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LangGraph vs Mastra

Mastra scores higher on the AgentReady, 42/100 against 34/100. They differ on 6 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

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

Where LangGraph is ahead

LangGraph 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 adopt: official python sdk. Mastra misses it.

Where Mastra is ahead

Mastra 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 adopt: cli available and mcp integration available. LangGraph misses those.

What neither does

Both fail structured api reference, openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, self-service signup, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, free trial or free allowance, fast time to first request, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, 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 is whether an agent can find the product at all without being told it exists. Mastra leads 93 to 73. LangGraph misses clear product positioning, mcp discoverable; Mastra misses llms-full.txt / full agent docs.

Understand. Both sit at 15/100 here. 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; 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. Mastra leads 35 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; 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. Both sit at 24/100 here. LangGraph misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, 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

LangGraph does not publish a machine-readable starting price. Mastra does not publish one.

Signal by signal

Which to pick

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

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