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", "--mcp", "--vault", "/path/to/brain"],
      "env": { "ORI_VAULT": "/path/to/brain" }
    }
  }
}

Start a session. The agent receives its identity automatically and begins onboarding on first run.

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Recursive Memory Harness

Ori is the first implementation of the Recursive Memory Harness (RMH) framework — a set of constraints on how persistent memory should behave for AI agents.

The core insight comes from Recursive Language Models (Zhang, Krassa & Khattab, 2026). RLM treats context not as input to be stuffed into a window, but as an environment to be navigated. The model doesn't get a bigger desk — it gets legs and walks into the library. RMH applies the same principle to persistent memory.

Three constraints define the framework:

1. Retrieval must follow the graph. Memory is not a flat vector store. Notes are nodes, wiki-links are edges. Retrieval walks the structure — Personalized PageRank at α=0.45, spreading activation along edges, community-aware traversal. The topology of the graph shapes what gets found.

2. Unresolved queries must recurse. When a single retrieval pass is insufficient, the system decomposes the question into sub-questions, retrieves against each, and synthesizes. Convergence detection stops recursion when new passes stop surfacing new information. This is what ori explore does.

3. Every retrieval must reshape the graph. Retrieval is not read-only. Co-occurrence edges grow between notes retrieved together (Hebbian learning). Q-values update based on whether retrieved notes were actually useful. The graph learns from how it is used — every query makes the next query better.

Most memory systems treat retrieval as search. RMH treats retrieval as navigation, recursion, and learning — on a graph that evolves with every session.

Read the full paper: Introducing Recursive Memory Harness

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What It Does

• Persistent identity. Agent state — name, personality, goals, methodology — is stored in plain markdown and auto-injected at session start via MCP instructions. Identity survives client switches, machine migrations, and model changes without reconfiguration.

• Knowledge graph. Every [[wiki-link]] is a directed edge. PageRank authority, Louvain community detection, betweenness centrality, bridge detection, orphan and dangling link analysis. Structure is queryable through MCP tools and CLI.

• Three memory spaces. Identity (self/) decays at 0.1x — barely fades. Knowledge (notes/) decays at 1.0x — lives and dies by relevance. Operations (ops/) decays at 3.0x — burns hot and clears itself. The separation is architectural, not cosmetic.

• Cognitive forgetting. Notes decay using ACT-R base-level learning equations, not arbitrary TTLs. Used notes stay alive. Their neighbors stay warm through spreading activation along wiki-link edges. Structurally critical nodes are protected by Tarjan's algorithm. ori prune analyzes the full activation topology before archiving anything.

• Four-signal fusion. Semantic embeddings, BM25 keyword matching, personalized PageRank, and associative warmth fused through score-weighted Reciprocal Rank Fusion. Intent classification (episodic, procedural, semantic, decision) shifts signal weights automatically.

• Dampening pipeline. Three post-fusion stages validated by ablation testing: gravity dampening halves cosine-similarity ghosts with zero query-term overlap, hub dampening applies a P90 degree penalty to prevent map notes from dominating results, and resolution boost surfaces actionable knowledge (decisions, learnings) over passive observation.

• Learning retrieval (v0.4.0). Three intelligence layers improve retrieval quality from session to session, synthesized from 63 research sources. See Retrieval Intelligence below.

• Capture-promote pipeline. ori add captures to inbox. ori promote classifies (idea, decision, learning, insight, blocker, opportunity), detects links, suggests areas. 50+ heuristic patterns. Optional LLM enhancement.

• Zero cloud dependencies. Local embeddings via all-MiniLM-L6-v2 running in-process. SQLite for vectors and intelligence state. Everything on your filesystem. Zero API keys required for core functionality.

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Retrieval Intelligence (v0.4.0)

Three learning layers that improve retrieval quality over time without manual tuning. Synthesized from 63 research sources across reinforcement learning, information retrieval, cognitive science, and bandit theory.

Layer 1 — Q-Value Reranking

Notes earn Q-values from session outcomes via exponential moving average updates. Over time, genuinely useful notes rise and noise sinks.

