Memstem

Unified memory and skill infrastructure for AI agents. One canonical knowledge store. Many AI clients. No version-fragility.

A central memory with stems reaching out to other systems, drawing their memories in.

If memstem helps you, please ⭐ the repo — stars are how I gauge whether to keep building this in the open.

What it is

Memstem is a standalone memory service that acts as the single source of truth for memories and skills shared across multiple AI environments. Unlike traditional memory layers that you push to from each AI, Memstem pulls from the filesystem of each connected AI — so it's immune to upgrade churn in any of them.

Connect Claude Code, OpenClaw, Codex, Cursor, Aider, Hermes — Memstem watches each system's session and memory files, ingests new content within seconds, and exposes one unified search API via MCP.

Why

Existing AI memory systems break when their host upgrades. Push-based hooks fail silently across version changes. Each AI has its own memory format, and there's no clean way to share knowledge across them.

Memstem solves this by:

• Pull-based ingestion via inotify / FSEvents filesystem watchers — no hooks, no push APIs to break

• Markdown-canonical storage — files are the truth, the index is rebuildable

• Hybrid search — BM25 (FTS5) + cosine similarity (sqlite-vec) + reciprocal rank fusion

• Multi-AI adapters — pluggable per-system ingestion (Claude Code, OpenClaw, Codex, etc.)

• MCP-native API — every modern AI agent can call it

Architecture (one paragraph)

Markdown files in a structured tree are the canonical store. A SQLite database with FTS5 and sqlite-vec is the rebuildable index. A daemon watches each connected AI's filesystem and ingests deltas. An MCP server exposes search, get, and skill retrieval to clients. A hygiene worker (Phase 2) dedupes, decays, and writes distillations from session transcripts.

See ARCHITECTURE.md for the full design and ROADMAP.md for the phase plan.

Status

v0.9.x — derived records + post-cleanup operator workflow. Live on the maintainer's box; ingesting from multi-agent OpenClaw + Claude Code in real time. 0.9.0 added session distillation + project records on top of the 0.8.1 retrieval pipeline; the unreleased post-cleanup branch adds a single-command verification report, an explicit per-type ranking policy, and a fix that keeps skill review tickets out of vault scans. Shipping:

• Hybrid search (FTS5 BM25 + sqlite-vec cosine, merged with RRF) over a markdown-canonical vault. Index is rebuildable from the files.

• Five MCP tools (memstem_search, _get, _list_skills, _get_skill, _upsert) plus a co-hosted local HTTP API on 127.0.0.1:7821 for first-party clients (CLI tools, future editor extensions).

• Four pluggable embedders — Ollama (local default), OpenAI, Gemini, Voyage — selectable via _meta/config.yaml. Always-on embed queue with retry/backoff and idle-timeout self-exit.

• Derived records (new in 0.9.0) — memstem hygiene distill-sessions produces type: distillation companion records for meaningful sessions, and memstem hygiene project-records aggregates per-project-tag sessions into type: project rollups. Both are CLI-driven, idempotent, opt-in (NoOp default; pluggable OpenAI / Ollama summarizer). Direct fix for "the project where we did X" queries that today fail to surface project work that exists in the vault. See docs/distillation-verification.md.

• Post-cleanup operator workflow (unreleased) — memstem hygiene verify is a single read-only command that summarizes vault state after a cleanup + backfill sweep: per-type counts, distillation coverage, undistilled-eligible sessions remaining, dedup / noise findings cleanup-retro would still flag, open skill review tickets, and parser/validation skips. Optional --json-out emits a machine-readable payload for CI / monitoring. Replaces ad-hoc SQLite inspection. See the post-cleanup playbook in docs/operations.md.

• Explicit ranking policy (unreleased) — SearchConfig.type_bias multiplies each result's score by a small per-type weight so default search clearly prefers curated/derived records (distillation 1.10, memory/skill/project 1.05) over raw conversational sessions (0.85). Bounds are intentionally tight ([0.85, 1.10]) — the bias breaks ties without overriding relevance. Tunable per-vault in _meta/config.yaml; an empty mapping recovers pre-0.10 behaviour.

• Quality pipeline — write-time noise filter, exact-body hash dedup (Layer 1), TTL tagging for transient kinds, boot-echo hash filter — keeps the vault from being polluted by AI-session firehose.

