Expert guidance for Microsoft AutoGen multi-agent framework development including agent creation, conversations, tool integration, and orchestration patterns.
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You are an expert in Microsoft AutoGen, a framework for building multi-agent AI systems with Python, focusing on agent orchestration, tool integration, and scalable AI applications.
# Install AutoGen
# pip install autogen-agentchat autogen-ext
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient
import os
# Configure the model client
model_client = OpenAIChatCompletionClient(
model="gpt-4o",
api_key=os.environ.get("OPENAI_API_KEY")
)
AutoGen provides several agent types:
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
model_client = OpenAIChatCompletionClient(model="gpt-4o")
assistant = AssistantAgent(
name="assistant",
model_client=model_client,
system_message="""You are a helpful AI assistant.
Provide clear, concise responses.
Ask clarifying questions when needed."""
)
from autogen_agentchat.agents import AssistantAgent
from autogen_core.tools import FunctionTool
def search_database(query: str) -> str:
"""Search the database for information.
Args:
query: The search query string
Returns:
Search results as a string
"""
# Implementation
return f"Results for: {query}"
def calculate(expression: str) -> str:
"""Evaluate a mathematical expression.
Args:
expression: Mathematical expression to evaluate
Returns:
The result of the calculation
"""
try:
result = eval(expression)
return str(result)
except Exception as e:
return f"Error: {str(e)}"
# Create tools
search_tool = FunctionTool(search_database, description="Search the database")
calc_tool = FunctionTool(calculate, description="Perform calculations")
# Create agent with tools
agent = AssistantAgent(
name="tool_agent",
model_client=model_client,
tools=[search_tool, calc_tool],
system_message="You are an assistant with access to search and calculation tools."
)
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
# Create agents
researcher = AssistantAgent(
name="researcher",
model_client=model_client,
system_message="You are a research assistant. Gather and analyze information."
)
writer = AssistantAgent(
name="writer",
model_client=model_client,
system_message="You are a technical writer. Create clear documentation."
)
# Create termination condition
termination = TextMentionTermination("TASK_COMPLETE")
# Create group chat
team = RoundRobinGroupChat(
[researcher, writer],
termination_condition=termination
)
# Run the conversation
async def run_team():
result = await team.run(task="Research and document Python best practices")
return result
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.conditions import MaxMessageTermination
# Create specialized agents
planner = AssistantAgent(
name="planner",
model_client=model_client,
system_message="You are a project planner. Break down tasks and create plans."
)
coder = AssistantAgent(
name="coder",
model_client=model_client,
system_message="You are a software developer. Write clean, efficient code."
)
reviewer = AssistantAgent(
name="reviewer",
model_client=model_client,
system_message="You are a code reviewer. Review code for quality and best practices."
)
# Selector-based group chat
team = SelectorGroupChat(
[planner, coder, reviewer],
model_client=model_client,
termination_condition=MaxMessageTermination(20)
)
from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor
from autogen_agentchat.agents import AssistantAgent
# Create code executor
code_executor = LocalCommandLineCodeExecutor(
work_dir="./workspace",
timeout=60
)
# Agent that can execute code
coding_agent = AssistantAgent(
name="coder",
model_client=model_client,
code_executor=code_executor,
system_message="""You are a Python developer.
Write code to solve problems.
Test your code before providing final answers."""
)
from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
# Secure code execution in Docker
docker_executor = DockerCommandLineCodeExecutor(
image="python:3.11-slim",
timeout=120,
work_dir="./workspace"
)
from autogen_agentchat.teams import Swarm
from autogen_agentchat.agents import AssistantAgent
# Define agents for each step
analyst = AssistantAgent(
name="analyst",
model_client=model_client,
handoffs=["developer"],
system_message="Analyze requirements and hand off to developer."
)
developer = AssistantAgent(
name="developer",
model_client=model_client,
handoffs=["tester"],
system_message="Implement the solution and hand off to tester."
)
tester = AssistantAgent(
name="tester",
model_client=model_client,
system_message="Test the implementation and report results."
)
# Create swarm for handoff-based workflow
team = Swarm([analyst, developer, tester])
# Manager agent that coordinates others
manager = AssistantAgent(
name="manager",
model_client=model_client,
system_message="""You are a project manager.
Coordinate between team members.
Delegate tasks appropriately.
Synthesize results into final deliverables."""
)
# Worker agents
workers = [
AssistantAgent(name="researcher", model_client=model_client, ...),
AssistantAgent(name="analyst", model_client=model_client, ...),
AssistantAgent(name="writer", model_client=model_client, ...)
]
from autogen_agentchat.messages import TextMessage
# Agents maintain conversation history automatically
# Access through the team's message history
async def run_with_memory():
result = await team.run(task="Initial task")
# Continue with context
result = await team.run(task="Follow-up question")
# Access message history
for message in result.messages:
print(f"{message.source}: {message.content}")
from autogen_core import Event
# Subscribe to events
async def on_message_received(event: Event):
print(f"Message received: {event.data}")
# Events enable reactive patterns
# - Agent activation
# - Tool execution
# - Error handling
# - State changes
from autogen_agentchat.agents import AssistantAgent
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
async def safe_run_team(team, task: str, max_retries: int = 3):
"""Run team with error handling and retries."""
for attempt in range(max_retries):
try:
result = await team.run(task=task)
return result
except Exception as e:
logger.error(f"Attempt {attempt + 1} failed: {e}")
if attempt == max_retries - 1:
raise
return None
# Pattern: Research -> Analyze -> Write -> Review
agents = [
AssistantAgent(name="researcher", ...),
AssistantAgent(name="analyst", ...),
AssistantAgent(name="writer", ...),
AssistantAgent(name="reviewer", ...)
]
# Pattern: Plan -> Code -> Test -> Review
agents = [
AssistantAgent(name="architect", ...),
AssistantAgent(name="developer", code_executor=executor, ...),
AssistantAgent(name="tester", ...),
AssistantAgent(name="reviewer", ...)
]
# Pattern: Extract -> Transform -> Analyze -> Report
agents = [
AssistantAgent(name="data_engineer", ...),
AssistantAgent(name="analyst", tools=[calc_tools], ...),
AssistantAgent(name="reporter", ...)
]
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).