This repository provides an example implementation of a multi-agent concierge system using LlamaIndex's Workflows abstraction. It enables users to build and extend custom multi-agent systems by defining agents with specific roles, tools, and system prompts, facilitating complex task completion through coordinated agent interactions and shared global state.

How It Works

The system orchestrates multiple agents, each responsible for distinct tasks (e.g., authentication, stock lookup, money transfer). A central "concierge" agent routes user requests to the appropriate task agent. Agents communicate and coordinate via a shared global state, allowing them to access and update information. Tools can be configured to require human approval before execution, adding a layer of control for sensitive operations.

Quick Start & Requirements

• Install: pip install llama-index (and potentially other dependencies as outlined in main.py).
• Prerequisites: Python environment, LlamaIndex installation. Specific tool integrations may require API keys or external service access.
• Demo: The README includes detailed interaction examples and a link to a visual representation of the workflow.

Highlighted Details

• Multi-Agent Coordination: Demonstrates complex task delegation and collaboration between specialized agents.
• Global State Management: Facilitates seamless information sharing and context maintenance across agents.
• Human-in-the-Loop: Supports user approval for critical tool executions.
• Workflow Visualization: Automatically generates diagrams of agent interactions and data flow.

Maintenance & Community

The project is associated with LlamaIndex, a growing ecosystem for LLM applications. Further community engagement and development support can likely be found through LlamaIndex's official channels.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README excerpt. Users should verify the license for compatibility with commercial or closed-source projects.

Limitations & Caveats

This is presented as an example implementation, suggesting it may require significant adaptation for production use. The complexity of managing numerous agents and tools could pose scalability challenges.