Inkstone — Your AI Work Becomes Searchable Knowledge. Automatically.

You run 20+ AI sessions a day. Decisions happen in them. Infrastructure details, deployment preferences, bug root causes, design choices — all discussed, none captured. Next session starts from zero. You repeat yourself.

Most "memory servers" are flat key-value stores — you manually write notes and they return them verbatim. That's not memory, that's a text file with search.

Inkstone is different. It's an automatic knowledge extraction pipeline:

Your AI sessions  ──►  Gemma 4 summarizes each session
                          │
                    Extracts decisions, preferences, context
                          │
                     ──►  Writes structured wiki entities
                          │
                     ──►  Indexes into searchable database
                          │
                     ──►  Dream cycle (14 steps)
                          │
                    Maintains itself nightly

Run the nightly pipeline. That's it. Your session history becomes a self-maintaining knowledge graph with hybrid search, exponential decay, and zero manual entry.

# Start the MCP server for your AI agent
inkstone

# Or run the full pipeline manually:
inkstone ingest-sessions   # Summarize today's sessions → wiki entities
inkstone index             # Sync wiki → database
inkstone dream             # 14-step maintenance cycle

# Search everything that was automatically captured
inkstone search "RDS decision"

Prerequisites

Inkstone uses Ollama with Gemma 4 for session summarization and nomic-embed-text for vector embeddings.

# 1. Install Ollama (macOS / Linux)
# macOS:
brew install --cask ollama
# Linux:
curl -fsSL https://ollama.ai/install.sh | sh

# 2. Pull required models
ollama pull gemma4:e4b     # Main summarization model (5.5 GB)
ollama pull nomic-embed-text  # Embedding model (274 MB)

# 3. Verify everything works
inkstone setup

All models run locally on your machine. No cloud API keys needed. If you prefer cloud LLMs, set OPENROUTER_API_KEY and Inkstone falls back to OpenRouter automatically.

What Makes Inkstone Different?

Most "memory servers" are passive storage — you write a note, it saves it. Inkstone is an active extraction pipeline. It reads your session history, distills knowledge, and maintains itself.

Automatic Ingestion (No Manual Entry)

# Nightly pipeline — runs this every day via cron:
inkstone nightly --root=~/projects

The pipeline does all of this automatically:

Step	What Happens
ingest-sessions	Reads session JSONL (OpenCode, Claude Code), filters noise (tool calls, system msgs, compactions), summarizes dialogue via Ollama Gemma 4, writes wiki entity markdown files
ingest-files	Walks workspace directories, detects new/modified files via hash manifest, feeds each through Gemma 4 for enrichment — extracts key facts, decisions, entities, relationships
index-wiki	Syncs wiki markdown → database chunks with embeddings
dream-fast	Steps 1-7: decay recalc, lifecycle promotion, entity extraction, trivia pruning, wiki reindex, prune expired, graph edges
dream-llm	Steps 8-14: contradiction detection, goal inference, failure patterns, causal links, hypotheses, self-model updates, cluster distillation

The pipeline is resumable — state saves after every step. A crash doesn't lose progress.

What You Get vs Other Memory Servers

Feature	Other servers	Inkstone
Data capture	Manual writes	Auto-ingest from sessions + files via Gemma 4
Search	Keyword match	FTS (Porter + BM25) + vector cosine + graph fusion
Decay	None	Exponential per type (corrections 10yr, emotions 3d)
Maintenance	None	14-step dream cycle (automated nightly)
Lifecycle	None	active → validated → stale → archived → pruned
Graph	None	Entity relations, neighbors, paths, centrality, contradiction traversal
SQL engine	sql.js WASM (13s load, 1.13GB export)	better-sqlite3 native (71ms load, direct WAL writes)
Multi-user	None	Namespace RBAC with API keys

Three Things No Other Memory Server Does

1. Session-to-Knowledge Pipeline Your AI sessions are the richest source of context — decisions made, infrastructure confirmed, bugs root-caused. Inkstone reads session JSONL from OpenCode/Claude Code, filters out tool noise and system messages, feeds the clean dialogue to Gemma 4 (local Ollama), and writes structured wiki entity files. No manual entry.

