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Python: [BREAKING] Moved to a single get_response and run API (#3379)
* 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
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@@ -14,6 +14,7 @@ This folder contains simple examples demonstrating direct usage of various chat
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| [`openai_assistants_client.py`](openai_assistants_client.py) | Direct usage of OpenAI Assistants Client for basic chat interactions with OpenAI assistants. |
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| [`openai_chat_client.py`](openai_chat_client.py) | Direct usage of OpenAI Chat Client for chat interactions with OpenAI models. |
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| [`openai_responses_client.py`](openai_responses_client.py) | Direct usage of OpenAI Responses Client for structured response generation with OpenAI models. |
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| [`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. |
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## Environment Variables
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@@ -37,4 +38,4 @@ Depending on which client you're using, set the appropriate environment variable
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- `OLLAMA_HOST`: Your Ollama server URL (defaults to `http://localhost:11434` if not set)
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- `OLLAMA_MODEL_ID`: The Ollama model to use for chat (e.g., `llama3.2`, `llama2`, `codellama`)
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> **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.
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> **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.
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@@ -36,7 +36,7 @@ async def main() -> None:
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print(f"User: {message}")
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if stream:
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print("Assistant: ", end="")
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async for chunk in client.get_streaming_response(message, tools=get_weather):
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async for chunk in client.get_response(message, tools=get_weather, stream=True):
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if str(chunk):
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print(str(chunk), end="")
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print("")
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@@ -36,7 +36,7 @@ async def main() -> None:
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print(f"User: {message}")
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if stream:
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print("Assistant: ", end="")
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async for chunk in client.get_streaming_response(message, tools=get_weather):
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async for chunk in client.get_response(message, tools=get_weather, stream=True):
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if str(chunk):
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print(str(chunk), end="")
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print("")
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@@ -36,7 +36,7 @@ async def main() -> None:
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print(f"User: {message}")
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if stream:
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print("Assistant: ", end="")
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async for chunk in client.get_streaming_response(message, tools=get_weather):
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async for chunk in client.get_response(message, tools=get_weather, stream=True):
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if str(chunk):
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print(str(chunk), end="")
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print("")
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@@ -42,21 +42,19 @@ async def main() -> None:
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stream = True
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print(f"User: {message}")
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if stream:
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response = await ChatResponse.from_update_generator(
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client.get_streaming_response(message, tools=get_weather, options={"response_format": OutputStruct}),
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response = await ChatResponse.from_chat_response_generator(
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client.get_response(message, tools=get_weather, options={"response_format": OutputStruct}, stream=True),
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output_format_type=OutputStruct,
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)
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try:
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result = response.value
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if result := response.try_parse_value(OutputStruct):
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print(f"Assistant: {result}")
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except Exception:
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else:
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print(f"Assistant: {response.text}")
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else:
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response = await client.get_response(message, tools=get_weather, options={"response_format": OutputStruct})
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try:
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result = response.value
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if result := response.try_parse_value(OutputStruct):
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print(f"Assistant: {result}")
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except Exception:
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else:
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print(f"Assistant: {response.text}")
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@@ -0,0 +1,197 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import random
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import sys
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from collections.abc import AsyncIterable, Awaitable, Mapping, Sequence
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from typing import Any, ClassVar, Generic, TypedDict
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from agent_framework import (
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BaseChatClient,
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ChatMessage,
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ChatMiddlewareLayer,
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ChatOptions,
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ChatResponse,
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ChatResponseUpdate,
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Content,
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FunctionInvocationLayer,
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ResponseStream,
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Role,
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)
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from agent_framework._clients import TOptions_co
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from agent_framework.observability import ChatTelemetryLayer
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if sys.version_info >= (3, 13):
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from typing import TypeVar
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else:
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from typing_extensions import TypeVar
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if sys.version_info >= (3, 12):
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from typing import override # type: ignore # pragma: no cover
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else:
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from typing_extensions import override # type: ignore[import] # pragma: no cover
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"""
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Custom Chat Client Implementation Example
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This sample demonstrates implementing a custom chat client and optionally composing
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middleware, telemetry, and function invocation layers explicitly.
