Multi-Agent AI Platform with Long-Term Memory

1. The Challenge

Autonomous agents losing critical context details during long or cross-session user conversations, leading to repetitive questions and degraded reasoning.

2. System Architecture

• FastAPI server hosting individual WebSocket channels for real-time agent coordination.
• Context-continuity router analyzing user queries and querying localized vector embeddings.
• Decoupled state database isolating agent conversation blocks to prevent cross-agent context leakage.

3. Technical Implementation

• Built a Retrieval-Augmented Generation (RAG) memory pipeline using LangChain to retrieve relevant past dialogue context on the fly.
• Implemented prompt-engineering injection layers that seamlessly append retrieved context summaries to the active LLM context window.
• Orchestrated custom state-machines capable of triggering agent-to-agent feedback loops to resolve complex tasks.