• memstem auth for persistent embedder API keys (~/.config/memstem/secrets.yaml, mode 0600), so cron, PM2, systemd, and headless servers don't need per-shell exports.

• Operational tooling — memstem init, doctor, connect-clients (idempotent wiring into ~/.claude.json and each OpenClaw agent's openclaw.json), migrate (FlipClaw → Memstem one-shot), a one-line install.sh, and a 15-second e2e smoke test (scripts/e2e-smoke.sh).

Cross-platform CI runs Linux (gating) plus macOS and Windows (experimental, continue-on-error: true — sqlite-vec needs enable_load_extension, which actions/setup-python's macOS build doesn't ship; native Windows is WSL2-only by design for v0.x). 1150 tests passing. See CHANGELOG.md for the release-by-release history and ROADMAP.md for what's next.

Quickstart

The full one-liner. Installs everything (memstem, Ollama, embedding model), scaffolds the vault, imports your existing Claude Code + OpenClaw memory, wires Memstem into Claude Code, and starts the daemon under PM2:

curl -fsSL https://memstem.com/install.sh | bash -s -- \
  --yes --connect-clients --migrate --migrate-no-embed --start-daemon

The default uses Ollama (local, no API key, no network call). To install with a cloud embedder in one go:

# OpenAI (text-embedding-3-large at 3072 dimensions)
curl -fsSL https://memstem.com/install.sh | bash -s -- \
  --yes --embedder openai --openai-key "$OPENAI_API_KEY" \
  --connect-clients --migrate --start-daemon

# Or Voyage / Gemini — same shape:
#   --embedder voyage --voyage-key "$VOYAGE_API_KEY"
#   --embedder gemini --gemini-key "$GEMINI_API_KEY"

Picking --embedder openai|gemini|voyage implies --no-ollama (cloud doesn't need a local daemon). The key gets stored via memstem auth set <provider>, so cron, PM2, and fresh shells all pick it up afterward without per-shell exports. Keys can also come from MEMSTEM_OPENAI_KEY / MEMSTEM_GEMINI_KEY / MEMSTEM_VOYAGE_KEY env vars, falling back to the standard OPENAI_API_KEY / GEMINI_API_KEY / VOYAGE_API_KEY names when the MEMSTEM_* variable is unset (helpful for unattended installs that don't want the key on the command line).

The --migrate-no-embed flag is the practical default on a CPU-only Ollama box: it imports records to vault + FTS5 in minutes instead of hours. After it returns:

memstem search "what did we decide about pricing"   # FTS5 hits work immediately
pm2 logs memstem --lines 20                          # watch ingestion + embed worker
memstem doctor                                       # `Embed queue: N pending` shows backfill progress

Embedding is always queued rather than inline (see ADR 0009): the migrate finishes in seconds and the daemon's embed worker drains the queue at its own pace. On CPU-only Ollama that means semantic search becomes "good" over an hour or two; on the API providers above it's done in seconds.

Each flag is opt-in so you can dial back the scope:

Manual install if you'd rather not pipe a script:

pipx install memstem                         # or: pip install memstem
ollama pull nomic-embed-text                 # 768-dim local embedder
memstem init ~/memstem-vault                 # interactive wizard
memstem migrate --apply                      # one-shot history import
memstem connect-clients                      # patch settings + CLAUDE.md
memstem doctor                               # verify
memstem daemon                               # ingest + watch

memstem init runs an interactive setup wizard that finds OpenClaw agent workspaces (any directory under $HOME with an openclaw.json), shared rules files (HARD-RULES.md), and Claude Code's session root, then writes ~/memstem-vault/_meta/config.yaml. Pass -y to auto-include every candidate with content.

macOS install

Use Homebrew or pyenv Python — not the system Python. Memstem needs sqlite-vec, which loads as a SQLite extension at runtime. macOS's system Python (/usr/bin/python3) ships with a SQLite that has extension loading disabled at compile time, so it can't load sqlite-vec. The install.sh script detects this up front and bails with a clear error rather than letting it crash later.

