multi-agent-concierge

multi-agent-concierge is a multi-agent concierge system that uses multiple specialized agents to complete complex tasks and a "concierge" agent to guide users to the correct agent. Such systems are designed to handle multiple tasks with interdependencies, using hundreds of tools. The system demonstrates how to create implicit "chains" between agents through natural language instructions and manage these chains through "continuation" agents, while using global state to track users and their current status.

Demand group:

"This product is suitable for developers and enterprises who need to deal with complex tasks and tool selection, especially in the field of customer service automation, to significantly improve efficiency and user experience."

Example of usage scenario:

Banking systems can use this model to implement functions such as user authentication, account inquiries, and fund transfers.

Online customer service platforms can utilize multi-agent systems to handle users' diverse requests.

In-house automation tools can use the system to coordinate task processes across different departments.

Product features:

Stock query agent: Responsible for querying stock prices and related subtasks.

User authentication agent: Responsible for collecting user names and passwords for user authentication.

Account balance query agent: responsible for selecting user accounts and querying balances.

Funds transfer agent: Responsible for handling transfer tasks, including inquiring about transfer accounts and amounts.

Concierge Agent: Interacts with the user for the first time, introducing available tasks and providing feedback on task completion.

Orchestration agent: does not provide output directly to the user, but responds to the corresponding agent name based on the user's current task requirements.

Continuation proxy: Used when a serial proxy is required to complete a task, such as a user who needs to be authenticated before transferring money.

Usage tutorial:

1. Visit the GitHub page to learn basic information about multi-agent-concierge .

2. Read the README file to obtain specific guidance on system construction and use.

3. Download or clone the code to the local environment and prepare for system deployment.

4. Set up the environment according to the project documentation and install the required dependent libraries.

5. Run the demo.py or workflows.py file to experience the workflow of the multi-agent concierge system.

6. Customize the development and optimization of the system according to actual needs.