# AutoGen

Multi-agent AI framework from Microsoft Research for building conversational agent systems with AgentChat, Core API, and Extensions.

## Summary
AutoGen is Microsoft Research's open-source framework for building multi-agent AI systems. It provides three layers — AgentChat for quick prototyping, Core API for fine-grained control, and Extensions for ecosystem integrations — giving developers a flexible foundation for agent collaboration patterns.


## Guide
AutoGen is Microsoft Research's framework for building multi-agent AI systems. It treats agent conversations as the fundamental building block and provides a layered architecture that scales from quick prototypes to production systems.

### What it is
AutoGen is an open-source framework for building conversational multi-agent systems. It offers three layers: AgentChat for rapid prototyping with pre-built conversation patterns, Core API for fine-grained control over agent state and messaging, and Extensions for integrating with external tools and services. Agents collaborate through structured message passing.

### Why it matters
Multi-agent systems are becoming the standard approach for complex AI tasks, but building them from scratch is hard. AutoGen provides the primitives — agent registration, message routing, conversation management, state handling — so developers can focus on designing agent behaviors rather than infrastructure.

### How it works
Install via pip and choose your entry layer. AgentChat gives you pre-built conversation patterns for quick experiments. Core API gives you full control over agent lifecycle, messaging, and state. Extensions connect to external tools and services. Define your agents, set up their conversation patterns, and let AutoGen orchestrate the collaboration.


## Use Cases
- Multi-agent code generation: Build a pipeline where a planner agent designs the implementation, a coder agent writes it, and a reviewer agent checks it before committing.
- Research synthesis: Create agents that specialize in different aspects of a research question and synthesize their findings into a coherent analysis.
- Customer support orchestration: Route customer queries through a triage agent to specialized support agents, with escalation patterns for complex issues.

## Alternatives
- Use CrewAI for a more accessible API vs CrewAI: AutoGen offers more control and flexibility. CrewAI has a gentler learning curve and more intuitive role-based design.
- Use a single agent for simple tasks vs Claude Code: If your task doesn't require multiple specialized agents, a single-agent tool like Claude Code or Aider will be simpler and faster.

### Getting Started
- Read the documentation: https://microsoft.github.io/autogen/docs/
- Inspect the repository: https://github.com/microsoft/autogen

### FAQ
- Is AutoGen open source?
  - Yes. AutoGen is released under the CC-BY-4.0 license, making the source code available for inspection, modification, and redistribution.
- Who should use AutoGen?
  - Developers building conversational multi-agent systems, especially those who need fine-grained control over agent state, messaging, and orchestration patterns.
- How does AutoGen compare to other multi-agent frameworks?
  - AutoGen's main advantage is its layered architecture and conversation-centric design. It offers more low-level control than CrewAI but has a steeper learning curve.
## Why It Matters
AutoGen matters because it comes from one of the most well-resourced AI research labs in the world and is designed around the principle that agent conversations are the fundamental primitive for multi-agent systems. Its layered architecture lets developers start simple and add complexity as needed.


## Best For
- Developers building conversational multi-agent systems
- Research teams studying agent collaboration patterns and emergent behavior
- Engineers who need fine-grained control over agent state, messaging, and orchestration

## Not For
- Developers who only need a single-agent terminal coding tool
- Teams looking for a drag-and-drop workflow builder with a visual UI

## What It Actually Does
- Layered architecture: AutoGen provides three layers: AgentChat for rapid prototyping, Core API for fine-grained control, and Extensions for ecosystem integrations.
  - Why it matters: Developers can start with the simplest layer and add complexity as their needs grow, without rewriting their codebase.
- Conversation-centric design: Agent conversations are the fundamental primitive. Agents collaborate through structured message passing with built-in conversation patterns.
  - Why it matters: Conversation-centric design makes it natural to model complex multi-agent interactions like debates, reviews, and iterative refinement.
- Microsoft ecosystem integration: Backed by Microsoft Research with deep integration into Azure AI services and the broader Microsoft developer ecosystem.
  - Why it matters: Teams already on Azure get first-class support, and the Microsoft backing means long-term maintenance and enterprise readiness.

## Typical Use Cases
- Agent research and experimentation: Use AutoGen to study how agents collaborate, debate, and refine outputs through structured conversations.
- Complex task decomposition: Break down complex tasks into sub-tasks handled by specialized agents that pass results and context between each other.
- Automated code generation pipelines: Build agent teams where one agent writes code, another reviews it, and a third tests it, iterating until quality thresholds are met.

## How It Compares
- Choose AutoGen for fine-grained agent control vs CrewAI: AutoGen's Core API offers more control over agent state and messaging. CrewAI's role-based API is more intuitive but less flexible for advanced patterns.

## Command Line
### Install AutoGen
Install the AgentChat package for quick prototyping, or install autogen-core for the full Core API.

```bash
pip install autogen-agentchat
```

## Facts
- Category: agents
- Resource type: agent
- Open source: yes
- License: CC-BY-4.0
- Last verified: 2026-05-27
- GitHub repo: microsoft/autogen
- GitHub stars: 58400

## Capabilities
- workflow-orchestration

## Structured Use Case Tags
- developer-workflow

## Getting Started
- Open the GitHub repository: https://github.com/microsoft/autogen
- Read the documentation: https://microsoft.github.io/autogen/docs/
- Visit the project website: https://microsoft.github.io/autogen/

## Links
- GitHub: https://github.com/microsoft/autogen
- Homepage: https://microsoft.github.io/autogen/
- Docs: https://microsoft.github.io/autogen/docs/

## Structured Outputs
- JSON: https://www.openagent.bot/agents/autogen.json
- Markdown: https://www.openagent.bot/agents/autogen.md
- Canonical: https://www.openagent.bot/agents/autogen