How to build AI agents with MCP: 12 framework comparison (2025)

The Model Context Protocol (MCP) is an open protocol standardizing interactions between applications and large language models (LLMs), which has become the standard for integrating systems with LLMs since its launch in late 2024. MCP servers, similar to REST APIs, define tools and resources for LLMs to interact with, allowing them to perform actions like running SQL queries or listing databases. There are now at least 12 major agent SDKs supporting MCP, each catering to different needs: Claude Agent SDK is security-focused, OpenAI Agents SDK emphasizes agent handoffs, CrewAI handles multi-agent workflows, LangChain offers ecosystem breadth, and Agno prioritizes minimal code and performance, among others. These SDKs facilitate the integration of MCP servers into applications, often handling complexities like authentication and tool discovery. MCP servers can be local or remote, with remote servers addressing operational concerns like rate limiting and authorization. The choice of agent SDK and LLM depends on the application's requirements and the developer's ecosystem preferences.