Files
agent-framework/python/samples/02-agents/devui/azure_responses_agent/agent.py
T
Eduard van Valkenburg 1e350ea22f Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850)
* PR2: Wire context provider pipeline and update all internal consumers

- Replace AgentThread with AgentSession across all packages
- Replace ContextProvider with BaseContextProvider across all packages
- Replace context_provider param with context_providers (Sequence)
- Replace thread= with session= in run() signatures
- Replace get_new_thread() with create_session()
- Add get_session(service_session_id) to agent interface
- DurableAgentThread -> DurableAgentSession
- Remove _notify_thread_of_new_messages from WorkflowAgent
- Wire before_run/after_run context provider pipeline in RawAgent
- Auto-inject InMemoryHistoryProvider when no providers configured

* fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files

* refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider)

* fix: update remaining ag-ui references (client docstring, getting_started sample)

* fix: make get_session service_session_id keyword-only to avoid confusion with session_id

* refactor: rename _RunContext.thread_messages to session_messages

* refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists

* rename: remove _new_ prefix from test files

* refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider)

* fix: read full history from session state directly instead of reaching into provider internals

* fix: update stale .pyi stubs, sample imports, and README references for new provider types

* fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples

* refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider

* refactor: UserInfoMemory stores state in session.state instead of instance attributes

* feat: add Pydantic BaseModel support to session state serialization

Pydantic models stored in session.state are now automatically serialized
via model_dump() and restored via model_validate() during to_dict()/from_dict()
round-trips. Models are auto-registered on first serialization; use
register_state_type() for cold-start deserialization.

Also export register_state_type as a public API.

* fix mem0

* Update sample README links and descriptions for session terminology

- Replace 'thread' with 'session' in sample descriptions across all READMEs
- Update file links for renamed samples (mem0_sessions, redis_sessions, etc.)
- Fix Threads section → Sessions section in main samples/README.md
- Update tools, middleware, workflows, durabletask, azure_functions READMEs
- Update architecture diagrams in concepts/tools/README.md
- Update migration guides (autogen, semantic-kernel)

* Fix broken Redis README link to renamed sample

* Fix Mem0 OSS client search: pass scoping params as direct kwargs

AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs,
while AsyncMemoryClient (Platform) expects them in a filters dict.
Adds tests for both client types.

Port of fix from #3844 to new Mem0ContextProvider.

* Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic

- Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase
- Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages
- Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests
- Add tests/workflow/__init__.py for relative import support
- Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep)

* Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection

Chat clients that store history server-side by default (OpenAI Responses API,
Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this
during auto-injection and skips InMemoryHistoryProvider unless the user
explicitly sets store=False.

* Fix broken markdown links in azure_ai and redis READMEs

* Fix getting-started samples to use session API instead of removed thread/ContextProvider API

* updates to workflow as agent

* fix group chat import

* Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread

- Fix: Propagate conversation_id from ChatResponse back to session.service_session_id
  in both streaming and non-streaming paths in _agents.py
- Rename AgentThreadException → AgentSessionException
- Remove stale AGUIThread from ag_ui lazy-loader
- Rename use_service_thread → use_service_session in ag-ui package
- Rename test functions from *_thread_* to *_session_*
- Rename sample files from *_thread* to *_session*
- Update docstrings and comments: thread → session
- Update _mcp.py kwargs filter: add 'session' alongside 'thread'
- Fix ContinuationToken docstring example: thread=thread → session=session
- Fix _clients.py docstring: 'Agent threads' → 'Agent sessions'

* Fix broken markdown links after thread→session file renames

* fix azure ai test
2026-02-12 21:00:32 +00:00

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4.3 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
"""Sample agent using Azure OpenAI Responses API for Agent Framework DevUI.
This agent uses the Responses API which supports:
- PDF file uploads
- Image uploads
- Audio inputs
- And other multimodal content
The Chat Completions API (AzureOpenAIChatClient) does NOT support PDF uploads.
Use this agent when you need to process documents or other file types.
Required environment variables:
- AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpoint
- AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: Deployment name for Responses API
(falls back to AZURE_OPENAI_CHAT_DEPLOYMENT_NAME if not set)
- AZURE_OPENAI_API_KEY: Your API key (or use Azure CLI auth)
"""
import logging
import os
from typing import Annotated
from agent_framework import Agent, tool
from agent_framework.azure import AzureOpenAIResponsesClient
logger = logging.getLogger(__name__)
# Get deployment name - try responses-specific env var first, fall back to chat deployment
_deployment_name = os.environ.get(
"AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME",
os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", ""),
)
# Get endpoint - try responses-specific env var first, fall back to default
_endpoint = os.environ.get(
"AZURE_OPENAI_RESPONSES_ENDPOINT",
os.environ.get("AZURE_OPENAI_ENDPOINT", ""),
)
def analyze_content(
query: Annotated[str, "What to analyze or extract from the uploaded content"],
) -> str:
"""Analyze uploaded content based on the user's query.
This is a placeholder - the actual analysis is done by the model
when processing the uploaded files.
"""
return f"Analyzing content for: {query}"
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def summarize_document(
length: Annotated[str, "Desired summary length: 'brief', 'medium', or 'detailed'"] = "medium",
) -> str:
"""Generate a summary of the uploaded document."""
return f"Generating {length} summary of the document..."
@tool(approval_mode="never_require")
def extract_key_points(
max_points: Annotated[int, "Maximum number of key points to extract"] = 5,
) -> str:
"""Extract key points from the uploaded document."""
return f"Extracting up to {max_points} key points..."
# Agent using Azure OpenAI Responses API (supports PDF uploads!)
agent = Agent(
name="AzureResponsesAgent",
description="An agent that can analyze PDFs, images, and other documents using Azure OpenAI Responses API",
instructions="""
You are a helpful document analysis assistant. You can:
1. Analyze uploaded PDF documents and extract information
2. Summarize document contents
3. Answer questions about uploaded files
4. Extract key points and insights
When a user uploads a file, carefully analyze its contents and provide
helpful, accurate information based on what you find.
For PDFs, you can read and understand the text, tables, and structure.
For images, you can describe what you see and extract any text.
""",
client=AzureOpenAIResponsesClient(
deployment_name=_deployment_name,
endpoint=_endpoint,
api_version="2025-03-01-preview", # Required for Responses API
),
tools=[summarize_document, extract_key_points],
)
def main():
"""Launch the Azure Responses agent in DevUI."""
from agent_framework_devui import serve
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger.info("=" * 60)
logger.info("Starting Azure Responses Agent")
logger.info("=" * 60)
logger.info("")
logger.info("This agent uses the Azure OpenAI Responses API which supports:")
logger.info(" - PDF file uploads")
logger.info(" - Image uploads")
logger.info(" - Audio inputs")
logger.info("")
logger.info("Try uploading a PDF and asking questions about it!")
logger.info("")
logger.info("Required environment variables:")
logger.info(" - AZURE_OPENAI_ENDPOINT")
logger.info(" - AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME")
logger.info(" - AZURE_OPENAI_API_KEY (or use Azure CLI auth)")
logger.info("")
serve(entities=[agent], port=8090, auto_open=True)
if __name__ == "__main__":
main()