Signal	Reward	What triggers it
Forward citation	+1.0	You [[link]] a retrieved note in new content
Update after retrieval	+0.5	You edit a note you just retrieved
Downstream creation	+0.6	You create a new note after retrieving
Within-session re-recall	+0.4	Same note surfaces across different queries
Dead end (top-3, no follow-up)	−0.15	Retrieved in top 3 but nothing follows

After RRF fusion, Phase B reranks the candidate set with a lambda blend of similarity score and learned Q-value, plus a UCB-Tuned exploration bonus that ensures under-retrieved notes still get discovered. Exposure-aware correction prevents the same notes from dominating every session. A cumulative bias cap (MAX=3.0, compression=0.3) prevents runaway score inflation.

Layer 2 — Co-Occurrence Edges

Notes that are retrieved together grow edges between them — Hebbian learning on the knowledge graph. Edge weights are computed using NPMI normalization (genuine association beyond base rate), GloVe power-law frequency scaling, and Ebbinghaus decay with strength accumulation (frequently co-retrieved pairs decay slower).

Per-node Turrigiano homeostasis prevents hub notes from absorbing all edge weight. Bibliographic coupling bootstraps day-0 edges from existing wiki-link structure before any queries have been run.

The combined wiki-link + co-occurrence graph feeds a Personalized PageRank walk (HippoRAG, α=0.5) that surfaces notes semantic search alone would never find.

Layer 3 — Stage Meta-Learning

Each pipeline stage (BM25, PageRank, warmth, hub dampening, Q-reranking, co-occurrence PPR) is wrapped in a LinUCB contextual bandit with an 8-dimensional query feature vector. The system learns which stages help for which query types and auto-skips stages that consistently hurt.

Three-way decisions per stage: run / skip / abstain (stop the pipeline early). Cost-sensitive thresholds ensure expensive stages face a higher bar. Essential stages (semantic search, RRF fusion) never skip. An ACQO two-phase curriculum runs all stages during exploration (first 50 samples), then optimizes.

Session Learning Loop

Query → Retrieve → Use (cite, update, create) → Reward signals
  ↓                                                    ↓
  Co-occurrence edges grow                Q-values update (session-end batch)
  ↓                                                    ↓
  Stage meta-learner updates              Better retrieval next session

All updates happen in a single SQLite transaction at session end, in order: co-occurrence → Q-values → stage learning.

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The Stack

Layer 6: MCP Server                    16 tools, 5 resources — any agent talks to this
Layer 5: Recursive Exploration         PPR graph traversal, sub-question decomposition, convergence detection
Layer 4: Retrieval Intelligence        Q-value reranking, co-occurrence learning, stage meta-optimization
Layer 3: Dampening Pipeline            gravity, hub, resolution — ablation-validated
Layer 2: Four-Signal Fusion            semantic + BM25 + PageRank + warmth → score-weighted RRF
Layer 1: Knowledge Graph + Vitality    wiki-links, ACT-R decay, spreading activation, zone classification
Layer 0: Markdown files on disk        git-friendly, human-readable, portable

16 MCP tools · 5 resources · 17 CLI commands · 579 tests

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Token Economics

Without retrieval, every question requires dumping the entire vault into context. With Ori, the cost stays flat.

Vault Size	Without Ori	With Ori	Savings
50 notes	10,100 tokens	850 tokens	91%
200 notes	40,400 tokens	850 tokens	98%
1,000 notes	202,000 tokens	850 tokens	99.6%
5,000 notes	1,010,000 tokens	850 tokens	99.9%

A typical session costs ~$0.10 with Ori. Without it: ~$6.00+.