2. Decay Scoring That Matches Reality Not all knowledge is equally important. A decision from today ranks above a random fact from 6 months ago. Configurable half-lives per type:

Type	Half-life	Example
Correction	10 years	"Root cause was a race condition in the auth module"
Preference	10 years	"We prefer DHL Express for international shipping"
Decision	90 days	"Switched from Postgres to DynamoDB for session store"
Emotion	3 days	"Frustrated with the CI pipeline speed"
Financial	7 days	"AWS bill was $4,200 this month"

3. Self-Maintaining Dream Cycle Run inkstone dream (or let the nightly pipeline do it) and Inkstone runs 14 maintenance steps — decay recalculation, lifecycle promotion, entity extraction, trivia pruning, contradiction detection, goal inference, failure patterns, causal links, hypothesis generation, self-model updates, and cluster distillation. Steps 1-7 require zero external calls. Steps 8-14 use your local LLM (Ollama or OpenRouter).

Quick Start

Install

npm install -g inkstone-mcp

Or install from source:

git clone https://github.com/jairodriguez/inkstone.git
cd inkstone
npm install && npm run build
npm install -g .

Configure

Inkstone works out of the box. The default database lives at ~/.inkstone/inkstone.db. Override with environment variables:

export INKSTONE_DB="$HOME/.inkstone/inkstone.db"

Run

# 1. Start the MCP server for your AI agent
inkstone

# 2. Ingest today's AI sessions (auto-summarizes via Gemma 4 → wiki)
inkstone ingest-sessions

# 3. Ingest your project files
inkstone ingest-files --root=~/my-project

# 4. Sync wiki → database
inkstone index

# 5. Run maintenance (do this nightly via cron)
inkstone dream

# 6. Search everything that was captured
inkstone search "database"

# Or run the whole thing as one resumable pipeline:
inkstone nightly --root=~/my-project

Using Inkstone with AI Agents

Claude Code

Add to ~/.claude.json:

{
  "mcpServers": {
    "inkstone": {
      "command": "inkstone",
      "args": []
    }
  }
}

opencode

Add to .opencode.json in your project root:

{
  "mcpServers": {
    "inkstone": {
      "command": "inkstone",
      "args": []
    }
  }
}

Cline / Roo Code

Add to cline_mcp_settings.json:

{
  "mcpServers": {
    "inkstone": {
      "command": "inkstone",
      "args": []
    }
  }
}

Continue.dev

Add to config.json:

{
  "experimental": {
    "mcpServers": {
      "inkstone": {
        "command": "inkstone",
        "args": []
      }
    }
  }
}

MCP Tools

Once connected, your agent can use these tools:

Tool	What it does
memory_search	Search everything — text, vector, graph, decay-ranked
memory_write	Save a memory with namespace, type, confidence
memory_get	Read one chunk by ID
memory_hybrid_answer	Answer a question using local memory first, deep archive as fallback
memory_deep_query	Same as hybrid_answer but returns citations
memory_summarize	Have the LLM condense text into structured memory
memory_goals	Track goals — list, create, complete, abandon
memory_hypotheses	Track hypotheses — create, confirm, reject
memory_failures	Log and query known failure patterns (don't repeat mistakes)
memory_contradictions	Find conflicting memories
memory_dream	Trigger the 14-step maintenance cycle
memory_consolidate	Merge related chunks by ID
memory_self_model	Read the agent's stored self-knowledge (capabilities, limits)
memory_graph_context	Get the full graph neighborhood around a chunk
memory_graph_neighbors	Direct neighbors of a chunk
memory_graph_path	Shortest path between two chunks
memory_graph_centrality	Most connected chunks
memory_graph_contradictions	Find chunks that contradict a specific chunk
memory_gemini_query	Query with Gemini File Search fallback
memory_gemini_sync	Upload chunks to Gemini File Search
memory_nlm_query	Query Google NotebookLM notebooks
memory_nlm_status	Show configured NotebookLM routes
memory_global_search	Cross-agent search (admin)

