Cerememory
A Living Memory Database for the Age of AI
What it does
Brain-inspired memory architecture. Connect AI agents to lifelike memory shaped by the concepts of time and dreams, building natural long-term recall. Cerememory is a living memory database for the age of AI. Built in Rust, it provides persistent, brain-inspired memory that any LLM can use -- regardless of provider. Memories stored in Cerememory are not static rows in a table; they decay, evolve, form associations, and respond to emotion, just as biological memory does.
Does a similar job
all alternatives →- AMAI memory with biological decay (52% recall)Apr 2026 · github.com · ▲98
Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
- COCore – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112
I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
- MLMnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)Jun 2026 · github.com · ▲60
- AMA memory database that forgets, consolidates, and detects contradictionApr 2026 · github.com · ▲48
Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier. YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does. Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA…
- ALA local-first memory store for LLM agents (SQLite)Dec 2025 · github.com · ▲48
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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