Taxi Call Center Agents is a Python-based demo illustrating a multi-agent system using OpenAI GPT models. It orchestrates Customer, Dispatch, Driver, and Notification agents to handle ride requests end-to-end, manage driver assignments, and send confirmations. Built with LangChain and asyncio, it provides a template for automating conversational workflows in transportation and service operations.
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Taxi Call Center Agents
Taxi Call Center Agents is a Python-based demo illustrating a multi-agent system using OpenAI GPT models. It orchestrates Customer, Dispatch, Driver, and Notification agents to handle ride requests end-to-end, manage driver assignments, and send confirmations. Built with LangChain and asyncio, it provides a template for automating conversational workflows in transportation and service operations.
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What is Taxi Call Center Agents?
This repository delivers a customizable multi-agent framework simulating a taxi call center. It defines distinct AI agents: CustomerAgent to request rides, DispatchAgent to select drivers based on proximity, DriverAgent to confirm assignments and update statuses, and NotificationAgent for billing and messages. Agents interact through an orchestrator loop using OpenAI GPT calls and memory, enabling asynchronous dialogue, error handling, and logging. Developers can extend or adapt agent prompts, integrate real-time systems, and prototype AI-driven customer service and dispatch workflows with ease.
Who will use Taxi Call Center Agents?
- AI developers and researchers
- Chatbot and conversational system builders
- Call center solution architects
- Transportation service providers
- Operations automation engineers
How to use the Taxi Call Center Agents?
- Step1: Clone the repository: git clone https://github.com/lreimer/taxi-callcenter-agents.git
- Step2: Install dependencies: pip install -r requirements.txt
- Step3: Set your OpenAI API key: export OPENAI_API_KEY=your_key
- Step4: Configure agent settings in config.py (model names, parameters)
- Step5: Run the simulation: python main.py
- Step6: Monitor terminal logs and observe agent dialogues
- Step7: Customize prompts or add new agents to extend functionality
Platform
- Linux
- Mac
- Windows
Taxi Call Center Agents's Core Features & Benefits
The Core Features
- Multi-agent orchestration with GPT
- Natural language customer booking
- Automated driver dispatch coordination
- Notification and billing messaging
- Configurable agent personalities and prompts
- Asynchronous dialogue management with asyncio
The Benefits
- Rapid prototyping of AI-driven call centers
- End-to-end simulated dispatch workflow
- Easy customization of agent behaviors
- Scalable demonstration of conversational AI
- Template for real-time service automation
Taxi Call Center Agents's Main Use Cases & Applications
- Simulating taxi ride booking workflows
- Prototyping AI call center automation
- Testing dispatch algorithms with conversational input
- Training AI agents for customer service scenarios
- Building proof-of-concept multi-agent applications
FAQs of Taxi Call Center Agents
Taxi Call Center Agents Company Information
Taxi Call Center Agents Reviews
5/5
Taxi Call Center Agents's Main Competitors and alternatives?
Rasa multi-agent conversational systems
AWS Connect with AI-driven bots
Google Dialogflow CX conversational workflows
Microsoft Bot Framework orchestration
Custom GPT orchestration scripts
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