Overview
Mastra is an all-in-one TypeScript framework designed for building production-ready AI applications and autonomous agents. It provides a comprehensive suite of primitives—including agents, workflows, RAG (Retrieval-Augmented Generation), memory, tools, and evaluations—that enable developers to go from prototype to deployment within a modern JavaScript stack. The framework is fully open-source under the Apache 2.0 license, giving teams complete control over their source code and infrastructure while integrating seamlessly with popular frameworks like Next.js, Express, and Hono.
Built for TypeScript developers who value type safety and modern tooling, Mastra eliminates the complexity of cobbling together disparate AI libraries. It connects to over 40 AI model providers through a unified interface and supports the Model Context Protocol (MCP) for universal plugin integration. Whether you're building a simple chatbot or orchestrating multi-step agentic workflows with parallel execution, Mastra provides the observability, guardrails, and deployment infrastructure needed for production environments.
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Key Features
Unified AI Model Integration — Connects to 40+ model providers (OpenAI, Anthropic, Gemini, etc.) through a single interface, allowing you to switch models or run A/B tests without rewriting application logic.
Graph-Based Workflow Engine — Orchestrate complex multi-step processes with intuitive
.then(),.branch(), and.parallel()syntax, providing explicit control over agent execution flow and error handling.Built-in Memory & RAG — Provides built-in components for message history, semantic recall, and retrieval-augmented generation. Once configured, automatically retrieves and injects context, reducing manual state management overhead.
Developer Studio & Observability — The open-source framework provides a local dev server (Studio) with visualization tools for agent development. The managed Platform service offers hosted observability dashboards with AI-aware tracing, structured logs, and eval metrics for production monitoring.
Production-Ready Infrastructure — Deploy agents as REST APIs or bundle them with your existing application, with built-in guardrails for prompt injection prevention, response sanitization, and authentication integration.
Model Context Protocol (MCP) — Functions as both an MCP client (connecting to external servers) and server (exposing Mastra tools), enabling seamless integration with the broader AI agent ecosystem.
Pricing & Plans
Mastra operates on a dual-model approach: the core framework is free and open-source, while the managed platform service will introduce paid tiers in Q1 2026.
Framework (Open Source)
- Price: Free
- License: Apache 2.0 (transitioned from ELv2 during beta phase)
- Includes: All core primitives (agents, workflows, RAG, memory, tools, evals), vector stores, datasets, and MCP support
- Self-Hosted: Full control over deployment and infrastructure
Mastra Platform (Managed Service)
- Current Status: Free to start with no seats or usage tiers (as of January 2026; specific quotas and rate limits subject to official terms)
- Launching: Paid pricing tiers will be introduced in Q1 2026
- Features:
- Observability dashboard with AI-aware tracing and eval metrics
- Interactive Studio UI for building and testing agents
- GitHub-based deployments with autoscaling REST endpoints
- Built-in authentication and guardrail enforcement
Enterprise
- Custom Pricing: Available for teams requiring hands-on support, SLAs, and on-premises deployments
- Contact: Sales inquiry required through website
Trial Policy: The current open-source version operates under Apache 2.0 license and can be used according to license terms indefinitely. The managed platform is currently free during beta, with pricing details for production tiers to be announced in Q1 2026. Future commercial policies are subject to official announcements.
Best For
- TypeScript-first engineering teams who prefer type-safe development and want to build AI agents within their existing JavaScript/Node.js stack without context switching to Python.
- Product teams iterating on agentic workflows who need rapid prototyping capabilities with the Developer Studio while maintaining production-grade observability and eval tools.
- Startups and scale-ups seeking full control over their AI infrastructure and source code through open-source licensing, avoiding vendor lock-in to proprietary agent platforms.
- Organizations building customer-facing AI applications that require robust guardrails, authentication integration, and deployment infrastructure for REST API-based agent services.
- Developers experienced with Next.js or Express who want to add AI capabilities to existing applications without rearchitecting their codebase or adopting unfamiliar frameworks.
Alternatives & Comparisons
For teams evaluating Mastra within the TypeScript AI ecosystem, here are key alternatives with different trade-offs:
LangChain.js
A JavaScript port of the popular Python framework with extensive ecosystem integrations. Choose LangChain.js if you prioritize ecosystem breadth and chain-based abstractions over opinionated workflow primitives. Trade-off: Less focus on production infrastructure (observability, evals) compared to Mastra.
Vercel AI SDK
A lightweight library for streaming AI responses and React integration, ideal for chat UIs and generative interfaces. Choose Vercel AI SDK if you're building conversational experiences within Next.js and don't need full workflow orchestration or agent tooling. Mastra actually integrates with Vercel AI SDK for enhanced React support.
