claude-memory-mcp/README.md

# Claude Memory MCP

Give Claude persistent memory that survives across sessions, context compactions, and machines.

```bash
claude plugins install github:ViktorBarzin/claude-memory-mcp
```

That's it. Claude now remembers things.

## What It Does

```
You:    "remember I prefer Svelte for all new frontend apps"
Claude: Stored. ✓

  ── 3 weeks later, new session, different machine ──

You:    "build me a dashboard for this API"
Claude: I'll set up a SvelteKit project since that's your preference...
```

**No configuration needed** for single-machine use. Memories are stored in a local SQLite database with full-text search. For multi-machine sync, point it at an API server (details below).

## Features

### Slash Commands
- **`/remember <fact>`** — store a fact, preference, decision, or person detail
- **`/recall <query>`** — search memories by topic

### Automatic Behaviors (via hooks)
- **Auto-recall** — before responding, Claude checks stored memories for relevant context (preferences, past corrections, decisions). Completely invisible to the user.
- **Auto-learn** — after each response, a background process analyzes the conversation for corrections, preferences, and decisions worth remembering. Uses haiku-as-judge for conservative extraction.
- **Compaction survival** — when Claude's context window compacts, key memories are saved to a marker file and re-injected on the next prompt. No knowledge is lost.
- **Auto-approve** — memory tool calls are approved automatically, no permission prompts.

### MCP Tools
Claude has direct access to these tools during conversation:

| Tool | Description |
|------|-------------|
| `memory_store` | Store a fact with category, tags, importance, and semantic search keywords |
| `memory_recall` | Search memories using full-text search with expanded query terms |
| `memory_list` | List recent memories, optionally filtered by category |
| `memory_delete` | Delete a memory by ID |
| `secret_get` | Retrieve the decrypted content of a sensitive memory |

### Memory Categories
Memories are organized into: `facts`, `preferences`, `projects`, `people`, `decisions`

### Sensitive Memory Support
Mark memories as sensitive with `force_sensitive: true`. When Vault is configured, sensitive content is encrypted at rest and only decryptable via `secret_get`.

## Architecture

```
Claude Code Session
┌──────────────────────────────────────────────────────┐
│  Hooks                        MCP Server             │
│  ┌──────────────────┐        ┌────────────────────┐  │
│  │ auto-recall      │───────▶│ memory_store       │  │
│  │ auto-learn       │        │ memory_recall      │  │
│  │ compaction       │        │ memory_list        │  │
│  │ auto-approve     │        │ memory_delete      │  │
│  └──────────────────┘        │ secret_get         │  │
│                              └─────────┬──────────┘  │
│                                        │             │
│              ┌─────────────────────────┼──────────┐  │
│              │ Local SQLite            │          │  │
│              │ (cache + FTS5)    SyncEngine       │  │
│              │ ◄──── always ────(background, 60s) │  │
│              │       used       push queued writes │  │
│              │                  pull server changes│  │
│              │  pending_ops ──────────┘            │  │
│              │  (offline write queue)              │  │
│              └────────────────────────────────────┘  │
└──────────────────────────────────────────────────────┘
                                │
              optional, for multi-machine sync
                                │
                     ┌──────────▼───────────┐
                     │  API Server (FastAPI) │
                     │  Docker / Kubernetes  │
                     └──────────┬───────────┘
                                │
                     ┌──────────▼───────────┐
                     │    PostgreSQL         │
                     │  (authoritative)      │
                     └──────────────────────┘
```

## Search Algorithm

Memory recall uses two different full-text search backends depending on the operating mode. Both follow the same query building pattern: the `context` and `expanded_query` fields from the user's recall request are concatenated into a single search string, then processed into a backend-specific query.

### Query Construction

When `memory_recall` is called, the tool receives:
- **`context`** — the topic or question being asked about
- **`expanded_query`** — REQUIRED, minimum 5 space-separated semantically related terms generated by Claude

These are concatenated: `all_terms = f"{context} {expanded_query}"`, then split into individual words for the search backend.

### SQLite: FTS5 with BM25

The local SQLite database uses an [FTS5 virtual table](https://www.sqlite.org/fts5.html) for full-text search with the [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) ranking algorithm.

