Mastra

What is Mastra?

Mastra is a TypeScript-based agent framework and platform for building AI agents and workflows. It includes a framework for defining agents with tools and memory, Mastra Studio for debugging and evaluation with traces, logs, and metrics, and Mastra Server for deploying agents as production APIs. Mastra also supports unified model access and production observability across development and deployment. It is built for developers, product teams, and operations teams that need to move agents from prototype to production use.

Key Features

• Observational Memory: Mastra uses Observer and Reflector background agents to compress older messages into structured observations, which helps keep context stable without a vector database.
• Model Router: It gives developers one API for more than 3,300 models across 94 providers, with autocomplete, fallbacks, and runtime model selection for easier model switching.
• Workflows: Workflows define multi-step execution as graphs with .then(), .branch(), and .parallel(), so teams can build sequential, parallel, conditional, and looped agent logic.
• RAG: Mastra handles document chunking, embeddings, vector storage, similarity search, and reranking, so retrieval systems can be built inside the same framework.
• Supervisor Agents: A coordinator agent can assign work to specialized sub-agents, which helps organize multi-agent task execution.
• Guardrails: Guardrails check inputs and outputs for issues such as prompt injection and PII, which adds safety controls before and after model generation.
• Tool Approval: Tool calls can require human or external system approval before execution, which adds control for actions that need review.
• Studio: Studio is an interactive playground for running agents and workflows, testing prompts, and viewing graphs, so users can inspect behavior in one place.

Use Cases

• Founder at Orange Collective: Uses Mastra to ingest portfolio and deal data, then run agents that generate investment memos and portfolio analysis. The reported outcome is days of manual work saved per report, with more time for founder relationships.

• HR Product Engineer at Factorial: Built "One," an AI agent that lets HR managers and employees query company data with permission controls. The reported outcome is secure, accurate data access and blocked unauthorized queries.

• Platform Engineer at SoftBank: Built Satto Workspace with Mastra for document processing and AI-assisted drafting. The reported outcome is document creation reduced from hours to minutes.

Pricing

• Platform Starter: $0/month. Unlimited users, unlimited deployments, Studio, and Server. Includes 100K observability events, 24 hours CPU time, 10GB data egress, and 1GB data storage. Overage is billed at $10 per 100K observability events, $0.00008 per second of CPU time, and $10 per GB of data egress.
• Platform Teams: $250/month per team. Includes everything in Starter, plus multiple teams, custom SSO, and SOC 2 documentation. Includes 100K observability events, 250 hours CPU time, 100GB data egress, and a $100/project persistent server with 24/7 uptime. Overage is billed at $10 per 100K observability events, $0.00008 per second of CPU time, and $10 per GB of data egress.
• Platform Enterprise: Custom. Includes everything in Teams, plus RBAC, support SLA, a dedicated support engineer, and uptime SLA. CPU time and data egress limits are custom, with 100GB data egress base and $10/GB add-on.

Memory Gateway Starter includes an initial $5 credit. Enterprise plans are available through sales.

Who Is It For?

Ideal for:

• Full-stack developer at a mid-market company building AI agents: Mastra fits teams that use TypeScript and need to prototype agents such as data analysts or retrieval-augmented generation systems. It supports complex workflows with tools, models, and storage, and avoids a lot of low-level setup.
• AI engineer at a growth-stage BI or data platform startup: Mastra suits teams building agent orchestration across sources such as Salesforce, Stripe, and BigQuery. It includes supervisor patterns and eval scorers for natural language querying and production-focused agent work.
• Indie hacker or developer advocate prototyping agents: Mastra works for small teams or solo builders who want to set up personal assistants or research agents in 5 to 20 minutes. It also supports routing across 3,300+ models and fits Vercel-adjacent stacks.

Not ideal for:

• Non-technical PMs or CEOs who need no-code agents: Mastra requires TypeScript coding, so tools like SmythOS or Flowise are a better fit.
• Teams committed to Python ecosystems: Mastra is TypeScript-only, so LangChain or LlamaIndex will fit better for Python-native development.

