ag2
AG2 is an open-source Python framework for building multi-agent AI systems where agents collaborate to solve tasks using LLMs and tools. It evolved from AutoGen and provides conversation patterns, autonomous workflows, and human-in-the-loop capabilities for agentic AI development.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ag2ai/ag2 |
| Owner | ag2ai |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 4.7k |
| Forks | 663 |
| Open issues | 20 |
| Latest release | v1.0.0b0 (2026-07-03) |
| Last updated | 2026-07-08 |
| Source | https://github.com/ag2ai/ag2 |
What ag2 is
AG2 is a protocol-driven agent framework supporting conversable agents, multi-agent orchestration patterns (swarms, group chats, nested chats, sequential chats), tool integration, LLM routing, structured outputs, and code execution. The v1.0 release removes the classic framework; Python ≥3.10 required.
Get the ag2 source
Clone the repository and explore it locally.
git clone https://github.com/ag2ai/ag2.gitcd ag2# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Migrate from AutoGen: code using old import paths (`autogen`) and classic framework must be refactored for `ag2` package and protocol-driven design.
- LLM configuration: requires external API keys (OpenAI shown in examples); manage via environment or config files to avoid secrets in code.
- Python environment: minimum Python 3.10; pin ag2 version tightly until v1.0 stable release to avoid breaking API changes.
- Code execution sandboxing: examples show Docker as optional (`use_docker=False`); evaluate Docker vs. local execution based on security requirements.
- Multi-agent orchestration learning curve: understand conversation patterns (group chat, nested, sequential) before designing agent topology.
When to avoid it — and what to weigh
- Require stable v1.0 API guarantees today — Latest release is v1.0.0b0 (beta). Classic framework (ConversableAgent, GroupChat) removed; breaking changes may occur. Requires careful dependency pinning.
- Need mature ecosystem of integrations — Integration ecosystem not detailed in provided data. Third-party LLM/tool support unclear. May require custom adapters or development.
- Building simple single-agent chatbots — AG2 is optimized for multi-agent orchestration; overhead unnecessary for single-agent deployments. Consider lighter frameworks like LangChain or direct API calls.
- Strict production stability requirements — Project 8 months old; volunteer-maintained. No documented SLA, uptime commitments, or commercial support model in provided data.
License & commercial use
Apache License 2.0 (permissive, OSI-approved). Allows commercial use, modification, distribution, and sublicensing with liability and trademark disclaimers. No royalties or restrictions on business use.
Apache 2.0 permits commercial use without restrictions. However, no warranty is provided. Maintenance and support are volunteer-driven; no SLA or commercial entity behind project. Assess risk tolerance for dependencies on community-maintained software in production.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Code execution risk: agents can execute arbitrary Python code if `code_execution_config` enabled; isolation depends on Docker or local environment security. API key exposure: examples use config files; ensure .gitignore applied. LLM prompt injection: multi-agent message passing may be vulnerable to prompt attacks from malicious inputs or agent responses. No security audit, vulnerability disclosure process, or hardening guidance documented.
Alternatives to consider
LangChain + LangGraph
Mature ecosystem, extensive LLM/tool integrations, better documentation, active commercial backing. Better for single-agent or simpler multi-agent flows.
Anthropic's Claude via Tool Use + API
Direct API with native tool use and streaming. Simpler if locked into Claude; no framework overhead. Lacks multi-agent orchestration patterns.
CrewAI
Purpose-built for multi-agent teams with role-based agents and task delegation. Smaller codebase, active development. Less flexible orchestration than AG2.
Build on ag2 with DEV.co software developers
AG2 is ideal for teams needing sophisticated agent collaboration and custom orchestration. Devco can help architect, integrate, and deploy agent systems tailored to your business logic.
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ag2 FAQ
Is AG2 backward compatible with AutoGen?
What LLM providers does AG2 support?
Can agents run without Docker?
Is there commercial support or SLA?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If ag2 is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to build multi-agent AI systems?
AG2 is ideal for teams needing sophisticated agent collaboration and custom orchestration. Devco can help architect, integrate, and deploy agent systems tailored to your business logic.