_data"
@@ -28,7 +28,7 @@ async def create_vector_store(client: AsyncOpenAI) -> tuple[str, HostedVectorSto
if result.last_error is not None:
raise Exception(f"Vector store file processing failed with status: {result.last_error.message}")
return file.id, HostedVectorStoreContent(vector_store_id=vector_store.id)
return file.id, Content.from_hosted_vector_store(vector_store_id=vector_store.id)
async def delete_vector_store(client: AsyncOpenAI, file_id: str, vector_store_id: str) -> None:
@@ -56,8 +56,10 @@ async def main() -> None:
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(
query, tool_resources={"file_search": {"vector_store_ids": [vector_store.vector_store_id]}}
async for chunk in agent.run(
query,
stream=True,
options={"tool_resources": {"file_search": {"vector_store_ids": [vector_store.vector_store_id]}}},
):
if chunk.text:
print(chunk.text, end="", flush=True)
python/samples/getting_started/agents/openai/openai_chat_client_basic.py
+1 -1
View File
@@ -54,7 +54,7 @@ async def streaming_example() -> None:
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
python/samples/getting_started/agents/openai/openai_chat_client_with_runtime_json_schema.py
+2 -1
View File
@@ -74,8 +74,9 @@ async def streaming_example() -> None:
print(f"User: {query}")
chunks: list[str] = []
async for chunk in agent.run_stream(
async for chunk in agent.run(
query,
stream=True,
options={
"response_format": {
"type": "json_schema",
python/samples/getting_started/agents/openai/openai_chat_client_with_web_search.py
+1 -1
View File
@@ -34,7 +34,7 @@ async def main() -> None:
if stream:
print("Assistant: ", end="")
async for chunk in agent.run_stream(message):
async for chunk in agent.run(message, stream=True):
if chunk.text:
print(chunk.text, end="")
print("")
python/samples/getting_started/agents/openai/openai_responses_client_basic.py
+51 -5
View File
@@ -1,10 +1,11 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from collections.abc import Awaitable, Callable
from random import randint
from typing import Annotated
from agent_framework import ChatAgent, tool
from agent_framework import ChatAgent, ChatContext, ChatMessage, ChatResponse, Role, chat_middleware, tool
from agent_framework.openai import OpenAIResponsesClient
from pydantic import Field
@@ -16,6 +17,47 @@ response generation, showing both streaming and non-streaming responses.
"""
@chat_middleware
async def security_and_override_middleware(
context: ChatContext,
next: Callable[[ChatContext], Awaitable[None]],
) -> None:
"""Function-based middleware that implements security filtering and response override."""
print("[SecurityMiddleware] Processing input...")
# Security check - block sensitive information
blocked_terms = ["password", "secret", "api_key", "token"]
for message in context.messages:
if message.text:
message_lower = message.text.lower()
for term in blocked_terms:
if term in message_lower:
print(f"[SecurityMiddleware] BLOCKED: Found '{term}' in message")
# Override the response instead of calling AI
context.result = ChatResponse(
messages=[
ChatMessage(
role=Role.ASSISTANT,
text="I cannot process requests containing sensitive information. "
"Please rephrase your question without including passwords, secrets, or other "
"sensitive data.",
)
]
)
# Set terminate flag to stop execution
context.terminate = True
return
# Continue to next middleware or AI execution
await next(context)
print("[SecurityMiddleware] Response generated.")
print(type(context.result))
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
@tool(approval_mode="never_require")
def get_weather(
@@ -47,25 +89,29 @@ async def streaming_example() -> None:
print("=== Streaming Response Example ===")
agent = ChatAgent(
chat_client=OpenAIResponsesClient(),
chat_client=OpenAIResponsesClient(
middleware=[security_and_override_middleware],
),
instructions="You are a helpful weather agent.",
tools=get_weather,
# tools=get_weather,
)
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
response = agent.run(query, stream=True)
async for chunk in response:
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
print(f"Final Result: {await response.get_final_response()}")
async def main() -> None:
print("=== Basic OpenAI Responses Client Agent Example ===")
await non_streaming_example()
await streaming_example()
await non_streaming_example()
if __name__ == "__main__":
python/samples/getting_started/agents/openai/openai_responses_client_image_generation.py
+2 -2
View File
@@ -3,7 +3,7 @@
import asyncio
import base64
from agent_framework import Content, HostedImageGenerationTool, ImageGenerationToolResultContent
from agent_framework import HostedImageGenerationTool
from agent_framework.openai import OpenAIResponsesClient
"""
@@ -70,7 +70,7 @@ async def main() -> None:
# Show information about the generated image
for message in result.messages:
for content in message.contents:
if isinstance(content, ImageGenerationToolResultContent) and content.outputs:
if content.type == "image_generation" and content.outputs:
for output in content.outputs:
if output.type in ("data", "uri") and output.uri:
show_image_info(output.uri)
python/samples/getting_started/agents/openai/openai_responses_client_reasoning.py
+1 -1
View File
@@ -55,7 +55,7 @@ async def streaming_reasoning_example() -> None:
print(f"User: {query}")
print(f"{agent.name}: ", end="", flush=True)
usage = None
async for chunk in agent.run_stream(query):
async for chunk in agent.run(query, stream=True):
if chunk.contents:
for content in chunk.contents:
if content.type == "text_reasoning":
python/samples/getting_started/agents/openai/openai_responses_client_streaming_image_generation.py
+1 -1
View File
@@ -67,7 +67,7 @@ async def main():
await output_dir.mkdir(exist_ok=True)
print(" Streaming response:")
async for update in agent.run_stream(query):
async for update in agent.run(query, stream=True):
for content in update.contents:
# Handle partial images
# The final partial image IS the complete, full-quality image. Each partial
python/samples/getting_started/agents/openai/openai_responses_client_with_agent_as_tool.py
+1 -1
View File
@@ -21,7 +21,7 @@ async def logging_middleware(
context: FunctionInvocationContext,
next: Callable[[FunctionInvocationContext], Awaitable[None]],
) -> None:
"""Middleware that logs tool invocations to show the delegation flow."""
"""MiddlewareTypes that logs tool invocations to show the delegation flow."""
print(f"[Calling tool: {context.function.name}]")
print(f"[Request: {context.arguments}]")
python/samples/getting_started/agents/openai/openai_responses_client_with_code_interpreter.py
+2 -5
View File
@@ -4,9 +4,6 @@ import asyncio
from agent_framework import (
ChatAgent,
CodeInterpreterToolCallContent,
CodeInterpreterToolResultContent,
Content,
HostedCodeInterpreterTool,
)
from agent_framework.openai import OpenAIResponsesClient
@@ -35,8 +32,8 @@ async def main() -> None:
print(f"Result: {result}\n")
for message in result.messages:
code_blocks = [c for c in message.contents if isinstance(c, CodeInterpreterToolCallContent)]
outputs = [c for c in message.contents if isinstance(c, CodeInterpreterToolResultContent)]
code_blocks = [c for c in message.contents if c.type == "code_interpreter_tool_input"]
outputs = [c for c in message.contents if c.type == "code_interpreter_tool_result"]
if code_blocks:
code_inputs = code_blocks[0].inputs or []
for content in code_inputs:
python/samples/getting_started/agents/openai/openai_responses_client_with_file_search.py
+4 -4
View File
@@ -2,7 +2,7 @@
import asyncio
from agent_framework import ChatAgent, HostedFileSearchTool, HostedVectorStoreContent
from agent_framework import ChatAgent, Content, HostedFileSearchTool
from agent_framework.openai import OpenAIResponsesClient
"""
@@ -15,7 +15,7 @@ for direct document-based question answering and information retrieval.
# Helper functions
async def create_vector_store(client: OpenAIResponsesClient) -> tuple[str, HostedVectorStoreContent]:
async def create_vector_store(client: OpenAIResponsesClient) -> tuple[str, Content]:
"""Create a vector store with sample documents."""
file = await client.client.files.create(
file=("todays_weather.txt", b"The weather today is sunny with a high of 75F."), purpose="user_data"
@@ -28,7 +28,7 @@ async def create_vector_store(client: OpenAIResponsesClient) -> tuple[str, Hoste
if result.last_error is not None:
raise Exception(f"Vector store file processing failed with status: {result.last_error.message}")
return file.id, HostedVectorStoreContent(vector_store_id=vector_store.id)
return file.id, Content.from_hosted_vector_store(vector_store_id=vector_store.id)
async def delete_vector_store(client: OpenAIResponsesClient, file_id: str, vector_store_id: str) -> None:
@@ -55,7 +55,7 @@ async def main() -> None:
if stream:
print("Assistant: ", end="")
async for chunk in agent.run_stream(message):
async for chunk in agent.run(message, stream=True):
if chunk.text:
print(chunk.text, end="")
print("")
python/samples/getting_started/agents/openai/openai_responses_client_with_hosted_mcp.py
+4 -4
View File
@@ -29,10 +29,10 @@ async def handle_approvals_without_thread(query: str, agent: "AgentProtocol"):
f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
f" with arguments: {user_input_needed.function_call.arguments}"
)
new_inputs.append(ChatMessage("assistant", [user_input_needed]))
new_inputs.append(ChatMessage(role="assistant", contents=[user_input_needed]))
user_approval = input("Approve function call? (y/n): ")
new_inputs.append(
ChatMessage("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
ChatMessage(role="user", contents=[user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
)
result = await agent.run(new_inputs)
@@ -70,8 +70,8 @@ async def handle_approvals_with_thread_streaming(query: str, agent: "AgentProtoc
new_input_added = True
while new_input_added:
new_input_added = False
new_input.append(ChatMessage("user", [query]))
async for update in agent.run_stream(new_input, thread=thread, store=True):
new_input.append(ChatMessage(role="user", text=query))
async for update in agent.run(new_input, thread=thread, stream=True, options={"store": True}):
if update.user_input_requests:
for user_input_needed in update.user_input_requests:
print(
python/samples/getting_started/agents/openai/openai_responses_client_with_local_mcp.py
+2 -2
View File
@@ -35,7 +35,7 @@ async def streaming_with_mcp(show_raw_stream: bool = False) -> None:
query1 = "How to create an Azure storage account using az cli?"
print(f"User: {query1}")
print(f"{agent.name}: ", end="")
async for chunk in agent.run_stream(query1):
async for chunk in agent.run(query1, stream=True):
if show_raw_stream:
print("Streamed event: ", chunk.raw_representation.raw_representation) # type:ignore
elif chunk.text:
@@ -46,7 +46,7 @@ async def streaming_with_mcp(show_raw_stream: bool = False) -> None:
query2 = "What is Microsoft Agent Framework?"
