AutoGen — Microsoft Multi-Agent Conversation Framework

What it is

AutoGen is an open-source framework by Microsoft Research for building multi-agent conversational AI systems. Multiple agents chat with each other, divide work, execute code, and collaborate to solve complex tasks. It supports human-in-the-loop interaction where a human can join the conversation at any point.

AutoGen targets AI researchers and developers building systems where specialized agents (coder, reviewer, planner) work together through structured conversation.

How it saves time or tokens

AutoGen's multi-agent architecture distributes work across specialized agents, so each agent handles a focused subtask. This reduces the context each agent needs, lowering per-agent token consumption. The framework manages conversation flow, retries, and termination conditions automatically. Estimated token usage for this workflow is around 2,800 tokens.

How to use

1. Install AutoGen:

pip install autogen-agentchat autogen-ext

1. Create agents and a team:

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

model = OpenAIChatCompletionClient(model='gpt-4o')
coder = AssistantAgent('coder', model_client=model)
reviewer = AssistantAgent('reviewer', model_client=model)
team = RoundRobinGroupChat([coder, reviewer])

1. Run the team with a task and agents collaborate through conversation rounds.

Example

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

model = OpenAIChatCompletionClient(model='gpt-4o')
coder = AssistantAgent('coder', model_client=model)
reviewer = AssistantAgent('reviewer', model_client=model)

team = RoundRobinGroupChat([coder, reviewer])
result = team.run(task='Write a Python function to merge two sorted lists')
print(result)

Related on TokRepo

• Multi-Agent Frameworks — AutoGen deep-dive and comparisons
• AI Tools for Agents — Browse agent frameworks and toolkits

Key considerations

When evaluating AutoGen for your workflow, consider the following factors. First, assess whether your team has the technical prerequisites to adopt this tool effectively. Second, evaluate the maintenance burden against the productivity gains. Third, check community activity and documentation quality to ensure long-term viability. Integration with your existing toolchain matters more than feature count alone. Start with a small pilot project before rolling out across the organization. Monitor resource usage during the initial adoption phase to identify bottlenecks early. Document your configuration decisions so team members can onboard independently.

Common pitfalls

• Agents can enter infinite conversation loops if termination conditions are not properly set; always define max rounds or stop criteria.
• Code execution happens locally by default; use Docker execution for safety when running untrusted code.
• The v0.2 and v0.4 APIs differ significantly; check which version your code targets.