CrewAI
Multi-agent automation framework & studio to build and run AI crews
AutoGen
Build autonomous multi-agent AI applications with a flexible framework
AutoGen is a modular Python framework for creating autonomous or collaborative multi-agent AI systems, offering layered APIs, extensions, and no-code tools like AutoGen Studio.
Overview
Overview
AutoGen provides a layered, extensible framework for building multi‑agent AI applications. Developers can choose the level of abstraction they need—from the low‑level Core API for fine‑grained control to the higher‑level AgentChat API for rapid prototyping—while extensions add LLM clients, code execution, and other capabilities.
Who It's For
The framework targets AI engineers, researchers, and product teams that need coordinated agents to browse the web, run code, or handle domain‑specific tasks. With cross‑language support for Python and .NET, it fits both prototype and production environments. Deployment is self‑hosted via standard Python packages, and a no‑code GUI (AutoGen Studio) lets non‑programmers design workflows before exporting code.
Key Capabilities
AutoGen ships with built‑in OpenAI integration, Playwright MCP for web browsing, and a plugin system for custom tools. Community resources—including Discord, weekly office hours, and a benchmarking suite (AutoGen Bench)—help users iterate quickly and share best practices.
Highlights
Layered architecture (Core, AgentChat, Extensions) for flexible abstraction levels
Built‑in OpenAI and Playwright MCP integrations for LLM and web browsing
AutoGen Studio offers a no‑code GUI for rapid workflow prototyping
Cross‑language runtime (Python and .NET) with an extensible plugin system
Pros
- Enables rapid development of complex multi‑agent workflows
- Extensible via third‑party extensions and custom tools
- Supports both autonomous operation and human‑in‑the‑loop interaction
- Active community with Discord, office hours, and regular updates
Considerations
- Requires Python 3.10+ and familiarity with async programming
- Documentation assumes prior knowledge of v0.2 patterns
- Limited out‑of‑the‑box model support beyond OpenAI; other providers need custom adapters
- No built‑in visual debugging beyond AutoGen Studio
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Fit guide
Great for
- Developers building AI assistants that need coordinated tool use
- Researchers prototyping multi‑agent experiments without low‑level boilerplate
- Teams wanting a GUI to prototype workflows before coding
- Organizations that prefer an extensible framework with community support
Not ideal when
- Projects that need only a single‑agent chatbot
- Environments without Python 3.10 or .NET runtime
- Users seeking a fully managed SaaS solution rather than self‑hosted code
- Scenarios requiring extensive pre‑trained multi‑agent models out of the box
How teams use it
Web‑enabled research assistant
Automatically browse the web, extract data, and summarize findings using the Playwright MCP integration.
Mathematics and chemistry tutoring bot
Leverages specialized math and chemistry agents to solve problems and explain concepts within a single conversational interface.
Enterprise workflow automation
Orchestrates multiple agents to handle ticket triage, code generation, and documentation updates, reducing manual effort.
Rapid prototype of custom AI team
Designs and tests a team of agents via AutoGen Studio’s no‑code UI, then exports the code for production deployment.
Tech snapshot
Python61%
C#25%
TypeScript13%
HTML1%
JavaScript1%
Jupyter Notebook1%
Frequently asked questions
Do I need an OpenAI account to use AutoGen?
Yes, the provided examples use OpenAI’s API; you must set the OPENAI_API_KEY environment variable. Other LLM providers can be integrated via extensions.
Can AutoGen run on Windows, macOS, and Linux?
AutoGen is pure Python and .NET compatible, so it works on all major operating systems that support Python 3.10+.
What is the difference between Core API and AgentChat API?
Core API offers low‑level message passing and runtime control, while AgentChat provides a higher‑level, opinionated interface for rapid prototyping built on top of Core.
How do I add custom tools or extensions?
Create a Python package that implements the Extensions API contract and install it alongside autogen‑ext; the framework will discover and load it automatically.
Is there a way to benchmark my agents?
Yes, the AutoGen Bench suite provides benchmarking utilities to evaluate performance and cost of agent workflows.
Project at a glance
Stable- Stars
- 53,708
- Watchers
- 53,708
- Forks
- 8,127
LicenseCC-BY-4.0
Repo age2 years old
Last commit3 months ago
Primary languagePython
Last synced 23 hours ago