Architecture

                    ┌──────────────────────────┐
                    │    MCP Client (Claude)    │
                    └──────────┬───────────────┘
                               │ stdio
                    ┌──────────▼───────────────┐
                    │  MCP Server (server.ts)   │
                    │  19+ tools: search, write │
                    │  dream, graph, goals, ... │
                    └──────────┬───────────────┘
                               │
                    ┌──────────▼───────────────┐
                    │    DB Layer (schema.ts)   │
                    │  better-sqlite3 (native)  │
                    │  WAL mode, no export      │
                    └──────────┬───────────────┘
                               │
          ┌────────────────────┼────────────────────┐
          ▼                    ▼                    ▼
┌─────────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ FTS Index (fts) │ │ LLM Client      │ │ Graph Traversal  │
│ Porter stemmer  │ │ Ollama OpenRouter│ │ neighbors, paths │
│ BM25 scoring    │ │ fallback chain   │ │ centrality, edges│
│ stop words      │ │ 3s timeout       │ └──────────────────┘
└─────────────────┘ └─────────────────┘

Search Pipeline

Query "huckleberry"
    ↓
Porter Stemmer → "huckleberri"
    ↓
FTS Index lookup (BM25)
    ↓
Fetch chunks + decay scores + graph edges
    ↓
Score fusion:
  composite = (ftsScore × 0.5 + 0.5) × sourceTrust × typeWeight
              × (1 + decayBoost) × (1 + graphBoost) × supersededPenalty
    ↓
If hybrid: rerank with cosine similarity
  fused = ftsScore × 0.4 + vectorScore × 0.4 + decayBoost × 0.1 + graphBoost × 0.1
    ↓
Return top N (default 10)

Scoring Weights

Factor	Effect
Source trust	evergreen 2.0, business 1.5, correction 1.5, raw 0.6
Knowledge type	correction 3.0, preference 2.0, fact 1.0, emotion 0.5
Decay	Exponential: score × 0.5^(age / halfLifeDays)
Graph	+5% per edge, capped at +30%
Superseded	×0.1 penalty (old version of something)

Memory Types (auto-detected)

Type	Detected From	Half-life	TTL
correction	fix, bug, root cause	10 years	10 years
preference	prefer, like, always	10 years	10 years
milestone	launched, shipped, released	10 years	10 years
decision	decided, chose, switched	90 days	1 year
lesson	lesson, learned, takeaway	90 days	1 year
procedural	how to, steps, process	180 days	180 days
contact	email, phone, dm	365 days	365 days
financial	$, cost, revenue	7 days	7 days
blocker	blocked, can't, failed	7 days	7 days
event	happened, occurred	14 days	30 days
context	background, situation	14 days	14 days
emotion	feel, frustrated, happy	3 days	7 days
fact	(default)	30 days	90 days

CLI Commands

# ── Server ─────────────────────────────────────────────────────────
inkstone                         Start MCP server (stdio transport)

# ── Search & Query ─────────────────────────────────────────────────
inkstone search <query>          Hybrid FTS + vector + graph search
inkstone deep-query <q>          Local + NLM deep archive (cached)
  --domain=business|content|system
  --force                        Bypass cache

# ── Write ──────────────────────────────────────────────────────────
inkstone write <text>            Write a memory chunk (with embedding)
  --ns=       Namespace          (default: /general)
  --source=   Source label       (default: cli)

# ── Maintenance ────────────────────────────────────────────────────
inkstone dream                   Full 14-step dream cycle
  --steps=1,3,5                  Specific steps only
  --step-timeout=600             Per-step timeout in seconds
inkstone embed-all               Generate embeddings for missing vectors
  --batch=50                     Commit batch size
inkstone index                   Re-index wiki directory

