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Python: Add DevUI to AgentFramework (#781)
* add initial backend service code for devui * add tests * add frontendcode * ui updates * update readme * ui updates and tweaks * update ui bundle * improve ui, add react flow base * add react flow ui, fix background * update ui, fix introspection bug * update readme * update ui build * add support for multimodal input - both backend and frontend * update ui build * refactor as main framework package * backend and tests refactor * ui build update * ui build update and refactor * update pyproject.toml, update uv.lock * update ui build * ui update to fit oai responses types * add backend updat and readme update * mypy and other fixes * add intial dev guide * update ui and fix workflow bug * update ui build, add thread support * type fixes * update workflow view * update uv.lock * fix workflow iport errors * lint and other fixes * mypy fixes * minor update * update ui build * refactor to use oai dependencies directly, update examples to samples, improve typing * readme update * update ui and ui build * fix workflow pyright error * update ui, fix issues with run workflow placement, miniamp menu, etc * make samples integrate serve --------- Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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# Copyright (c) Microsoft. All rights reserved.
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"""
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Message Capture Script - Debug message flow
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- This script is intended to provide a reference for the types of events
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that are emitted by the server when agents and workflows are executed
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"""
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import asyncio
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import contextlib
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import http.client
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import json
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import threading
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import time
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from pathlib import Path
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from typing import Any
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import uvicorn
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from openai import OpenAI
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from agent_framework_devui import DevServer
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def start_server() -> tuple[str, Any]:
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"""Start server with samples directory."""
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# Get samples directory
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current_dir = Path(__file__).parent
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samples_dir = current_dir.parent / "samples"
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# Create and start server with simplified parameters
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server = DevServer(
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entities_dir=str(samples_dir.resolve()),
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host="127.0.0.1",
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port=8085, # Use different port
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ui_enabled=False,
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)
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app = server.get_app()
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server_config = uvicorn.Config(
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app=app,
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host="127.0.0.1",
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port=8085,
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log_level="info", # More verbose to see tracing setup
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)
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server_instance = uvicorn.Server(server_config)
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def run_server():
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asyncio.run(server_instance.serve())
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server_thread = threading.Thread(target=run_server, daemon=True)
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server_thread.start()
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# Wait for server to start
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time.sleep(5) # Increased wait time
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# Verify server is running with retries
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max_retries = 10
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for attempt in range(max_retries):
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try:
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conn = http.client.HTTPConnection("127.0.0.1", 8085, timeout=5)
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try:
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conn.request("GET", "/health")
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response = conn.getresponse()
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if response.status == 200:
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break
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finally:
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conn.close()
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except Exception as e:
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if attempt < max_retries - 1:
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time.sleep(2)
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else:
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raise RuntimeError(f"Server failed to start after {max_retries} attempts: {e}") from e
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return "http://127.0.0.1:8085", server_instance
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def capture_agent_stream_with_tracing(client: OpenAI, agent_id: str, scenario: str = "success") -> list[dict[str, Any]]:
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"""Capture agent streaming events."""
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try:
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stream = client.responses.create(
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model="agent-framework",
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input="Tell me about the weather in Tokyo. I want details.",
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stream=True,
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extra_body={"entity_id": agent_id},
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)
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events = []
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for event in stream:
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# Serialize the entire event object
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try:
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event_dict = json.loads(event.model_dump_json())
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except Exception:
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# Fallback to dict conversion if model_dump_json fails
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event_dict = event.__dict__ if hasattr(event, "__dict__") else str(event)
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events.append(event_dict)
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# Just capture everything as-is
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if len(events) >= 200: # Increased limit
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break
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return events
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except Exception as e:
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# Return error information as events
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error_event = {
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"type": "error",
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"scenario": scenario,
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"error_message": str(e),
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"error_type": type(e).__name__,
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"timestamp": time.time(),
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}
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return [error_event]
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def capture_workflow_stream_with_tracing(
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client: OpenAI, workflow_id: str, scenario: str = "success"
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) -> list[dict[str, Any]]:
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"""Capture workflow streaming events."""
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try:
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stream = client.responses.create(
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model="agent-framework",
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input=(
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"Process this spam detection workflow with multiple emails: "
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"'Buy now!', 'Hello mom', 'URGENT: Click here!'"
