> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/microsoft/autogen/llms.txt > Use this file to discover all available pages before exploring further. # Welcome to AutoGen > Build autonomous AI agents and multi-agent applications with ease AutoGen Hero Light AutoGen Hero Dark # Welcome to AutoGen AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans. Build sophisticated AI systems where multiple agents collaborate, share context, and solve complex tasks together. Get started in under 5 minutes with your first AI agent High-level API for rapid multi-agent prototyping Event-driven runtime for scalable agent systems LLM clients, tools, and integrations ecosystem ## Why AutoGen? AutoGen provides everything you need to create AI agents and multi-agent workflows through a layered, extensible framework designed for both rapid prototyping and production deployment. ### Three-Layer Architecture AutoGen uses a modular architecture with clearly divided responsibilities. You can start at the high level and drop down to lower layers when you need more control. **Event-driven foundation** Message passing, agent runtime, and distributed execution based on the Actor model. Supports both Python and .NET with cross-language interoperability. * Standalone and distributed runtimes * Topic-based messaging * Agent lifecycle management * Cross-platform support **High-level abstractions** Intuitive defaults for rapid development. Built on Core API with preset agents and team patterns. * Pre-configured agent types * Multi-agent teams (RoundRobin, Selector, Swarm) * Built-in termination conditions * Streaming support **Extensible ecosystem** First and third-party extensions for models, tools, and capabilities. * OpenAI, Azure, Anthropic, Gemini clients * Code execution sandboxes * MCP server integration * Custom tool support ## Key Features Create teams of specialized agents that work together on complex tasks. Agents can: * Share context and communicate via messages * Take turns in round-robin fashion or use intelligent selection * Hand off tasks between agents with the Swarm pattern * Form hierarchical structures with orchestrator agents Built-in team patterns include RoundRobinGroupChat, SelectorGroupChat, Swarm, and GraphFlow for workflows. Start with high-level agents or build custom ones: * **AssistantAgent**: LLM-powered agent with tool use and reflection * **CodeExecutorAgent**: Safely executes Python or Docker-based code * **UserProxyAgent**: Human-in-the-loop interactions * **Custom Agents**: Implement your own with full control All agents support streaming, tool calling, and state management. Agents can use tools to interact with external systems: * **Function Tools**: Wrap Python functions with automatic schema generation * **MCP Servers**: Connect to Model Context Protocol servers (Playwright, filesystem, etc.) * **Custom Tools**: Implement any tool interface * **Agent Tools**: Use other agents as tools for hierarchical orchestration Tools support streaming results and error handling. Use any LLM provider through a unified interface: * OpenAI (GPT-4, GPT-4o, o1) * Azure OpenAI with AAD authentication * Anthropic Claude * Google Gemini * Local models via Ollama or llama.cpp * Custom model clients All model clients support streaming and function calling where available. Built for reliability and scale: * **Distributed Runtime**: Scale across multiple machines * **Memory Systems**: Persistent context with ChromaDB, Redis, or custom stores * **Logging & Tracing**: OpenTelemetry integration for observability * **Serialization**: Save and restore agent configurations * **Termination Control**: Flexible stopping conditions for teams Supports both standalone and distributed deployment models. Designed for productivity: * **AutoGen Studio**: No-code GUI for prototyping multi-agent workflows * **Type Safety**: Full type hints for IDE autocomplete * **Async/Await**: Native async support throughout * **Streaming UI**: Built-in console interface with progress tracking * **Rich Ecosystem**: Active community and extensive examples ## Real-World Applications AutoGen powers diverse AI applications: * **Magentic-One**: State-of-the-art multi-agent team for web browsing, code execution, and file handling * **Research Assistants**: Agents that search, analyze, and synthesize information * **Code Generation**: Multi-agent systems with coder, reviewer, and executor roles * **Customer Support**: Conversational agents with tool access and escalation * **Data Analysis**: Teams that query databases, run computations, and generate reports * **Creative Workflows**: Collaborative agents for writing, editing, and reviewing content ## Architecture Philosophy AutoGen's layered design lets you choose the right level of abstraction: 1. **Start High**: Use AgentChat for rapid prototyping with sensible defaults 2. **Go Deep**: Drop to Core API when you need event-driven patterns or distributed execution 3. **Extend Freely**: Add custom models, tools, and agents without framework modifications 4. **Deploy Anywhere**: Run on a single machine or distribute across a cluster **New to AutoGen?** We recommend starting with [Quickstart](/quickstart) to build your first agent, then exploring [Core Concepts](/concepts) to understand the architecture. ## Community and Support Join a thriving ecosystem of AI developers: * **Discord**: Real-time chat at [aka.ms/autogen-discord](https://aka.ms/autogen-discord) * **GitHub Discussions**: Q\&A and community support * **Weekly Office Hours**: Live sessions with maintainers * **Blog**: Tutorials and updates at [devblogs.microsoft.com/autogen](https://devblogs.microsoft.com/autogen/) ## What's Next? Follow the quickstart guide to create a working agent in minutes Learn about agents, teams, tools, and the layered architecture Detailed setup instructions for Python and .NET Browse sample applications and use cases **Coming from AutoGen v0.2?** Check the [Migration Guide](/migration-guide) for instructions on updating your code.