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Architecture

                          Any MCP Client
                    (Claude, Cursor, Windsurf,
                     Cline, Hermes, custom agents, VPS)
                              │
                        MCP Protocol
                        (stdio / JSON-RPC)
                              │
                    ┌───────────────────┐
                    │    Ori MCP Server  │
                    │                   │
                    │  instructions     │   identity auto-injected
                    │  resources  (5)   │   ori:// endpoints
                    │  tools    (16)    │   full memory operations
                    └─────────┬─────────┘
                              │
            ┌─────────────────┼─────────────────┐
            │                 │                 │
      ┌───────────┐    ┌───────────┐    ┌───────────┐
      │ Knowledge │    │ Identity  │    │Operations │
      │   Graph   │    │  Layer    │    │  Layer    │
      │           │    │           │    │           │
      │  notes/   │    │  self/    │    │  ops/     │
      │  inbox/   │    │  identity │    │  daily    │
      │  templates│    │  goals    │    │  reminders│
      └─────┬─────┘    │  method.  │    │  sessions │
            │          └───────────┘    └───────────┘
      ┌─────┴──────┐
      │            │
   Wiki-link   Embedding        ┌──────────────────────┐
    Graph       Index            │ Retrieval Intelligence│
   (in-mem)    (SQLite)          │                      │
      │            │             │  Q-values  (note_q)  │
   PageRank    Semantic          │  Co-occur  (edges)   │
   Spreading   BM25              │  Stage Q   (LinUCB)  │
   Activation  4-Signal          │  Dampening (3 stages)│
   Communities Fusion            │  Explore   (PPR+RMH) │
                                 └──────────────────────┘

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MCP Tools

Tool	What it does
ori_orient	Session briefing: daily status, goals, reminders, vault health, index freshness
ori_update	Write to identity, goals, methodology, daily, or reminders
ori_status	Vault overview
ori_health	Full diagnostics
ori_add	Capture to inbox
ori_promote	Promote with classification, linking, and area assignment
ori_validate	Schema validation
ori_query	Graph queries: orphans, dangling, backlinks, cross-project
ori_query_ranked	Full retrieval with Q-value reranking, co-occurrence PPR, and stage meta-learning
ori_warmth	Inspect the associative warmth field
ori_query_similar	Semantic search (vector only, faster)
ori_query_important	PageRank authority ranking
ori_query_fading	Vitality-based decay detection
ori_explore	Recursive graph traversal — PPR, sub-question decomposition, convergence detection
ori_prune	Activation topology analysis and archive candidates
ori_index_build	Build/update embedding index and bootstrap co-occurrence edges

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CLI

# Vault management
ori init [dir]                    # Scaffold a new vault
ori status                        # Vault overview
ori health                        # Full diagnostics

# Note lifecycle
ori add <title> [--type <type>]   # Capture to inbox
ori promote [note] [--all]        # Promote to knowledge graph
ori validate <path>               # Schema validation
ori archive [--dry-run]           # Archive stale notes
ori prune [--apply] [--verbose]   # Topology analysis + archive candidates

# Retrieval
ori explore <query>               # Recursive graph traversal (RMH)
ori query ranked <query>          # Full intelligent retrieval
ori query similar <query>         # Semantic search
ori query important               # PageRank ranking
ori query fading                  # Vitality detection
ori query orphans                 # Notes with no incoming links
ori query dangling                # Broken wiki-links
ori query backlinks <note>        # What links to this note
ori query cross-project           # Multi-project notes

# Infrastructure
ori index build [--force]         # Build embedding index
ori index status                  # Index statistics
ori graph metrics                 # PageRank, centrality
ori graph communities             # Louvain clustering
ori serve --mcp [--vault <path>]                                # Run MCP server
ori bridge claude-code [--scope <s>] [--activation <a>] [--vault <p>]  # Claude Code (hooks + MCP + instructions)
ori bridge hermes [--scope <s>] [--activation <a>] [--vault <p>]       # Hermes Agent (plugin + MCP + instructions)
ori bridge cursor [--scope <s>] [--vault <p>]                          # Cursor (MCP config)
ori bridge codex [--scope <s>] [--vault <p>]                           # Codex (TOML config)
ori bridge generic [--scope <s>] [--vault <p>] [--json]                # Any MCP client (prints config)
ori bridge status [--json]                                             # Inspect all bridge installs
ori bridge <target> --uninstall                                        # Remove Ori config for a target

Path-taking commands treat relative file paths as vault-relative. Absolute paths continue to work unchanged.

