Mastra is an open-source TypeScript agent framework developed by the team behind Gatsby.js, released in 2024 and growing rapidly as the leading agent development framework in the JavaScript/TypeScript ecosystem. The framework is built around the insight that most enterprise applications are built in TypeScript, yet the majority of the AI agent framework ecosystem is Python-first. Mastra fills this gap by providing agents, workflows, memory, RAG, and tool integration as a cohesive TypeScript SDK that integrates naturally into existing Node.js and Next.js applications. Its design reflects production experience with scalable JavaScript systems, offering durable workflow execution, vector database integrations, and a suite of observability tooling that the Python ecosystem has had for years but TypeScript developers have largely had to build from scratch.
Key Features#
TypeScript-Native Agents Mastra agents are defined entirely in TypeScript with end-to-end type safety. Tool definitions, agent configurations, workflow steps, and memory schemas are all typed — meaning TypeScript's compiler catches integration errors before runtime. This is a significant improvement over frameworks where tool schemas are defined as JSON or untyped strings, where schema mismatches only appear at runtime.
Durable Workflows Mastra's workflow system allows developers to define multi-step processes that combine deterministic code (data fetching, validation, transformation) with LLM-powered steps in a single workflow graph. Workflows support conditional branching, parallel execution, and retry logic. Crucially, workflows are durable — they can survive application restarts and are designed for long-running processes that may span minutes or hours.
Built-in Memory System Every Mastra agent can be configured with persistent memory that stores conversation history, semantic memories (facts extracted from conversations), and structured working memory. Memory backends include LibSQL (SQLite-compatible), PostgreSQL, and Upstash for serverless environments. The semantic memory layer uses embeddings to retrieve relevant past context, enabling agents to maintain coherent behavior across many separate sessions with the same user.
RAG and Vector Store Integration Mastra includes a complete RAG pipeline abstraction with document ingestion, chunking, embedding, and retrieval built in. Supported vector stores include Pinecone, Pgvector, Chroma, Weaviate, Qdrant, and Upstash Vector. The retrieval system integrates directly with the agent memory and tool systems, allowing agents to query knowledge bases as naturally as calling any other tool.
MCP Integration Mastra supports the Model Context Protocol (MCP) both as a client (connecting to MCP servers) and as a server (exposing Mastra tools to MCP clients). This means Mastra agents can access the growing ecosystem of MCP-compatible tools and data sources, and Mastra tool definitions can be consumed by any MCP-compatible client.
Pricing#
Mastra's open-source framework is available under the Elastic License 2.0, which permits self-hosted use for building applications but restricts providing Mastra itself as a managed service to third parties. The core framework is free for typical application development use cases. A managed cloud platform is available for teams who need hosted workflow execution and monitoring without self-managed infrastructure. LLM API costs are billed by the respective providers separately.
Who It's For#
Mastra is the right choice for:
- TypeScript and Node.js backend teams: Engineers building production API services, Next.js applications, or serverless functions who want to add agent capabilities without adopting a Python dependency.
- Full-stack JavaScript developers: Teams who want a single language (TypeScript) across their entire stack, including the AI layer, maintaining type safety from database schemas through LLM tool definitions.
- Teams needing durable workflows: Organizations building business automation that combines deterministic logic and LLM reasoning in sequences that may run over extended periods and need to be resumable.
It is less suitable for data science teams who need Python's data ecosystem (NumPy, Pandas, Scikit-learn), organizations already invested in a Python agent framework, or use cases requiring the most mature ecosystem with the largest community.
Strengths#
Genuine TypeScript-first design. Unlike Python frameworks with a TypeScript SDK bolted on, Mastra was built for TypeScript from day one. The ergonomics and type coverage reflect this.
Durable workflow execution. The combination of deterministic steps and LLM steps in a resumable, durable workflow is a capability that few agent frameworks offer regardless of language, and is essential for production business automation.
MCP client and server support. Bidirectional MCP integration gives Mastra agents access to the growing ecosystem of external data sources and tools while also making Mastra agents discoverable to MCP-compatible platforms.
Limitations#
Smaller ecosystem than Python frameworks. The TypeScript AI ecosystem is maturing rapidly but still has fewer community integrations, tutorials, and third-party plugins than Python frameworks like LangChain or LlamaIndex.
Elastic License restrictions. The Elastic License 2.0 (ELv2) restricts offering Mastra as a hosted service. Teams with specific open-source compliance requirements should review whether ELv2 is compatible with their policies.
Related Resources#
Browse the full AI Agent Tools Directory to compare TypeScript and Python agent frameworks.
- Learn about the MCP server standard that Mastra implements natively
- Understand multi-agent system patterns applicable in Mastra workflows
- Read the foundational AI Agents overview for context on agent architectures
- Compare general frameworks in our LangChain vs CrewAI analysis
- Explore the LangChain directory entry for the leading Python alternative
- Understand tool use patterns central to Mastra's agent capabilities