**Schema:**
```sql
CREATE VIRTUAL TABLE memories_fts USING fts5(
    content, category, tags, expanded_keywords,
    content='memories', content_rowid='id'
);
```

This is a [content table](https://www.sqlite.org/fts5.html#contentless_and_content_external_tables) backed by the `memories` table — FTS5 reads column data from `memories` on demand rather than duplicating it. Triggers on INSERT, UPDATE, and DELETE keep the FTS index synchronized:

```sql
-- Insert trigger
CREATE TRIGGER memories_ai AFTER INSERT ON memories BEGIN
    INSERT INTO memories_fts(rowid, content, category, tags, expanded_keywords)
    VALUES (new.id, new.content, new.category, new.tags, new.expanded_keywords);
END;

-- Delete trigger (FTS5 special 'delete' command)
CREATE TRIGGER memories_ad AFTER DELETE ON memories BEGIN
    INSERT INTO memories_fts(memories_fts, rowid, content, category, tags, expanded_keywords)
    VALUES ('delete', old.id, old.content, old.category, old.tags, old.expanded_keywords);
END;

-- Update trigger (delete old + insert new)
CREATE TRIGGER memories_au AFTER UPDATE ON memories BEGIN
    INSERT INTO memories_fts(memories_fts, rowid, ...)
    VALUES ('delete', old.id, old.content, old.category, old.tags, old.expanded_keywords);
    INSERT INTO memories_fts(rowid, content, category, tags, expanded_keywords)
    VALUES (new.id, new.content, new.category, new.tags, new.expanded_keywords);
END;
```

**Query building:** Each word is quoted and joined with OR:
```python
words = all_terms.split()
fts_query = " OR ".join(f'"{w}"' for w in words if w)
# Example: "kubernetes" OR "deployment" OR "pod" OR "scaling" OR "replicas"
```

**Ranking:** Two sort modes:
- `sort_by="relevance"` — `ORDER BY bm25(memories_fts), importance DESC` — FTS5's built-in BM25 relevance score, with importance as tiebreaker
- `sort_by="importance"` (default) — `ORDER BY importance DESC, created_at DESC` — user-assigned importance score, with recency as tiebreaker

**Fallback:** If the FTS5 MATCH query fails (e.g. syntax error from special characters), the search falls back to a `LIKE %query%` scan against `content` and `tags` columns.

### PostgreSQL: tsvector with Weighted Ranking

The API server uses PostgreSQL's native [full-text search](https://www.postgresql.org/docs/current/textsearch.html) with a generated `tsvector` column and weighted ranking.

**Schema** (from migration `001`):
```sql
CREATE TABLE memories (
    id SERIAL PRIMARY KEY,
    content TEXT NOT NULL,
    category VARCHAR(50) DEFAULT 'facts',
    tags TEXT DEFAULT '',
    expanded_keywords TEXT DEFAULT '',
    importance REAL DEFAULT 0.5,
    -- ...
    search_vector tsvector GENERATED ALWAYS AS (
        setweight(to_tsvector('english', coalesce(content, '')), 'A') ||
        setweight(to_tsvector('english', coalesce(expanded_keywords, '')), 'B') ||
        setweight(to_tsvector('english', coalesce(tags, '')), 'C') ||
        setweight(to_tsvector('english', coalesce(category, '')), 'D')
    ) STORED
);

CREATE INDEX idx_memories_search ON memories USING GIN(search_vector);
```

The `search_vector` is a [generated column](https://www.postgresql.org/docs/current/ddl-generated-columns.html) that PostgreSQL maintains automatically on every INSERT/UPDATE — no triggers needed. It combines four fields with different weights:

| Weight | Field | Purpose |
|--------|-------|---------|
| **A** (highest) | `content` | The actual memory text — most important for matching |
| **B** | `expanded_keywords` | Semantic search terms generated by Claude at store time |
| **C** | `tags` | User-provided comma-separated tags |
| **D** (lowest) | `category` | Category name (facts, preferences, etc.) |

A [GIN index](https://www.postgresql.org/docs/current/gin.html) on `search_vector` enables fast lookup without sequential scans.

**Query execution:**
```sql
SELECT id, content, category, tags, importance, is_sensitive,
       ts_rank(search_vector, query) AS rank,
       created_at, updated_at
FROM memories, plainto_tsquery('english', $2) query
WHERE user_id = $1
  AND deleted_at IS NULL
  AND (search_vector @@ query OR $2 = '')
ORDER BY importance DESC, ts_rank(search_vector, query) DESC
LIMIT $3
```

[`plainto_tsquery`](https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES) processes the concatenated query text through PostgreSQL's English text search dictionary — it handles stemming (e.g. "running" → "run"), stop word removal, and normalization. The `@@` operator matches the resulting `tsquery` against the stored `tsvector`.

