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LangGraph vs LlamaIndex

LlamaIndex scores higher on the AgentReady, 52/100 against 34/100. They differ on 9 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

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

LlamaIndex. LlamaIndex provides document parsing and extraction tools that turn complex documents into AI-ready context.

Where LangGraph is ahead

LangGraph passes clear canonical domain, and LlamaIndex does not. That is discover, whether an agent can find the product at all without being told it exists.

Where LlamaIndex is ahead

LlamaIndex passes structured api reference, pricing understandable and limits / constraints documented, and LangGraph does not. That is understand, whether an agent can read the docs and work out how the API behaves before calling it.

It also holds adopt: self-service signup, agent-compatible signup flow, free trial or free allowance and fast time to first request. LangGraph misses those.

And on operate, observable execution. LangGraph misses it.

What neither does

Both fail clear product positioning, mcp discoverable, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, no mandatory sales call, programmatic credential creation, cli available, mcp integration 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. LangGraph leads 73 to 67. LangGraph misses clear product positioning, mcp discoverable; LlamaIndex misses clear canonical domain, clear product positioning, mcp discoverable.

Understand. LlamaIndex leads 46 to 15. 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; LlamaIndex misses openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented.

Adopt is whether an agent can get a key and make its first successful call without a human in the loop. LlamaIndex leads 60 to 25. 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; LlamaIndex misses no mandatory sales call, programmatic credential creation, cli available, mcp integration available.

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

Pricing

LangGraph does not publish a machine-readable starting price. LlamaIndex starts at $0/mo and has a free tier.

Signal by signal

Which to pick

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

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