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* Fix MCP tool result serialization for list[TextContent] When MCP tools return results containing list[TextContent], they were incorrectly serialized to object repr strings like: '[<agent_framework._types.TextContent object at 0x...>]' This fix properly extracts text content from list items by: 1. Checking if items have a 'text' attribute (TextContent) 2. Using model_dump() for items that support it 3. Falling back to str() for other types 4. Joining single items as plain text, multiple items as JSON array Fixes #2509 * Address PR review feedback for MCP tool result serialization - Extract serialize_content_result() to shared _utils.py - Fix logic: use texts[0] instead of join for single item - Add type annotation: texts: list[str] = [] - Return empty string for empty list instead of '[]' - Move import json to file top level - Add comprehensive unit tests for serialization * Address PR review feedback: fix type checking and double serialization - Add isinstance(item.text, str) check to ensure text attribute is a string - Fix double-serialization issue by keeping model_dump results as dicts until final json.dumps (removes escaped JSON strings in arrays) - Improve docstring with detailed return value documentation - Add test for non-string text attribute handling - Add tests for list type tool results in _events.py path * Simplify PR: minimal changes to fix MCP tool result serialization Addresses reviewer feedback about excessive refactoring: - Reset _events.py to original structure - Only add import and use serialize_content_result in one location - All review comments addressed in serialize_content_result(): - Added isinstance(item.text, str) check - Use model_dump(mode="json") to avoid double-serialization - Improved docstring with explicit return value documentation - Empty list returns "" instead of "[]" * Refactor: Move MCP TextContent serialization to core prepare_function_call_results Per reviewer feedback, moved the TextContent serialization logic from ag-ui's serialize_content_result to the core package's prepare_function_call_results function. Changes: - Added handling for objects with 'text' attribute (like MCP TextContent) in _prepare_function_call_results_as_dumpable - Removed serialize_content_result from ag-ui/_utils.py - Updated _events.py and _message_adapters.py to use prepare_function_call_results from core package - Updated tests to match the core function's behavior * Fix failing tests for prepare_function_call_results behavior - test_tool_result_with_none: Update expected value to 'null' (JSON serialization of None) - test_tool_result_with_model_dump_objects: Use Pydantic BaseModel instead of plain class * Fix B903 linter error: Convert MockTextContent to dataclass The ruff linter was reporting B903 (class could be dataclass or namedtuple) for the MockTextContent test helper classes. This commit converts them to dataclasses to satisfy the linter check.
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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 ChatAgent
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint
# Create your agent
agent = ChatAgent(
name="my_agent",
instructions="You are a helpful assistant.",
chat_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 import TextContent
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_streaming_response("Hello!"):
for content in update.contents:
if isinstance(content, TextContent):
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
ChatAgentfor 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
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