lastmile-ai/mcp-agent

Examples
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Building Effective Agents
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MCP
Overview
**mcp-agent** is a simple, composable framework to build effective agents using Model Context Protocol.
> [!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 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 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, feedback and your help in improving this project.
**Minimal example**
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 available at **docs.mcp-agent.com**, including full SDK guides, CLI reference, and advanced patterns. This readme gives a high-level overview to get you started.
- llms-full.txt: contains entire documentation.
- llms.txt: sitemap listing key pages in the docs.
- docs MCP server
Table of Contents
- Overview
- Minimal example
- Quickstart
- Why mcp-agent
- Core concepts
- MCPApp
- Agents & AgentSpec
- Augmented LLM
- Workflows & decorators
- Configuration & secrets
- MCP integration
- Workflow patterns
- CLI reference
- Authentication
- Advanced
- Observability & controls
- Composing workflows
- Durable execution
- Agent servers
- Signals & human input
- App configuration
- Icons
- MCP server management
- Cloud deployment
- Examples
- FAQs
- Community & contributions
Get Started
> [!TIP]
> The CLI is available via uvx mcp-agent.
> To get up and running,
> scaffold a project with uvx mcp-agent init and deploy with uvx mcp-agent deploy my-agent.
>
> You can get up and running in 2 minutes by running these commands:
>
>
Installation
We recommend using uv to manage your Python projects (uv init).
Alternatively:
Also add optional packages for LLM providers (e.g. uv add "mcp-agent[openai, anthropic, google, azure, bedrock]").
Quickstart
> [!TIP]
> The examples directory has several example applications to get started with.
> To run an example, clone this repo (or generate one with uvx mcp-agent init --template basic --dir my-first-agent)
>
>
Here is a basic "finder" agent that uses the fetch and filesystem servers to look up a file, read a blog and write a tweet. Example link:
finder_agent.py
mcp_agent.config.yaml
Agent output
Why use mcp-agent?
There are too many AI frameworks out there already. But mcp-agent is the only one that is purpose-built for a shared protocol - MCP.mcp-agent pairs Anthropic’s Building Effective Agents patterns with a batteries-included MCP runtime so you can focus on behaviour, not boilerplate. Teams pick it because it is:
- **Composable** – every pattern ships as a reusable workflow you can mix and match.