Multi-Agent AI System

This project provides a framework for building sophisticated multi-agent AI systems using LangGraph and LangSmith. It addresses common challenges in complex agent architectures, such as reducing hallucinations, managing conversational flow, incorporating human-in-the-loop interactions, and evaluating agent performance. The system is designed for developers and researchers looking to implement advanced agentic workflows with robust memory and routing capabilities.

How It Works

The system employs a supervisor-based multi-agent architecture. A supervisor agent routes user queries to specialized sub-agents (e.g., for music catalog or invoice information) based on the query's intent. Each sub-agent utilizes the ReAct (Reasoning and Acting) pattern, powered by LLMs and LangChain tools, to interact with a SQLite database (Chinook sample dataset). It incorporates both short-term memory (conversation state) and long-term memory (user preferences) using LangGraph's MemorySaver and InMemoryStore, respectively. A human-in-the-loop mechanism allows for verification steps before sensitive data access.

Quick Start & Requirements

• Installation: Clone the repository, navigate to the directory, and install dependencies using pip install -r requirements.txt.
• Prerequisites: Python 3.10+ is required. Environment variables for OPENAI_API_KEY and LANGSMITH_API_KEY must be set. LangSmith tracing is enabled by default.
• Documentation: The project follows the structure and concepts outlined in LangChain's official notebook documentation.

Highlighted Details

• Supervisor Architecture: Centralized routing mechanism for directing queries to appropriate specialized sub-agents.
• Sub-Agent Specialization: Dedicated agents for distinct tasks like music catalog queries and invoice information retrieval.
• Memory Management: Integrates short-term conversation memory and long-term user preference storage.
• Human-in-the-Loop: Implements verification steps using LangGraph's interrupt feature for enhanced security and user interaction.
• Evaluation Framework: Demonstrates how to set up evaluations using LangSmith datasets and custom evaluators.

Maintenance & Community

No specific details on maintenance frequency, community channels (like Discord/Slack), or notable contributors are provided in the README.

Licensing & Compatibility

The project is licensed under the MIT License. This license is generally permissive for commercial use, allowing integration into closed-source applications.

Limitations & Caveats

The project heavily relies on OpenAI models and a specific SQLite database setup. While LangChain supports various providers, the provided code is tailored to these components. The complexity of multi-agent systems and LangGraph concepts may present a learning curve for new adopters. Evaluation examples focus on final response correctness, with other evaluation types mentioned but not fully detailed in the provided text.