print(f"User: {query2}")
print(f"{agent.name}: ", end="")
async for chunk in agent.run_stream(query2):
async for chunk in agent.run(query2, stream=True):
if show_raw_stream:
print("Streamed event: ", chunk.raw_representation.raw_representation) # type:ignore
elif chunk.text:
python/samples/getting_started/agents/openai/openai_responses_client_with_runtime_json_schema.py
+2 -1
View File
@@ -74,8 +74,9 @@ async def streaming_example() -> None:
print(f"User: {query}")
chunks: list[str] = []
async for chunk in agent.run_stream(
async for chunk in agent.run(
query,
stream=True,
options={
"response_format": {
"type": "json_schema",
python/samples/getting_started/agents/openai/openai_responses_client_with_structured_output.py
+4 -4
View File
@@ -59,16 +59,16 @@ async def streaming_example() -> None:
query = "Tell me about Tokyo, Japan"
print(f"User: {query}")
# Get structured response from streaming agent using AgentResponse.from_agent_response_generator
# Get structured response from streaming agent using AgentResponse.from_update_generator
# This method collects all streaming updates and combines them into a single AgentResponse
result = await AgentResponse.from_agent_response_generator(
agent.run_stream(query, options={"response_format": OutputStruct}),
result = await AgentResponse.from_update_generator(
agent.run(query, stream=True, options={"response_format": OutputStruct}),
output_format_type=OutputStruct,
)
# Access the structured output using the parsed value
if structured_data := result.value:
print("Structured Output (from streaming with AgentResponse.from_agent_response_generator):")
print("Structured Output (from streaming with AgentResponse.from_update_generator):")
print(f"City: {structured_data.city}")
print(f"Description: {structured_data.description}")
else:
python/samples/getting_started/agents/openai/openai_responses_client_with_web_search.py
+1 -1
View File
@@ -34,7 +34,7 @@ async def main() -> None:
if stream:
print("Assistant: ", end="")
async for chunk in agent.run_stream(message):
async for chunk in agent.run(message, stream=True):
if chunk.text:
print(chunk.text, end="")
print("")
python/samples/getting_started/chat_client/README.md
+2 -1
View File
@@ -14,6 +14,7 @@ This folder contains simple examples demonstrating direct usage of various chat
| [`openai_assistants_client.py`](openai_assistants_client.py) | Direct usage of OpenAI Assistants Client for basic chat interactions with OpenAI assistants. |
| [`openai_chat_client.py`](openai_chat_client.py) | Direct usage of OpenAI Chat Client for chat interactions with OpenAI models. |
| [`openai_responses_client.py`](openai_responses_client.py) | Direct usage of OpenAI Responses Client for structured response generation with OpenAI models. |
| [`custom_chat_client.py`](custom_chat_client.py) | Demonstrates how to create custom chat clients by extending the `BaseChatClient` class. Shows a `EchoingChatClient` implementation and how to integrate it with `ChatAgent` using the `as_agent()` method. |
## Environment Variables
@@ -37,4 +38,4 @@ Depending on which client you're using, set the appropriate environment variable
- `OLLAMA_HOST`: Your Ollama server URL (defaults to `http://localhost:11434` if not set)
- `OLLAMA_MODEL_ID`: The Ollama model to use for chat (e.g., `llama3.2`, `llama2`, `codellama`)
> **Note**: For Ollama, ensure you have Ollama installed and running locally with at least one model downloaded. Visit [https://ollama.com/](https://ollama.com/) for installation instructions.
> **Note**: For Ollama, ensure you have Ollama installed and running locally with at least one model downloaded. Visit [https://ollama.com/](https://ollama.com/) for installation instructions.
python/samples/getting_started/chat_client/azure_ai_chat_client.py
+1 -1
View File
@@ -36,7 +36,7 @@ async def main() -> None:
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/chat_client/azure_assistants_client.py
+1 -1
View File
@@ -36,7 +36,7 @@ async def main() -> None:
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/chat_client/azure_chat_client.py
+1 -1
View File
@@ -36,7 +36,7 @@ async def main() -> None:
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/chat_client/azure_responses_client.py
+6 -8
View File
@@ -42,21 +42,19 @@ async def main() -> None:
stream = True
print(f"User: {message}")
if stream:
response = await ChatResponse.from_update_generator(
client.get_streaming_response(message, tools=get_weather, options={"response_format": OutputStruct}),
response = await ChatResponse.from_chat_response_generator(
client.get_response(message, tools=get_weather, options={"response_format": OutputStruct}, stream=True),
output_format_type=OutputStruct,
)
try:
result = response.value
if result := response.try_parse_value(OutputStruct):
print(f"Assistant: {result}")
except Exception:
else:
print(f"Assistant: {response.text}")
else:
response = await client.get_response(message, tools=get_weather, options={"response_format": OutputStruct})
try:
result = response.value
if result := response.try_parse_value(OutputStruct):
print(f"Assistant: {result}")
except Exception:
else:
print(f"Assistant: {response.text}")
python/samples/getting_started/agents/custom/custom_chat_client.py → python/samples/getting_started/chat_client/custom_chat_client.py
+57 -35
View File
@@ -3,40 +3,54 @@
import asyncio
import random
import sys
from collections.abc import AsyncIterable, MutableSequence
from typing import Any, ClassVar, Generic
from collections.abc import AsyncIterable, Awaitable, Mapping, Sequence
from typing import Any, ClassVar, Generic, TypedDict
from agent_framework import (
BaseChatClient,
ChatMessage,
ChatMiddlewareLayer,
ChatOptions,
ChatResponse,
ChatResponseUpdate,
Content,
use_chat_middleware,
use_function_invocation,
FunctionInvocationLayer,
ResponseStream,
Role,
)
from agent_framework._clients import TOptions_co
from agent_framework.observability import ChatTelemetryLayer
if sys.version_info >= (3, 13):
from typing import TypeVar
else:
from typing_extensions import TypeVar
if sys.version_info >= (3, 12):
from typing import override # type: ignore # pragma: no cover
else:
from typing_extensions import override # type: ignore[import] # pragma: no cover
"""
Custom Chat Client Implementation Example
This sample demonstrates implementing a custom chat client by extending BaseChatClient class,
showing integration with ChatAgent and both streaming and non-streaming responses.
This sample demonstrates implementing a custom chat client and optionally composing
middleware, telemetry, and function invocation layers explicitly.
"""
TOptions_co = TypeVar(
"TOptions_co",
bound=TypedDict, # type: ignore[valid-type]
default="ChatOptions",
covariant=True,
)
@use_function_invocation
@use_chat_middleware
class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
"""A custom chat client that echoes messages back with modifications.
This demonstrates how to implement a custom chat client by extending BaseChatClient
and implementing the required _inner_get_response() and _inner_get_streaming_response() methods.
and implementing the required _inner_get_response() method.
"""
OTEL_PROVIDER_NAME: ClassVar[str] = "EchoingChatClient"
@@ -52,13 +66,14 @@ class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
self.prefix = prefix
@override
async def _inner_get_response(
def _inner_get_response(
self,
*,
messages: MutableSequence[ChatMessage],
options: dict[str, Any],
messages: Sequence[ChatMessage],
stream: bool = False,
options: Mapping[str, Any],
**kwargs: Any,
) -> ChatResponse:
) -> Awaitable[ChatResponse] | ResponseStream[ChatResponseUpdate, ChatResponse]:
"""Echo back the user's message with a prefix."""
if not messages:
response_text = "No messages to echo!"
@@ -66,7 +81,7 @@ class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
# Echo the last user message
last_user_message = None
for message in reversed(messages):
if message.role == "user":
if message.role == Role.USER:
last_user_message = message
break
@@ -75,39 +90,46 @@ class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
else:
response_text = f"{self.prefix} [No text message found]"
response_message = ChatMessage("assistant", [Content.from_text(text=response_text)])
response_message = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(response_text)])
return ChatResponse(
response = ChatResponse(
messages=[response_message],
model_id="echo-model-v1",
response_id=f"echo-resp-{random.randint(1000, 9999)}",
)
@override
async def _inner_get_streaming_response(
self,
*,
messages: MutableSequence[ChatMessage],
options: dict[str, Any],
**kwargs: Any,
) -> AsyncIterable[ChatResponseUpdate]:
"""Stream back the echoed message character by character."""
# Get the complete response first
response = await self._inner_get_response(messages=messages, options=options, **kwargs)
if not stream:
if response.messages:
response_text = response.messages[0].text or ""
async def _get_response() -> ChatResponse:
return response
# Stream character by character
for char in response_text:
return _get_response()
async def _stream() -> AsyncIterable[ChatResponseUpdate]:
response_text_local = response_message.text or ""
for char in response_text_local:
yield ChatResponseUpdate(
contents=[Content.from_text(text=char)],
role="assistant",
contents=[Content.from_text(char)],
role=Role.ASSISTANT,
response_id=f"echo-stream-resp-{random.randint(1000, 9999)}",
model_id="echo-model-v1",
)
await asyncio.sleep(0.05)
return ResponseStream(_stream(), finalizer=lambda updates: response)
class EchoingChatClientWithLayers( # type: ignore[misc,type-var]
ChatMiddlewareLayer[TOptions_co],
ChatTelemetryLayer[TOptions_co],
FunctionInvocationLayer[TOptions_co],
EchoingChatClient[TOptions_co],
Generic[TOptions_co],
):
"""Echoing chat client that explicitly composes middleware, telemetry, and function layers."""
OTEL_PROVIDER_NAME: ClassVar[str] = "EchoingChatClientWithLayers"
async def main() -> None:
"""Demonstrates how to implement and use a custom chat client with ChatAgent."""
