Syne – AI agent that remembers everything, built on PostgreSQL
PostgreSQL-native memory with semantic search beats ephemeral ChatGPT sessions.
Neuroscience-inspired memory for AI agents — sleep cycles that rewrite and strengthen knowledge over time. TypeScript, PostgreSQL, MCP.
Sleep cycles that actively rewrite memories beat passive vector stores like LangChain memory.
AI agent developers, LLM application builders
LangChain Memory · LlamaIndex Vector Stores · Zep
The architecture is based on a rough simulation of human memory and uses sleep cycle (agent inactivity) where an LLM reorganizes and stores memory in a multi-tier database with a number of sorting, ordering, and prioritization mechanisms. I also attempted to build this with security built in from the beginning.
Since I first published this, it has evolved quite a bit based on some feedback and integrating some ideas from many of the other fantastic memory management systems that show up daily. Hopefully my contribution to the community can help move things forward and bring some value to people as a full solution, or just to carry some ideas forward in other projects.
Looking for feedback on how the sleep cycle works, the bet on a PostgreSQL only solution - particularly from anyone willing to run this under real workloads. Full transparency -- it's Alpha now, but has undergone multiple rounds of stress testing, hardening, and benchmarking.
PostgreSQL-native memory with semantic search beats ephemeral ChatGPT sessions.
Team-wide memory pool for agents when most tools stay siloed on one workstation.
Temporal knowledge graph memory and trace-to-test evals beat standard vector RAG.
Sandboxed agent that writes its own Python tools and remembers mistakes in JSON.
Direct weight editing for persistent memory—MEMIT meets LoRA consolidation with null-space math.
Automatic session memory for agents when Cline and Cursor already track context.