The article provides an overview of the core components and workflows of the `mcp-agent`, including the MCPApp, Agent, AugmentedLLM, and various workflow patterns like Parallel, Router, and Evaluator-Optimizer. It also highlights the framework's interoperability, composability, and support for human input and signals. The article outlines different ways to set up `mcp-agent` applications, such as standalone, as an MCP server, or embedded in an MCP client. The roadmap for future development includes durable execution, long-term memory, and expanded MCP capabilities.
Key takeaways:
- mcp-agent is a lightweight framework designed to build AI agents using the Model Context Protocol (MCP), offering a composable approach to agent development.
- The framework simplifies the management of MCP server connections and implements patterns for building production-ready AI agents, including OpenAI's Swarm pattern for multi-agent orchestration.
- mcp-agent supports various workflows such as Parallel, Router, Intent-Classifier, Evaluator-Optimizer, and Orchestrator-Workers, allowing for flexible and complex AI applications.
- The framework emphasizes interoperability, composability, programmatic control flow, and human input integration, making it a versatile tool for AI application developers.