agentic-ai-mcp

# Agentic AI MCP Lightweight agentic AI with MCP tools. Supports multiple LLM providers (Anthropic, OpenAI), multi-agent orchestration, and distributed setups where tools run on one machine and agents on another. ## Install ```bash pip install agentic-ai-mcp ``` ## Setup Set your API key in `.env` file (only needed on the client/agent machine): ```bash # For Anthropic (default) ANTHROPIC_API_KEY=sk-ant-... # For OpenAI OPENAI_API_KEY=sk-... ``` ## Quick Start See the example notebooks: - [`examples/quickstart_server.ipynb`](examples/quickstart_server.ipynb) - Run on machine exposing tools - [`examples/quickstart_client.ipynb`](examples/quickstart_client.ipynb) - Run on machine executing agents (single & multi-agent) ## Usage ### Server (expose tools) Run this on the machine where you want to host tools: ```python 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: ```python 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: `…