Agent Orchestrator FAQs

Question 1

What core technologies does Agent Orchestrator integrate?

Accepted Answer

Agent Orchestrator integrates the Model Context Protocol (MCP) via FastMCP for tool decoupling, LangGraph for stateful, multi-turn agent orchestration, Ollama as the local LLM host, and Streamlit for a modern, real-time user interface.

Question 2

What is Agent Orchestrator?

Accepted Answer

Agent Orchestrator is a production-ready starter template designed for building local-first agentic AI systems. It connects a FastMCP Python server with a LangGraph orchestrator and Ollama to create robust, self-contained AI agents.

Question 3

How does Agent Orchestrator ensure privacy?

Accepted Answer

It ensures privacy through 100% local execution. By using Ollama for local LLM hosting (Llama 3.1/Mistral), it eliminates the need for external API keys or sending data to third-party services, keeping all processing on your local machine.

Question 4

What are the main benefits of using Agent Orchestrator?

Accepted Answer

Key benefits include standardized tool integration (MCP), robust multi-turn conversations with stateful LangGraph agents, enhanced privacy through local LLM execution (Ollama), a user-friendly Streamlit interface, and simplified custom tool development.

Question 5

Can I define custom tools with Agent Orchestrator?

Accepted Answer

Yes, you can easily define custom Python tools using the FastMCP framework. It simplifies tool definition and automatically generates schemas, enabling seamless integration of your unique functionalities with the LangGraph orchestrator.

Agent Orchestrator

Agent Orchestrator

Key Features

Use Cases

Key Features

Use Cases