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AutoGen (AG2) vs LangGraph

AutoGen (AG2) scores higher on the AgentReady, 53/100 against 34/100. They differ on 7 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

AutoGen (AG2). AG2 (formerly AutoGen) is an open-source multi-agent AI framework and AgentOS for building, orchestrating, and evolving systems of AI agents.

LangGraph. LangGraph is a low-level orchestration framework for advanced needs combining deterministic and agentic workflows, part of the LangChain ecosystem

Where AutoGen (AG2) is ahead

AutoGen (AG2) passes clear product positioning and mcp discoverable, and LangGraph does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: pricing understandable. LangGraph misses it.

And on adopt, agent-compatible signup flow, free trial or free allowance and mcp integration available. LangGraph misses those.

Finally, on operate, observable execution. LangGraph misses it.

What neither does

Both fail structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, self-service signup, no mandatory sales call, programmatic credential creation, fast time to first request, cli available, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover. AutoGen (AG2) leads 100 to 73. AutoGen (AG2) misses nothing; LangGraph misses clear product positioning, mcp discoverable.

Understand. AutoGen (AG2) leads 23 to 15. AutoGen (AG2) misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; LangGraph misses structured api reference, openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt is whether an agent can get a key and make its first successful call without a human in the loop. AutoGen (AG2) leads 55 to 25. AutoGen (AG2) misses self-service signup, no mandatory sales call, programmatic credential creation, fast time to first request, cli available; LangGraph misses self-service signup, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, free trial or free allowance, fast time to first request, cli available, mcp integration available.

Operate. AutoGen (AG2) leads 35 to 24. AutoGen (AG2) misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified; LangGraph misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, agent compatibility verified.

Pricing

AutoGen (AG2) does not publish a machine-readable starting price and has a free tier. LangGraph does not publish one.

Signal by signal

Which to pick

AutoGen (AG2) clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: AutoGen (AG2) and LangGraph. Alternatives to each: AutoGen (AG2), LangGraph.

An agent can fetch this as data: POST /v1/compare {"slugs": ["ag2", "langchain"]}