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.

v0.5.0 · npm · Paper · Apache-2.0

Benchmarks

HotpotQA — Multi-Hop Retrieval

Head-to-head against Mem0, the most widely adopted agent memory system. HotpotQA tests multi-hop reasoning — questions that require connecting information across multiple documents to answer.

| Metric | Ori Mnemos | Mem0 | Δ | |--------|:----------:|:----:|:-:| | Recall@5 | 90% | 29% | 3.1× | | F1 Score | 0.68 | 0.33 | 2.1× | | Latency (avg) | 120ms | 1,140ms | 9.5× faster | | Infrastructure | Markdown + SQLite | Redis + Qdrant + cloud | — |

Ori retrieves the right information 3× more often, scores 2× higher on answer quality, and does it 9.5× faster — on markdown files with a SQLite index. No cloud services. No API keys. Full evaluation code in .