Keywords AI

LangChain vs Mastra

Compare LangChain and Mastra side by side. Both are tools in the Agent Frameworks category.

Quick Comparison

	LangChain	Mastra
Category	Agent Frameworks	Agent Frameworks
Pricing	Open Source	Open Source
Best For	Developers building complex LLM applications who need a comprehensive orchestration framework	TypeScript developers who want a modern framework for building production AI agents
Website	langchain.com	mastra.ai
Key Features	LangChain Expression Language (LCEL) LangGraph for stateful multi-agent workflows 1000+ integrations LangSmith observability companion Python and JavaScript SDKs	TypeScript agent framework Built-in tool calling and workflows Sync engine for agent state Integration marketplace Production-ready agent primitives
Use Cases	Building RAG applications Multi-agent orchestration with LangGraph Tool-using conversational agents Document processing pipelines Complex chain-of-thought workflows	TypeScript-native agent development Building AI workflows with tool use Agent state management Production agent deployment Multi-step task automation

When to Choose LangChain vs Mastra

Choose LangChain if you need

Building RAG applications
Multi-agent orchestration with LangGraph
Tool-using conversational agents

Pricing: Open Source

Choose Mastra if you need

TypeScript-native agent development
Building AI workflows with tool use
Agent state management

Pricing: Open Source

How to Choose a Agent Frameworks Tool

Key criteria to evaluate when comparing Agent Frameworks solutions:

Programming languagePython, TypeScript, or both — must match your team skills and existing codebase.

Architecture patternSingle-agent, multi-agent, or graph-based orchestration depending on task complexity.

Tool ecosystemBuilt-in tools and ease of creating custom tools for your specific needs.

ObservabilityBuilt-in tracing, debugging, and monitoring for understanding agent behavior.

Production readinessError handling, retries, streaming, and deployment options for production use.

About LangChain

LangChain is the most widely adopted framework for building LLM-powered applications and AI agents. It provides abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems. LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring. The LangChain ecosystem is the largest in the AI application development space.

View LangChain profile →Visit website

About Mastra

Mastra is a TypeScript-first agent framework for building production AI applications. It provides primitives for agents, workflows, RAG, integrations, and memory with a focus on developer experience and type safety. Mastra is designed for full-stack TypeScript developers who want to build AI features without leaving their existing tech stack.

View Mastra profile →Visit website

What is Agent Frameworks?

Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.

Browse all Agent Frameworks tools →

Frequently Asked Questions

What is an AI agent framework?▾

An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.

Do I need a framework or can I build agents with raw API calls?▾

For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.

Which agent framework should I choose?▾

LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.