Mayura vs Mastra

Mastra and Mayura are both TypeScript frameworks for building AI agents and workflows, and on a quick read their feature lists overlap. Underneath, they aim at different things. Mastra is a broad, batteries-included platform: hundreds of model providers, dozens of storage backends, RAG, voice, evals and a local studio. Mayura is narrower and stricter: nothing is allowed until you grant it, every run has a hard budget, and a durable step that may have acted is never run again.

We make Mayura, so read this with that in mind. We checked every statement about Mastra against its own documentation, repository and npm listing, and link the sources at the end. If something here is wrong or out of date, please tell us.

Compared on September 29, 2026: Mastra (@mastra/core) 1.71.0 and Mayura 1.0.0. Mayura's side updated for Mayura 1.1 on October 12, 2026; Mastra's side is as checked on September 29.

#At a glance

#Where they differ most

#Permissions: allowed unless removed, or denied unless granted

In Mastra, an agent can call the tools you give it. You narrow them per call with activeTools or toolChoice, and resource-level authorization (for example tools:execute) is part of the enterprise edition.

In Mayura, a runtime starts with nothing allowed. To call a paid model, an agent needs model:<adapter>. To call a tool, it needs tool:<id>, each capability the tool declares (such as payments:refund), and effect:write if the tool writes. A missing grant blocks the call before it runs. The point is that a prompt injection, or a model that simply gets it wrong, can't reach anything you didn't list. See Permissions.

#Budgets: approximate or reserved

Mastra's TokenCostControl tracks spending per thread or resource over a time window. Its documentation says the limit is approximate: metrics are persisted asynchronously, so a run may briefly exceed it, and it needs observability storage.

Mayura's budget is a hard limit per run. Every model and tool call declares the most it can cost, and the runtime reserves that amount before the call starts. A call that doesn't fit never starts, and a paid model is refused until you set a limit at all. See Costs and budgets.

#Durability: resume or never twice

This is the deepest difference.

Mastra persists workflow snapshots in the storage you configure; the default in-memory store loses them on exit. Its deployment documentation says the default in-process workers don't survive process crashes: a run interrupted during a step stays running until it is restarted, and restarting resumes from the last active step, which runs that step again. Distributed setups may deliver events more than once, so the docs ask for idempotent handlers. For stronger guarantees, Mastra integrates with Inngest and Temporal.

Mayura records each step as started before it calls the tool, and never calls it again. If the process dies mid-step, the next process to advance the run settles that step as unknown once its deadline has passed, and the run stops for reconciliation rather than paying twice. Runs stay pinned to the definition they started with until an explicit, reviewed migration moves them. Never twice covers the mechanism and its limits.

Neither approach is free. Re-running a step is right when steps are idempotent, and it recovers without a human. Never re-running is right when a duplicate is worse than a pause, as with payments, emails or deletions, and it means someone occasionally reconciles an outcome_unknown run.

#Where Mastra is ahead

• Breadth. RAG utilities, evals, a studio, more storage and vector backends, and more integrations. Mayura has no RAG utilities or built-in eval framework.
• Models. Mastra's router reaches far more providers out of the box.
• Edge deployments. Both run on Bun, Deno and Cloudflare. Mayura is tested on them in CI and on a Vercel Edge deployment, but deployed Cloudflare Workers and Deno Deploy are still experimental.
• Ecosystem. A larger community, more examples, a hosted platform, and a longer track record.

#When to choose which

Choose Mastra if you want the widest set of building blocks in one framework: RAG over your documents, voice, evals, many models behind one router, and a studio for trying things out.

Choose Mayura if your agents act on things that matter, like money, customer records or messages, and you want the framework to enforce the rules: nothing allowed by default, a hard budget on every run, approvals bound to the exact input, and durable steps that never run twice. Also if you want one Apache-2.0 license for everything.

#Sources

Mastra facts were checked on September 29, 2026:

• npm: @mastra/core (version 1.71.0, release dates, downloads)
• GitHub: mastra-ai/mastra, LICENSE.md and ee/LICENSE
• Agents, Tools, Human in the loop and Guardrails
• Models
• Workflows, Snapshots, Error handling and Workers
• Memory, Storage and Evals
• MCP, Deployment and Integrations
• Fine-grained authorization

Mayura facts come from its documentation.