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supermem

Persistent AI memory without RAG — local-first, lifecycle-aware retrieval backed by SQLite FTS5, an embedded graph database, and your markdown vault.

PyPI Python 3.11 License: Apache 2.0 MCP Docker CI

An MCP (Model Context Protocol) server for trusted local stdio clients and authenticated loopback HTTP clients. It provides persistent, structured memory backed by SQLite plus an optional graph database. Supported memory retrieval stops at lifecycle-aware tiers 1–3; raw-vault Agent navigation (Tier 4) is unavailable until a source-aware lifecycle broker exists.

Highlights

Capability

What it gives you

Boundary-aware retrieval

Fast FTS5 first, graph expansion second, and optional vector search third. Every supported transport stops at Tier 3.

Local-first vault

Markdown files remain portable and inspectable; SQLite/Kuzu/Chroma indexes can be rebuilt.

Memory lifecycle

Observations carry provenance, confidence, sensitivity, validity, TTL, and active/retracted status metadata.

Retraction workflow

Stale or sensitive observations can be retracted from FTS, vector-backed retrieval, timelines, and derived summaries.

Local productivity insights

Heuristic open-task extraction, follow-up suggestions, and day summaries without an LLM call.

Safer operations

Path-safe backup restore, shared MCP auth/rate guards, PR-safe CI release validation, and a documented security posture.


Related MCP server: tartarus-mcp

Quick Start (Personal, No GPU)

pip install supermem

# Point supermem at a directory of markdown files
export SUPERMEM_VAULT_PATH=~/notes
export SUPERMEM_LLM_PROVIDER=openrouter
export OPENROUTER_API_KEY=your_key_here

# Start the MCP server (add to Claude Desktop's mcp.json)
supermem serve

Add to Claude Desktop mcp.json:

{
  "mcpServers": {
    "supermem": {
      "command": "supermem",
      "args": ["serve"]
    }
  }
}

Quick Start (Local Docker)

# Clone and configure
git clone https://github.com/lamenting-hawthorn/supermem
cp .env.example .env
# Edit .env: set SUPERMEM_VAULT_PATH, SUPERMEM_LLM_PROVIDER, API keys

# MCP server only (stdio, for Claude Desktop)
docker compose up supermem-mcp

# MCP server + HTTP dashboard
docker compose --profile worker up

# Dashboard at http://localhost:37777

Architecture: Boundary-Aware Retrieval

Retrieval proceeds in order and short-circuits when enough results are found. Tiers 1–3 never call an LLM. Every supported transport is capped at Tier 3. Tier 4 raw-vault Agent navigation is deliberately unavailable until a source-aware lifecycle broker can enforce the same retraction and deletion policy as indexed retrieval.

Query
  │
  ├─ Tier 1: SQLite FTS5 full-text search          ~1ms    always available
  │          porter tokenizer, WAL mode
  │
  ├─ Tier 2: Kuzu embedded graph expansion         ~5ms    optional (install kuzu)
  │          BFS traversal via [[wikilink]] edges
  │
  ├─ Tier 3: ChromaDB vector similarity            ~50ms   optional (SUPERMEM_VECTOR=true)
  │          sentence-transformer embeddings
  │
  └─ Tier 4: raw-vault Agent navigation            unavailable
             pending a source-aware lifecycle broker

Short-circuit rule: if tier 1 returns ≥ min_results (default 3), tiers 2–3 are skipped entirely. Candidate IDs are filtered through observation lifecycle status before being returned, so retracted memories are excluded from search, timeline context, and derived summaries.


Memory Lifecycle and Retraction

Each observation is stored with lifecycle/provenance metadata designed for source-grounded memory:

Field group

Examples

Purpose

Source

source_id, source_span, observed_at

Trace a memory back to an import, file, conversation, or time span.

Validity

valid_from, valid_until, confidence, trust_level

Represent changing facts and retrieval confidence.

Governance

sensitivity, status, expires_at

Support privacy labels, TTL cleanup, and active/retracted filtering.

Use retract_observation or POST /observations/{id}/retract to mark stale or sensitive records as retracted. Retraction removes the observation from FTS, filters it from hybrid retrieval, deletes vector chunks when available through the MCP/worker path, removes it from timelines and recent-session context, and invalidates derived session summaries. Retraction reasons are stored in a non-FTS audit table so the value being forgotten is not re-indexed as an active memory.


