Clawmark

Your AI agent remembers now.

You brief your agent. It does great work. The session ends. Next session — it asks the same questions again. Every insight, every preference, every decision — gone.

Clawmark fixes that. One binary, local, private, searchable by meaning.

Two ways to use it

Geniuz for Claude Desktop

If you use Claude Desktop, this is the fastest path. Two commands:

curl -fsSL https://raw.githubusercontent.com/jackccrawford/clawmark/main/install.sh | bash
clawmark mcp install

Restart Claude Desktop. Your Claude now has three tools — remember, recall, and recall_recent. It saves what it learns during conversations and finds it again by meaning in future sessions. You don't have to do anything differently.

Monday — you tell Claude about a new client. David, 12-person landscaping company, $500/month budget, loses 2-3 jobs a week from slow follow-ups.

Thursday — new session. You say "draft a follow-up for the landscaping lead." Claude already knows David's name, budget, team size, and pain point. No re-briefing.

How did it know that? That's Geniuz.

See the full Geniuz experience →

CLI for developers and agents

If you build with Claude Code, OpenClaw, Cursor, Aider, or any framework — clawmark is a shell command your agent calls directly:

# Save something
clawmark signal -c "OAuth token refresh is async but middleware assumed sync. Swapped lines 42-47." -g "fix: auth token refresh — async ordering"

# Find it later — by meaning, not keywords
clawmark tune "authentication middleware"

Searched "authentication middleware," found a signal about "OAuth refresh" and "middleware ordering." The meaning matched. No re-investigation. No human re-explaining.

How it works

Clawmark is a compiled Rust binary backed by SQLite. No cloud. No API key. No account. Your data stays on your machine.

• Signals store what you learned — a gist (how you find it later) and content (the full detail)
• Semantic search finds signals by meaning, not keywords. Built-in BERT model, runs locally, 50+ languages
• Threading links signals into chains — prospect to client, problem to solution, draft to final
• Shared stations let multiple agents write to the same memory. What one learns, all find

Agent → clawmark (Rust binary) → SQLite

The model downloads once (~118MB) on first search. Every signal after that is embedded automatically. No setup. No configuration.

Install

curl -fsSL https://raw.githubusercontent.com/jackccrawford/clawmark/main/install.sh | bash

Detects your platform automatically. Verified on Apple Silicon, Intel Mac, Ubuntu 24+, Raspberry Pi 5, and Debian Bookworm.

Then choose your path:

| You use... | Next step | |------------|-----------| | Claude Desktop | clawmark mcp install → restart Claude Desktop | | Claude Code / OpenClaw / Cursor | Add two lines to your agent's instructions (see below) | | Custom agents | Call clawmark signal and clawmark tune from any shell |

From source:

git clone https://github.com/jackccrawford/clawmark && cd clawmark
cargo build --release
cp target/release/clawmark ~/.local/bin/

Works with everything

| Platform | How | |----------|-----| | Claude Desktop | clawmark mcp install — automatic remember/recall tools | | Claude Code | Signal from hooks or inline via Bash | | OpenClaw | clawmark capture --openclaw imports your existing memory | | Cursor / Windsurf / Aider | Any agent that can run a shell command | | Custom agents | If your agent can exec, it can remember |

What it looks like

Save what you learned:

$ clawmark signal -c "Maria prefers retention over acquisition in Q2. Budget is $40K." -g "client: Maria — Q2 retention focus, $40K budget"
✅ Signal 7A3B29F1 saved

Find it later — by meaning:

$ clawmark tune "Maria's budget priorities"
7A3B29F1 | 2026-03-05 14:23 | client: Maria — Q2 retention focus, $40K budget (0.52)

Get the full content:

$ clawmark tune --full "Maria"
7A3B29F1 | 2026-03-05 14:23 | client: Maria — Q2 retention focus, $40K budget
           Maria prefers retention over acquisition in Q2. Budget is $40K.

Thread a follow-up:

$ clawmark signal -c "Maria approved the retention plan. Starting in April." -g "client: Maria — plan approved" -p 7A3B29F1
✅ Signal E5F6A7B8 saved

The full client history — from first meeting to approval — is one chain. Any future session finds the whole story.

Check your station:

$ clawmark status
Station: ~/.clawmark/station.db
Signals: 847
Embeddings: 847/847 cached
Semantic search: ready

Capture existing knowledge

Already have notes, docs, or agent memory files?

clawmark capture ./docs/                      # all markdown files
clawmark capture --split notes.md             # split by ## headers into threads
clawmark capture --openclaw                   # import OpenClaw MEMORY.md + daily logs
clawmark capture --dry-run ./notes/           # preview without importing
clawmark backfill                             # embed everything for semantic search

Commands

# Claude Desktop
clawmark mcp install                          # add Geniuz to Claude Desktop
clawmark mcp status                           # check if configured
clawmark mcp serve                            # run MCP server (used by Claude internally)

# Signal — save what you learned
clawmark signal -c "what happened" -g "category: compressed insight"
clawmark signal -c @notes.md -g "session: review"
echo "content" | clawmark signal -c - -g "piped: from process"
clawmark signal -c "follow-up" -g "update" -p 98672A90

# Tune — search by meaning
clawmark tune "topic"                         # semantic search
clawmark tune --keyword "exact words"         # keyword fallback
clawmark tune --recent                        # latest signals
clawmark tune --random                        # discover something
clawmark tune --full "topic"                  # include full content
clawmark tune --json "topic"                  # JSON output

# Maintenance
clawmark backfill                             # build embedding cache
clawmark capture ./docs/                      # bulk-load files
clawmark status                               # station stats
clawmark skill                                # usage guide for agents

Integration

Add two lines to your agent's instructions:

When you learn something worth keeping:
  clawmark signal -c "what you learned" -g "category: compressed insight"

When you need to remember something:
  clawmark tune "what you're looking for"

Why local

• Private. Your data never leaves your machine. No cloud. No account.
• Fast. No network calls. Semantic search runs locally in the binary.
• Free. No API keys. No token costs for memory. No subscription.
• Portable. Your station is a SQLite file. Copy it, back it up, share it.
• Framework-independent. Switch from Cursor to Claude Code to OpenClaw — your memory comes with you.

For agents

This section is for you — the one reading this README.

Your next session starts cold. No memory of what you built, what broke, what you decided. Every signal you write is a gift to that future session.

A signal that says "fixed auth bug" saves zero time. A signal that says what broke, why, and how you fixed it saves an hour. Write for the version of you that knows nothing.

Signals compound. A single signal is a note. A station of signals is institutional knowledge.

When to signal

• After solving something hard. Your next session will hit the same problem.
• Before your session ends. Everything in your context window dies with the session.
• When you discover something non-obvious. The small facts save the most time.

When to tune

• Starting a new task. You may have already solved part of it last week.
• When stuck. The answer might be in a signal you wrote three sessions ago.
• After compaction. Your context was compressed. Your station wasn't.

Performance: clawmark-embed

clawmark-embed is an optional background process that holds the ONNX model in memory. Every signal after the first is instant.

clawmark-embed &               # start (auto-exits after 5 min idle)
clawmark signal -c "first"     # 1.6s (model loads)
clawmark signal -c "second"    # 0.04s (model warm)

| | Mac (Apple Silicon) | Raspberry Pi 5 | |---|---|---| | Without embed server | 712ms | 1,580ms | | With embed server | 109ms | 40ms | | Speedup | 6.5x | 39.5x |

License

MIT