Multi-Agent-Seller is a Python-based framework that enables simulation of dynamic e-commerce negotiations between buyer and seller AI agents. It supports defining negotiation protocols, pricing strategies, offer and counteroffer exchanges, and logs result visualization. Developers can customize agent behaviors, integrate new strategies, and analyze negotiation outcomes for research, education, or prototyping multi-agent systems in commerce scenarios.
Multi-Agent-Seller is a Python-based framework that enables simulation of dynamic e-commerce negotiations between buyer and seller AI agents. It supports defining negotiation protocols, pricing strategies, offer and counteroffer exchanges, and logs result visualization. Developers can customize agent behaviors, integrate new strategies, and analyze negotiation outcomes for research, education, or prototyping multi-agent systems in commerce scenarios.
Multi-Agent-Seller provides a modular environment for simulating e-commerce negotiations using AI agents. It includes pre-built buyer and seller agents with customizable negotiation strategies, such as dynamic pricing, time-based concessions, and utility-based decision-making. Users can define custom protocols, message formats, and market conditions. The framework handles session management, offer tracking, and result logging with built-in visualization tools for analyzing agent interactions. It integrates easily with machine learning libraries for strategy development, enabling experimentation with reinforcement learning or rule-based agents. Its extensible architecture allows adding new agent types, negotiation rules, and visualization plugins. Multi-Agent-Seller is ideal for testing multi-agent algorithms, studying negotiation behaviors, and teaching concepts in AI and e-commerce domains.
Who will use Multi-Agent-Seller?
AI researchers in multi-agent systems
Machine learning engineers
Educational instructors in AI and e-commerce
Students learning agent-based modeling
Developers prototyping negotiation algorithms
How to use the Multi-Agent-Seller?
Step1: Clone the repository from GitHub.
Step2: Install Python (3.7+) and required dependencies via pip install -r requirements.txt.
Step3: Configure agent parameters and negotiation protocols in config files.
Step4: Run the simulation script (python run_simulation.py).
Step5: Access generated logs and visualization outputs for analysis.
Platform
mac
windows
linux
Multi-Agent-Seller's Core Features & Benefits
The Core Features
Pre-built buyer and seller AI agents
Customizable negotiation protocols
Dynamic pricing and concession strategies
Session management and offer tracking
Result logging and visualization tools
Integration with machine learning libraries
The Benefits
Rapid prototyping of negotiation strategies
Extensible architecture for new agents and rules
Visualization for easy analysis of interactions
Supports research and educational use cases
Open-source and customizable
Multi-Agent-Seller's Main Use Cases & Applications
Academic research on multi-agent negotiation
Teaching AI and e-commerce concepts
Prototyping negotiation algorithms
Experimenting with reinforcement learning strategies
Benchmarking agent behaviors under varied market conditions