MCP Tool Reference

Tool

Parameters

Returns

Notes

use_memory_agent

question: str

Formatted answer

Compatibility name; uses lifecycle-aware tiers 1–3 only

supermem_hybrid

query: str, tier_limit: int = 3

JSON with obs_ids, source_tier, latency_ms

Requests above 3 are capped for every transport; Tier 4 is unavailable

get_observations

ids: list[int]

JSON array of observation dicts

Fetch full content for specific IDs

get_timeline

obs_id: int, window: int = 5

JSON array of chronological observations

Context around a specific observation

list_open_tasks

days: int = 14, limit: int = 20

JSON with likely unresolved tasks

Local heuristic open-loop inbox inspired by ambient memory tools

suggest_followups

days: int = 14, limit: int = 10

JSON with next-action suggestions

Turns open tasks into concise follow-up prompts

list_day_summaries

days: int = 7

JSON day summaries

Keywords, highlights, and open-loop counts from recent observations

retract_observation

obs_id: int, reason: str = ""

JSON retraction status

Marks stale or incorrect memories as retracted so retrieval ignores them

Progressive Disclosure Pattern

# 1. Search — cheap, returns IDs only
result = await supermem_hybrid("Alice's project status", tier_limit=2)
# {"obs_ids": [42, 17, 88], "source_tier": 1, "latency_ms": 2.1}

# 2. Fetch — only for IDs you actually need
obs = await get_observations([42, 17])
# [{"id": 42, "content": "...", "tier_used": 1}, ...]

# 3. Timeline — context around interesting observations
ctx = await get_timeline(42, window=3)

# 4. Retract — remove stale/sensitive memory from retrieval
await retract_observation(obs_id=42, reason="superseded by current roadmap")

Local Insight Pattern

# Open-loop inbox for recent memory
tasks = await list_open_tasks(days=14, limit=20)

# Turn open tasks into concise next-action prompts
followups = await suggest_followups(days=14, limit=10)

# Summarize recent days without an LLM call
summaries = await list_day_summaries(days=7)

Environment Variables

Variable

Default

Description

SUPERMEM_LLM_PROVIDER

openrouter

openrouter | ollama | claude | lmstudio

SUPERMEM_LLM_MODEL

provider default

Model string (e.g. openai/gpt-4o-mini, llama3)

SUPERMEM_DB_PATH

~/.supermem/supermem.db

SQLite database path

SUPERMEM_VAULT_PATH

.memory_path file

Markdown vault directory

SUPERMEM_VECTOR

false

Set true to enable ChromaDB tier

SUPERMEM_DEFAULT_TIER_LIMIT

3

Default lifecycle-aware retrieval ceiling; values above 3 are capped

SUPERMEM_API_KEY

(none)

Bearer token required for primary MCP HTTP and protected Worker HTTP endpoints (fail closed if unset)

SUPERMEM_RATE_LIMIT

60

Requests/minute limit per client identity across MCP tools

SUPERMEM_WORKER_PORT

37777

HTTP dashboard port

SUPERMEM_COMPRESS_EVERY

50

Observations written before LLM compression

SUPERMEM_OBS_TTL_DAYS

90

Retention window for regular observations (0 disables TTL expiry)

OPENROUTER_API_KEY

(required for openrouter)

OpenRouter API key

ANTHROPIC_API_KEY

(required for claude)

Anthropic API key

OLLAMA_HOST

http://localhost:11434

Ollama server URL

LMSTUDIO_HOST

http://localhost:1234

LM Studio server URL

Note: Local model inference (vLLM/CUDA) is an optional extra. Install with pip install supermem[local] if you need it. Not included in the default install.


Connector Guide

Import external data into your vault with one command:

# ChatGPT export (Settings → Data controls → Export data → .zip)
supermem connect chatgpt ~/Downloads/chatgpt_export.zip

# Notion workspace export (.zip)
supermem connect notion ~/Downloads/notion_export.zip

# Nuclino workspace export (.zip)
supermem connect nuclino ~/Downloads/nuclino_export.zip

# GitHub repositories (live via API)
supermem connect github owner/repo1,owner/repo2 --token ghp_xxx

# Google Docs (OAuth, opens browser)
supermem connect google_docs "My Doc Name"

All connectors write markdown to your vault, then automatically index the files into SQLite + graph. Private content wrapped in <private>...</private> tags is stripped before indexing.


