Multi-Agent Chatbot
Apr 1, 2025
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2 min read
Overview
This Multi-Agent Chatbot system leverages advanced AI techniques to create an intelligent conversational interface. Built with Python and powered by LangChain, it features sophisticated memory management that enables context retention over extended conversations.
Key Features
- Multi-Agent Architecture: Multiple specialized agents working together to provide comprehensive responses
- Context Retention: Advanced memory management using LangChain for maintaining conversation history
- Intelligent Routing: Smart routing of queries to the most appropriate agent
- Extensible Design: Easy to add new agents with specialized capabilities
Technical Implementation
Core Technologies
- Python: Primary development language
- LangChain: Framework for building LLM applications with memory management
- Ollama: Local LLM deployment for privacy and performance
Architecture Components
- Agent Manager: Orchestrates communication between different agents
- Memory System: Implements both short-term and long-term memory storage
- Context Analyzer: Determines relevant context from conversation history
- Response Generator: Combines outputs from multiple agents into coherent responses
Key Innovations
- Hierarchical Memory: Implements a tiered memory system that prioritizes recent and important information
- Agent Specialization: Each agent is optimized for specific types of queries or tasks
- Dynamic Context Window: Automatically adjusts context based on conversation complexity
Use Cases
- Customer support automation
- Educational tutoring systems
- Research assistance
- Complex query resolution requiring multiple domains of knowledge
The system demonstrates the power of combining multiple AI agents with sophisticated memory management to create more intelligent and context-aware conversational experiences.