@@ -116,7 +138,7 @@ async def main() -> None:
# Create the custom chat client
print("--- EchoingChatClient Example ---")
echo_client = EchoingChatClient(prefix="🔊 Echo:")
echo_client = EchoingChatClientWithLayers(prefix="🔊 Echo:")
# Use the chat client directly
print("Using chat client directly:")
@@ -141,7 +163,7 @@ async def main() -> None:
query2 = "Stream this message back to me"
print(f"\nUser: {query2}")
print("Agent: ", end="", flush=True)
async for chunk in echo_agent.run_stream(query2):
async for chunk in echo_agent.run(query2, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
python/samples/getting_started/chat_client/openai_assistants_client.py
+1 -1
View File
@@ -34,7 +34,7 @@ async def main() -> None:
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/chat_client/openai_chat_client.py
+1 -1
View File
@@ -34,7 +34,7 @@ async def main() -> None:
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if chunk.text:
print(chunk.text, end="")
print("")
python/samples/getting_started/chat_client/openai_responses_client.py
+5 -5
View File
@@ -30,14 +30,14 @@ def get_weather(
async def main() -> None:
client = OpenAIResponsesClient()
message = "What's the weather in Amsterdam and in Paris?"
stream = False
stream = True
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
if chunk.text:
print(chunk.text, end="")
print("")
response = client.get_response(message, stream=True, tools=get_weather)
# TODO: review names of the methods, could be related to things like HTTP clients?
response.with_update_hook(lambda chunk: print(chunk.text, end=""))
await response.get_final_response()
else:
response = await client.get_response(message, tools=get_weather)
print(f"Assistant: {response}")
python/samples/getting_started/chat_client/typed_options.py
-182
View File
@@ -1,182 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Literal
from agent_framework import ChatAgent
from agent_framework.anthropic import AnthropicClient
from agent_framework.openai import OpenAIChatClient, OpenAIChatOptions
"""TypedDict-based Chat Options.
In Agent Framework, we have made ChatClient and ChatAgent generic over a ChatOptions typeddict, this means that
you can override which options are available for a given client or agent by providing your own TypedDict subclass.
And we include the most common options for all ChatClient providers out of the box.
This sample demonstrates the TypedDict-based approach for chat client and agent options,
which provides:
1. IDE autocomplete for available options
2. Type checking to catch errors at development time
3. An example of defining provider-specific options by extending the base options,
including overriding unsupported options.
The sample shows usage with both OpenAI and Anthropic clients, demonstrating
how provider-specific options work for ChatClient and ChatAgent. But the same approach works for other providers too.
"""
async def demo_anthropic_chat_client() -> None:
"""Demonstrate Anthropic ChatClient with typed options and validation."""
print("\n=== Anthropic ChatClient with TypedDict Options ===\n")
# Create Anthropic client
client = AnthropicClient(model_id="claude-sonnet-4-5-20250929")
# Standard options work great:
response = await client.get_response(
"What is the capital of France?",
options={
"temperature": 0.5,
"max_tokens": 1000,
# Anthropic-specific options:
"thinking": {"type": "enabled", "budget_tokens": 1000},
# "top_k": 40, # <-- Uncomment for Anthropic-specific option
},
)
print(f"Anthropic Response: {response.text}")
print(f"Model used: {response.model_id}")
async def demo_anthropic_agent() -> None:
"""Demonstrate ChatAgent with Anthropic client and typed options."""
print("\n=== ChatAgent with Anthropic and Typed Options ===\n")
client = AnthropicClient(model_id="claude-sonnet-4-5-20250929")
# Create a typed agent for Anthropic - IDE knows Anthropic-specific options!
agent = ChatAgent(
chat_client=client,
name="claude-assistant",
instructions="You are a helpful assistant powered by Claude. Be concise.",
default_options={
"temperature": 0.5,
"max_tokens": 200,
"top_k": 40, # Anthropic-specific option, uncomment to try
},
)
# Run the agent
response = await agent.run("Explain quantum computing in one sentence.")
print(f"Agent Response: {response.text}")
class OpenAIReasoningChatOptions(OpenAIChatOptions, total=False):
"""Chat options for OpenAI reasoning models (o1, o3, o4-mini, etc.).
Reasoning models have different parameter support compared to standard models.
This TypedDict marks unsupported parameters with ``None`` type.
Examples:
.. code-block:: python
from agent_framework.openai import OpenAIReasoningChatOptions
options: OpenAIReasoningChatOptions = {
"model_id": "o3",
"reasoning_effort": "high",
"max_tokens": 4096,
}
"""
# Reasoning-specific parameters
reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh"]
# Unsupported parameters for reasoning models (override with None)
temperature: None
top_p: None
frequency_penalty: None
presence_penalty: None
logit_bias: None
logprobs: None
top_logprobs: None
stop: None # Not supported for o3 and o4-mini
async def demo_openai_chat_client_reasoning_models() -> None:
"""Demonstrate OpenAI ChatClient with typed options for reasoning models."""
print("\n=== OpenAI ChatClient with TypedDict Options ===\n")
# Create OpenAI client
client = OpenAIChatClient[OpenAIReasoningChatOptions]()
# With specific options, you get full IDE autocomplete!
# Try typing `client.get_response("Hello", options={` and see the suggestions
response = await client.get_response(
"What is 2 + 2?",
options={
"model_id": "o3",
"max_tokens": 100,
"allow_multiple_tool_calls": True,
# OpenAI-specific options work:
"reasoning_effort": "medium",
# Unsupported options are caught by type checker (uncomment to see):
# "temperature": 0.7,
# "random": 234,
},
)
print(f"OpenAI Response: {response.text}")
print(f"Model used: {response.model_id}")
async def demo_openai_agent() -> None:
"""Demonstrate ChatAgent with OpenAI client and typed options."""
print("\n=== ChatAgent with OpenAI and Typed Options ===\n")
# Create a typed agent - IDE will autocomplete options!
# The type annotation can be done either on the agent like below,
# or on the client when constructing the client instance:
# client = OpenAIChatClient[OpenAIReasoningChatOptions]()
agent = ChatAgent[OpenAIReasoningChatOptions](
chat_client=OpenAIChatClient(),
name="weather-assistant",
instructions="You are a helpful assistant. Answer concisely.",
# Options can be set at construction time
default_options={
"model_id": "o3",
"max_tokens": 100,
"allow_multiple_tool_calls": True,
# OpenAI-specific options work:
"reasoning_effort": "medium",
# Unsupported options are caught by type checker (uncomment to see):
# "temperature": 0.7,
# "random": 234,
},
)
# Or pass options at runtime - they override construction options
response = await agent.run(
"What is 25 * 47?",
options={
"reasoning_effort": "high", # Override for a run
},
)
print(f"Agent Response: {response.text}")
async def main() -> None:
"""Run all Typed Options demonstrations."""
# # Anthropic demos (requires ANTHROPIC_API_KEY)
await demo_anthropic_chat_client()
await demo_anthropic_agent()
# OpenAI demos (requires OPENAI_API_KEY)
await demo_openai_chat_client_reasoning_models()
await demo_openai_agent()
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/context_providers/azure_ai_search/azure_ai_with_search_context_agentic.py
+1 -1
View File
@@ -130,7 +130,7 @@ async def main() -> None:
print("Agent: ", end="", flush=True)
# Stream response
async for chunk in agent.run_stream(user_input):
async for chunk in agent.run(user_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
python/samples/getting_started/context_providers/azure_ai_search/azure_ai_with_search_context_semantic.py
+1 -1
View File
@@ -86,7 +86,7 @@ async def main() -> None:
print("Agent: ", end="", flush=True)
# Stream response
async for chunk in agent.run_stream(user_input):
async for chunk in agent.run(user_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
python/samples/getting_started/devui/weather_agent_azure/agent.py
+6 -4
View File
@@ -14,6 +14,8 @@ from agent_framework import (
ChatResponseUpdate,
Content,
FunctionInvocationContext,
Role,
TextContent,
chat_middleware,
function_middleware,
tool,
@@ -42,7 +44,7 @@ async def security_filter_middleware(
# Check only the last message (most recent user input)
last_message = context.messages[-1] if context.messages else None
if last_message and last_message.role == "user" and last_message.text:
if last_message and last_message.role == Role.USER and last_message.text:
message_lower = last_message.text.lower()
for term in blocked_terms:
if term in message_lower:
@@ -52,12 +54,12 @@ async def security_filter_middleware(
"or other sensitive data."
)
if context.is_streaming:
if context.stream:
# Streaming mode: return async generator
async def blocked_stream() -> AsyncIterable[ChatResponseUpdate]:
yield ChatResponseUpdate(
contents=[Content.from_text(text=error_message)],
role="assistant",
role=Role.ASSISTANT,
)
context.result = blocked_stream()
@@ -66,7 +68,7 @@ async def security_filter_middleware(
context.result = ChatResponse(
messages=[
ChatMessage(
role="assistant",
role=Role.ASSISTANT,
text=error_message,
)
]
python/samples/getting_started/durabletask/01_single_agent/worker.py
+7 -7
View File
@@ -3,8 +3,8 @@
This worker registers agents as durable entities and continuously listens for requests.
The worker should run as a background service, processing incoming agent requests.
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
- Start a Durable Task Scheduler (e.g., using Docker)
"""
@@ -25,7 +25,7 @@ logger = logging.getLogger(__name__)
def create_joker_agent() -> ChatAgent:
"""Create the Joker agent using Azure OpenAI.
Returns:
ChatAgent: The configured Joker agent
"""
@@ -41,12 +41,12 @@ def get_worker(
log_handler: logging.Handler | None = None
) -> DurableTaskSchedulerWorker:
"""Create a configured DurableTaskSchedulerWorker.
Args:
taskhub: Task hub name (defaults to TASKHUB env var or "default")
endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
log_handler: Optional logging handler for worker logging
Returns:
Configured DurableTaskSchedulerWorker instance
"""
@@ -69,10 +69,10 @@ def get_worker(
def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
"""Set up the worker with agents registered.
Args:
worker: The DurableTaskSchedulerWorker instance
Returns:
DurableAIAgentWorker with agents registered
"""
python/samples/getting_started/durabletask/02_multi_agent/worker.py
+14 -15
View File
@@ -4,8 +4,8 @@ This worker registers two agents - a weather assistant and a math assistant - ea
with their own specialized tools. This demonstrates how to host multiple agents
with different capabilities in a single worker process.
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
- Start a Durable Task Scheduler (e.g., using Docker)
"""
@@ -15,6 +15,7 @@ import logging
import os
from typing import Any
from agent_framework import tool
from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentWorker
from azure.identity import AzureCliCredential, DefaultAzureCredential
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
@@ -28,6 +29,7 @@ WEATHER_AGENT_NAME = "WeatherAgent"
MATH_AGENT_NAME = "MathAgent"
@tool
def get_weather(location: str) -> dict[str, Any]:
"""Get current weather for a location."""
logger.info(f"🔧 [TOOL CALLED] get_weather(location={location})")
@@ -41,11 +43,10 @@ def get_weather(location: str) -> dict[str, Any]:
return result
@tool
def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> dict[str, Any]:
"""Calculate tip amount and total bill."""
logger.info(
f"🔧 [TOOL CALLED] calculate_tip(bill_amount={bill_amount}, tip_percentage={tip_percentage})"
)
logger.info(f"🔧 [TOOL CALLED] calculate_tip(bill_amount={bill_amount}, tip_percentage={tip_percentage})")
tip = bill_amount * (tip_percentage / 100)
total = bill_amount + tip
result = {
@@ -60,7 +61,7 @@ def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> dict[str,
def create_weather_agent():
"""Create the Weather agent using Azure OpenAI.