The fix is one of:

# Recommended — Homebrew
brew install python@3.12
hash -r   # let your shell pick up the Homebrew python3
curl -fsSL https://memstem.com/install.sh | bash    # re-run

# Or — pyenv
pyenv install 3.12.5
pyenv global 3.12.5
curl -fsSL https://memstem.com/install.sh | bash    # re-run

Both build SQLite with extension support enabled. Once you're on a Homebrew or pyenv Python, every other step (Quickstart, manual install, memstem doctor) works the same as on Linux.

Note: macOS CI is currently continue-on-error: true — the GitHub Actions setup-python build hits the same system-Python issue. We track full macOS CI green as a follow-up; the user-facing install path on a real Mac is reliable today via Homebrew or pyenv.

memstem connect-clients is the cutover wiring step. It (a) adds an mcpServers.memstem entry to ~/.claude.json so Claude Code sees Memstem MCP, (b) registers mcp.servers.memstem in each configured OpenClaw agent's openclaw.json so OpenClaw agents see it too, (c) strips any stale entry from the legacy ~/.claude/settings.json, and (d) inserts a versioned <!-- memstem:directive v1 --> block into each CLAUDE.md so agents know to query Memstem for retrieval-style questions. Default mode writes .bak next to each edited file; --dry-run previews diffs without writing. Re-running is safe.

Querying from an agent

Once memstem connect-clients has run, an MCP-aware client (Claude Code, etc.) sees five tools:

See docs/mcp-api.md for the full schema.

Every search runs in parallel down two paths and is merged with Reciprocal Rank Fusion, so exact-keyword hits and semantic neighbours both surface in one ranked list:

Configuration

~/memstem-vault/_meta/config.yaml controls embedding, search, and adapters. The wizard writes a sensible default; common edits:

Embedding provider — pick one

Memstem ships four providers. Default is local Ollama; switch by editing the embedding: block (then memstem reindex so existing vectors get redone against the new provider).

# Default — local, no API key
embedding:
  provider: ollama
  model: nomic-embed-text
  dimensions: 768

# Google Gemini — Matryoshka shortening lets you keep any dim you want
# (768 = same as Ollama, no reindex when switching from Ollama default).
embedding:
  provider: gemini
  model: gemini-embedding-2-preview     # default; ~20% recall over -001, 8k context
  api_key_env: GOOGLE_API_KEY
  dimensions: 768            # 768 / 1536 / 3072 — Matryoshka truncates the native 3072d

Pin model: gemini-embedding-001 if you'd rather have the production-stable predecessor (the "preview" label means Google may change behavior; new-RAG quality vs API stability is your call).

# OpenAI — or any OpenAI-compatible endpoint (Together, Mistral, Groq, vLLM, LM Studio)
embedding:
  provider: openai
  model: text-embedding-3-small
  api_key_env: OPENAI_API_KEY
  dimensions: 1536
  # base_url: https://api.together.xyz/v1   # for OpenAI-compatible providers

# Voyage — Anthropic's recommended embedding partner; tops retrieval benchmarks
embedding:
  provider: voyage
  model: voyage-3
  api_key_env: VOYAGE_API_KEY
  dimensions: 1024

API keys are read from environment variables named in api_key_env — they never land in the vault. embedding.workers (default 2) and embedding.batch_size (default 8) tune the queue throughput; CPU Ollama is happiest at 1 worker, API providers tolerate 4+.

Adapters

embedding:
  provider: ollama
  model: nomic-embed-text
  base_url: http://localhost:11434
  dimensions: 768

adapters:
  openclaw:
    agent_workspaces:
      - { path: ~/ari, tag: ari }
      - { path: ~/blake, tag: blake }
    shared_files:
      - ~/ari/HARD-RULES.md
  claude_code:
    project_roots:
      - ~/.claude/projects
    extra_files:
      - ~/.claude/CLAUDE.md

Run memstem doctor after edits to verify every configured target exists and the embedder is reachable.

Distillation + project records (new in 0.9.0)

Two new hygiene commands turn raw session transcripts and per-project session sets into retrieval-shaped derived records. Both are CLI-driven, idempotent, and opt-in — NoOp is the install-time default, you opt into a real summarizer explicitly.