# ── Ingestion ──────────────────────────────────────────────────────
inkstone ingest-files            Ingest files from workspace
  --root=DIR    Root to scan     (default: cwd)
  --force       Re-ingest unchanged
  --no-llm      Skip LLM enrichment
  --dry-run     Preview only
  --limit=N     Max files
  --skip-enriched                Skip already-enriched files
inkstone ingest-sessions         Summarize Claude sessions
  --days=N      Look back        (default: 1)
  --force       Re-process
  --dry-run     Preview only

# ── Diagnostics ────────────────────────────────────────────────────
inkstone status                  DB stats (chunks, types, decays)
inkstone setup                   Check prerequisites (Ollama, models)
inkstone check                   Integrity check
inkstone nlm-status              Show NLM notebook domain routes

# ── Users (multi-user mode) ────────────────────────────────────────
inkstone user add <name>         Create user (first = enables auth)
  --role=admin
inkstone user list               List all users
inkstone user remove <id>        Delete a user
inkstone user grant <ns> <uid> <perm>   Grant namespace access
inkstone user revoke <ns> <uid>        Revoke namespace access

# ── Migration ──────────────────────────────────────────────────────
inkstone migrate                 Migrate from legacy systems

# ── Help ───────────────────────────────────────────────────────────
inkstone help                    This page

Dream Cycle (Automated Maintenance)

The dream cycle is a 14-step pipeline that keeps Inkstone healthy. Run it nightly:

# Full cycle
inkstone dream

# Specific steps
inkstone dream --steps=1,3,5

# With per-step timeout
inkstone dream --step-timeout=600

Step	Name	What It Does
1	exponential_decay	Recalculates decay scores for all chunks
2	lifecycle_transitions	Promotes/demotes chunks based on access patterns
3	entity_extraction	Scans for [[wiki-link]] patterns, extracts entities
4	trivia_pruning	Lowers score for generic/trivial content
5	wiki_reindex	Syncs wiki directory changes to DB
6	prune_expired	Archives chunks below decay threshold
7	graph_edges	Builds entity co-occurrence graph
8	contradiction_detection	Finds conflicting memories (requires LLM)
9	goal_inference	Extracts tracked goals from content (requires LLM)
10	failure_patterns	Identifies recurring failures (requires LLM)
11	causal_links	Links cause and effect between chunks (requires LLM)
12	hypothesis_scan	Generates open hypotheses (requires LLM)
13	self_model_update	Updates agent self-knowledge (requires LLM)
14	distill_clusters	Distills thematic summaries (requires LLM)

Steps 1-7 run with zero external calls. Steps 8-14 need Ollama or OpenRouter.

Configuration

Env Var	Default	Description
INKSTONE_DB	~/.inkstone/inkstone.db	Database path
INKSTONE_ROOT	~/.inkstone	Root directory
INKSTONE_WIKI	~/.inkstone/wiki	Wiki directory
INKSTONE_API_KEY	—	Default MCP API key
INKSTONE_OLLAMA_MODEL	gemma4:e4b	Ollama chat model
INKSTONE_EMBED_MODEL	nomic-embed-text	Embedding model
INKSTONE_EMBEDDING_PROVIDER	local	local (Ollama) or openai
INKSTONE_OR_MODEL	google/gemini-2.0-flash-001	OpenRouter chat model
INKSTONE_OR_FALLBACK	minimax/minimax-m2.5:free	OpenRouter fallback
OPENROUTER_API_KEY	—	Required for OpenRouter
OLLAMA_URL	http://localhost:11434	Ollama endpoint

Multi-User Mode

By default, Inkstone runs without auth. Create your first user to enable multi-user mode:

inkstone user add jairo --role=admin
# → User created. API Key: isk_abc123...

After that, all MCP requests require _apiKey. Users see only their granted namespaces. Admins see everything.