Returns:
ChatAgent: The configured Weather agent with weather tool
"""
@@ -73,7 +74,7 @@ def create_weather_agent():
def create_math_agent():
"""Create the Math agent using Azure OpenAI.
Returns:
ChatAgent: The configured Math agent with calculation tools
"""
@@ -85,17 +86,15 @@ def create_math_agent():
def get_worker(
taskhub: str | None = None,
endpoint: str | None = None,
log_handler: logging.Handler | None = None
taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
) -> DurableTaskSchedulerWorker:
"""Create a configured DurableTaskSchedulerWorker.
Args:
taskhub: Task hub name (defaults to TASKHUB env var or "default")
endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
log_handler: Optional logging handler for worker logging
Returns:
Configured DurableTaskSchedulerWorker instance
"""
@@ -112,16 +111,16 @@ def get_worker(
secure_channel=endpoint_url != "http://localhost:8080",
taskhub=taskhub_name,
token_credential=credential,
log_handler=log_handler
log_handler=log_handler,
)
def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
"""Set up the worker with multiple agents registered.
Args:
worker: The DurableTaskSchedulerWorker instance
Returns:
DurableAIAgentWorker with agents registered
"""
python/samples/getting_started/durabletask/03_single_agent_streaming/tools.py
+4 -1
View File
@@ -4,10 +4,12 @@
In a real application, these would call actual weather and events APIs.
"""
from typing import Annotated
from agent_framework import tool
@tool
def get_weather_forecast(
destination: Annotated[str, "The destination city or location"],
date: Annotated[str, 'The date for the forecast (e.g., "2025-01-15" or "next Monday")'],
@@ -64,6 +66,7 @@ Low: {low_f}°F ({low_c}°C)
Recommendation: {recommendation}"""
@tool
def get_local_events(
destination: Annotated[str, "The destination city or location"],
date: Annotated[str, 'The date to search for events (e.g., "2025-01-15" or "next week")'],
python/samples/getting_started/middleware/agent_and_run_level_middleware.py
+3 -3
View File
@@ -18,7 +18,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Agent-Level and Run-Level Middleware Example
Agent-Level and Run-Level MiddlewareTypes Example
This sample demonstrates the difference between agent-level and run-level middleware:
@@ -107,7 +107,7 @@ async def debugging_middleware(
"""Run-level debugging middleware for troubleshooting specific runs."""
print("[Debug] Debug mode enabled for this run")
print(f"[Debug] Messages count: {len(context.messages)}")
print(f"[Debug] Is streaming: {context.is_streaming}")
print(f"[Debug] Is streaming: {context.stream}")
# Log existing metadata from agent middleware
if context.metadata:
@@ -163,7 +163,7 @@ async def function_logging_middleware(
async def main() -> None:
"""Example demonstrating agent-level and run-level middleware."""
print("=== Agent-Level and Run-Level Middleware Example ===\n")
print("=== Agent-Level and Run-Level MiddlewareTypes Example ===\n")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
python/samples/getting_started/middleware/chat_middleware.py
+10 -10
View File
@@ -18,7 +18,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Chat Middleware Example
Chat MiddlewareTypes Example
This sample demonstrates how to use chat middleware to observe and override
inputs sent to AI models. Chat middleware intercepts chat requests before they reach
@@ -31,8 +31,8 @@ the underlying AI service, allowing you to:
The example covers:
- Class-based chat middleware inheriting from ChatMiddleware
- Function-based chat middleware with @chat_middleware decorator
- Middleware registration at agent level (applies to all runs)
- Middleware registration at run level (applies to specific run only)
- MiddlewareTypes registration at agent level (applies to all runs)
- MiddlewareTypes registration at run level (applies to specific run only)
"""
@@ -137,7 +137,7 @@ async def security_and_override_middleware(
async def class_based_chat_middleware() -> None:
"""Demonstrate class-based middleware at agent level."""
print("\n" + "=" * 60)
print("Class-based Chat Middleware (Agent Level)")
print("Class-based Chat MiddlewareTypes (Agent Level)")
print("=" * 60)
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
@@ -161,7 +161,7 @@ async def class_based_chat_middleware() -> None:
async def function_based_chat_middleware() -> None:
"""Demonstrate function-based middleware at agent level."""
print("\n" + "=" * 60)
print("Function-based Chat Middleware (Agent Level)")
print("Function-based Chat MiddlewareTypes (Agent Level)")
print("=" * 60)
async with (
@@ -191,7 +191,7 @@ async def function_based_chat_middleware() -> None:
async def run_level_middleware() -> None:
"""Demonstrate middleware registration at run level."""
print("\n" + "=" * 60)
print("Run-level Chat Middleware")
print("Run-level Chat MiddlewareTypes")
print("=" * 60)
async with (
@@ -204,14 +204,14 @@ async def run_level_middleware() -> None:
) as agent,
):
# Scenario 1: Run without any middleware
print("\n--- Scenario 1: No Middleware ---")
print("\n--- Scenario 1: No MiddlewareTypes ---")
query = "What's the weather in Tokyo?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Response: {result.text if result.text else 'No response'}")
# Scenario 2: Run with specific middleware for this call only (both enhancement and security)
print("\n--- Scenario 2: With Run-level Middleware ---")
print("\n--- Scenario 2: With Run-level MiddlewareTypes ---")
print(f"User: {query}")
result = await agent.run(
query,
@@ -223,7 +223,7 @@ async def run_level_middleware() -> None:
print(f"Response: {result.text if result.text else 'No response'}")
# Scenario 3: Security test with run-level middleware
print("\n--- Scenario 3: Security Test with Run-level Middleware ---")
print("\n--- Scenario 3: Security Test with Run-level MiddlewareTypes ---")
query = "Can you help me with my secret API key?"
print(f"User: {query}")
result = await agent.run(
@@ -235,7 +235,7 @@ async def run_level_middleware() -> None:
async def main() -> None:
"""Run all chat middleware examples."""
print("Chat Middleware Examples")
print("Chat MiddlewareTypes Examples")
print("========================")
await class_based_chat_middleware()
python/samples/getting_started/middleware/class_based_middleware.py
+2 -2
View File
@@ -20,7 +20,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Class-based Middleware Example
Class-based MiddlewareTypes Example
This sample demonstrates how to implement middleware using class-based approach by inheriting
from AgentMiddleware and FunctionMiddleware base classes. The example includes:
@@ -95,7 +95,7 @@ class LoggingFunctionMiddleware(FunctionMiddleware):
async def main() -> None:
"""Example demonstrating class-based middleware."""
print("=== Class-based Middleware Example ===")
print("=== Class-based MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
python/samples/getting_started/middleware/decorator_middleware.py
+6 -6
View File
@@ -12,7 +12,7 @@ from agent_framework.azure import AzureAIAgentClient
from azure.identity.aio import AzureCliCredential
"""
Decorator Middleware Example
Decorator MiddlewareTypes Example
This sample demonstrates how to use @agent_middleware and @function_middleware decorators
to explicitly mark middleware functions without requiring type annotations.
@@ -52,22 +52,22 @@ def get_current_time() -> str:
@agent_middleware # Decorator marks this as agent middleware - no type annotations needed
async def simple_agent_middleware(context, next): # type: ignore - parameters intentionally untyped to demonstrate decorator functionality
"""Agent middleware that runs before and after agent execution."""
print("[Agent Middleware] Before agent execution")
print("[Agent MiddlewareTypes] Before agent execution")
await next(context)
print("[Agent Middleware] After agent execution")
print("[Agent MiddlewareTypes] After agent execution")
@function_middleware # Decorator marks this as function middleware - no type annotations needed
async def simple_function_middleware(context, next): # type: ignore - parameters intentionally untyped to demonstrate decorator functionality
"""Function middleware that runs before and after function calls."""
print(f"[Function Middleware] Before calling: {context.function.name}") # type: ignore
print(f"[Function MiddlewareTypes] Before calling: {context.function.name}") # type: ignore
await next(context)
print(f"[Function Middleware] After calling: {context.function.name}") # type: ignore
print(f"[Function MiddlewareTypes] After calling: {context.function.name}") # type: ignore
async def main() -> None:
"""Example demonstrating decorator-based middleware."""
print("=== Decorator Middleware Example ===")
print("=== Decorator MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
python/samples/getting_started/middleware/exception_handling_with_middleware.py
+2 -2
View File
@@ -10,7 +10,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Exception Handling with Middleware
Exception Handling with MiddlewareTypes
This sample demonstrates how to use middleware for centralized exception handling in function calls.
The example shows:
@@ -54,7 +54,7 @@ async def exception_handling_middleware(
async def main() -> None:
"""Example demonstrating exception handling with middleware."""
print("=== Exception Handling Middleware Example ===")
print("=== Exception Handling MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
python/samples/getting_started/middleware/function_based_middleware.py
+2 -2
View File
@@ -16,7 +16,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Function-based Middleware Example
Function-based MiddlewareTypes Example
This sample demonstrates how to implement middleware using simple async functions instead of classes.
The example includes:
@@ -80,7 +80,7 @@ async def logging_function_middleware(
async def main() -> None:
"""Example demonstrating function-based middleware."""
print("=== Function-based Middleware Example ===")
print("=== Function-based MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
python/samples/getting_started/middleware/middleware_termination.py
+6 -6
View File
@@ -17,7 +17,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Middleware Termination Example
MiddlewareTypes Termination Example
This sample demonstrates how middleware can terminate execution using the `context.terminate` flag.
The example includes:
@@ -40,7 +40,7 @@ def get_weather(
class PreTerminationMiddleware(AgentMiddleware):
"""Middleware that terminates execution before calling the agent."""
"""MiddlewareTypes that terminates execution before calling the agent."""
def __init__(self, blocked_words: list[str]):
self.blocked_words = [word.lower() for word in blocked_words]
@@ -79,7 +79,7 @@ class PreTerminationMiddleware(AgentMiddleware):
class PostTerminationMiddleware(AgentMiddleware):
"""Middleware that allows processing but terminates after reaching max responses across multiple runs."""