Custom Toolchain
Building from scratch with raw OpenAI/Anthropic SDKs plus separate libraries for observability (Langfuse, LangSmith) and orchestration. Choose this if you need maximum flexibility and have engineering resources to maintain integration code. Trade-off: Higher development overhead versus Mastra's integrated approach.
AutoGen (TypeScript ports)
Microsoft's multi-agent conversation framework, focusing on agent-to-agent collaboration patterns. Consider if your use case centers on autonomous agent negotiations rather than human-in-the-loop workflows. Less mature TypeScript support compared to Mastra.
Flowise / LangFlow
No-code/low-code visual workflow builders with TypeScript export capabilities. Choose if non-technical team members need to design flows. Trade-off: Less control over code structure and production deployment compared to code-first Mastra.
Decision Framework: Choose Mastra if you value (1) TypeScript-native development with full type safety, (2) integrated production tooling (Studio, observability, evals, guardrails), and (3) framework flexibility for embedding agents into existing Next.js/Express apps. Consider alternatives if you need Python ecosystem compatibility (LangChain), minimal UI-only integration (Vercel AI SDK), or visual workflow design (Flowise).
FAQ
Is Mastra completely free to use?
Yes, the core Mastra framework is free and open-source under the Apache 2.0 license (transitioned from ELv2 during beta). You can build, deploy, and run agents in your own infrastructure according to the license terms. The managed Mastra Platform is currently free during beta, but paid pricing tiers will launch in Q1 2026 for teams requiring hosted observability, deployment automation, and enterprise support.
What AI models does Mastra support?
Mastra connects to over 40 AI model providers through a unified interface, including OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), Google (Gemini), and many others. You can switch between models or run A/B tests without modifying your agent logic, as the framework abstracts provider-specific APIs into a consistent TypeScript interface.
Can I use Mastra with Python instead of TypeScript?
No, Mastra is built exclusively for TypeScript/JavaScript environments. If your team works primarily in Python, you may want to consider alternatives like LangChain or LlamaIndex, which offer similar agent and workflow capabilities in Python ecosystems.
How does Mastra differ from LangChain?
While both frameworks enable agent and workflow development, Mastra is TypeScript-first with a focus on production infrastructure (observability, evals, guardrails), whereas LangChain is Python-dominant with broader ecosystem integrations. Mastra provides opinionated primitives (.then(), .branch(), .parallel()) for workflow orchestration, while LangChain uses chain-based abstractions. Choose Mastra if you prioritize type safety and JavaScript stack integration.
What is the Model Context Protocol (MCP) support in Mastra?
Mastra implements MCP as both a client and server, meaning your agents can consume tools from external MCP servers (like database connectors or API wrappers) and expose Mastra-built tools to other MCP-compatible systems. This enables interoperability across the AI agent ecosystem without vendor lock-in.
Do I need to learn React to use Mastra?
No, Mastra's core framework works with any Node.js environment, including Express, Hono, or standalone scripts. However, the Developer Studio (visual agent builder) and Vercel AI SDK integration provide enhanced experiences when used with React-based frameworks like Next.js. Backend-focused teams can build and deploy agents entirely through code without touching React.
How does Mastra handle agent memory and context?
Mastra includes built-in memory management with three layers: message history (conversation logs), semantic recall (vector-based retrieval), and working memory (session state). For RAG applications, it integrates with vector stores to automatically fetch relevant context before agent execution, eliminating the need for manual state handling.
Is my data secure when using Mastra?
When self-hosting the open-source framework, you maintain full control over data storage and transmission—no data is sent to Mastra's servers. The managed Platform service (launching paid tiers in Q1 2026) will handle observability data, but authentication and access control integrate with your existing identity systems. Review the license terms and upcoming Platform privacy policy for managed service details.
Can I deploy Mastra agents in my existing infrastructure?
Yes, Mastra is designed to integrate with your existing deployment pipeline. You can bundle agents with your Next.js app, deploy them as standalone Express/Hono services, or use containerized environments. The framework does not require proprietary hosting—GitHub-based deployments in the managed Platform are optional conveniences, not requirements.
What kind of observability does Mastra provide?
The framework includes AI-aware tracing (tracking token usage, latency, and tool calls), structured logs, and eval dashboards. You can integrate with external observability platforms through standard logging and tracing hooks. The managed Platform will offer a built-in dashboard for visualizing agent performance, but self-hosted deployments can use tools like Datadog or New Relic.
