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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/custom/custom_chat_client.py
-175
View File
@@ -1,175 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import random
import sys
from collections.abc import AsyncIterable, MutableSequence
from typing import Any, ClassVar, Generic
from agent_framework import (
BaseChatClient,
ChatMessage,
ChatResponse,
ChatResponseUpdate,
Content,
use_chat_middleware,
use_function_invocation,
)
from agent_framework._clients import TOptions_co
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.
"""
@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.
"""
OTEL_PROVIDER_NAME: ClassVar[str] = "EchoingChatClient"
def __init__(self, *, prefix: str = "Echo:", **kwargs: Any) -> None:
"""Initialize the EchoingChatClient.
Args:
prefix: Prefix to add to echoed messages.
**kwargs: Additional keyword arguments passed to BaseChatClient.
"""
super().__init__(**kwargs)
self.prefix = prefix
@override
async def _inner_get_response(
self,
*,
messages: MutableSequence[ChatMessage],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
"""Echo back the user's message with a prefix."""
if not messages:
response_text = "No messages to echo!"
else:
# Echo the last user message
last_user_message = None
for message in reversed(messages):
if message.role == "user":
last_user_message = message
break
if last_user_message and last_user_message.text:
response_text = f"{self.prefix} {last_user_message.text}"
else:
response_text = f"{self.prefix} [No text message found]"
response_message = ChatMessage("assistant", [Content.from_text(text=response_text)])
return 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 response.messages:
response_text = response.messages[0].text or ""
# Stream character by character
for char in response_text:
yield ChatResponseUpdate(
contents=[Content.from_text(text=char)],
role="assistant",
response_id=f"echo-stream-resp-{random.randint(1000, 9999)}",
model_id="echo-model-v1",
)
await asyncio.sleep(0.05)
async def main() -> None:
"""Demonstrates how to implement and use a custom chat client with ChatAgent."""
print("=== Custom Chat Client Example ===\n")
# Create the custom chat client
print("--- EchoingChatClient Example ---")
echo_client = EchoingChatClient(prefix="🔊 Echo:")
# Use the chat client directly
print("Using chat client directly:")
direct_response = await echo_client.get_response("Hello, custom chat client!")
print(f"Direct response: {direct_response.messages[0].text}")
# Create an agent using the custom chat client
echo_agent = echo_client.as_agent(
name="EchoAgent",
instructions="You are a helpful assistant that echoes back what users say.",
)
print(f"\nAgent Name: {echo_agent.name}")
# Test non-streaming with agent
query = "This is a test message"
print(f"\nUser: {query}")
result = await echo_agent.run(query)
print(f"Agent: {result.messages[0].text}")
# Test streaming with agent
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):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
# Example: Using with threads and conversation history
print("\n--- Using Custom Chat Client with Thread ---")
thread = echo_agent.get_new_thread()
# Multiple messages in conversation
messages = [
"Hello, I'm starting a conversation",
"How are you doing?",
"Thanks for chatting!",
]
for msg in messages:
result = await echo_agent.run(msg, thread=thread)
print(f"User: {msg}")
print(f"Agent: {result.messages[0].text}\n")
# Check conversation history
if thread.message_store:
thread_messages = await thread.message_store.list_messages()
print(f"Thread contains {len(thread_messages)} messages")
else:
print("Thread has no message store configured")
if __name__ == "__main__":
asyncio.run(main())
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("")