Agent
Build effective agents using Model Context Protocol and simple workflow patterns
安装配置
暂未识别到可直接复制的 MCP 配置，请查看 GitHub README。后台管理员可以补充配置。
README 摘要
Build effective agents with Model Context Protocol using simple, composable patterns. Examples | Building Effective Agents | MCP ## Overview **`mcp-agent`** is a simple, composable framework to build effective agents using [Model Context Protocol](https://modelcontextprotocol.io/introduction). > [!Note] > mcp-agent's vision is that _MCP is all you need to build agents, and that simple patterns are more robust than complex architectures for shipping high-quality agents_. `mcp-agent` gives you the following: 1. **Full MCP support**: It _fully_ implements MCP, and handles the pesky business of managing the lifecycle of MCP server connections so you don't have to. 2. **Effective agent patterns**: It implements every pattern described in Anthropic's [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) in a _composable_ way, allowing you to chain these patterns together. 3. **Durable agents**: It works for simple agents and scales to sophisticated workflows built on [Temporal](https://temporal.io/) so you can pause, resume, and recover without any API changes to your agent. Altogether, this is the simplest and easiest way to build robust agent applications. We welcome all kinds of [contributions](/CONTRIBUTING.md), feedback and your help in improving this project. **Minimal example** ```python import asyncio from mcp_agent.app import MCPApp from mcp_agent.agents.agent import Agent from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM app = MCPApp(name="hello_world") async def main(): async with app.run(): agent = Agent( name="finder", instruction="Use filesystem and fetch to answer questions.", server_names=["filesystem", "fetch"], ) async with agent: llm = await agent.attach_llm(OpenAIAugmentedLLM) answer = await llm.generate_str("Summarize README.md in two sentences.") print(answer) if __name__ == "__main__": asyncio.run(main()) # Add your LLM API key to `mcp_agent.secrets.yaml` or set it in env. # The [Getting Started guide](https://docs.mcp-agent.com/get-started/overview) walks through configuration and secrets in detail. ``` ## At a glance Build an Agent Connect LLMs to MCP servers in simple, composable patterns like map-reduce, orchestrator, evaluator-optimizer, router & more. Quick Start ↗ | Docs ↗ Create any kind of MCP Server Create MCP servers with a FastMCP-compatible API. You can even expose agents as MCP servers. MCP Agent Server ↗ | 🎨 Build a ChatGPT App ↗ | Examples ↗ Full MCP Support Core: Tools ✅ Resources ✅ Prompts ✅ Notifications ✅ Advanced: OAuth ✅ Sampling ✅ Elicitation ✅ Roots ✅ Examples ↗ | MCP Docs ↗ Durable Execution (Temporal) Scales to production workloads using Temporal as the agent runtime backend without any API changes. Docs ↗ | Examples ↗ ☁️ Deploy to Cloud Beta: Deploy agents yourself, or use mcp-c for a managed agent runtime. All apps are deployed as MCP servers. Demo ↗ | Cloud Quickstart ↗ | Examples ↗ ## Documentation & build with LLMs mcp-agent's complete documentation is avai...
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