If you're building anything that involves an AI "agent" — software that can decide what to do next instead of just responding to one prompt — you eventually need a framework to handle the messy plumbing: remembering context, calling tools, recovering when something breaks midway through a multi-step task. Two of the most talked-about options for this in 2026 are Mastra and LangGraph. They solve the same underlying problem in very different ways, so the right pick depends heavily on what language your team already uses and how much control you want over the internals.
Mastra
Mastra is a TypeScript framework built for teams already living in the Node.js/JavaScript world. It bundles typed tools, graph-based orchestration, memory, RAG and evals into one package, and it can talk to 90+ model providers through a single interface — so switching from, say, OpenAI to Anthropic doesn't mean rewriting your integration.
Pricing: the core framework is Apache 2.0 and free. Mastra Cloud has a free Starter tier, with paid Teams plans above that — some enterprise features carry a separate license worth checking before shipping to production.
Strengths: arguably the most complete TypeScript-native agent framework available, with memory, RAG, evals and MCP support built in rather than bolted on, and 90+ provider coverage that limits vendor lock-in.
Limits: the API has shifted between releases as the framework matures, and the dual-licensing on some enterprise features is worth reading closely before building a production dependency on it.
LangGraph
LangGraph, from the LangChain team, takes a lower-level approach: it's a library for building agents that can pause mid-task, remember exactly where they left off, and resume later — even after a server restart — instead of losing all progress if something crashes. That durability is the whole point.
Pricing: open source and free to install via pip. LangSmith, the companion observability tool most teams pair it with, has its own separate paid tier.
Strengths: durable execution that survives failures instead of restarting from scratch, built-in human-in-the-loop and short/long-term memory primitives, native token-by-token streaming, and real production mileage — running inside Klarna, Replit and Elastic, backed by 40,000+ GitHub stars.
Limits: it's a low-level library, not a batteries-included app — you write code and think in state graphs, a steeper learning curve than a no-code tool like Dify, and it can be overkill if all you need is one simple stateless AI feature.
Side-by-side
| Mastra | LangGraph | |
|---|---|---|
| Primary language | TypeScript | Python (JS supported) |
| Core license | Apache 2.0 (some enterprise features licensed separately) | Open source, fully free |
| Built-in memory/RAG/evals | Yes, bundled | Memory primitives yes; RAG/evals via separate tools |
| Standout feature | 90+ model providers behind one API | Durable execution — resumes after crashes |
| Production track record | Newer, fast-growing | Klarna, Replit, Elastic; 40k+ stars |
Other frameworks worth a look
If neither fits exactly, two more real options from the same space: CrewAI trades fine-grained control for the fastest way to prototype role-based multi-agent "crews" in Python, backed by a 55,000+ star community — though its high-level abstractions can hide what agents are actually doing. Agno is a Python framework that unifies 30+ model providers behind one API and adds AgentOS, a control plane for actually running agents in production, not just building them — genuinely open source, but Python-only, and its managed control plane starts at $150/month.
Verdict
Pick Mastra if: your team already writes TypeScript/Node.js and you want one framework that bundles memory, RAG, evals and broad model support without stitching together separate tools.
Pick LangGraph if: you're building in Python, need agents that survive crashes and pick up where they left off, and you're comfortable writing code and thinking in state graphs rather than using a higher-level abstraction.
Both are moving fast and neither is going away — if you're unsure, prototype the riskiest part of your agent (usually the failure-recovery path) in each for a day before committing.