Mind-mem – Zero-infra agent memory with 19 MCP tools (BM25+vector+RRF)
Shared memory across Claude Code, Cursor, Windsurf—solves agent drift via hybrid search and audit trails.

Ebbinghaus decay prunes memory automatically, unlike standard RAG hoarding.
AI agent developers, teams building persistent assistant memory
Mem0 · LangChain Memory · Zep
In the current state of agentic memory most of the context is stored in the form of a MD file or is derived through a RAG model where you store each and everything. Both of the solution leads to bloated context which does not optimize the usage of any tokens.
In this system we only keep relevant data in our memory and prune all the unnecessary data. The relevance of a data is derived through multiple factors such as recall rate, importance, category, to which memory chain it's connected to etc. These parameters are fine tuned so that we can cater to both episodic memory and semantic memory.
Our memory layer keeps the size flat in this manner. You can draw correlation of this infrastructure with how Human brain store and prune memory.
The enterprise model is something very exciting as we can extract relevant memories from each user, agent and sub agent in this layer and that can be used by any one in the org, ensuring memory optimization at an enterprise level.
Shared memory across Claude Code, Cursor, Windsurf—solves agent drift via hybrid search and audit trails.
Yet another agent orchestration platform competing with LangGraph and CrewAI.
Persistent memory and time travel for AI agents using local SQLite.
Biologically-inspired memory consolidation that prunes unused facts and strengthens associations overnight.
Cross-project memory for AI agents when single-project solutions already exist.
SQLite F32_BLOB vector search with Git drift detection for agent memory.