> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mcp-agent.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Welcome to mcp-agent

> Build effective agents with Model Context Protocol using simple, composable patterns.

<img src="https://mintcdn.com/lastmileai-94a5811a/xKc1LECUXZUL786H/logo/mcp-agent-logo.png?fit=max&auto=format&n=xKc1LECUXZUL786H&q=85&s=ec696e1141d4591a3d5b029223bb8e03" alt="mcp-agent logo" noZoom className="rounded-2xl block" width="2704" height="876" data-path="logo/mcp-agent-logo.png" />

[`mcp-agent`](https://github.com/lastmile-ai/mcp-agent) is a simple, composable framework to build effective agents using [Model Context Protocol](https://modelcontextprotocol.io/introduction).

**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.
When you're ready to deploy, [`mcp-c`](https://docs.mcp-agent.com/get-started/cloud) let's you deploy any kind of MCP server to a managed Cloud. You can even deploy agents as MCP servers!

## Why teams pick mcp-agent

<CardGroup cols={2}>
  <Card title="MCP-native" icon="plug">
    Fully implements the MCP spec, including auth, elicitation, sampling, and notifications.
  </Card>

  <Card title="Composable patterns" icon="puzzle-piece">
    Map-reduce, router, deep research, evaluator — every pattern from Anthropic's [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) guide ships as a first-class workflow.
  </Card>

  <Card title="Built for Production" icon="shield">
    Durable execution with Temporal, OpenTelemetry observability, and cloud deployment via the CLI.
  </Card>

  <Card title="Lightweight & Pythonic" icon="feather">
    Define an agent with a few lines of Python—mcp-agent handles the lifecycle, connections, and MCP server wiring for you.
  </Card>
</CardGroup>

```python {1} theme={null}
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="researcher")

async def main():
    async with app.run() as session:
        agent = Agent(
            name="researcher",
            instruction="Use available tools to gather concise answers.",
            server_names=["fetch", "filesystem"],
        )

        async with agent:
            llm = await agent.attach_llm(OpenAIAugmentedLLM)
            report = await llm.generate_str("Summarize the latest MCP news")
            print(report)

if __name__ == "__main__":
    asyncio.run(main())
```

## Next steps

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket-launch" href="/get-started/quickstart">
    Scaffold an agent with `uvx mcp-agent init` and run it locally in under 5 minutes.
  </Card>

  <Card title="Deploy to Cloud" icon="cloud" href="/get-started/cloud">
    Deploy any kind of MCP server using `mcp-c`. Use `uvx mcp-agent deploy` to host your agent as a managed MCP server.
  </Card>

  <Card title="Explore the patterns" icon="diagram-project" href="/mcp-agent-sdk/effective-patterns/overview">
    Learn how to combine planner, router, evaluator, and more.
  </Card>
</CardGroup>

### Build with LLMs

The docs are also available in [llms.txt format](https://llmstxt.org/):

* [llms.txt](https://docs.mcp-agent.com/llms.txt) - A sitemap listing all documentation pages
* [llms-full.txt](https://docs.mcp-agent.com/llms-full.txt) - The entire documentation in one file (may exceed context windows)
* [docs MCP server](https://docs.mcp-agent.com/mcp) - Directly connect the docs to an MCP-compatible AI coding assistant.