"""MiddlewareTypes that allows processing but terminates after reaching max responses across multiple runs."""
def __init__(self, max_responses: int = 1):
self.max_responses = max_responses
@@ -109,7 +109,7 @@ class PostTerminationMiddleware(AgentMiddleware):
async def pre_termination_middleware() -> None:
"""Demonstrate pre-termination middleware that blocks requests with certain words."""
print("\n--- Example 1: Pre-termination Middleware ---")
print("\n--- Example 1: Pre-termination MiddlewareTypes ---")
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(credential=credential).as_agent(
@@ -136,7 +136,7 @@ async def pre_termination_middleware() -> None:
async def post_termination_middleware() -> None:
"""Demonstrate post-termination middleware that limits responses across multiple runs."""
print("\n--- Example 2: Post-termination Middleware ---")
print("\n--- Example 2: Post-termination MiddlewareTypes ---")
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(credential=credential).as_agent(
@@ -170,7 +170,7 @@ async def post_termination_middleware() -> None:
async def main() -> None:
"""Example demonstrating middleware termination functionality."""
print("=== Middleware Termination Example ===")
print("=== MiddlewareTypes Termination Example ===")
await pre_termination_middleware()
await post_termination_middleware()
python/samples/getting_started/middleware/override_result_with_middleware.py
+149 -44
View File
@@ -1,7 +1,8 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from collections.abc import AsyncIterable, Awaitable, Callable
import re
from collections.abc import Awaitable, Callable
from random import randint
from typing import Annotated
@@ -9,16 +10,19 @@ from agent_framework import (
AgentResponse,
AgentResponseUpdate,
AgentRunContext,
ChatContext,
ChatMessage,
Content,
ChatResponse,
ChatResponseUpdate,
ResponseStream,
Role,
tool,
)
from agent_framework.azure import AzureAIAgentClient
from azure.identity.aio import AzureCliCredential
from agent_framework.openai import OpenAIResponsesClient
from pydantic import Field
"""
Result Override with Middleware (Regular and Streaming)
Result Override with MiddlewareTypes (Regular and Streaming)
This sample demonstrates how to use middleware to intercept and modify function results
after execution, supporting both regular and streaming agent responses. The example shows:
@@ -26,7 +30,7 @@ after execution, supporting both regular and streaming agent responses. The exam
- How to execute the original function first and then modify its result
- Replacing function outputs with custom messages or transformed data
- Using middleware for result filtering, formatting, or enhancement
- Detecting streaming vs non-streaming execution using context.is_streaming
- Detecting streaming vs non-streaming execution using context.stream
- Overriding streaming results with custom async generators
The weather override middleware lets the original weather function execute normally,
@@ -45,10 +49,8 @@ def get_weather(
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def weather_override_middleware(
context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]]
) -> None:
"""Middleware that overrides weather results for both streaming and non-streaming cases."""
async def weather_override_middleware(context: ChatContext, next: Callable[[ChatContext], Awaitable[None]]) -> None:
"""Chat middleware that overrides weather results for both streaming and non-streaming cases."""
# Let the original agent execution complete first
await next(context)
@@ -57,56 +59,159 @@ async def weather_override_middleware(
if context.result is not None:
# Create custom weather message
chunks = [
"Weather Advisory - ",
"due to special atmospheric conditions, ",
"all locations are experiencing perfect weather today! ",
"Temperature is a comfortable 22°C with gentle breezes. ",
"Perfect day for outdoor activities!",
]
if context.is_streaming:
# For streaming: create an async generator that yields chunks
async def override_stream() -> AsyncIterable[AgentResponseUpdate]:
for chunk in chunks:
yield AgentResponseUpdate(contents=[Content.from_text(text=chunk)])
if context.stream and isinstance(context.result, ResponseStream):
index = {"value": 0}
context.result = override_stream()
def _update_hook(update: ChatResponseUpdate) -> ChatResponseUpdate:
for content in update.contents or []:
if not content.text:
continue
content.text = f"Weather Advisory: [{index['value']}] {content.text}"
index["value"] += 1
return update
context.result.with_update_hook(_update_hook)
else:
# For non-streaming: just replace with the string message
custom_message = "".join(chunks)
context.result = AgentResponse(messages=[ChatMessage("assistant", [custom_message])])
# For non-streaming: just replace with a new message
current_text = context.result.text or ""
custom_message = f"Weather Advisory: [0] {''.join(chunks)} Original message was: {current_text}"
context.result = ChatResponse(messages=[ChatMessage(role=Role.ASSISTANT, text=custom_message)])
async def validate_weather_middleware(context: ChatContext, next: Callable[[ChatContext], Awaitable[None]]) -> None:
"""Chat middleware that simulates result validation for both streaming and non-streaming cases."""
await next(context)
validation_note = "Validation: weather data verified."
if context.result is None:
return
if context.stream and isinstance(context.result, ResponseStream):
def _append_validation_note(response: ChatResponse) -> ChatResponse:
response.messages.append(ChatMessage(role=Role.ASSISTANT, text=validation_note))
return response
context.result.with_finalizer(_append_validation_note)
elif isinstance(context.result, ChatResponse):
context.result.messages.append(ChatMessage(role=Role.ASSISTANT, text=validation_note))
async def agent_cleanup_middleware(
context: AgentRunContext, next: Callable[[AgentRunContext], Awaitable[None]]
) -> None:
"""Agent middleware that validates chat middleware effects and cleans the result."""
await next(context)
if context.result is None:
return
validation_note = "Validation: weather data verified."
state = {"found_prefix": False}
def _sanitize(response: AgentResponse) -> AgentResponse:
found_prefix = state["found_prefix"]
found_validation = False
cleaned_messages: list[ChatMessage] = []
for message in response.messages:
text = message.text
if text is None:
cleaned_messages.append(message)
continue
if validation_note in text:
found_validation = True
text = text.replace(validation_note, "").strip()
if not text:
continue
if "Weather Advisory:" in text:
found_prefix = True
text = text.replace("Weather Advisory:", "")
text = re.sub(r"\[\d+\]\s*", "", text)
cleaned_messages.append(
ChatMessage(
role=message.role,
text=text.strip(),
author_name=message.author_name,
message_id=message.message_id,
additional_properties=message.additional_properties,
raw_representation=message.raw_representation,
)
)
if not found_prefix:
raise RuntimeError("Expected chat middleware prefix not found in agent response.")
if not found_validation:
raise RuntimeError("Expected validation note not found in agent response.")
cleaned_messages.append(ChatMessage(role=Role.ASSISTANT, text=" Agent: OK"))
response.messages = cleaned_messages
return response
if context.stream and isinstance(context.result, ResponseStream):
def _clean_update(update: AgentResponseUpdate) -> AgentResponseUpdate:
for content in update.contents or []:
if not content.text:
continue
text = content.text
if "Weather Advisory:" in text:
state["found_prefix"] = True
text = text.replace("Weather Advisory:", "")
text = re.sub(r"\[\d+\]\s*", "", text)
content.text = text
return update
context.result.with_update_hook(_clean_update)
context.result.with_finalizer(_sanitize)
elif isinstance(context.result, AgentResponse):
context.result = _sanitize(context.result)
async def main() -> None:
"""Example demonstrating result override with middleware for both streaming and non-streaming."""
print("=== Result Override Middleware Example ===")
print("=== Result Override MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(credential=credential).as_agent(
name="WeatherAgent",
instructions="You are a helpful weather assistant. Use the weather tool to get current conditions.",
tools=get_weather,
middleware=[weather_override_middleware],
) as agent,
):
# Non-streaming example
print("\n--- Non-streaming Example ---")
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}")
agent = OpenAIResponsesClient(
middleware=[validate_weather_middleware, weather_override_middleware],
).as_agent(
name="WeatherAgent",
instructions="You are a helpful weather assistant. Use the weather tool to get current conditions.",
tools=get_weather,
middleware=[agent_cleanup_middleware],
)
# Non-streaming example
print("\n--- Non-streaming Example ---")
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}")
# Streaming example
print("\n--- Streaming Example ---")
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
if chunk.text:
print(chunk.text, end="", flush=True)
# Streaming example
print("\n--- Streaming Example ---")
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
response = agent.run(query, stream=True)
async for chunk in response:
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
print(f"Final Result: {(await response.get_final_response()).text}")
if __name__ == "__main__":
python/samples/getting_started/middleware/runtime_context_delegation.py
+11 -11
View File
@@ -16,9 +16,9 @@ session data, etc.) to tools and sub-agents.
Patterns Demonstrated:
1. **Pattern 1: Single Agent with Middleware & Closure** (Lines 130-180)
1. **Pattern 1: Single Agent with MiddlewareTypes & Closure** (Lines 130-180)
- Best for: Single agent with multiple tools
- How: Middleware stores kwargs in container, tools access via closure
- How: MiddlewareTypes stores kwargs in container, tools access via closure
- Pros: Simple, explicit state management
- Cons: Requires container instance per agent
@@ -28,7 +28,7 @@ Patterns Demonstrated:
- Pros: Automatic, works with nested delegation, clean separation
- Cons: None - this is the recommended pattern for hierarchical agents
3. **Pattern 3: Mixed - Hierarchical with Middleware** (Lines 250-300)
3. **Pattern 3: Mixed - Hierarchical with MiddlewareTypes** (Lines 250-300)
- Best for: Complex scenarios needing both delegation and state management
- How: Combines automatic kwargs propagation with middleware processing
- Pros: Maximum flexibility, can transform/validate context at each level
@@ -36,7 +36,7 @@ Patterns Demonstrated:
Key Concepts:
- Runtime Context: Session-specific data like API tokens, user IDs, tenant info
- Middleware: Intercepts function calls to access/modify kwargs
- MiddlewareTypes: Intercepts function calls to access/modify kwargs
- Closure: Functions capturing variables from outer scope
- kwargs Propagation: Automatic forwarding of runtime context through delegation chains
"""
@@ -56,7 +56,7 @@ class SessionContextContainer:
context: FunctionInvocationContext,
next: Callable[[FunctionInvocationContext], Awaitable[None]],
) -> None:
"""Middleware that extracts runtime context from kwargs and stores in container.
"""MiddlewareTypes that extracts runtime context from kwargs and stores in container.
This middleware runs before tool execution and makes runtime context
available to tools via the container instance.