CLI Reference

supermem serve            # Start MCP server (stdio transport, for Claude Desktop)
supermem serve --worker   # Start MCP server + HTTP dashboard on :37777
supermem chat             # Interactive terminal REPL (no client required)
supermem backup           # Create timestamped .tar.gz (vault + SQLite)
supermem backup --output /path/to/archive.tar.gz
supermem restore <archive.tar.gz>
supermem connect <type> <source> [--token TOKEN] [--max-items N]

HTTP Dashboard (Optional)

Start with supermem serve --worker or docker compose --profile worker up.

Endpoint

Method

Description

/.well-known/oauth-protected-resource

GET

Protected-resource metadata only; no OAuth issuer/JWKS/scope validation

/health

GET

{"status":"ok","db":true,"graph":false,"vector":false}

/sessions

GET

Paginated session list with summaries

/observations

GET

Active observations only; filter by session/type

/search

POST

{"query": "...", "tier_limit": 3} (HTTP cap)

/index/rebuild

POST

Reindex entire vault

/backup

GET

Streams vault + DB as .tar.gz

/stats

GET

{obs_count, entity_count, session_count, db_size_mb}

/open-tasks

GET

Local heuristic open-loop/task extraction

/followups

GET

Follow-up suggestions derived from recent open tasks

/day-summaries

GET

Local day summaries with keywords and highlights

/observations/{id}/retract

POST

Mark an observation retracted so retrieval ignores it

Auth: protected Worker endpoints require Authorization: Bearer <SUPERMEM_API_KEY> and fail closed when the key is unset. The dashboard prompts for this key and keeps it only in the current page's JavaScript memory; it does not put the credential in the URL or browser storage, and a refresh clears it.

The primary MCP HTTP profile is authenticated, loopback-only, and stateless: each request creates no resumable MCP transport session and no MCP session ID is issued. Clients needing persistent protocol sessions should use local stdio.

Keep HTTP on a trusted loopback/private boundary. Static API-key auth does not establish remote-production or multi-tenant support; see SECURITY.md.


Privacy and Security

Wrap sensitive content in <private>...</private> tags. It is stripped before writing to any storage layer (SQLite, Kuzu, ChromaDB). Raw Agent vault content and metadata inspection are unavailable until a source-aware lifecycle broker exists. The restricted executor is not a hostile-code sandbox; remote execution remains unsupported.

# Meeting Notes

Alice discussed the roadmap.
<private>Budget: $2.4M approved for Q3</private>
Next steps: ship v2 by June.

Additional safeguards:

  • Backup restore rejects archive members that would escape the configured vault.

  • Notion/Nuclino ZIP imports bound member count, central-directory size, compression ratio, total extraction, parsed Markdown/CSV size, and attachment size separately.

  • MCP tools share one auth/rate-limit guard and one per-client rate bucket.

  • The Python executor blocks denied imports (including direct platform raw-I/O modules), scrubs inherited environment variables, and wraps common filesystem APIs; it is still a restricted local executor, not a substitute for container/OS isolation for hostile code.

  • Keep current HTTP surfaces off untrusted networks. OAuth identities, scopes, installed-artifact E2E, staging, and remote-production proof remain future work; see SECURITY.md.


CI and Release Checks

Pull requests run lint, formatting, type-checking, tests with coverage, Docker build validation, and package build validation. Docker pushes and PyPI publishing remain gated to version-tag pushes (v*) so PRs validate release artifacts without publishing them.


Running Tests

uv run pytest tests/ -v                          # all tests
uv run pytest tests/unit/ -v                     # unit only (fast, no network)
uv run pytest tests/integration/ -v              # integration (real storage)
uv run pytest tests/ --cov=supermem --cov-report=term-missing  # with coverage

Coverage gate: 60% (CI enforced). Kuzu and Anthropic tests are auto-skipped if packages are not installed.


License

Apache 2.0 — see LICENSE.

A
license - permissive license
-
quality - not tested
A
maintenance

Maintenance

Maintainers
Response time
Release cycle
1Releases (12mo)
Commit activity

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