Mastra provides an all-in-one TypeScript framework for developing and deploying AI-powered applications and agents. The platform integrates core components like Workflows, RAG, Memory, and Observability to streamline the build-to-production lifecycle. Developers can iterate locally, tune agent accuracy with custom evaluations, and expose agents as scalable APIs.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing AI applications with JavaScript and TypeScript can be challenging due to the lack of comprehensive tooling and frameworks, especially when compared to Python-centric solutions. This gap hinders frontend and full-stack developers from efficiently prototyping and deploying AI-powered features.
Solution
Mastra is an open-source TypeScript framework designed to streamline the development of AI agents and applications. It provides developers with the necessary primitives to build, test, and deploy AI features, offering a unified interface for interacting with various LLM providers through the Vercel AI SDK. Mastra supports agent memory, tool calling, graph-based workflows, and retrieval-augmented generation (RAG), enabling the creation of robust and reusable AI processes. The framework also includes a local development environment for agent interaction and automated evaluation metrics for assessing LLM outputs.
Target Audience
Mastra is targeted towards JavaScript and TypeScript developers, including frontend, full-stack, and AI engineers, who want to build and deploy AI-powered applications and features efficiently.
Features
- Unified API for interacting with LLMs from providers like OpenAI, Anthropic, and Google Gemini via Vercel AI SDK
- Agent memory management with options for persistence and retrieval based on recency, semantic similarity, or conversation thread
- Graph-based workflow engine for deterministic execution of LLM calls, featuring branching and chaining capabilities
- Local agent development environment for real-time interaction and state inspection
- Retrieval-augmented generation (RAG) APIs for processing documents, creating embeddings, and querying vector databases (Pinecone, pgvector, etc.)
- Automated evaluation metrics for assessing LLM outputs, including toxicity, bias, relevance, and factual accuracy
- CLI tool for running agents behind an API, facilitating easy integration into existing projects
- Support for structured output using JSON Schema or Zod schemas