Mastra

What do they actually do

Mastra is an open‑source TypeScript/JavaScript framework and toolset for building AI “agents” — programs that combine LLM calls with tools, retrieval (RAG), and memory, with typed, observable workflows. It’s aimed at developers who want to write agents in plain TypeScript and run them as part of an app or as an API/service, without stitching together many separate libraries Mastra docs, GitHub.

A typical workflow is: create a project via the CLI, write agents and tools in TypeScript, run a local dev server and test in Mastra’s Studio with tracing, then deploy to a Node host or to Mastra’s hosted option (Mastra Cloud) for managed deployments and team observability. The product includes built‑in tracing, evals/scorers, and examples/templates to speed up iteration Install, Examples, Evals/observability, Mastra Cloud.

Mastra also exposes a model/provider interface to route requests across many model vendors without rewriting code, and is shipping stronger typed workflows (vNext) and MCP-based document integrations to improve reliability and access to authoritative sources Models, vNext workflows, MCP docs server. Early users include independent developers and some YC companies using it for support automation, scraping, transcriptions, and code helpers YC profile.

Who are their target customer(s)

• Early-stage product teams at startups building AI features (support, code helpers, transcription).: They spend time stitching together libraries/services to get a reliable agent running and need an easy path to deploy and monitor in production examples, Mastra Cloud.
• Backend/platform engineers at growing teams operating model‑driven features.: They need predictable behavior, tracing, and model/provider flexibility so features don’t break in production and can switch vendors without rewrites models, evals/observability.
• Independent developers and agencies building demos or client prototypes.: They want fast starter projects and a local dev workflow to show working demos quickly instead of wiring infra from scratch examples, templates, course.
• Teams building apps that answer questions over company documents/knowledge bases.: They struggle with indexing and keeping answers grounded; they want built‑in document serving and retrieval integrations (MCP, vector stores) instead of fragile prompting MCP.
• ML/ML‑ops engineers responsible for safety, testing, and evaluation of agent behavior.: They need evals, typed control flow, and tracing to reproduce failures and move from prototype to production vNext workflows, changelog.

How would they acquire their first 10, 50, and 100 customers

• First 10: Invite existing GitHub watchers/contributors and YC users to white‑glove onboarding sessions, build a working agent with them live, and offer short, free Mastra Cloud trials to remove integration friction GitHub, YC profile, Mastra Cloud.
• First 50: Publish 3–5 starter templates (support bot, code helper, transcription) plus step‑by‑step workshops and live Q&A; drive adoption via targeted posts in developer communities and GitHub outreach, ending with a one‑click deploy flow templates, examples/workshops.
• First 100: Ship integrations with popular hosts/vector stores, publish 3–5 case studies (incl. YC users), and launch a partner program with deployment recipes; complement with sales outreach to product/platform teams that need observability, evals, MCP, and multi‑provider flexibility models/providers, MCP, evals.

What is the rough total addressable market

Top-down context:

Near‑term SAM is the LLMOps/LLM platform market, estimated around US$1.3B in 2024 DataIntelo. Longer‑term, the broader generative‑AI applications market is in the tens of billions (e.g., ~US$59B in 2025) Statista.

Bottom-up calculation:

From ~27M developers worldwide and ~17M already using AI tools, assume a reachable subset building agent features; if 100k–300k teams adopt a hosted framework at ~$50–$200 per team per month, that implies a US$60M–$720M bottom‑up SAM Evans Data, StackOverflow 2024.

Assumptions:

• A material subset of AI‑using developers work on agent/RAG features inside product teams.
• Teams are willing to pay for hosted deploy/observe/eval features (ARPA ~$600–$2,400/yr).
• Pricing and adoption reflect SMB/startup skew before larger enterprise uptake.

Who are some of their notable competitors

• LangChain: Widely used framework for building LLM apps and agents (Python/JS) with rich integrations and LangGraph for workflow orchestration.
• LlamaIndex: Data framework for LLM applications with strong RAG, indexing, and agent tooling; includes evals and observability options.
• Vercel AI SDK: TypeScript SDK for building AI features with streaming UX and serverless deploys; overlaps on model routing and JS developer ergonomics.
• Microsoft AutoGen: Open-source multi‑agent framework focused on agent collaboration and tool use (primarily Python); popular for complex agent setups.
• CrewAI: Agent framework (primarily Python) for orchestrating multiple specialized agents; notable community and production‑oriented patterns.