File Structure

src/
├── config.ts          — Paths, weights, decay params, memory types, domain detection
├── index.ts           — CLI entry point (all commands)
├── db/
│   ├── schema.ts      — DB layer: better-sqlite3, write, search, decay, lifecycle, wiki
│   └── fts.ts         — Full-text search: Porter stemmer, inverted index, BM25
├── ingest/
│   ├── files.ts       — File walker + LLM enrichment pipeline
│   └── sessions.ts    — Session summarization → wiki
├── mcp/
│   └── server.ts      — MCP server (stdio, 19+ tools, auth middleware)
├── llm/
│   └── client.ts      — LLM abstraction: Ollama (default) → OpenRouter (fallback)
├── dream/
│   └── cycle.ts       — 14-step dream cycle with AbortController timeouts
├── graph/
│   └── traversal.ts   — BFS/Dijkstra, neighbors, path, contradictions, centrality
├── nlm/
│   ├── client.ts      — NotebookLM API wrapper
│   ├── deep-query.ts  — Cached deep-archive queries
│   ├── router.ts      — Domain-based notebook routing
│   └── state.ts       — Active notebook state
├── gemini/
│   ├── client.ts      — Gemini File Search API
│   ├── query.ts       — Hybrid Inkstone + Gemini search
│   └── sync.ts        — Upload chunks to Gemini
└── auth/
    └── auth.ts        — API key auth + namespace RBAC

Ingestion Pipeline

Inkstone captures knowledge automatically. You don't write memories — Inkstone extracts them from your AI sessions and project files.

Session Ingestion (Gemma 4 → Wiki Entities)

inkstone ingest-sessions               # Summarize today's sessions
inkstone ingest-sessions --days=3      # Last 3 days
inkstone ingest-sessions --force       # Re-process already-ingested
inkstone ingest-sessions --dry-run     # Preview without writing

What happens: Reads session JSONL files from ~/.hermes/sessions/ and ~/.opencode/sessions/, filters out noise (tool calls, system messages, context compactions, session metadata), extracts clean user + assistant dialogue, sends the full session to Ollama Gemma 4 for summarization, and writes structured wiki entity markdown files to ~/.inkstone/wiki/entities/.

Each wiki entity captures decisions made, infrastructure details confirmed, blockers encountered, preferences stated. The wiki indexer (inkstone index) syncs these into the database automatically.

Dedup: Manifest at .ingest-manifest.json tracks processed sessions. Changed sessions are re-summarized, old entities are marked superseded.

File Ingestion (Workspace → Gemma 4 → Wiki + DB)

inkstone ingest-files --root=/path/to/project    # Index project directory
inkstone ingest-files --root=. --skip-enriched    # Skip already-enriched files
inkstone ingest-files --root=. --dry-run          # Preview only
inkstone ingest-files --root=. --force            # Re-process everything
inkstone ingest-files --root=. --limit=50         # Cap at 50 files
inkstone ingest-files --root=. --no-llm           # Skip LLM (not recommended)

What happens: Walks a workspace directory, detects new/modified files via content hash manifest (.file-manifest.json), feeds each changed file through Gemma 4 with a structured enrichment prompt that extracts: summary, key facts, entities mentioned, decisions recorded, and cross-project relationships. Delta tracking means repeated runs are fast.

Nightly Pipeline (Orchestrator)

inkstone nightly --root=~/projects                # Full pipeline (resumable)
inkstone nightly --root=~/projects --resume       # Resume from last saved state
inkstone nightly --root=~/projects --dry-run      # Preview steps
inkstone nightly status                           # Show last run state

Runs 5 sequential steps with individual timeouts. State saves after each step — a crash doesn't lose progress:

#	Step	Timeout	What It Does
1	ingest-sessions --days=1	10 min	Summarize today's sessions via Gemma 4 → wiki
2	ingest-files	60 min	Enrich new/changed workspace files → wiki + DB
3	index	30 min	Sync wiki → database with embeddings
4	dream --steps=1-7	15 min	Decay, lifecycle, entity extraction, pruning, graph edges (no LLM)
5	dream --steps=8-14	60 min	Contradictions, goals, failures, causal links, clusters (LLM)

Automation

Inkstone is designed to run unattended. Set up the cron once; it captures and maintains itself nightly.

