Hey everyone! I've been working on this project for a while and finally got it to a point where I'm comfortable sharing it with the community. Eion is a shared memory storage system that provides unified knowledge graph capabilities for AI agent systems. Think of it as the "Google Docs of AI Agents" that connects multiple AI agents together, allowing them to share context, memory, and knowledge in real-time.
When building multi-agent systems, I kept running into the same issues: limited memory space, context drifting, and knowledge quality dilution. Eion tackles these issues by:
• Unifying API that works for single LLM apps, AI agents, and complex multi-agent systems
• No external cost via in-house knowledge extraction + all-MiniLM-L6-v2 embedding
• PostgreSQL + pgvector for conversation history and semantic search
• Neo4j integration for temporal knowledge graphs
I'm curious what the HN community thinks about this approach. Are there specific use cases you'd find valuable? Any architectural concerns or missing features?
• No external cost via in-house knowledge extraction + all-MiniLM-L6-v2 embedding
• PostgreSQL + pgvector for conversation history and semantic search
• Neo4j integration for temporal knowledge graphs
I'm curious what the HN community thinks about this approach. Are there specific use cases you'd find valuable? Any architectural concerns or missing features?