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

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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 bench/.

LoCoMo — Long-Term Conversational Memory

Evaluated on LoCoMo (Maharana et al., 2024) — the standard benchmark for long-term conversational memory. 10 conversations, 695 questions across single-hop, multi-hop, and temporal reasoning.

System	Single-hop	Multi-hop	Infrastructure
MemoryBank	5.00	—	Custom server
ReadAgent	9.15	—	LLM-based
A-Mem	20.76	—	Cloud APIs
MemGPT / Letta	26.65	—	PostgreSQL + cloud
LangMem	35.51	26.04	Cloud APIs
OpenAI Memory	34.30	—	OpenAI proprietary
Zep	35.74	19.37	PostgreSQL + cloud
Mem0	38.72	28.64	Redis + Qdrant + cloud
Ori Mnemos	37.69	29.31	Markdown on disk

Baseline numbers from Mem0 paper (Table 1). Ori evaluated with GPT-4.1-mini for answer generation, BM25 + embedding + PageRank fusion for retrieval.

More benchmarks coming — including LoCoMo-Plus (Level-2 cognitive memory) and adversarial refusal evaluation.

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Quick Start

npm install -g ori-memory
ori init my-agent
cd my-agent

Connect to your agent:

# Full adapters — auto-orient at session start, capture at session end
ori bridge claude-code --vault ~/brain                 # hooks + MCP + CLAUDE.md
ori bridge hermes --vault ~/brain                      # native plugin + MCP + HERMES.md

# MCP-only adapters — tools available, no lifecycle automation
ori bridge cursor --vault ~/brain                      # .cursor/mcp.json
ori bridge codex --vault ~/brain                       # ~/.codex/config.toml

# Any MCP client
ori bridge generic --vault ~/brain                     # prints config for manual setup

Claude Code and Hermes Agent get full lifecycle integration — the agent orients at session start, captures insights at session end, and validates notes on write. Cursor, Codex, and other MCP clients get access to all 16 tools but manage their own session lifecycle.

Manual MCP config (works with any client that speaks MCP):

{
  "mcpServers": {
    "ori": {
      "command": "ori",
      "args": ["serve", "

... [View full README on GitHub](https://github.com/aayoawoyemi/Ori-Mnemos#readme)