Install Nightly Cron

# One command — adds "inkstone nightly --root=~/projects" at 2 AM
inkstone cron-install

# Or manually:
crontab -e
# Add:
0 2 * * * cd ~ && inkstone nightly --root=~/projects 2>&1 | logger -t inkstone-nightly

The nightly pipeline runs 5 steps: ingest sessions (Gemma 4 → wiki), ingest files (workspace → wiki), index wiki → DB, dream fast (steps 1-7, no LLM), dream LLM (steps 8-14). Each step has a timeout. State saves between steps — a crash doesn't lose progress.

What to Tell Your AI Agent

Add this to your project's CLAUD.md, AGENTS.md, or agent instructions:

## Inkstone Memory

Inkstone is a persistent memory server running on this machine.
It auto-ingests AI sessions nightly and maintains a searchable knowledge graph.

### Setup (one-time)
1. Install: `npm install -g inkstone-mcp`
2. Pull models: `ollama pull gemma4:e4b && ollama pull nomic-embed-text`
3. Verify: `inkstone setup`
4. Install cron: `inkstone cron-install` (or `crontab -e`)
5. Connect: add the MCP server config to your client

### Daily use
- Before answering, call `memory_search` for relevant context.
- After making a decision or confirming a fact, call `memory_write` to persist it immediately (don't wait for nightly ingest).
- The nightly pipeline auto-ingests session logs, so repeated context won't be needed tomorrow.

### Commands
inkstone                          Start MCP server (stdio)
inkstone status                   DB statistics
inkstone search <query>           Search memories
inkstone write <text>             Write a memory (use for immediate persistence)
inkstone ingest-sessions          Summarize today's sessions
inkstone nightly --root=.         Full pipeline
inkstone cron-install             Install nightly cron

Connect Inkstone to your AI agent via MCP. The config is identical across all clients:

{
  "mcpServers": {
    "inkstone": {
      "command": "inkstone",
      "args": []
    }
  }
}

Put this in ~/.claude.json (Claude Code), opencode.json (OpenCode), or your client's MCP config. Cline, Continue.dev, and any MCP-compatible client use the same pattern.

Backup & Recovery

Automatic backups rotate on every schema change:

~/.inkstone/inkstone.db         ← Live database
~/.inkstone/inkstone.db.bak     ← Most recent backup
~/.inkstone/inkstone.db.bak.2   ← Second backup
~/.inkstone/inkstone.db.bak.3   ← Third backup

If the DB is corrupted or you need to revert:

inkstone check                          # Check integrity
cp ~/.inkstone/inkstone.db.bak ~/.inkstone/inkstone.db   # Restore
inkstone check                          # Verify

Development

npm run build        # TypeScript → dist/
npm run dev          # tsc --watch
npm test             # Run tests

Database Schema

Table	Purpose
chunks	Knowledge entries with text, namespace, type, lifecycle, embeddings
fts_index	Custom inverted index (term → chunk_id → positions) with BM25
files	File tracking (path, hash, mtime) for delta detection
memory_decay	Per-chunk exponential decay scores
memory_relations	Graph edges between chunks
embedding_cache	LLM embedding cache
manifest	Session ingestion tracking
goals	Tracked goals (active/complete/abandoned)
hypotheses	Hypotheses with evidence
failure_patterns	Recurring failure patterns
nlm_sync	NotebookLM sync state
users	Multi-user auth
namespace_permissions	Per-user RBAC grants

Chunk ID Scheme

Source	ID Pattern
Direct write	direct::<sha256-prefix>
Wiki file	wiki::<relative-path>::<line>
Session ingest	session::<session-id>::<hash>
Graph edge	edge:entity:<from>:<to> or edge:ns:<ns>:<from>:<to>

Chunk Lifecycle

  active ──► validated ──► stale ──► archived ──► pruned (deleted)
   │           │              │            │
   │ 3+        │ 14 days      │ 28 days    │ decay < 0.05
   │ accesses  │ no access    │ stale       │ AND expired

License

MIT