The next-generation AG2 platform with AgentOS runtime, framework interoperability, teachable agents, and enhanced planning for production multi-agent systems.
Next-generation version of Microsoft's AutoGen framework — enables AI agent teams to collaborate through natural conversations for complex problem solving.
AG2 Framework (v0.11+) is the evolved version of AG2, pushing beyond basic multi-agent conversations into a full agent operating system. If AG2 is the conversation framework, AG2 Framework is the production platform.
The big addition: AgentOS. This runtime lets you connect agents built in different frameworks into one team. An AG2 agent, a LangChain agent, a Google ADK agent, and an OpenAI SDK agent can all participate in the same workflow. No other framework offers this level of cross-platform interoperability.
Unlike base AG2 (self-host only), AG2 Framework offers a hosted option. You get 50 free executions/month to test. The $25/month Pro tier gives 100 executions. Enterprise scales to 30,000 executions with self-hosted Kubernetes deployment.
Source: ag2.ai
"Executions" on the hosted platform mean workflow runs, not individual API calls. A single execution might contain 20 agent conversations burning $2-5 in LLM tokens. So 100 executions at $25/month could mean $200-500 in underlying API costs on top of the platform fee. Factor that into your budget.
Developers building with AG2 Framework call it "the right level of abstraction" for rapid iteration. The teachability feature gets specific praise for reducing prompt engineering over time. The main complaint: token consumption. Conversation-driven coordination generates verbose agent exchanges that cost more than structured task pipelines in CrewAI. Some users note confusion about the relationship between AG2, AutoGen, and Microsoft's Agent Framework.
AG2 Framework Hosted Pro at $25/month + estimated $200-500/month in API costs for 100 executions. Compare to CrewAI Enterprise at $200/month (includes managed hosting) or LangGraph Cloud at usage-based pricing. AG2 Framework is cheaper at low volumes but less battle-tested. For self-hosted deployments, the framework is free and you pay only API costs.
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AG2 Framework adds production features (persistent memory, cross-framework agents, hosted platform) on top of AG2's conversation-driven foundation. The AgentOS interoperability is unique. Token costs run high, and production readiness trails LangGraph and CrewAI.
Agents coordinate through natural language conversations rather than rigid task definitions, enabling flexible problem-solving approaches.
Use Case:
Building a research team where agents debate methodologies and collaboratively refine their approach to complex analysis tasks.
Improved planning algorithms that can handle complex, multi-step workflows with dependencies and conditional logic.
Use Case:
Creating software development workflows where agents plan features, implement code, and conduct reviews collaboratively.
Better context preservation across conversation turns and agent interactions for long-running collaborative sessions.
Use Case:
Long-term research projects where agents need to remember findings and decisions from previous sessions.
Seamless integration of human oversight and guidance within multi-agent conversations.
Use Case:
Complex decision-making scenarios where human expertise is needed to guide agent collaboration.
Built-in sandboxed environments for collaborative code development, testing, and debugging by multiple agents.
Use Case:
Software development teams where multiple agents write, review, and test code together.
Sophisticated conversation ending criteria based on goal achievement, consensus, or custom conditions.
Use Case:
Research collaborations that continue until agents reach consensus or meet quality thresholds.
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View Pricing Options →Complex research and analysis projects requiring multiple specialized agent perspectives and collaborative reasoning
Software development workflows where agents collaboratively plan features, implement code, and conduct reviews
Open-ended problem solving scenarios where solution paths are not predetermined and benefit from agent debate and consensus
Educational environments where understanding multi-agent conversations provides insight into collaborative AI approaches
Human-AI collaborative workflows requiring seamless integration of human expertise with agent capabilities
Content creation and review processes benefiting from multiple agent perspectives and iterative refinement
Strategic planning and decision-making processes where multiple viewpoints enhance outcome quality
We believe in transparent reviews. Here's what AG2 Framework doesn't handle well:
AG2 includes enhanced planning, better memory management, more flexible termination conditions, and improved conversation patterns.
Yes, AG2 supports any OpenAI-compatible API including local models through Ollama, vLLM, or LiteLLM.
Yes, but consider token costs and conversation management for high-volume applications. Best for complex, high-value tasks.
AG2 maintains backward compatibility with most AutoGen patterns while offering new features. Migration guides are available in the documentation.
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AG2 v0.11.2 released February 2026. Introduced AgentOS vision for universal framework interoperability. Enhanced planning engine and memory management. Captain Agents and teachability features. Featured as leading framework in 2026 Agentic Frameworks Guide.
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