Multi Agent Concierge

Multi-agent concierge system

This repo contains an implementation of a multi-agent concierge system using LlamaIndex's Workflows abstraction. Using this example, you can plug in your own agents and tools to build your own multi-agent system, or hack and extend the underlying code to suit your needs.

In this example, agents are represented by a set name, description, set of tools, and system prompt, which all define how the agent acts and how that agent is selected.

In addition, all agent tools have access to the global state in the workflow, which allows agents to coordinate with each other and share information easily. Tools can also be marked as requiring human confirmation, which will cause the system to ask the user to confirm the tool call before it's sent.

The resulting workflow is rendered automatically using the built-in `draw_all_possible_flows()` and looks like this:


Why build this?

Interactive chat bots are by this point a familiar solution to customer service, and agents are a frequent component of chat bot implementations. They provide memory, introspection, tool use and other features necessary for a competent bot.

We have become interested in larger-scale chatbots: ones that can complete dozens of tasks, some of which have dependencies on each other, using hundreds of tools. What would that agent look like? It would have an enormous system prompt and a huge number of tools to choose from, which can be confusing for an agent.

Imagine a bank implementing a system that can:

• Look up the price of a specific stock
• Authenticate a user
• Check your account balance
  • Which requires the user be authenticated
• Transfer money between accounts
  • Which requires the user be authenticated
  • And also that the user checks their account balance first

Each of these top-level tasks has sub-tasks, for instance:

• The stock price lookup might need to look up the stock symbol first