Mastra is best for TypeScript developers and AI engineers at SaaS, BI, dev tools, or media and transcription companies, especially teams of 2 to 10 engineers working with Vercel, Next.js, PostgreSQL, BigQuery, or vector databases. Use it when you need production agents with RAG, multi-tool orchestration, evals, and fast iteration. Skip it if you want no-code builders, Python-first tooling, or a simple chatbot without tools and workflows.

Alternatives and Comparisons

• LangChain/LangGraph: Mastra does native memory and state handling, tracing, evals, and observability better out of the box, and it is positioned for TypeScript-friendly open source and custom stacks. LangGraph does mature Python-based agent graphs and broad adoption better, with 34.5M monthly downloads and a lead in custom agent orchestration. Choose Mastra if you want built-in production tools and a quick TypeScript start with npm create mastra@latest; choose LangGraph if your stack is Python-first or you need a more established orchestration ecosystem. Switching from LangGraph is medium difficulty.

• AutoGen: Mastra does TypeScript workflows, built-in memory, streaming, evals, and Studio for development and testing better. AutoGen does Python multi-agent systems better, and forks such as AG2 keep backward-compatible multi-agent APIs and stronger community-driven event handling. Choose Mastra if you want self-hosted TypeScript agents with observability and an interactive UI; choose AutoGen if you are building around Python and Microsoft ecosystem ties.

• CrewAI: Mastra does full AI app development with native observability, memory, tracing, evals, and Studio better. CrewAI does role-based multi-agent crew orchestration and task delegation better. Choose Mastra if you want a TypeScript-first framework with built-in production tooling; choose CrewAI if your main goal is to assemble agent teams quickly.

Getting Started

Setup:

• Signup: Public sources here do not list signup requirements or trial details.
• Time to first result: A minimal example is achievable within a few minutes for developers familiar with TypeScript.

Learning curve:

• Mastra appears easiest for developers. Public docs point to a TypeScript based API, async and await patterns, step creation with createStep(), workflow registration in a Mastra instance, and LLM model configuration. It is likely steep for non-developers.
• Beginner: TypeScript fluency and familiarity with LLM concepts are expected. Experienced: developers with that background can get to a minimal example within a few minutes.

Where to get help:

• Official guidance starts with the workflows overview docs and workshops at mastra.ai. These are the main public learning resources identified.
• Discord has 1000+ members and appears to be growing. Answers come primarily from an experimental AI bot, with team and community members adding help, and the team monitors during business hours.
• A docs chatbot gives real-time retrieval for documentation questions. Third-party learning material appears limited, with one engineering deep-dive blog noted and no public YouTube tutorials or courses identified.

Watch out for:

• The first setup steps assume comfort with TypeScript, schema-based step definitions, and async code patterns.
• You may need to piece together the workflow setup yourself, since the public starting points focus on overview docs and workshops rather than ready-made templates.

Integration Ecosystem

Users describe Mastra's integration ecosystem as fairly limited and focused on core AI building blocks, rather than a broad set of external app connections. Public examples and user reports point to an API-first approach, and the integrations people mention are generally described as working reliably in real builds. Research data also notes that an MCP server is available.

• Vercel AI SDK: Users describe this as the base layer for model routing, and say it supports dynamic LLM selection for lighter tasks or more complex reasoning in agent workflows.
• Vector stores: Developers report using vector store integrations for retrieval-augmented generation, with embeddings from document chunks upserted into indexes such as transcript_embeddings for agent knowledge retrieval.
• MCP server: Research data notes that Mastra has MCP server availability as part of its integration approach.

User discussion in the available research centers on these core integrations, and not on requests for additional external app connections.

Developer Experience

Mastra is a TypeScript and JavaScript framework for building autonomous AI agents with multi step reasoning, integrations, workflows, and persistent memory in Node.js and Deno environments. Public documentation is geared toward getting started, and developers report a low barrier to entry for core setup, but less depth once work moves past basic agent initialization. Teams report a working agent scaffold in 5 to 15 minutes with the CLI and example code, while production ready setups often take 1 to 2 hours when custom integrations or knowledge sources are involved.

