agent-mcp-framework 0.1.0

A Python framework for building multi-agent MCP servers

agent-mcp-framework

A Python framework for building multi-agent MCP (Model Context Protocol) servers.

Build production-ready multi-agent systems that expose their capabilities as MCP tools — ready to integrate with Claude, VSCode, and any MCP-compatible client.

Features

• Agent abstractions — Agent, LLMAgent, FunctionAgent with lifecycle hooks
• Pipeline composition — Sequential, Parallel, Conditional, and MapReduce patterns
• MCP integration — Expose agent pipelines as MCP tools over stdio or SSE
• Output formatting — JSON, Markdown, and plain text output modes
• CLI — Run servers and pipelines from the command line

Installation

pip install agent-mcp-framework

Quick Start

from agent_mcp_framework import Agent, AgentContext, AgentResult, SequentialPipeline, AgentMCPServer


class AnalyzerAgent(Agent):
    async def run(self, context: AgentContext) -> AgentResult:
        code = context.get("code", "")
        issues = []
        if len(code.splitlines()) > 500:
            issues.append("File exceeds 500 lines — consider splitting")
        if "import *" in code:
            issues.append("Wildcard imports detected")
        context.set("issues", issues)
        return AgentResult(success=True, output={"issues": issues, "count": len(issues)})


class ScorerAgent(Agent):
    async def run(self, context: AgentContext) -> AgentResult:
        issues = context.get("issues", [])
        score = max(0, 100 - len(issues) * 15)
        return AgentResult(success=True, output={"score": score, "grade": "A" if score >= 90 else "B" if score >= 70 else "C"})


# Compose agents into a pipeline
pipeline = SequentialPipeline("code-review", agents=[
    AnalyzerAgent("analyzer", description="Find code issues"),
    ScorerAgent("scorer", description="Score code quality"),
])

# Expose as an MCP server
server = AgentMCPServer("code-review-server", description="Multi-agent code review")
server.add_pipeline_tool(
    pipeline,
    name="review_code",
    description="Analyze code quality and return a score",
)

if __name__ == "__main__":
    server.run()  # Starts MCP server on stdio

Agent Types

Agent — Base class

Implement run() to define your agent's logic.

LLMAgent — Claude-powered agent

Built-in Anthropic client with complete() helper for LLM calls.

FunctionAgent — Quick inline agents

Wrap any async function as an agent without subclassing.

Pipeline Patterns

Pattern	Description
SequentialPipeline	Run agents one after another, each seeing updated context
ParallelPipeline	Run agents concurrently with optional concurrency limits
ConditionalPipeline	Route to agents based on a condition function
MapReducePipeline	Split work, fan out, and reduce results

CLI

# Start an MCP server
agent-mcp serve my_project.server

# Run a pipeline directly
agent-mcp run my_project.pipeline --input '{"code": "import *"}'

# Show framework info
agent-mcp info

License

MIT