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"""
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TOptions_co = TypeVar(
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"TOptions_co",
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bound=TypedDict, # type: ignore[valid-type]
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default="ChatOptions",
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covariant=True,
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)
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class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
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"""A custom chat client that echoes messages back with modifications.
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This demonstrates how to implement a custom chat client by extending BaseChatClient
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and implementing the required _inner_get_response() method.
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"""
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OTEL_PROVIDER_NAME: ClassVar[str] = "EchoingChatClient"
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def __init__(self, *, prefix: str = "Echo:", **kwargs: Any) -> None:
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"""Initialize the EchoingChatClient.
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Args:
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prefix: Prefix to add to echoed messages.
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**kwargs: Additional keyword arguments passed to BaseChatClient.
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"""
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super().__init__(**kwargs)
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self.prefix = prefix
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@override
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def _inner_get_response(
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self,
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*,
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messages: Sequence[ChatMessage],
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stream: bool = False,
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options: Mapping[str, Any],
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**kwargs: Any,
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) -> Awaitable[ChatResponse] | ResponseStream[ChatResponseUpdate, ChatResponse]:
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"""Echo back the user's message with a prefix."""
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if not messages:
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response_text = "No messages to echo!"
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else:
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# Echo the last user message
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last_user_message = None
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for message in reversed(messages):
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if message.role == Role.USER:
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last_user_message = message
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break
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if last_user_message and last_user_message.text:
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response_text = f"{self.prefix} {last_user_message.text}"
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else:
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response_text = f"{self.prefix} [No text message found]"
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response_message = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(response_text)])
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response = ChatResponse(
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messages=[response_message],
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model_id="echo-model-v1",
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response_id=f"echo-resp-{random.randint(1000, 9999)}",
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)
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if not stream:
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async def _get_response() -> ChatResponse:
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return response
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return _get_response()
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async def _stream() -> AsyncIterable[ChatResponseUpdate]:
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response_text_local = response_message.text or ""
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for char in response_text_local:
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yield ChatResponseUpdate(
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contents=[Content.from_text(char)],
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role=Role.ASSISTANT,
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response_id=f"echo-stream-resp-{random.randint(1000, 9999)}",
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model_id="echo-model-v1",
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)
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await asyncio.sleep(0.05)
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return ResponseStream(_stream(), finalizer=lambda updates: response)
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class EchoingChatClientWithLayers( # type: ignore[misc,type-var]
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ChatMiddlewareLayer[TOptions_co],
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ChatTelemetryLayer[TOptions_co],
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FunctionInvocationLayer[TOptions_co],
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EchoingChatClient[TOptions_co],
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Generic[TOptions_co],
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):
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"""Echoing chat client that explicitly composes middleware, telemetry, and function layers."""
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OTEL_PROVIDER_NAME: ClassVar[str] = "EchoingChatClientWithLayers"
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async def main() -> None:
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"""Demonstrates how to implement and use a custom chat client with ChatAgent."""
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print("=== Custom Chat Client Example ===\n")
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# Create the custom chat client
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print("--- EchoingChatClient Example ---")
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echo_client = EchoingChatClientWithLayers(prefix="🔊 Echo:")
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# Use the chat client directly
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print("Using chat client directly:")
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direct_response = await echo_client.get_response("Hello, custom chat client!")
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print(f"Direct response: {direct_response.messages[0].text}")
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# Create an agent using the custom chat client
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echo_agent = echo_client.as_agent(
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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(query2, stream=True):
|
||||
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())
|
||||
@@ -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("")
|
||||
|
||||
@@ -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("")
|
||||
|
||||
@@ -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}")
|
||||
|
||||
@@ -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())
|
||||
Reference in New Issue
Block a user