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),
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stream=True,
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extra_body={"entity_id": workflow_id},
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)
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events = []
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for event in stream:
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# Serialize the entire event object
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try:
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event_dict = json.loads(event.model_dump_json())
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except Exception:
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# Fallback to dict conversion if model_dump_json fails
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event_dict = event.__dict__ if hasattr(event, "__dict__") else str(event)
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events.append(event_dict)
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# Just capture everything as-is
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if len(events) >= 200: # Increased limit
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break
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return events
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except Exception as e:
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# Return error information as events
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error_event = {
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"type": "error",
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"scenario": scenario,
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"error_message": str(e),
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"error_type": type(e).__name__,
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"timestamp": time.time(),
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"entity_type": "workflow",
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}
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return [error_event]
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def capture_agent_with_bad_config(base_url: str, agent_id: str) -> list[dict[str, Any]]:
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"""Capture agent events with intentionally bad configuration to test error handling."""
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# Test with invalid API key
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bad_client = OpenAI(base_url=f"{base_url}/v1", api_key="invalid-api-key-123")
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try:
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return capture_agent_stream_with_tracing(bad_client, agent_id, "bad_api_key")
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except Exception as e:
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return [
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{
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"type": "error",
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"scenario": "bad_api_key",
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"error_message": str(e),
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"error_type": type(e).__name__,
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"timestamp": time.time(),
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}
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]
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def capture_agent_with_wrong_model(base_url: str, agent_id: str) -> list[dict[str, Any]]:
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"""Capture agent events with wrong model name to test error handling."""
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client = OpenAI(
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base_url=f"{base_url}/v1",
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api_key="dummy-key", # Use the same key as success case
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)
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try:
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stream = client.responses.create(
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model="gpt-4-nonexistent-model", # Wrong model name
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input="Tell me about the weather in Tokyo. I want details.",
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stream=True,
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extra_body={"entity_id": agent_id},
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)
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events = []
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for event in stream:
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# Serialize the entire event object
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try:
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event_dict = json.loads(event.model_dump_json())
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except Exception:
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# Fallback to dict conversion if model_dump_json fails
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event_dict = event.__dict__ if hasattr(event, "__dict__") else str(event)
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events.append(event_dict)
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if len(events) >= 200:
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break
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return events
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except Exception as e:
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return [
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{
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"type": "error",
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"scenario": "wrong_model",
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"error_message": str(e),
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"error_type": type(e).__name__,
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"timestamp": time.time(),
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}
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]
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def main():
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"""Main capture script - testing both success and failure scenarios."""
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# Setup
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output_dir = Path(__file__).parent / "captured_messages"
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output_dir.mkdir(exist_ok=True)
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# Start server
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base_url, server_instance = start_server()
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try:
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# Create OpenAI client for success scenario
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client = OpenAI(base_url=f"{base_url}/v1", api_key="dummy-key")
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# Discover entities
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conn = http.client.HTTPConnection("127.0.0.1", 8085, timeout=10)
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try:
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conn.request("GET", "/v1/entities")
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response = conn.getresponse()
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response_data = response.read().decode("utf-8")
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entities = json.loads(response_data)["entities"]
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finally:
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conn.close()
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all_results = {}
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# Test each entity
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for entity in entities:
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entity_type = entity["type"]
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entity_id = entity["id"]
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if entity_type == "agent":
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events = capture_agent_stream_with_tracing(client, entity_id, "success")
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elif entity_type == "workflow":
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events = capture_workflow_stream_with_tracing(client, entity_id, "success")
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else:
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continue
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all_results[f"{entity_type}_{entity_id}"] = {"entity_info": entity, "events": events}
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# Save results
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file_path = output_dir / "entities_stream_events.json"
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with open(file_path, "w") as f:
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json.dump(
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{"timestamp": time.time(), "server_type": "DevServer", "entities_tested": all_results},
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f,
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indent=2,
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default=str,
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)
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finally:
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# Cleanup server
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with contextlib.suppress(Exception):
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server_instance.should_exit = True
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if __name__ == "__main__":
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main()
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