lastmile-ai/mcp-agent

★ 8,321PythonAudience · developerComplexity · 4/5Setup · moderate

Mindmap

mindmap
  root((mcp-agent))
    What it does
      AI agent framework
      MCP tool connections
      Workflow patterns
    Tech Stack
      Python
      Temporal
      YAML config
    Patterns
      Orchestrator
      Map-reduce
      Evaluator optimizer
    Features
      Durable workflows
      MCP server exposure
      Multi-model support

mindmap root((mcp-agent)) What it does AI agent framework MCP tool connections Workflow patterns Tech Stack Python Temporal YAML config Patterns Orchestrator Map-reduce Evaluator optimizer Features Durable workflows MCP server exposure Multi-model support

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Things people build with this

USE CASE 1

Build an AI agent that connects to external tools like file systems and databases using the MCP standard.

USE CASE 2

Chain orchestrator, map-reduce, and evaluator workflow patterns together to create a multi-step AI pipeline.

USE CASE 3

Deploy a durable agent workflow that can pause, resume, and recover automatically from failures using Temporal.

USE CASE 4

Expose your custom agent as an MCP server so other tools and agents can call it as a tool.

Tech stack

PythonYAMLTemporal

Getting it running

Difficulty · moderate Time to first run · 30min

Production durable workflows require a running Temporal server, basic local use needs only Python and a YAML config file.

No license information stated in the explanation, check the repository directly for terms.

In plain English

mcp-agent is a Python framework for building AI agents that use the Model Context Protocol, or MCP. MCP is a standard developed by Anthropic that defines how AI systems connect to external tools and data sources, such as file systems, web browsers, databases, or custom APIs. This library handles the connection management for those tools so you can focus on what the agent should actually do. The central idea is that simple, composable patterns produce more reliable agents than complex architectures. The library implements a set of agent workflow patterns drawn from Anthropic's published guidance on building effective agents: patterns like map-reduce, orchestrator, evaluator-optimizer, and router. You can chain these patterns together to build more sophisticated behavior without writing everything from scratch. For production use, the framework integrates with Temporal, which is a system for running long-running or failure-prone workflows in a way that can pause, resume, and recover automatically. This integration is available without changing the agent code, meaning you can start with a simple setup and scale to durable production workflows using the same API. Agents built with this library can themselves be exposed as MCP servers, which means other agents or tools can connect to them the same way they connect to any other tool. A cloud deployment option is also in beta, allowing agents to be hosted as managed services. The framework supports the full MCP specification, including tools, resources, prompts, notifications, OAuth authentication, and sampling. Multiple AI model providers can be used, including OpenAI. Setup is done through configuration YAML files, and a command-line tool is available for scaffolding and deploying projects. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Create an mcp-agent Python project with a YAML config that connects to a filesystem MCP server and runs a simple task.

Prompt 2

Show me how to implement the orchestrator workflow pattern in mcp-agent to break a large task into parallel subtasks and aggregate results.

Prompt 3

How do I expose my mcp-agent as an MCP server so that Claude or another AI tool can call it as an external tool?

Prompt 4

Add Temporal integration to an existing mcp-agent project so my workflow can pause and resume automatically after a failure.

Prompt 5

Write an mcp-agent evaluator-optimizer workflow that generates a draft, evaluates it against criteria, and iterates until it passes.

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