What developers like:

• Core setup is simple and supports a low barrier to entry.
• Developers highlight tight TypeScript integration and native async/await support.
• The tool system is described as flexible, and public signals point to active maintenance.

Common frustrations:

• Developers report unclear errors during implementation and debugging.
• Integration work can become complex once projects move beyond basic scaffolding.
• Reported gaps include knowledge base limitations, limited deployment guidance, and type safety issues.

Security and Privacy

• SOC 2: SOC 2 Type 2 certified, per Mastra's trust center. (https://trust.mastra.ai)
• Encryption at rest: AES-256 encryption is enabled, per the vendor's security information. (https://trust.mastra.ai)
• Encryption in transit: TLS 1.3 is enabled for data in transit, per the vendor's security information. (https://trust.mastra.ai)

Product Momentum

• Release pace: Users and coverage describe Mastra as "furiously, frantically shipping" since early 2025.
• Recent releases: Mastra released Version 1.0 in January 2026. Public coverage tied that release to production readiness and 1.77M monthly NPM downloads.
• Growth: The trajectory is growing, and Mastra is VC-backed. Public sources also point to production use at Replit, Marsh McLennan, SoftBank, Adobe, PayPal, Elastic, and Docker, plus support for 81 LLM providers.
• Search interest: Google Trends data is flat over the measured period, with +0.0% change, a latest score of 0/100, and a peak score of 0/100.
• Risks: No notable risks are reported in public sources. The main constraint noted is a TypeScript-only approach for Python and ML teams, while broad LLM support reduces reliance on a single provider.

FAQ

What is Mastra?

Mastra is an open-source TypeScript framework for building AI agents, workflows, and retrieval-augmented generation pipelines. It includes abstractions for agents, deterministic workflows, persistent memory, and evaluation tools on top of Vercel's AI SDK.

What is Mastra used for?

Mastra is used to build AI agents and applications that go beyond basic chatbots. Public sources mention customer support agents and multi-agent orchestration systems, along with production-focused memory and evaluation features.

What is a Mastra agent?

A Mastra agent is an autonomous AI component built with the Mastra TypeScript framework. It can reason, use tools, handle multi-step tasks, and work with observational memory.

Who are the founders of Mastra?

Public sources say Mastra was founded by Shane Thomas and Abhi Aiyer. The same sources note they started the company after a NYC AI hackathon 18 months earlier.

Who owns Mastra AI?

Public sources describe Mastra AI as owned by its founders, Shane Thomas and Abhi Aiyer. The company is also described as an independent open-source framework provider with backing from Spark Capital.

Is Mastra open source?

Yes. Public sources describe Mastra as an open-source framework for TypeScript developers building AI agents, workflows, and RAG pipelines.

Does Mastra support workflows?

Yes. Mastra includes deterministic workflows, and its setup documentation references creating steps with createStep(), defining input and output schemas, and registering workflows in a Mastra instance.

Does Mastra include memory features?

Yes. Mastra includes persistent memory, and its feature set includes Observational Memory. Public sources describe this as using Observer and Reflector background agents to compress older conversation history into structured observations.

Does Mastra have observability tools?

Yes. Mastra documentation includes observability support, and public positioning describes tracing as part of the product. Studio is also listed as an interactive UI for development and testing.

Does Mastra support RAG?

Yes. Public sources describe Mastra as a framework for building retrieval-augmented generation pipelines. Its ideal use cases also mention production agents that work over data sources such as BigQuery or Postgres.

What languages and SDKs does Mastra use?

Mastra is built as a TypeScript framework. Public sources also describe it as working on top of Vercel's AI SDK.

Is Mastra free?

Mastra lists a free forever tier for both Platform and Memory Gateway, with core usage limits. Pricing notes also mention an Enterprise tier with dedicated support, custom limits, and advanced security.

Who is Mastra for?

Mastra targets TypeScript developers and AI engineers building production agents. Public sources point to teams working on RAG, multi-tool orchestration, evaluations, and data-connected agent systems.

How does Mastra compare with basic chatbot tools?

Public sources position Mastra for more complex agent systems rather than simple chatbots. Its feature set includes workflows, memory, evals, tracing, and an interactive Studio UI for development and testing.