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mcp-agent Project Description

What is the project about?

mcp-agent is a framework for building AI agents that utilize the Model Context Protocol (MCP). It's designed to be simple and composable, allowing developers to easily create agents that can interact with various services and perform complex tasks. It draws inspiration from Anthropic's research on building effective agents and OpenAI's Swarm project.

What problem does it solve?

It simplifies the development of robust, controllable AI agents that can leverage a growing ecosystem of MCP-aware services. It handles the complexities of managing MCP server connections, provides pre-built workflow patterns (inspired by Anthropic's research), and allows for easy composition of these patterns. It also provides a model-agnostic implementation of multi-agent orchestration, similar to OpenAI's Swarm.

What are the features of the project?

  • MCP Server Management: Easy connection to and lifecycle management of MCP servers (using gen_client, connect, disconnect, MCPConnectionManager).
  • Agent Abstraction: The Agent class represents an entity that can access and utilize MCP servers, exposing their functionalities as tools to LLMs.
  • Augmented LLMs: The AugmentedLLM interface provides a way to enhance language models with tools from MCP servers, supporting generation, string output, and structured output.
  • Workflow Patterns: Implementations of various agent patterns, including:
    • Parallel: Distributes tasks to multiple sub-agents.
    • Router: Routes requests to the most relevant agent or function (embedding-based and LLM-based).
    • IntentClassifier: Identifies the intents that best match an input (embedding-based and LLM-based).
    • Evaluator-Optimizer: Iteratively refines responses using an optimizer and evaluator LLM.
    • Orchestrator-Workers: A higher-level LLM creates a plan and assigns tasks to sub-agents.
    • Swarm: A model-agnostic implementation of OpenAI's Swarm multi-agent pattern.
  • Composability: Workflows are implemented as AugmentedLLMs, allowing them to be nested and chained.
  • Signaling and Human Input: Support for pausing workflows and requesting human input via a __human_input__ tool.
  • Configuration: Uses YAML files (mcp_agent.config.yaml and mcp_agent.secrets.yaml) for application and MCP server configuration.
  • MCP Server Aggregator: The MCPAggregator combines multiple MCP servers into a single interface.
  • Examples: Includes example applications demonstrating integration with Streamlit, Marimo, and Claude Desktop, as well as standalone Python examples.

What are the technologies used in the project?

  • Python: The primary programming language.
  • Model Context Protocol (MCP): The core protocol for communication between agents and services.
  • LLMs: Integration with LLM providers (Anthropic, OpenAI) via AugmentedLLM implementations.
  • Asyncio: For asynchronous programming and managing concurrent operations.
  • Pydantic: For data validation and structured output.
  • Instructor: (Optional) For generating structured output from LLMs.
  • uv: (Recommended) For Python project management.
  • Streamlit, Marimo: (Optional) For building interactive web applications.
  • Temporal: (In Roadmap) For durable execution.

What are the benefits of the project?

  • Simplified Agent Development: Provides a higher-level abstraction over MCP, making it easier to build complex agents.
  • Robustness: Offers pre-built patterns for creating reliable and fault-tolerant agents.
  • Interoperability: Works seamlessly with any MCP-compliant service.
  • Extensibility: Designed to be easily extended with new workflows and integrations.
  • Model Agnostic: Many components, especially the workflow patterns, are designed to work with different LLM providers.
  • Lightweight: Closer to a pattern library than a heavy framework.

What are the use cases of the project?

  • Multi-Agent Systems: Building applications where multiple agents collaborate to achieve a goal (e.g., customer service, research assistants).
  • Human-in-the-Loop Workflows: Creating agents that can interact with users for input or approval.
  • Retrieval-Augmented Generation (RAG): Developing chatbots and Q&A systems that can access and utilize external knowledge sources.
  • Automation: Automating tasks that require interaction with various services (e.g., email management, data processing).
  • Integration with MCP Clients: Building agents that can be used within MCP clients like Claude Desktop.
  • Standalone AI Applications: Creating AI-powered applications that leverage MCP servers without requiring an MCP client.
  • Complex Task Decomposition: Breaking down complex tasks into smaller, manageable subtasks handled by specialized agents.
mcp-agent screenshot