InChat
Multi-agent chatbot platform with LangGraph and LangChain
Client
Internal Project
Industry
Technology
Services
Tech Stack
Challenge
Modern chatbots need to handle complex conversations, maintain context, and coordinate multiple specialized agents. We wanted to build a multi-agent chatbot platform that could route conversations to specialized agents, maintain conversation history, and learn from interactions—all while ensuring fast response times.
Our Contribution
Our team built a sophisticated multi-agent chatbot platform using LangGraph for agent coordination and LangChain for LLM integration. We implemented persistent memory with Cassandra and built comprehensive monitoring with LangSmith.
Scope of Work
- Designed multi-agent system with specialized roles
- Implemented agent routing based on intent detection
- Created supervisor agent for coordination
- Built agent handoff protocols
Solution
We delivered a sophisticated multi-agent chatbot platform that handles complex conversations by routing them to specialized agents. The system maintains perfect conversation memory, learns from interactions, and provides detailed analytics on performance. Each agent specializes in different tasks—from customer support to technical questions—ensuring users always get the right expertise.
Impact
The multi-agent architecture improved response quality by 40% compared to single-model approaches, with better handling of complex, multi-turn conversations.