@@ -68,7 +68,7 @@ class SessionContextContainer:
# Log what we captured (for demonstration)
if self.api_token or self.user_id:
print("[Middleware] Captured runtime context:")
print("[MiddlewareTypes] Captured runtime context:")
print(f" - API Token: {'[PRESENT]' if self.api_token else '[NOT PROVIDED]'}")
print(f" - User ID: {'[PRESENT]' if self.user_id else '[NOT PROVIDED]'}")
print(f" - Session Metadata Keys: {list(self.session_metadata.keys())}")
@@ -140,7 +140,7 @@ async def send_notification(
async def pattern_1_single_agent_with_closure() -> None:
"""Pattern 1: Single agent with middleware and closure for runtime context."""
print("\n" + "=" * 70)
print("PATTERN 1: Single Agent with Middleware & Closure")
print("PATTERN 1: Single Agent with MiddlewareTypes & Closure")
print("=" * 70)
print("Use case: Single agent with multiple tools sharing runtime context")
print()
@@ -234,7 +234,7 @@ async def pattern_1_single_agent_with_closure() -> None:
print(f"\nAgent: {result4.text}")
print("\n✓ Pattern 1 complete - Middleware & closure pattern works for single agents")
print("\n✓ Pattern 1 complete - MiddlewareTypes & closure pattern works for single agents")
# Pattern 2: Hierarchical agents with automatic kwargs propagation
@@ -353,7 +353,7 @@ async def pattern_2_hierarchical_with_kwargs_propagation() -> None:
class AuthContextMiddleware:
"""Middleware that validates and transforms runtime context."""
"""MiddlewareTypes that validates and transforms runtime context."""
def __init__(self) -> None:
self.validated_tokens: list[str] = []
@@ -387,7 +387,7 @@ async def protected_operation(operation: Annotated[str, Field(description="Opera
async def pattern_3_hierarchical_with_middleware() -> None:
"""Pattern 3: Hierarchical agents with middleware processing at each level."""
print("\n" + "=" * 70)
print("PATTERN 3: Hierarchical with Middleware Processing")
print("PATTERN 3: Hierarchical with MiddlewareTypes Processing")
print("=" * 70)
print("Use case: Multi-level validation/transformation of runtime context")
print()
@@ -433,7 +433,7 @@ async def pattern_3_hierarchical_with_middleware() -> None:
)
print(f"\n[Validation Summary] Validated tokens: {len(auth_middleware.validated_tokens)}")
print("✓ Pattern 3 complete - Middleware can validate/transform context at each level")
print("✓ Pattern 3 complete - MiddlewareTypes can validate/transform context at each level")
async def main() -> None:
python/samples/getting_started/middleware/shared_state_middleware.py
+2 -2
View File
@@ -14,7 +14,7 @@ from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Shared State Function-based Middleware Example
Shared State Function-based MiddlewareTypes Example
This sample demonstrates how to implement function-based middleware within a class to share state.
The example includes:
@@ -88,7 +88,7 @@ class MiddlewareContainer:
async def main() -> None:
"""Example demonstrating shared state function-based middleware."""
print("=== Shared State Function-based Middleware Example ===")
print("=== Shared State Function-based MiddlewareTypes Example ===")
# Create middleware container with shared state
middleware_container = MiddlewareContainer()
python/samples/getting_started/middleware/thread_behavior_middleware.py
+6 -6
View File
@@ -14,7 +14,7 @@ from azure.identity import AzureCliCredential
from pydantic import Field
"""
Thread Behavior Middleware Example
Thread Behavior MiddlewareTypes Example
This sample demonstrates how middleware can access and track thread state across multiple agent runs.
The example shows:
@@ -48,13 +48,13 @@ async def thread_tracking_middleware(
context: AgentRunContext,
next: Callable[[AgentRunContext], Awaitable[None]],
) -> None:
"""Middleware that tracks and logs thread behavior across runs."""
"""MiddlewareTypes that tracks and logs thread behavior across runs."""
thread_messages = []
if context.thread and context.thread.message_store:
thread_messages = await context.thread.message_store.list_messages()
print(f"[Middleware pre-execution] Current input messages: {len(context.messages)}")
print(f"[Middleware pre-execution] Thread history messages: {len(thread_messages)}")
print(f"[MiddlewareTypes pre-execution] Current input messages: {len(context.messages)}")
print(f"[MiddlewareTypes pre-execution] Thread history messages: {len(thread_messages)}")
# Call next to execute the agent
await next(context)
@@ -64,12 +64,12 @@ async def thread_tracking_middleware(
if context.thread and context.thread.message_store:
updated_thread_messages = await context.thread.message_store.list_messages()
print(f"[Middleware post-execution] Updated thread messages: {len(updated_thread_messages)}")
print(f"[MiddlewareTypes post-execution] Updated thread messages: {len(updated_thread_messages)}")
async def main() -> None:
"""Example demonstrating thread behavior in middleware across multiple runs."""
print("=== Thread Behavior Middleware Example ===")
print("=== Thread Behavior MiddlewareTypes Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
python/samples/getting_started/observability/advanced_manual_setup_console_output.py
+1 -1
View File
@@ -107,7 +107,7 @@ async def run_chat_client() -> None:
message = "What's the weather in Amsterdam and in Paris?"
print(f"User: {message}")
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/observability/advanced_zero_code.py
+1 -1
View File
@@ -81,7 +81,7 @@ async def run_chat_client(client: "ChatClientProtocol", stream: bool = False) ->
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/observability/agent_observability.py
+2 -1
View File
@@ -50,9 +50,10 @@ async def main():
for question in questions:
print(f"\nUser: {question}")
print(f"{agent.name}: ", end="")
async for update in agent.run_stream(
async for update in agent.run(
question,
thread=thread,
stream=True,
):
if update.text:
print(update.text, end="")
python/samples/getting_started/observability/agent_with_foundry_tracing.py
+1 -4
View File
@@ -87,10 +87,7 @@ async def main():
for question in questions:
print(f"\nUser: {question}")
print(f"{agent.name}: ", end="")
async for update in agent.run_stream(
question,
thread=thread,
):
async for update in agent.run(question, thread=thread, stream=True):
if update.text:
print(update.text, end="")
python/samples/getting_started/observability/azure_ai_agent_observability.py
+1 -4
View File
@@ -67,10 +67,7 @@ async def main():
for question in questions:
print(f"\nUser: {question}")
print(f"{agent.name}: ", end="")
async for update in agent.run_stream(
question,
thread=thread,
):
async for update in agent.run(question, thread=thread, stream=True):
if update.text:
print(update.text, end="")
python/samples/getting_started/observability/configure_otel_providers_with_env_var.py
+1 -1
View File
@@ -71,7 +71,7 @@ async def run_chat_client(client: "ChatClientProtocol", stream: bool = False) ->
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, tools=get_weather, stream=True):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/observability/configure_otel_providers_with_parameters.py
+1 -1
View File
@@ -71,7 +71,7 @@ async def run_chat_client(client: "ChatClientProtocol", stream: bool = False) ->
print(f"User: {message}")
if stream:
print("Assistant: ", end="")
async for chunk in client.get_streaming_response(message, tools=get_weather):
async for chunk in client.get_response(message, stream=True, tools=get_weather):
if str(chunk):
print(str(chunk), end="")
print("")
python/samples/getting_started/observability/workflow_observability.py
+1 -1
View File
@@ -92,7 +92,7 @@ async def run_sequential_workflow() -> None:
print(f"Starting workflow with input: '{input_text}'")
output_event = None
async for event in workflow.run_stream("Hello world"):
async for event in workflow.run("Hello world", stream=True):
if isinstance(event, WorkflowOutputEvent):
# The WorkflowOutputEvent contains the final result.
output_event = event
python/samples/getting_started/orchestrations/group_chat_agent_manager.py
+1 -1
View File
@@ -87,7 +87,7 @@ async def main() -> None:
# Keep track of the last response to format output nicely in streaming mode
last_response_id: str | None = None
async for event in workflow.run_stream(task):
async for event in workflow.run(task, stream=True):
if isinstance(event, WorkflowOutputEvent):
data = event.data
if isinstance(data, AgentResponseUpdate):
python/samples/getting_started/orchestrations/group_chat_philosophical_debate.py
+1 -1
View File
@@ -240,7 +240,7 @@ Share your perspective authentically. Feel free to:
# Keep track of the last response to format output nicely in streaming mode
last_response_id: str | None = None
async for event in workflow.run_stream(f"Please begin the discussion on: {topic}"):
async for event in workflow.run(f"Please begin the discussion on: {topic}", stream=True):
if isinstance(event, WorkflowOutputEvent):
data = event.data
if isinstance(data, AgentResponseUpdate):
python/samples/getting_started/orchestrations/group_chat_simple_selector.py
+1 -1
View File
@@ -105,7 +105,7 @@ async def main() -> None:
# Keep track of the last response to format output nicely in streaming mode
last_response_id: str | None = None
async for event in workflow.run_stream(task):
async for event in workflow.run(task, stream=True):
if isinstance(event, WorkflowOutputEvent):
data = event.data
if isinstance(data, AgentResponseUpdate):
python/samples/getting_started/orchestrations/handoff_autonomous.py
+1 -1
View File
@@ -111,7 +111,7 @@ async def main() -> None:
print("Request:", request)
last_response_id: str | None = None
async for event in workflow.run_stream(request):
async for event in workflow.run(request, stream=True):
if isinstance(event, HandoffSentEvent):
print(f"\nHandoff Event: from {event.source} to {event.target}\n")
elif isinstance(event, WorkflowOutputEvent):
python/samples/getting_started/orchestrations/handoff_simple.py
+3 -3
View File
@@ -233,12 +233,12 @@ async def main() -> None:
]
# Start the workflow with the initial user message
# run_stream() returns an async iterator of WorkflowEvent
# run(..., stream=True) returns an async iterator of WorkflowEvent
print("[Starting workflow with initial user message...]\n")
initial_message = "Hello, I need assistance with my recent purchase."
