Ori Memory MCP server

Ori Mnemos

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 for you.

The export surface is deliberately small and is a semver contract; the rest of src/core is internal. Versions before 0.7.1 shipped no main and no exports, so a bare import threw and the library path did not exist — but the CLI and MCP paths always worked, and existing users were unaffected.

ori-memory/cli resolves to the CLI entry, for callers that need to locate the binary and spawn it rather than link against it. require.resolve on it is the intended use; importing it runs the CLI.

Benchmarks

ForgetEval — Can It Forget On Command?

deeplethe/lethe, bench/forgeteval/, MIT. No API key, no network, no LLM judge. The scorer is a ~20-line deterministic substring check in GeneratedCase.run(): it calls recalltexts(query, k=10) itself, joins the top 10, and tests mustcontain / mustnotcontain. Generation is random.Random(42) over templates. The optional LLM hook is llm=None by default and was not used.

Not fitted to the suite: unseen seeds give 97.2% (seed 7) and 98.0% (seed 123). The fixes were structural bugs in the matcher, not case-specific patches.

Shortened. The full README is on GitHub.