Mastra
The TypeScript AI agent framework with durable execution & memory
About Mastra
Mastra is an open-source, TypeScript-first framework designed for building, testing, and deploying production-grade AI agents and autonomous workflows. It provides native support for multi-agent graph orchestration, persistent state management, semantic memory, and automated evaluation metrics without requiring complex Python bridges.
Built for modern Node.js and Next.js developer environments, Mastra unifies LLM routing, tool execution, and vector retrieval into a single composable SDK. Its engine features built-in observability for tracing agent decision trees, deterministic schema validation via Zod, and instant deployment pipelines for serverless infrastructure.
Define agent tools, prompts, and schema validations in native TypeScript using Zod.
Assemble multi-agent workflows as stateful directed acyclic graphs (DAGs) with step-level checkpointing.
Connect persistent memory stores and vector databases for contextual retrieval across long-running sessions.
Run automated synthetic evaluation tests to score agent accuracy, hallucination rates, and latency.
Deploy directly to Vercel, Cloudflare Workers, or Dockerized Node.js runtime environments.
Capabilities & Features
Common Use Cases
agent-orchestration
multi-agent-systems
workflow-automation
typescript-ai
Frequently Asked Questions
Is Mastra completely free and open-source?
Yes, Mastra is 100% open-source under the MIT license and can be self-hosted without licensing fees.
How does Mastra differ from LangGraph and CrewAI?
While LangGraph and CrewAI primarily target the Python ecosystem, Mastra is purpose-built from the ground up for TypeScript and Node.js developers, integrating workflows, memory, and evals natively.
Which LLM providers are supported in Mastra?
Mastra supports OpenAI, Anthropic, Google Gemini, Groq, Mistral, and local models via Ollama through unified standard interfaces.
Free Plan
100% Free / Open Source (MIT Licensed)
Paid Plan
Enterprise support and managed cloud orchestration available on request.
Direct link · Verified & reader-supported
Pros & Cons
100% native TypeScript ergonomics with zero Python bridge overhead.
Durable workflow engine with step-level checkpointing and retry logic.
Integrated evaluation framework for measuring agent performance and drift.
Built-in support for major model providers including OpenAI, Anthropic, and Groq.
Open-source and fully self-hostable with MIT licensing.
Younger ecosystem and community compared to mature Python frameworks like LangChain.
Advanced state machine routing requires strong familiarity with async TypeScript patterns.
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