agentic-ai-mcp 0.6.3

A lightweight agentic AI framework with MCP tool serving

Agentic AI MCP

Lightweight agentic AI with MCP tools. Supports multiple LLM providers (Anthropic, OpenAI) and distributed setups where tools run on one machine and agents on another.

Install

pip install agentic-ai-mcp

Setup

Set your API key in .env file (only needed on the client/agent machine):

# For Anthropic (default)
ANTHROPIC_API_KEY=sk-ant-...

# For OpenAI
OPENAI_API_KEY=sk-...

Quick Start

See the example notebooks:

• examples/quickstart_server.ipynb - Run on machine exposing tools
• examples/quickstart_client.ipynb - Run on machine executing agents

Usage

Server (expose tools)

Run this on the machine where you want to host tools:

from agentic_ai_mcp import AgenticAIServer

def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

def greet(name: str, times: int = 1) -> str:
    """Greet someone."""
    return ("Hello, " + name + "! ") * times

# Create server and register tools
server = AgenticAIServer(host="0.0.0.0", port=8888)
server.register_tool(add)
server.register_tool(greet)

print(f"Tools: {server.tools}")
print(f"URL: {server.mcp_url}")

# Start server in background
server.start()

# ... do other things ...

# Stop when done
server.stop()

Client (run agents)

Run this on another machine to connect to the server and execute agents:

from agentic_ai_mcp import AgenticAIClient

# Connect to MCP server
client = AgenticAIClient(mcp_url="http://<server-ip>:8888/mcp")

# Simple agent workflow
result = await client.run("Calculate 2+1, then greet 'Alice' that many times.")
print(result)

# Planning-based workflow for complex tasks
result = await client.run_with_planning("Calculate ((0+2) + (1+1) + 1), then greet 'Bob' that many times.")
print(result)

Multiple MCP Servers

Connect to tools spread across multiple servers:

from agentic_ai_mcp import AgenticAIClient

# Connect to multiple MCP servers
client = AgenticAIClient(
    mcp_urls=[
        "http://<server-1>:8888/mcp",  # math tools
        "http://<server-2>:9999/mcp",  # greeting tools
    ]
)

# The agent can use tools from all servers
result = await client.run("Calculate 2+3, then greet 'Alice' that many times.")
print(result)

Using OpenAI

from agentic_ai_mcp import AgenticAIClient

# Use OpenAI instead of Anthropic
client = AgenticAIClient(
    mcp_url="http://<server-ip>:8888/mcp",
    provider="openai",
    model="gpt-4o-mini"
)

result = await client.run("Calculate -1+2")

Passing API Key Directly

from agentic_ai_mcp import AgenticAIClient

# Pass API key directly (instead of using .env)
client = AgenticAIClient(
    mcp_url="http://<server-ip>:8888/mcp",
    api_key="sk-ant-..."
)

API Reference

AgenticAIServer

Property/Method	Description
server.tools	List of registered tool names
server.mcp_url	Server URL
server.is_running	Check if server is running
server.register_tool(func)	Register a function as an MCP tool
server.start()	Start MCP server in background
server.stop()	Stop MCP server

AgenticAIClient

Property/Method	Description
client.mcp_url	Primary MCP server URL (first in the list)
client.mcp_urls	List of all MCP server URLs
client.tools	List of loaded tool names from all servers
client.run(prompt)	Simple agentic workflow
client.run_with_planning(prompt)	Planning-based workflow for complex tasks

License

MIT