[`ts_rank`](https://www.postgresql.org/docs/current/textsearch-controls.html#TEXTSEARCH-RANKING) scores each match considering the weight of the matched field — a match in `content` (weight A) scores higher than a match in `tags` (weight C).

**Sort modes:**
- `sort_by="importance"` (default) — `ORDER BY importance DESC, ts_rank(search_vector, query) DESC`
- `sort_by="relevance"` — `ORDER BY ts_rank(search_vector, query) DESC`
- `sort_by="recency"` — `ORDER BY created_at DESC`

### Semantic Expansion at Store Time

When Claude stores a memory via `memory_store`, it generates an `expanded_keywords` field — at least 5 space-separated semantically related terms. For example, storing "User prefers Svelte for frontends" might produce:

```
expanded_keywords: "svelte frontend framework ui web sveltekit javascript component reactive"
```

These keywords are indexed alongside the memory content. When a future `memory_recall` searches for "what framework should I use for the dashboard?", the expanded keywords create additional matching surface that pure content matching would miss. This is a form of query expansion performed at write time by the LLM.

### SQLite vs PostgreSQL Comparison

| Feature | SQLite (FTS5) | PostgreSQL (tsvector) |
|---------|---------------|----------------------|
| Tokenization | ICU/unicode61 tokenizer | English text search dictionary |
| Stemming | No (exact word matching) | Yes (Porter stemmer via `english` config) |
| Stop words | No | Yes (removes "the", "is", "at", etc.) |
| Ranking | BM25 (term frequency + inverse document frequency) | `ts_rank` (weighted field matching) |
| Field weighting | No (all FTS5 columns equal) | Yes (A/B/C/D weights per column) |
| Index type | B-tree on FTS content | GIN (Generalized Inverted Index) |
| Query syntax | Quoted OR terms (`"word1" OR "word2"`) | `plainto_tsquery` with implicit AND |
| Fallback | LIKE scan on failure | None needed (robust parser) |
| Maintenance | Triggers sync content ↔ FTS table | Generated column, auto-maintained |

## Operating Modes

| Mode | When | What happens |
|------|------|--------------|
| **SQLite-only** | No env vars set | Everything local. Zero config. Works offline. |
| **Hybrid** | `MEMORY_API_KEY` set | Local SQLite for reads + background sync to API. Writes queue if API is down. |
| **HTTP-only** | `MEMORY_API_KEY` + `MEMORY_SYNC_DISABLE=1` | Direct API calls, no local cache. Legacy mode. |
| **Full** | Any mode + Vault configured | Above + Vault for encrypting sensitive memories at rest. |

## Setup

### Option 1: Plugin Install (recommended)

```bash
claude plugins install github:ViktorBarzin/claude-memory-mcp
```

Works immediately with SQLite-only mode. To enable multi-machine sync:

```bash
export MEMORY_API_URL="https://your-server.example.com"
export MEMORY_API_KEY="your-api-key"
```

### Option 2: Manual MCP Config

Add to `~/.claude/settings.json`:

```json
{
  "mcpServers": {
    "claude_memory": {
      "type": "stdio",
      "command": "python3",
      "args": ["-m", "claude_memory.mcp_server"],
      "env": {
        "MEMORY_API_URL": "https://your-server.example.com",
        "MEMORY_API_KEY": "your-api-key"
      }
    }
  }
}
```

Omit the `env` block for SQLite-only mode. Requires `pip install claude-memory-mcp`.

### Verify

```
You: /remember "I prefer dark mode in all applications"
You: /recall "UI preferences"
```

## Running the API Server

Only needed for multi-machine sync. Skip this if you're using SQLite-only mode.

