LangChain vs Mastra: Agent Frameworks Comparison
Compare LangChain and Mastra side by side. Both are tools in the Agent Frameworks category.
Quick Comparison
| 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 |
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When to Choose LangChain vs Mastra
- Building RAG applications
- Multi-agent orchestration with LangGraph
- Tool-using conversational agents
- TypeScript-native agent development
- Building AI workflows with tool use
- Agent state management
How to Choose a Agent Frameworks Tool
Key criteria to evaluate when comparing Agent Frameworks solutions:
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.
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.
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 →