LangChain vs Mastra

LangChain and Mastra are both open-source, free-at-the-core frameworks for building AI agents, but they start from opposite premises about what language and what shape a framework should have. LangChain provides model, tool, retrieval and memory abstractions and, through LangGraph, an explicit graph of nodes and edges for controlling multi-step, stateful agent flows — Python is its home language, with an official JavaScript/TypeScript SDK as a well-supported but secondary target. Mastra is the one framework in this category built TypeScript-native from the ground up: it unifies typed agents, durable typed workflows (retries, branching), observational and semantic memory, and observability (evals, metrics, traces) into a single package designed to drop directly into an existing Next.js or Node codebase — there is no Python variant at all.

Checked live via the GitHub API on 2026-07-29: LangChain's root repository has 142,836 stars and 23,779 forks, and LangGraph — its dedicated agent-orchestration engine — has 38,381 stars on its own. Mastra has 26,672 stars and 2,523 forks. The gap reflects LangChain's head start (created October 2022, two years before Mastra) and its use well beyond agent-building specifically, but Mastra's growth is real for a framework whose company was only founded in 2024: it raised a $22 million Series A led by Spark Capital in mid-2026, bringing total funding to $35 million with Y Combinator and Gradient Ventures among its seed backers, and lists named production customers including Replit, Sanity, MongoDB, Brex and Marsh. LangChain is the better-capitalized company by a wide margin — a $125 million Series B in October 2025 at a $1.25 billion unicorn valuation, led by IVP with Sequoia and Benchmark returning — though because both frameworks' core libraries are open-source and self-hostable, neither company's fate determines whether an existing deployment keeps working.

Pricing is free-vs-free at the framework level, and the paid layers above each core are structured differently. LangChain's core (LangChain + LangGraph) is MIT-licensed and free; its optional LangSmith observability layer is $0/seat for up to 5,000 traces/month on one seat (Developer), $39/seat/month for up to 10,000 traces/month with unlimited seats (Plus, includes one free small serverless deployment), then custom-priced Enterprise, plus usage-based compute/storage units beyond the included limits. Mastra's core (agents, workflows, memory) is Apache 2.0-licensed and free to self-host anywhere; its optional Mastra Cloud platform starts at $0/month (Starter: 100K observability events then $10/100K, 24 CPU hours then $0.35/hr, 15-day retention), scales to $250/month (Teams: 1M events then $8/100K, 250 CPU hours then $0.25/hr, 6-month retention, SSO and SOC 2 docs), then custom Enterprise — plus a separate Enterprise Self-Hosted tier billed as a flat annual fee instead of per-trace metering. One real asymmetry: Mastra's deepest enterprise controls (RBAC, SSO, IAM) sit behind that separately-licensed Enterprise tier rather than the open Apache-2.0 core, while LangChain's core MIT license carries no such split.

The practical trade-off is language fit and ecosystem maturity against an integrated, TypeScript-first design. LangChain is model-agnostic through its own provider integrations and ships hundreds of pre-built model, vector-store and tool connectors — a real advantage for teams working in Python, or that need the widest possible integration surface and the deepest well of tutorials and battle-tested production patterns. Mastra routes to 90+ model providers through one standard interface and ships built-in deploy targets for Vercel, Netlify and Cloudflare, but is TypeScript/JavaScript-only with no first-party Python support at all — a hard constraint for any team standardized on Python, and a direct fit for any team that isn't. Where LangChain assembles orchestration (LangGraph), observability (LangSmith) and retrieval as related-but-separate products, Mastra ships agents, durable workflows, memory and observability as one integrated framework from the start — the trade being a much younger ecosystem (six years less GitHub history, roughly a fifth of the stars) against a framework designed for the full-stack JS world LangChain treats as a secondary target.