### Docker Compose

```bash
cd docker
docker compose up -d
```

Starts the API server + PostgreSQL. Optionally add Vault:

```bash
docker compose --profile vault up -d
```

### Manual

```bash
pip install claude-memory-mcp[api]
export DATABASE_URL="postgresql://user:pass@localhost:5432/claude_memory"
export API_KEY="your-secret-key"
alembic upgrade head
uvicorn claude_memory.api.app:app --host 0.0.0.0 --port 8000
```

### Kubernetes

Designed for high availability: 2 replicas with pod anti-affinity, PodDisruptionBudget, and startup probes.

```bash
helm install claude-memory deploy/helm/claude-memory \
  --set env.DATABASE_URL="postgresql://user:pass@host:5432/db" \
  --set env.API_KEY="your-key" \
  --set ingress.host="claude-memory.yourdomain.com"
```

Or raw manifests:

```bash
kubectl apply -f deploy/kubernetes/namespace.yaml
kubectl create secret generic claude-memory-secrets \
  -n claude-memory \
  --from-literal=database-url="postgresql://user:pass@host:5432/db" \
  --from-literal=api-key="your-key"
kubectl apply -f deploy/kubernetes/
```

## Multi-User Setup

For team deployments, use `API_KEYS` with a JSON mapping:

```bash
export API_KEYS='{"alice": "key-alice-xxx", "bob": "key-bob-yyy"}'
```

Each user gets isolated memory storage. Users cannot see each other's memories.

**Adding a user:**
1. Generate a key: `openssl rand -base64 32`
2. Add to `API_KEYS` JSON
3. Restart the API server
4. Share the key with the user

## Sensitive Memory & Secrets

Sensitive data is handled through a multi-layer system:

### Automatic Detection
`credential_detector.py` scans all incoming content for secrets using regex patterns with confidence scores (0.5–0.95):

| Confidence | Pattern Types |
|------------|--------------|
| 0.95 | Private keys (RSA/EC/DSA/OPENSSH), AWS access keys (`AKIA`/`ASIA`), GitHub tokens (`ghp_`/`gho_`/etc.) |
| 0.85–0.90 | Connection strings (postgres/mysql/mongodb/redis/amqp), Basic auth headers, Bearer tokens |
| 0.75–0.80 | `api_key=`, `password=`, `token=` assignments |
| 0.60–0.65 | Generic `secret=`/`credential=` patterns, hex keys |

Detected content is **redacted** in the main DB (e.g., `[REDACTED:private_key]`) and the original stored in Vault or encrypted storage.

### Encryption at Rest

Three tiers, checked in order:
1. **HashiCorp Vault** (KV v2) — if `VAULT_ADDR` + `VAULT_TOKEN` set. Secrets stored at `claude-memory/{user_id}/mem-{id}`. Supports Kubernetes Service Account auto-authentication.
2. **AES-256-GCM** — if `MEMORY_ENCRYPTION_KEY` set. Key can be hex-encoded 32 bytes or a passphrase (derived via SHA-256). Stored as `nonce (12 bytes) + ciphertext + GCM tag (16 bytes)` in the `encrypted_content` column.
3. **Plaintext fallback** — content stored as-is (still redacted in main column if detected as sensitive).

Retrieve the original via `secret_get(id=<memory_id>)`.

## Plugin Hooks

| Hook | Event | What it does |
|------|-------|-------------|
| `pre-compact-backup.sh` | PreCompact | Saves top 20 memories to a marker file before context compaction |
| `post-compact-recovery.sh` | UserPromptSubmit | Re-injects saved memories after compaction (one-time, then deletes marker) |
| `user-prompt-recall.py` | UserPromptSubmit | Tells Claude to check `memory_recall` before responding to each message |
| `auto-learn.py` | Stop (async) | Runs haiku-as-judge on the last exchange to extract durable facts worth storing |
| `auto-allow-memory-tools.py` | PermissionRequest | Auto-approves `memory_store`, `memory_recall`, `memory_list`, `memory_delete`, `secret_get` |

### Auto-Learn Details

The auto-learn hook runs asynchronously after each Claude response. It operates in two modes:

- **Single-turn** (every turn) — analyzes just the last user/assistant exchange. Cheap and fast. Extracts corrections, preferences, decisions, and facts.
- **Deep multi-turn** (every 5 turns) — analyzes the last 5 exchanges as a window. Also extracts debugging insights, workarounds, and operational knowledge.

**Judge fallback chain:** Claude CLI (haiku model) → local Ollama (qwen2.5:3b/llama3.2:3b/gemma2:2b/phi3:mini) → heuristic pattern matching (keyword-based extraction from user messages).

Extracted events are deduplicated via SHA-256 content hashing. Each event is stored to both the MCP memory database and a `~/.claude/projects/<project>/memory/auto-learned.md` markdown file.