print(f"- User: {initial_message}")
workflow_result = await workflow.run(initial_message)
pending_requests = _handle_events(workflow_result)
workflow_result = workflow.run(initial_message, stream=True)
pending_requests = _handle_events([event async for event in workflow_result])
# Process the request/response cycle
# The workflow will continue requesting input until:
python/samples/getting_started/orchestrations/handoff_with_code_interpreter_file.py
+1 -1
View File
@@ -187,7 +187,7 @@ async def main() -> None:
all_file_ids: list[str] = []
print(f"User: {user_inputs[0]}")
events = await _drain(workflow.run_stream(user_inputs[0]))
events = await _drain(workflow.run(user_inputs[0], stream=True))
requests, file_ids = _handle_events(events)
all_file_ids.extend(file_ids)
input_index += 1
python/samples/getting_started/orchestrations/magentic.py
+1 -1
View File
@@ -104,7 +104,7 @@ async def main() -> None:
# Keep track of the last executor to format output nicely in streaming mode
last_response_id: str | None = None
async for event in workflow.run_stream(task):
async for event in workflow.run(task, stream=True):
if isinstance(event, MagenticOrchestratorEvent):
print(f"\n[Magentic Orchestrator Event] Type: {event.event_type.name}")
if isinstance(event.data, ChatMessage):
python/samples/getting_started/orchestrations/magentic_checkpoint.py
+3 -3
View File
@@ -109,7 +109,7 @@ async def main() -> None:
# request_id we must reuse on resume. In a real system this is where the UI would present
# the plan for human review.
plan_review_request: MagenticPlanReviewRequest | None = None
async for event in workflow.run_stream(TASK):
async for event in workflow.run(TASK, stream=True):
if isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest:
plan_review_request = event.data
print(f"Captured plan review request: {event.request_id}")
@@ -148,7 +148,7 @@ async def main() -> None:
# Resume execution and capture the re-emitted plan review request.
request_info_event: RequestInfoEvent | None = None
async for event in resumed_workflow.run_stream(checkpoint_id=resume_checkpoint.checkpoint_id):
async for event in resumed_workflow.run(checkpoint_id=resume_checkpoint.checkpoint_id, stream=True):
if isinstance(event, RequestInfoEvent) and isinstance(event.data, MagenticPlanReviewRequest):
request_info_event = event
@@ -221,7 +221,7 @@ async def main() -> None:
final_event_post: WorkflowOutputEvent | None = None
post_emitted_events = False
post_plan_workflow = build_workflow(checkpoint_storage)
async for event in post_plan_workflow.run_stream(checkpoint_id=post_plan_checkpoint.checkpoint_id):
async for event in post_plan_workflow.run(checkpoint_id=post_plan_checkpoint.checkpoint_id, stream=True):
post_emitted_events = True
if isinstance(event, WorkflowOutputEvent):
final_event_post = event
python/samples/getting_started/orchestrations/magentic_human_plan_review.py
+1 -1
View File
@@ -142,7 +142,7 @@ async def main() -> None:
# Initiate the first run of the workflow.
# Runs are not isolated; state is preserved across multiple calls to run or send_responses_streaming.
stream = workflow.run_stream(task)
stream = workflow.run(task, stream=True)
pending_responses = await process_event_stream(stream)
while pending_responses is not None:
python/samples/getting_started/orchestrations/sequential_agents.py
+1 -1
View File
@@ -47,7 +47,7 @@ async def main() -> None:
# 3) Run and collect outputs
outputs: list[list[ChatMessage]] = []
async for event in workflow.run_stream("Write a tagline for a budget-friendly eBike."):
async for event in workflow.run("Write a tagline for a budget-friendly eBike.", stream=True):
if isinstance(event, WorkflowOutputEvent):
outputs.append(cast(list[ChatMessage], event.data))
python/samples/getting_started/purview_agent/sample_purview_agent.py
+3 -3
View File
@@ -157,7 +157,7 @@ async def run_with_agent_middleware() -> None:
middleware=[purview_agent_middleware],
)
print("-- Agent Middleware Path --")
print("-- Agent MiddlewareTypes Path --")
first: AgentResponse = await agent.run(
ChatMessage("user", ["Tell me a joke about a pirate."], additional_properties={"user_id": user_id})
)
@@ -200,7 +200,7 @@ async def run_with_chat_middleware() -> None:
name=JOKER_NAME,
)
print("-- Chat Middleware Path --")
print("-- Chat MiddlewareTypes Path --")
first: AgentResponse = await agent.run(
ChatMessage(
role="user",
@@ -305,7 +305,7 @@ async def run_with_custom_cache_provider() -> None:
async def main() -> None:
print("== Purview Agent Sample (Middleware with Automatic Caching) ==")
print("== Purview Agent Sample (MiddlewareTypes with Automatic Caching) ==")
try:
await run_with_agent_middleware()
python/samples/getting_started/tools/function_tool_with_approval.py
+6 -6
View File
@@ -88,7 +88,7 @@ async def handle_approvals_streaming(query: str, agent: "AgentProtocol") -> None
user_input_requests: list[Any] = []
# Stream the response
async for chunk in agent.run_stream(current_input):
async for chunk in agent.run(current_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
@@ -123,9 +123,9 @@ async def handle_approvals_streaming(query: str, agent: "AgentProtocol") -> None
current_input = new_inputs
async def run_weather_agent_with_approval(is_streaming: bool) -> None:
async def run_weather_agent_with_approval(stream: bool) -> None:
"""Example showing AI function with approval requirement."""
print(f"\n=== Weather Agent with Approval Required ({'Streaming' if is_streaming else 'Non-Streaming'}) ===\n")
print(f"\n=== Weather Agent with Approval Required ({'Streaming' if stream else 'Non-Streaming'}) ===\n")
async with ChatAgent(
chat_client=OpenAIResponsesClient(),
@@ -136,7 +136,7 @@ async def run_weather_agent_with_approval(is_streaming: bool) -> None:
query = "Can you give me an update of the weather in LA and Portland and detailed weather for Seattle?"
print(f"User: {query}")
if is_streaming:
if stream:
print(f"\n{agent.name}: ", end="", flush=True)
await handle_approvals_streaming(query, agent)
print()
@@ -148,8 +148,8 @@ async def run_weather_agent_with_approval(is_streaming: bool) -> None:
async def main() -> None:
print("=== Demonstration of a tool with approvals ===\n")
await run_weather_agent_with_approval(is_streaming=False)
await run_weather_agent_with_approval(is_streaming=True)
await run_weather_agent_with_approval(stream=False)
await run_weather_agent_with_approval(stream=True)
if __name__ == "__main__":
python/samples/getting_started/workflows/_start-here/step3_streaming.py
+3 -2
View File
@@ -52,8 +52,9 @@ async def main():
last_author: str | None = None
# Run the workflow with the user's initial message and stream events as they occur.
async for event in workflow.run_stream(
ChatMessage("user", ["Create a slogan for a new electric SUV that is affordable and fun to drive."])
async for event in workflow.run(
ChatMessage("user", ["Create a slogan for a new electric SUV that is affordable and fun to drive."]),
stream=True,
):
# The outputs of the workflow are whatever the agents produce. So the events are expected to
# contain `AgentResponseUpdate` from the agents in the workflow.
python/samples/getting_started/workflows/_start-here/step4_using_factories.py
+1 -1
View File
@@ -84,7 +84,7 @@ async def main():
)
first_update = True
async for event in workflow.run_stream("hello world"):
async for event in workflow.run("hello world", stream=True):
# The outputs of the workflow are whatever the agents produce. So the events are expected to
# contain `AgentResponseUpdate` from the agents in the workflow.
if isinstance(event, WorkflowOutputEvent) and isinstance(event.data, AgentResponseUpdate):
python/samples/getting_started/workflows/agents/azure_ai_agents_streaming.py
+4 -2
View File
@@ -38,13 +38,15 @@ async def main() -> None:
)
# Build the workflow by adding agents directly as edges.
# Agents adapt to workflow mode: run_stream() for incremental updates, run() for complete responses.
# Agents adapt to workflow mode: run(stream=True) for complete responses, run() for incremental updates.
workflow = WorkflowBuilder().set_start_executor(writer_agent).add_edge(writer_agent, reviewer_agent).build()
# Track the last author to format streaming output.
last_author: str | None = None
events = workflow.run_stream("Create a slogan for a new electric SUV that is affordable and fun to drive.")
events = workflow.run(
"Create a slogan for a new electric SUV that is affordable and fun to drive.", stream=True
)
async for event in events:
# The outputs of the workflow are whatever the agents produce. So the events are expected to
# contain `AgentResponseUpdate` from the agents in the workflow.
python/samples/getting_started/workflows/agents/azure_chat_agents_and_executor.py
+2 -2
View File
@@ -118,8 +118,8 @@ async def main() -> None:
.build()
)
events = workflow.run_stream(
"Create quick workspace wellness tips for a remote analyst working across two monitors."
events = workflow.run(
"Create quick workspace wellness tips for a remote analyst working across two monitors.", stream=True
)
# Track the last author to format streaming output.
python/samples/getting_started/workflows/agents/azure_chat_agents_streaming.py
+2 -2
View File
@@ -39,13 +39,13 @@ async def main():
# Build the workflow using the fluent builder.
# Set the start node and connect an edge from writer to reviewer.
# Agents adapt to workflow mode: run_stream() for incremental updates, run() for complete responses.
# Agents adapt to workflow mode: run(stream=True) for incremental updates, run() for complete responses.
workflow = WorkflowBuilder().set_start_executor(writer_agent).add_edge(writer_agent, reviewer_agent).build()
# Track the last author to format streaming output.
last_author: str | None = None
events = workflow.run_stream("Create a slogan for a new electric SUV that is affordable and fun to drive.")
events = workflow.run("Create a slogan for a new electric SUV that is affordable and fun to drive.", stream=True)
async for event in events:
# The outputs of the workflow are whatever the agents produce. So the events are expected to
# contain `AgentResponseUpdate` from the agents in the workflow.
python/samples/getting_started/workflows/agents/azure_chat_agents_tool_calls_with_feedback.py
+325
View File
@@ -0,0 +1,325 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import json
from dataclasses import dataclass, field
from typing import Annotated
from agent_framework import (
AgentExecutorRequest,
AgentExecutorResponse,
AgentResponse,
AgentRunUpdateEvent,
ChatAgent,
ChatMessage,
Executor,
FunctionCallContent,
FunctionResultContent,
RequestInfoEvent,
WorkflowBuilder,
WorkflowContext,
WorkflowOutputEvent,
handler,
response_handler,
tool,
)
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
from pydantic import Field
from typing_extensions import Never
"""
Sample: Tool-enabled agents with human feedback
Pipeline layout:
writer_agent (uses Azure OpenAI tools) -> Coordinator -> writer_agent
-> Coordinator -> final_editor_agent -> Coordinator -> output
The writer agent calls tools to gather product facts before drafting copy. A custom executor
packages the draft and emits a RequestInfoEvent so a human can comment, then replays the human
guidance back into the conversation before the final editor agent produces the polished output.
Demonstrates:
- Attaching Python function tools to an agent inside a workflow.
- Capturing the writer's output for human review.