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Vault Structure

vault/
├── .ori                       # Vault marker
├── ori.config.yaml            # Configuration
├── notes/                     # Knowledge graph (flat, no subfolders)
│   └── index.md               # Hub entry point
├── inbox/                     # Capture buffer
├── templates/                 # Note and map schemas
├── self/                      # Agent identity
│   ├── identity.md            # Name, personality, values
│   ├── goals.md               # Active threads, priorities
│   ├── methodology.md         # Processing principles
│   └── memory/                # Agent's accumulated insights
└── ops/                       # Operational state
    ├── daily.md               # Today's completed and pending
    ├── reminders.md           # Time-bound commitments
    └── sessions/              # Session logs

Every file is plain markdown. Open it in any text editor, Obsidian, or your file browser. git log is your audit trail.

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Deployment

Local. Install globally, ori init, connect your MCP client. Done.

VPS / headless. Install on the server. ori serve --mcp --vault /path/to/vault. Memory persists on the filesystem. Back up with git push.

Remote terminals. Hermes Agent supports Docker, SSH, Modal, and Daytona backends. If your agent runs in a remote terminal, ori must be installed and on PATH inside that environment, and the vault must be on persistent storage (not ephemeral). For serverless backends like Modal where environments hibernate, mount the vault on a persistent volume.

Multi-vault. Separate Ori instances for separate agents. Each vault is self-contained: its own identity, knowledge graph, and operational state.

Scriptable. CLI returns structured JSON. Use in cron jobs, webhook handlers, or orchestration loops.

Install Model

Ori separates three install concepts:

• scope: global follows one vault across the machine, project stays inside one repo/workspace
• activation: auto runs ori_orient at session start where the adapter supports it, manual leaves tools available but does not auto-orient
• vault: explicit --vault wins; otherwise Ori resolves by install scope

Precedence rules:

• project install overrides global install
• explicit --vault overrides inferred vault
• project activation overrides global activation

Bridge lifecycle:

• rerun the same ori bridge ... command to update vault path or activation in place
• use --uninstall to remove Ori-owned config from supported adapters
• generic installs emit manual uninstall instructions because Ori does not own that client config surface

Claude Code and Hermes Agent are fully automated adapters with lifecycle hooks. Claude Code uses hook scripts; Hermes uses a native Python plugin installed at ~/.hermes/plugins/ori/. Both auto-orient at session start and capture insights at session end. Cursor and Codex have native MCP config install support. Codex writes to ~/.codex/config.toml and uses a single global config surface; "project" scope there means project-like runtime vault discovery, not a separate project config file. Other MCP-capable clients can use ori bridge generic now and wire the emitted config into their own client surface.

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Configuration

ori.config.yaml controls all tunable parameters. Generated with sensible defaults on ori init.

Section	Controls
vitality	Decay parameters, metabolic rates, zone thresholds, bridge bonus
activation	Spreading activation: damping, max hops, min boost
retrieval	Signal weights, exploration budget, RRF k
engine	Embedding model, database path
warmth	Surprise threshold, PPR parameters, graph weight
promote	Auto-promotion, project routing
llm	Optional: Anthropic, OpenAI-compatible, or local models

LLM integration is optional. Every operation works deterministically with heuristics alone. When configured, LLM improves classification and link suggestions.

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Why Sovereignty Matters

Most memory systems store your agent's knowledge in infrastructure you do not control. A proprietary database. A cloud service. A vendor's format.

Ori stores memory as files you own. The vault is portable. Move it to a new machine, push it to a git remote, open it in a text editor. Switch MCP clients by changing one config line. The memory survives any platform change because it was never locked to a platform.

This is not ideological. It is architectural. Portable memory is composable memory.

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Development

git clone https://github.com/aayoawoyemi/Ori-Mnemos.git
cd Ori-Mnemos
npm install
npm run build
npm link
ori --version

npm test              # 579+ tests
npm run lint          # Type check
npm run dev           # Watch mode

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License

Apache-2.0

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Memory is sovereignty. Ori gives your agent a mind.