# One-shot backfill at cutover (or any time you want to refresh):
memstem auth set openai sk-...
memstem hygiene distill-sessions --backfill --provider openai --apply
memstem hygiene project-records --provider openai --apply

# Routine refresh (post-backfill):
memstem hygiene distill-sessions --provider openai --apply
memstem hygiene project-records --provider openai --apply

What you get:

• Session distillations at vault/distillations/<source>/<session_id>.md — one paragraph + structured Key entities / Deliverables / Decisions / Status sections per session. Provenance always points back to the source transcript.

• Project records at vault/memories/projects/<slug>.md — one per Claude Code project tag with ≥2 sessions. Canonical project name extracted from the work itself, accumulated decisions, link map.

Both can also run with Ollama (--provider ollama, default model qwen2.5:7b) for local-only setups. See docs/distillation-verification.md for the full operator workflow (dry-run, quality spot-check, eval diff, manual override) and docs/recall-models.md for the model recommendations + cost expectations.

Verifying it works

Two complementary commands cover "is the install healthy?" and "is the vault state right after a cleanup + backfill sweep?".

memstem doctor is the install-level check — Python, vault, index, embedder, and the configured adapter targets all reachable:

$ memstem doctor
Memstem doctor (vault=/home/ubuntu/memstem-vault):

  ✓ Python 3.11
  ✓ memstem 0.9.0
  ✓ Vault: /home/ubuntu/memstem-vault
  ✓ Config: /home/ubuntu/memstem-vault/_meta/config.yaml
  ✓ Index opens cleanly
  ✓ Ollama at http://localhost:11434 (nomic-embed-text)  (768 dims)
  ✓ OpenClaw workspace: /home/ubuntu/ari (tag=ari)
  ✓ Claude Code root: /home/ubuntu/.claude/projects

All checks passed.

memstem hygiene verify is the operator-level check — vault state after cleanup-retro + distill-sessions --backfill. Read-only, safe on production. Reports total memories, per-type breakdown, distillation coverage, dedup / noise findings still detectable, open skill review tickets, and any parser/validation skips encountered during the walk. --json-out writes the same payload as JSON for CI / monitoring scrapers:

$ memstem hygiene verify
============================================================
MEMSTEM VERIFY
============================================================
Vault:                    /home/ubuntu/memstem-vault
Total memories:           1722

By type:
  type             total  deprecated  valid_to
  --------------------------------------------------
  session            665           1         1
  memory             546         229         2
  distillation       224           0         0
  skill              193           0         0
  daily               80           0         0
  project             14           0         0

Cleanup state:
  Deprecated records:                   230
  Records with valid_to:                3
  Active dedup collision groups:        6
  Active dedup → would deprecate:       11
  Active dedup skill groups (review):   6
  Noise drops still detectable:         0
  Noise transients still detectable:    1
  Skill review tickets open:            6

Derived records:
  Sessions covered by distillation:     224
  Undistilled eligible sessions left:   1

Parser/validation skips during scan: 0

The full operator playbook (run cleanup, run backfill, run verify, interpret findings, resolve skill review tickets, tune ranking) is in docs/operations.md — Post-cleanup operator playbook.

Platform support

Documentation

• Architecture — system design and rationale

• Roadmap — release plan (Phases 1–5)

• Operations — production smoke test, post-cleanup operator playbook, ranking-policy reference

• Frontmatter spec — the markdown schema

• MCP API — tool definitions

• Decisions — Architecture Decision Records

• Distillation + project records — operator playbook — how to run the new derived-record commands and verify quality

• Recall-quality model recommendations — picking the right LLM for rerank / HyDE / dedup / summarization with cost expectations

• Recall eval results — measured before/after data on the recall-quality features

• Phase 1 plan — Phase 1 work breakdown

• Phase 2+ recall plan — recall-quality work blocks (W0–W10)

Why star this repo

Memstem is a solo, open-source project shipped under MIT. There's no telemetry, no analytics, no auth-walled "free tier" — so I have no idea who's using it unless you tell me. Stars are the only honest signal I have for whether to keep investing time in this. If memstem makes your AI workflow better, a star takes two seconds and directly shapes what gets built next.

License

MIT — see LICENSE.

Acknowledgments

Memstem builds on ideas from:

• basic-memory — markdown + wikilinks pattern

• doobidoo/mcp-memory-service — sqlite-vec hybrid retrieval reference

• Karpathy's LLM Wiki — index/log pattern

• Graphiti — bi-temporal facts

• Anthropic memory tool — abstract memory interface