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LlamaIndex vs Mastra

LlamaIndex scores higher on the AgentReady, 52/100 against 42/100. They differ on 15 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

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

Mastra. Mastra is a TypeScript framework for building AI agents and applications.

Where LlamaIndex is ahead

LlamaIndex passes llms-full.txt / full agent docs, and Mastra does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: structured api reference, pricing understandable and limits / constraints documented. Mastra misses those.

And on adopt, self-service signup, agent-compatible signup flow, free trial or free allowance, fast time to first request and official python sdk. Mastra misses those.

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

Where Mastra is ahead

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

It also holds adopt: cli available and mcp integration available. LlamaIndex misses those.

What neither does

Both fail openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, no mandatory sales call, programmatic credential creation, 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. Mastra leads 93 to 67. LlamaIndex misses clear canonical domain, clear product positioning, mcp discoverable; Mastra misses llms-full.txt / full agent docs.

Understand is whether an agent can read the docs and work out how the API behaves before calling it. LlamaIndex leads 46 to 15. LlamaIndex misses openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented; Mastra 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. LlamaIndex leads 60 to 35. LlamaIndex misses no mandatory sales call, programmatic credential creation, cli available, mcp integration available; Mastra 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, official python sdk.

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

Pricing

LlamaIndex starts at $0/mo and has a free tier. Mastra does not publish one.

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: LlamaIndex and Mastra. Alternatives to each: LlamaIndex, Mastra.

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