- Streaming AgentRunUpdateEvent updates alongside human-in-the-loop pauses.
Prerequisites:
- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
- Authentication via azure-identity. Run `az login` before executing.
"""
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
@tool(approval_mode="never_require")
def fetch_product_brief(
product_name: Annotated[str, Field(description="Product name to look up.")],
) -> str:
"""Return a marketing brief for a product."""
briefs = {
"lumenx desk lamp": (
"Product: LumenX Desk Lamp\n"
"- Three-point adjustable arm with 270° rotation.\n"
"- Custom warm-to-neutral LED spectrum (2700K-4000K).\n"
"- USB-C charging pad integrated in the base.\n"
"- Designed for home offices and late-night study sessions."
)
}
return briefs.get(product_name.lower(), f"No stored brief for '{product_name}'.")
@tool(approval_mode="never_require")
def get_brand_voice_profile(
voice_name: Annotated[str, Field(description="Brand or campaign voice to emulate.")],
) -> str:
"""Return guidance for the requested brand voice."""
voices = {
"lumenx launch": (
"Voice guidelines:\n"
"- Friendly and modern with concise sentences.\n"
"- Highlight practical benefits before aesthetics.\n"
"- End with an invitation to imagine the product in daily use."
)
}
return voices.get(voice_name.lower(), f"No stored voice profile for '{voice_name}'.")
@dataclass
class DraftFeedbackRequest:
"""Payload sent for human review."""
prompt: str = ""
draft_text: str = ""
conversation: list[ChatMessage] = field(default_factory=list) # type: ignore[reportUnknownVariableType]
class Coordinator(Executor):
"""Bridge between the writer agent, human feedback, and final editor."""
def __init__(self, id: str, writer_id: str, final_editor_id: str) -> None:
super().__init__(id)
self.writer_id = writer_id
self.final_editor_id = final_editor_id
@handler
async def on_writer_response(
self,
draft: AgentExecutorResponse,
ctx: WorkflowContext[Never, AgentResponse],
) -> None:
"""Handle responses from the other two agents in the workflow."""
if draft.executor_id == self.final_editor_id:
# Final editor response; yield output directly.
await ctx.yield_output(draft.agent_response)
return
# Writer agent response; request human feedback.
# Preserve the full conversation so the final editor
# can see tool traces and the initial prompt.
conversation: list[ChatMessage]
if draft.full_conversation is not None:
conversation = list(draft.full_conversation)
else:
conversation = list(draft.agent_response.messages)
draft_text = draft.agent_response.text.strip()
if not draft_text:
draft_text = "No draft text was produced."
prompt = (
"Review the draft from the writer and provide a short directional note "
"(tone tweaks, must-have detail, target audience, etc.). "
"Keep it under 30 words."
)
await ctx.request_info(
request_data=DraftFeedbackRequest(prompt=prompt, draft_text=draft_text, conversation=conversation),
response_type=str,
)
@response_handler
async def on_human_feedback(
self,
original_request: DraftFeedbackRequest,
feedback: str,
ctx: WorkflowContext[AgentExecutorRequest],
) -> None:
note = feedback.strip()
if note.lower() == "approve":
# Human approved the draft as-is; forward it unchanged.
await ctx.send_message(
AgentExecutorRequest(
messages=original_request.conversation
+ [ChatMessage("user", text="The draft is approved as-is.")],
should_respond=True,
),
target_id=self.final_editor_id,
)
return
# Human provided feedback; prompt the writer to revise.
conversation: list[ChatMessage] = list(original_request.conversation)
instruction = (
"A human reviewer shared the following guidance:\n"
f"{note or 'No specific guidance provided.'}\n\n"
"Rewrite the draft from the previous assistant message into a polished final version. "
"Keep the response under 120 words and reflect any requested tone adjustments."
)
conversation.append(ChatMessage("user", text=instruction))
await ctx.send_message(
AgentExecutorRequest(messages=conversation, should_respond=True), target_id=self.writer_id
)
def create_writer_agent() -> ChatAgent:
"""Creates a writer agent with tools."""
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
name="writer_agent",
instructions=(
"You are a marketing writer. Call the available tools before drafting copy so you are precise. "
"Always call both tools once before drafting. Summarize tool outputs as bullet points, then "
"produce a 3-sentence draft."
),
tools=[fetch_product_brief, get_brand_voice_profile],
tool_choice="required",
)
def create_final_editor_agent() -> ChatAgent:
"""Creates a final editor agent."""
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
name="final_editor_agent",
instructions=(
"You are an editor who polishes marketing copy after human approval. "
"Correct any legal or factual issues. Return the final version even if no changes are made. "
),
)
def display_agent_run_update(event: AgentRunUpdateEvent, last_executor: str | None) -> None:
"""Display an AgentRunUpdateEvent in a readable format."""
printed_tool_calls: set[str] = set()
printed_tool_results: set[str] = set()
executor_id = event.executor_id
update = event.data
# Extract and print any new tool calls or results from the update.
function_calls = [c for c in update.contents if isinstance(c, FunctionCallContent)] # type: ignore[union-attr]
function_results = [c for c in update.contents if isinstance(c, FunctionResultContent)] # type: ignore[union-attr]
if executor_id != last_executor:
if last_executor is not None:
print()
print(f"{executor_id}:", end=" ", flush=True)
last_executor = executor_id
# Print any new tool calls before the text update.
for call in function_calls:
if call.call_id in printed_tool_calls:
continue
printed_tool_calls.add(call.call_id)
args = call.arguments
args_preview = json.dumps(args, ensure_ascii=False) if isinstance(args, dict) else (args or "").strip()
print(
f"\n{executor_id} [tool-call] {call.name}({args_preview})",
flush=True,
)
print(f"{executor_id}:", end=" ", flush=True)
# Print any new tool results before the text update.
for result in function_results:
if result.call_id in printed_tool_results:
continue
printed_tool_results.add(result.call_id)
result_text = result.result
if not isinstance(result_text, str):
result_text = json.dumps(result_text, ensure_ascii=False)
print(
f"\n{executor_id} [tool-result] {result.call_id}: {result_text}",
flush=True,
)
print(f"{executor_id}:", end=" ", flush=True)
# Finally, print the text update.
print(update, end="", flush=True)
async def main() -> None:
"""Run the workflow and bridge human feedback between two agents."""
# Build the workflow.
workflow = (
WorkflowBuilder()
.register_agent(create_writer_agent, name="writer_agent")
.register_agent(create_final_editor_agent, name="final_editor_agent")
.register_executor(
lambda: Coordinator(
id="coordinator",
writer_id="writer_agent",
final_editor_id="final_editor_agent",
),
name="coordinator",
)
.set_start_executor("writer_agent")
.add_edge("writer_agent", "coordinator")
.add_edge("coordinator", "writer_agent")
.add_edge("final_editor_agent", "coordinator")
.add_edge("coordinator", "final_editor_agent")
.build()
)
# Switch to turn on agent run update display.
# By default this is off to reduce clutter during human input.
display_agent_run_update_switch = False
print(
"Interactive mode. When prompted, provide a short feedback note for the editor.",
flush=True,
)
pending_responses: dict[str, str] | None = None
completed = False
initial_run = True
while not completed:
last_executor: str | None = None
if initial_run:
stream = workflow.run(
"Create a short launch blurb for the LumenX desk lamp. Emphasize adjustability and warm lighting.",
stream=True,
)
initial_run = False
elif pending_responses is not None:
stream = workflow.send_responses_streaming(pending_responses)
pending_responses = None
else:
break
requests: list[tuple[str, DraftFeedbackRequest]] = []
async for event in stream:
if isinstance(event, AgentRunUpdateEvent) and display_agent_run_update_switch:
display_agent_run_update(event, last_executor)
if isinstance(event, RequestInfoEvent) and isinstance(event.data, DraftFeedbackRequest):
# Stash the request so we can prompt the human after the stream completes.
requests.append((event.request_id, event.data))
last_executor = None
elif isinstance(event, WorkflowOutputEvent):
last_executor = None
response = event.data
print("\n===== Final output =====")
final_text = getattr(response, "text", str(response))
print(final_text.strip())
completed = True
if requests and not completed:
responses: dict[str, str] = {}
for request_id, request in requests:
print("\n----- Writer draft -----")
print(request.draft_text.strip())
print("\nProvide guidance for the editor (or 'approve' to accept the draft).")
answer = input("Human feedback: ").strip() # noqa: ASYNC250
if answer.lower() == "exit":
print("Exiting...")
return
responses[request_id] = answer
pending_responses = responses
print("Workflow complete.")
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/workflows/agents/magentic_workflow_as_agent.py
+1 -1
View File
@@ -85,7 +85,7 @@ async def main() -> None:
workflow_agent = workflow.as_agent(name="MagenticWorkflowAgent")
last_response_id: str | None = None
async for update in workflow_agent.run_stream(task):
async for update in workflow_agent.run(task, stream=True):
# Fallback for any other events with text
if last_response_id != update.response_id:
if last_response_id is not None:
python/samples/getting_started/workflows/agents/workflow_as_agent_kwargs.py
+7 -6
View File
@@ -4,8 +4,9 @@ import asyncio
import json
from typing import Annotated, Any
from agent_framework import SequentialBuilder, tool
from agent_framework import tool
from agent_framework.openai import OpenAIChatClient
from agent_framework.orchestrations import SequentialBuilder
from pydantic import Field
"""
@@ -17,7 +18,7 @@ through a workflow exposed via .as_agent() to @tool functions using the **kwargs
Key Concepts:
- Build a workflow using SequentialBuilder (or any builder pattern)
- Expose the workflow as a reusable agent via workflow.as_agent()
- Pass custom context as kwargs when invoking workflow_agent.run() or run_stream()
- Pass custom context as kwargs when invoking workflow_agent.run()
- kwargs are stored in State and propagated to all agent invocations
- @tool functions receive kwargs via **kwargs parameter
@@ -121,12 +122,12 @@ async def main() -> None:
print("-" * 70)
# Run workflow agent with kwargs - these will flow through to tools
# Note: kwargs are passed to workflow_agent.run_stream() just like workflow.run_stream()
# Note: kwargs are passed to workflow.run()
print("\n===== Streaming Response =====")
async for update in workflow_agent.run_stream(
async for update in workflow_agent.run(
"Please get my user data and then call the users API endpoint.",
custom_data=custom_data,
user_token=user_token,
additional_function_arguments={"custom_data": custom_data, "user_token": user_token},
stream=True,
):
if update.text:
print(update.text, end="", flush=True)

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