### Debug

```bash
# See what hooks are doing
export DEBUG_CLAUDE_MEMORY_HOOKS=1

# Disable auto-approve to see permission prompts
export DISABLE_CLAUDE_MEMORY_AUTO_APPROVE=1
```

## Background Sync Engine

In hybrid mode, a `SyncEngine` runs in a daemon thread with its own SQLite connection (thread-safe via `threading.Lock`).

**Push cycle:** Reads the `pending_ops` queue (SQLite table) and sends each operation to the API. On success, removes the operation from the queue and updates the local `server_id` mapping. On failure, the operation stays queued for the next cycle.

**Pull cycle:** Calls `GET /api/memories/sync?since=<last_sync_ts>` for incremental changes. Server wins on conflicts — remote updates overwrite local rows (matched by `server_id`). Soft-deleted rows on the server are hard-deleted locally.

**Write path:** Every `memory_store` first writes to local SQLite (instant), then attempts an immediate sync to the API. If the immediate sync fails, the write is enqueued in `pending_ops` for the next background cycle.

**Sync interval:** Configurable via `MEMORY_SYNC_INTERVAL` (default: 60 seconds).

## API Reference

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/health` | GET | Health check |
| `/api/memories` | POST | Store a memory |
| `/api/memories` | GET | List memories (`?category=facts&limit=20`) |
| `/api/memories/recall` | POST | Search memories by context and expanded query |
| `/api/memories/{id}` | DELETE | Soft-delete a memory |
| `/api/memories/sync` | GET | Incremental sync (`?since=ISO-timestamp`) |
| `/api/memories/{id}/secret` | POST | Get decrypted sensitive memory content |
| `/api/memories/migrate-secrets` | POST | Re-encrypt existing secrets with current Vault config |
| `/api/memories/import` | POST | Bulk import memories (JSON array) |

All endpoints except `/health` require `Authorization: Bearer <api-key>`.

## Environment Variables

### MCP Server (client-side)

| Variable | Description | Default |
|----------|-------------|---------|
| `MEMORY_API_URL` | API server URL | `http://localhost:8080` |
| `MEMORY_API_KEY` | API key (enables hybrid mode) | None |
| `MEMORY_HOME` | Local storage directory | `~/.claude/claude-memory` |
| `MEMORY_DB` | SQLite database path override | `$MEMORY_HOME/memory/memory.db` |
| `MEMORY_SYNC_INTERVAL` | Background sync interval in seconds | `60` |
| `MEMORY_SYNC_DISABLE` | Set to `1` for HTTP-only mode | None |

Aliases `CLAUDE_MEMORY_API_URL` and `CLAUDE_MEMORY_API_KEY` are also supported.

### API Server

| Variable | Description | Default |
|----------|-------------|---------|
| `DATABASE_URL` | PostgreSQL connection string | Required |
| `API_KEY` | Single-user API key | None |
| `API_KEYS` | Multi-user JSON map `{"user": "key"}` | None |
| `VAULT_ADDR` | Vault server address | None |
| `VAULT_TOKEN` | Vault authentication token | None |
| `VAULT_ROLE` | Kubernetes auth role name | `claude-memory` |
| `MEMORY_ENCRYPTION_KEY` | AES-256 key for non-Vault encryption | None |

## Database Migrations

Migrations run automatically on API server startup. To run manually:

```bash
export DATABASE_URL="postgresql://user:pass@localhost:5432/claude_memory"
alembic upgrade head
```

Three migrations:
1. `001` — Creates `memories` table with generated `tsvector` column and GIN index
2. `002` — Adds `user_id` (multi-user), `is_sensitive`, `vault_path`, `encrypted_content`
3. `003` — Adds `deleted_at` (soft delete) and `updated_at` index for incremental sync

All migrations are idempotent (check column/table existence before altering).

## Development

```bash
git clone https://github.com/ViktorBarzin/claude-memory-mcp.git
cd claude-memory-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e ".[api,dev]"
pytest tests/ -v
ruff check src/ tests/
mypy src/claude_memory/ --strict
```

The MCP server itself (`mcp_server.py` and `sync.py`) uses **stdlib only** — no pip install needed on the client side. The `[api]` extra adds FastAPI, asyncpg, uvicorn, etc. for the server.

## License

Apache License 2.0. See [LICENSE](LICENSE) for details.