Master AG2 (AutoGen 2.0) with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install AG2 with pip install ag2 (requires Python
9+), then set your LLM API key as an environment variable (e.g., export OPENAI_API_KEY=your
key) — the framework supports OpenAI, Anthropic, Google, Azure, and local models Build your first two
agent conversation by following the quickstart at docs.ag
ai — create a ConversableAgent with a system prompt, pair it with a second agent, and call initiate_chat() to see them interact on a task Explore conversation patterns by building a group chat with 3+ agents: define a researcher agent, a writer agent, and an editor agent with distinct system prompts and tool access, then use GroupChat and GroupChatManager to orchestrate their collaboration Add tool integration by registering Python functions as tools that agents can call — start with simple tools like web search or file reading, then extend to database queries and API calls as your system grows Join the AG2 Discord community (discord.gg/pAbnFJrkgZ) and explore the example notebooks in the GitHub repository (github.com/ag2ai/build
ag2) for production patterns, advanced configurations, and real
world implementation guidance
💡 Quick Start: Follow these 7 steps in order to get up and running with AG2 (AutoGen 2.0) quickly.
Explore the key features that make AG2 (AutoGen 2.0) powerful for multi-agent builders workflows.
AG2 is the community-maintained evolution of AutoGen, built by the original creators after the project was forked. It preserves the core conversable-agent and group-chat abstractions but extends them with a full AgentOS — adding cross-framework interoperability (Google ADK, OpenAI, LangChain), A2A and MCP protocol support, unified state management, and an enterprise-ready Studio and Orchestrator layer that the original AutoGen does not provide.
Yes. The AG2 framework is open source under a permissive license and can be used freely for commercial production workloads, including self-hosted deployments. There is a separate enterprise AgentOS platform available via Request Access for teams that want managed orchestration, security controls, and SLAs, but the core multi-agent framework carries no license fee.
AG2 is LLM-agnostic. It works out of the box with OpenAI, Anthropic Claude, Azure OpenAI, and any OpenAI-compatible endpoint. Local and open-weight models are supported through integrations like Ollama, making it possible to run fully offline or mix cloud and local models across agents in the same team.
Yes. Universal Framework Interoperability is a headline feature. The AG2 Orchestrator lets agents from AG2, Google ADK, OpenAI Assistants, and LangChain join the same team, share state, and communicate through standardized A2A and MCP protocols — so teams do not have to re-implement existing agents to participate.
AG2 is best suited for complex, multi-step AI workflows that benefit from specialization and collaboration — for example research assistants, code generation pipelines, customer-support triage with escalation, data analysis pipelines with tool use, and enterprise automations that require human-in-the-loop review. It is overkill for simple single-prompt chatbots.
Now that you know how to use AG2 (AutoGen 2.0), it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
Check pros, cons, and user feedback
See how it stacks against alternatives
Follow our tutorial and master this powerful multi-agent builders tool in minutes.
Tutorial updated March 2026