Ori MnemosCAUTION

Open-source persistent memory infrastructure for AI agents.

Ori implements human cognition as mathematical models on a knowledge graph. Activation decay from ACT-R. Spreading activation along wiki-link edges. Hebbian co-occurrence from retrieval patterns. Reinforcement learning on retrieval itself. Recursive graph traversal with sub-question decomposition. The system learns what matters, forgets what doesn't, and optimizes its own retrieval pipeline.

Persistent memory across sessions, clients, and machines. Zero-infrastructure retrieval that matches and in several cases strongly outperforms incumbents on benchmarks — and you own every byte of your data. Markdown on disk. Wiki-links as graph edges. Git as version control. No database lock-in, no cloud dependency, no vendor capture.

v0.7.0 · npm · Paper · Apache-2.0

Use

Ori is three surfaces over one index. The markdown is the truth; the index is derived. The learned half — Q-values, LinUCB arms, retrieval history — is not, so export it before deleting anything (see When to rebuild).

CLI

npx ori init          # scaffold a vault
npx ori index build   # derive the index
npx ori explore "..."   # navigated retrieval
npx ori sql "..."       # read-only SQL over the index

MCP server — ori serve, registered in a client config. This is how an agent uses it.

Library — recall is the same wired entry the CLI and the MCP ori_recall tool both go through, so the programmatic path and the agent path cannot drift.

import { recall } from "ori-memory";

const res = await recall("./vault", "what did we decide about caching?", { limit: 5 });
for (const hit of res.data.results) console.log(hit.title, hit.score);

searchComposite is also exported for callers that have already assembled vectors, graph metrics and a config; recall does that assembly fo