Ori Mnemos

Persistent memory infrastructure for AI agents.

If models are compute and tools are actions, Ori is continuity.

Every agent session starts from near-zero context. Decisions get repeated. Learnings disappear. Identity resets. Ori fixes this with a markdown-native memory layer that any agent runtime can read, write, and reason over — no database, no cloud dependency, no lock-in.

Install it. Point your agent at it. It wakes up knowing who it is.

npm install -g ori-memory

Why This Exists

The industry has invested heavily in reasoning, tool use, and context windows. Memory is still an afterthought — most agents store nothing between sessions, or dump everything into a vector database that loses structure, relationships, and meaning.

Ori takes a different position: memory should be files on disk. Plain markdown, YAML frontmatter, wiki-links as graph edges, git as version control. The same format humans read and write. The same format that diffs, merges, and deploys.

This is not a note-taking app. It is infrastructure — the layer between an agent's context window and its long-term knowledge.

Architecture

                          Any MCP Client
                    (Claude, Cursor, Windsurf,
                     Cline, custom agents, VPS)
                              |
                        MCP Protocol
                        (stdio / JSON-RPC)
                              |
                    +-------------------+
                    |    Ori MCP Server  |
                    |                   |
                    |  instructions     |  <-- Agent identity auto-injected at connect
                    |  resources        |  <-- 5 identity resources (ori://)
                    |  13 tools         |  <-- 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
 (in-memory)  (SQLite)
    |            |
 PageRank    Semantic
 Backlinks   Search
 Orphans     BM25
 Communities 3-Signal Fusion

Three memory spaces, one protocol surface, zero external dependencies.

What v0.3 Ships

Agent Identity Layer

The defining feature of v0.3. Agents that connect to an Ori vault wake up as themselves.

How it works: When an MCP client connects, Ori reads the vault's self/ directory and injects the agent's identity, goals, and methodology into the MCP instructions field. The agent receives this context automatically — no configuration on the client side, no CLAUDE.md, no system prompts to maintain.

Identity travels with the vault. Move the vault to a new machine, connect a different client, deploy to a VPS — the agent is the same agent.

On first connection to a fresh vault, Ori detects the empty identity and guides the agent through onboarding: naming, purpose, initial context. The agent writes its own identity using ori_update. Every session after that, it wakes up knowing who it is.

MCP Resources expose identity as readable endpoints:

ori_orient is the session briefing tool. Call it at session start — it returns daily status, active goals, reminders, vault health, and (optionally) full identity context. The agent starts every session with situational awareness.

ori_update lets the agent write back to its identity files. Goals shift, methodology evolves, daily state changes — the agent maintains its own continuity.

Three-Signal Retrieval Engine

Most memory systems rely on a single retrieval signal — usually vector similarity. Ori fuses three independent signals to surface the right notes:

Semantic signal. Local embeddings via all-MiniLM-L6-v2 running in-process. No API calls, no external service. Notes are embedded with composite metadata (title, description, body, type, community). Stored in SQLite. Incrementally indexed — only changed notes get re-embedded.

Keyword signal. BM25 with field-level boosting. Title matches weight 3x, description 2x, body 1x. Handles exact terminology that embedding models smooth over.

Graph signal. PageRank importance from the wiki-link graph. Notes with high structural authority rank higher. Community membership from Louvain clustering adds topical coherence.

These signals combine through score-weighted Reciprocal Rank Fusion (RRF) with per-signal breakdown in the response. The engine classifies query intent (episodic, procedural, semantic, decision) to adjust signal weights dynamically.

ori query ranked "token incentive mechanisms"
ori query similar "how does session capture work"
ori query important --limit 20
ori query fading --threshold 0.3

Vitality Model

Notes decay. Ori models this explicitly using activation functions from ACT-R cognitive science literature.

Each note has a vitality score computed from:

• Access frequency and recency — how often and how recently the note was touched
• Structural connectivity — incoming links stabilize notes against decay
• Metabolic rate — identity files (self/) decay 10x slower than operational files (ops/)
• Bridge bonus — notes connecting otherwise disconnected clusters get a vitality floor
• Revival spike — new connections to dormant notes trigger a 14-day renewal

ori query fading surfaces notes losing vitality. These are candidates for reconnection, updating, or archival — not deletion.

Knowledge Graph

Every wiki-link ([[note title]]) is a directed edge. Ori builds the graph in memory and exposes structural queries:

Graph metrics include PageRank, betweenness centrality, and community detection via Louvain algorithm. Communities inform the retrieval engine — notes in the same cluster get a relevance boost.

Note Lifecycle

Notes follow a controlled path:

Capture (inbox/) --> Promote (notes/) --> Connect --> Decay/Archive

Capture. ori add "Title as a complete claim" writes to inbox/ with YAML frontmatter. Titles are prose — they work as statements when wiki-linked.

Promote. ori promote moves notes from inbox to the knowledge graph. Automatic type classification (idea, decision, learning, insight, blocker, opportunity) via 50+ heuristic patterns. Automatic link detection and area suggestion. Optional LLM enhancement.

Validate. Schema enforcement against YAML templates. Required fields, enum constraints, description quality checks. Run on individual notes or vault-wide.

Archive. ori archive moves isolated or stale notes out of the active graph.

Quick Start

1. Install

npm install -g ori-memory

Requires Node.js 18+.

2. Initialize a vault

ori init my-agent
cd my-agent

This creates the full vault structure: knowledge graph, identity layer, operational state, templates, and configuration.

