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* 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
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2026-02-12 21:00:32 +00:00
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Agent Framework AG-UI Integration
AG-UI protocol integration for Agent Framework, enabling seamless integration with AG-UI's web interface and streaming protocol.
Installation
pip install agent-framework-ag-ui
Quick Start
Server (Host an AI Agent)
from fastapi import FastAPI
from agent_framework import Agent
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
# Create your agent
agent = Agent(
name="my_agent",
instructions="You are a helpful assistant.",
client=AzureOpenAIChatClient(
endpoint="https://your-resource.openai.azure.com/",
deployment_name="gpt-4o-mini",
api_key="your-api-key",
),
)
# Create FastAPI app and add AG-UI endpoint
app = FastAPI()
add_agent_framework_fastapi_endpoint(app, agent, "/")
# Run with: uvicorn main:app --reload
Client (Connect to an AG-UI Server)
import asyncio
from agent_framework.ag_ui import AGUIChatClient
async def main():
async with AGUIChatClient(endpoint="http://localhost:8000/") as client:
# Stream responses
async for update in client.get_response("Hello!", stream=True):
for content in update.contents:
if content.type == "text" and content.text:
print(content.text, end="", flush=True)
print()
asyncio.run(main())
The AGUIChatClient supports:
- Streaming and non-streaming responses
- Hybrid tool execution (client-side + server-side tools)
- Automatic thread management for conversation continuity
- Integration with
Agentfor client-side history management
Documentation
- Getting Started Tutorial - Step-by-step guide to building AG-UI servers and clients
- Server setup with FastAPI
- Client examples using
AGUIChatClient - Hybrid tool execution (client-side + server-side)
- Thread management and conversation continuity
- Examples - Complete examples for AG-UI features
Features
This integration supports all 7 AG-UI features:
- Agentic Chat: Basic streaming chat with tool calling support
- Backend Tool Rendering: Tools executed on backend with results streamed to client
- Human in the Loop: Function approval requests for user confirmation before tool execution
- Agentic Generative UI: Async tools for long-running operations with progress updates
- Tool-based Generative UI: Custom UI components rendered on frontend based on tool calls
- Shared State: Bidirectional state sync between client and server
- Predictive State Updates: Stream tool arguments as optimistic state updates during execution
Security: Authentication & Authorization
The AG-UI endpoint does not enforce authentication by default. For production deployments, you should add authentication using FastAPI's dependency injection system via the dependencies parameter.
API Key Authentication Example
import os
from fastapi import Depends, FastAPI, HTTPException, Security
from fastapi.security import APIKeyHeader
from agent_framework import Agent
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
# Configure API key authentication
API_KEY_HEADER = APIKeyHeader(name="X-API-Key", auto_error=False)
EXPECTED_API_KEY = os.environ.get("AG_UI_API_KEY")
async def verify_api_key(api_key: str | None = Security(API_KEY_HEADER)) -> None:
"""Verify the API key provided in the request header."""
if not api_key or api_key != EXPECTED_API_KEY:
raise HTTPException(status_code=401, detail="Invalid or missing API key")
# Create agent and app
agent = Agent(name="my_agent", instructions="...", client=...)
app = FastAPI()
# Register endpoint WITH authentication
add_agent_framework_fastapi_endpoint(
app,
agent,
"/",
dependencies=[Depends(verify_api_key)], # Authentication enforced here
)
Other Authentication Options
The dependencies parameter accepts any FastAPI dependency, enabling integration with:
- OAuth 2.0 / OpenID Connect - Use
fastapi.security.OAuth2PasswordBearer - JWT Tokens - Validate tokens with libraries like
python-jose - Azure AD / Entra ID - Use
azure-identityfor Microsoft identity platform - Rate Limiting - Add request throttling dependencies
- Custom Authentication - Implement your organization's auth requirements
For a complete authentication example, see getting_started/server.py.
Architecture
The package uses a clean, orchestrator-based architecture:
- AgentFrameworkAgent: Lightweight wrapper that delegates to orchestrators
- Orchestrators: Handle different execution flows (default, human-in-the-loop, etc.)
- Confirmation Strategies: Domain-specific confirmation messages (extensible)
- AgentFrameworkEventBridge: Converts Agent Framework events to AG-UI events
- Message Adapters: Bidirectional conversion between AG-UI and Agent Framework message formats
- FastAPI Endpoint: Streaming HTTP endpoint with Server-Sent Events (SSE)
Next Steps
- New to AG-UI? Start with the Getting Started Tutorial
- Want to see examples? Check out the Examples for AG-UI features
License
MIT