Choosing an agent framework: LangChain vs LangGraph vs CrewAI vs PydanticAI vs Mastra vs Vercel AI SDK
Blog post from Speakeasy
Choosing the right agent framework is crucial for efficient AI development, as improper selection can lead to significant technical debt. The comparison involves seven frameworks and two SDKs: LangChain, LangGraph, CrewAI, PydanticAI, Mastra, Vercel AI SDK, n8n, and Vellum, evaluated on criteria like developer experience, agent capabilities, context and memory management, deployment and hosting, and security. Agent frameworks offer different features such as role-based multi-agent systems, type safety, or graph-based orchestration, each suited to specific needs like simple tool loops, stateful workflows, or non-technical team integration. For instance, LangChain is known for its wide integration range, while PydanticAI offers strong type safety, and Mastra provides robust TypeScript support. Vercel AI SDK excels in streaming AI UIs, and n8n and Vellum are tailored for visual workflows. The choice ultimately depends on factors like primary programming language, complexity of orchestration, and the team's technical composition. Each framework has its strengths and limitations, making it essential to align the choice with the specific project requirements and team capabilities.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 19 | 6,078 | 960 | 218 | +18% |
| Developer Experience | 18 | 482 | 254 | 106 | +18% |
| MCP | 16 | 4,488 | 443 | 150 | +34% |
| AI Agents | 15 | 4,545 | 963 | 231 | +27% |
| Multi-agent systems | 13 | 574 | 146 | 66 | +51% |
| Serverless | 11 | 729 | 189 | 89 | -11% |
| Real-time | 5 | 6,457 | 1,307 | 242 | +28% |
| RAG | 4 | 1,806 | 326 | 91 | +5% |
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