3. Connect your agent

Add Ori to your MCP client configuration:

{
  "mcpServers": {
    "ori": {
      "command": "ori",
      "args": ["serve", "--mcp"]
    }
  }
}

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

MCP Integration

Ori exposes a full memory surface over the Model Context Protocol. Any MCP-compatible runtime can connect — Claude Code, Cursor, Windsurf, Cline, custom agent loops, headless agents on a VPS.

The server runs over stdio (JSON-RPC 2.0). No ports to configure, no authentication layer, no network exposure. If the runtime can spawn a subprocess, it can use Ori.

Tools

Explicit vault path

For agents running outside the vault directory (VPS deployments, global configurations, multi-vault setups):

{
  "mcpServers": {
    "ori": {
      "command": "ori",
      "args": ["serve", "--mcp", "--vault", "/path/to/vault"]
    }
  }
}

Response format

All tools return a consistent envelope:

{
  "success": true,
  "data": {},
  "warnings": []
}

Vault Structure

vault/
├── .ori                       Vault marker
├── ori.config.yaml            Configuration
├── notes/                     Knowledge graph
│   └── index.md               Hub — entry point to all maps
├── inbox/                     Capture buffer
├── templates/
│   ├── note.md                Note schema (6 types, required/optional fields)
│   └── map.md                 Map of Content schema
├── self/                      Agent identity
│   ├── identity.md            Name, personality, values
│   ├── goals.md               Active threads, priorities
│   ├── methodology.md         Processing principles, session rhythm
│   └── memory/                Atomic agent insights
└── ops/                       Operational state
    ├── daily.md               Today's work — completed, pending
    ├── reminders.md           Time-bound commitments
    ├── sessions/              Session logs
    └── observations/          Friction signals, process gaps

Three spaces with distinct purposes:

• self/ — Who the agent is. Decays slowly. Persists across sessions, clients, and machines.
• notes/ — What the agent knows. The knowledge graph. Wiki-links, frontmatter, flat structure.
• ops/ — What the agent is doing. Daily state, session captures, coordination. Decays fast.

Every file is plain markdown. Every file is diffable, mergeable, inspectable. git log is your audit trail.

CLI Reference

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

ori add <title> [--type <type>]                 Capture to inbox
ori promote [note] [--all] [--dry-run]          Promote to knowledge graph
    [--type] [--description] [--links] [--project]
ori validate <path>                             Schema validation
ori archive [--dry-run]                         Archive stale notes

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
ori query ranked <query> [--limit N]            Three-signal retrieval
ori query similar <query> [--limit N]           Semantic search
ori query important [--limit N]                 PageRank ranking
ori query fading [--threshold N] [--limit N]    Vitality-based detection

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 [--global]               Install Claude Code integration

Claude Code Bridge

For Claude Code users, the bridge adds lifecycle automation on top of the MCP server:

ori bridge claude-code           # Project scope
ori bridge claude-code --global  # All projects

This installs three hooks:

• SessionStart — Runs vault health check and displays status on every session open.
• PostToolUse — Validates notes after creation against schema.
• Stop — Captures session summary to inbox before exit.

Global mode installs hooks to ~/.claude/hooks/ori/ and merges settings into ~/.claude/settings.json. Idempotent on repeat runs. Exits cleanly in non-vault directories.

Configuration

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

Key sections:

LLM integration

LLM enhancement is optional. Every operation works deterministically with heuristics alone. When configured, LLM improves classification accuracy, link suggestions, and metadata quality.

Supported providers:

• Anthropic — Claude models via API
• OpenAI-compatible — Any provider exposing the OpenAI chat completions endpoint (OpenAI, Ollama, LM Studio, Together, Groq, local models)

llm:
  provider: openai-compat
  model: gpt-4o
  apiKeyEnv: OPENAI_API_KEY
  baseUrl: https://api.openai.com/v1

For local models:

llm:
  provider: openai-compat
  model: llama3
  baseUrl: http://localhost:11434/v1

Deployment

Ori runs anywhere Node.js runs.

Local development. Install globally, ori init, connect your editor's MCP client.

VPS / headless agents. Install Ori on the server. Point the MCP config to the vault path with --vault. The agent connects over stdio — no ports, no HTTP, no auth layer needed. Memory persists on the filesystem. Back up with git push.

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

CI / automation. Ori's CLI is scriptable. ori health returns structured JSON. ori query ranked searches the knowledge graph. ori add captures from any pipeline. Use it in cron jobs, webhook handlers, or agent orchestration loops.

Design Principles

Markdown-native. Plain files. No proprietary format, no binary blobs, no opaque database. If Ori disappears tomorrow, your knowledge is still readable markdown files in a git repository.

Agent-native. Designed for autonomous loops, not manual note-taking. The capture-promote-validate pipeline enforces quality without requiring human intervention. The agent maintains its own memory.

Human-legible. Every file is inspectable. Open the vault in any text editor, file browser, or Obsidian. Frontmatter is YAML. Links are wiki-links. Structure is directories.

No lock-in. Git is the sync layer. The filesystem is the database. Switching agents, clients, or providers requires changing one MCP config line. The vault is portable.

Deterministic core. Every operation works without an LLM. Heuristic classification, pattern-based link detection, graph algorithms, BM25 search — all run locally with zero API calls. LLM enhancement is opt-in and gracefully degrades.

Development

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

npm test              # Run test suite
npm run lint          # Type check
npm run dev           # Watch mode
npm run smoke:bin     # Verify CLI binary
npm run pack:check    # Validate package structure

License

Apache-2.0