remindb

Agentic memory in a single SQLite file.
Stop letting your agent re-read the same notes every session.

Why I built this

Coding agents already have memory. CLAUDE.md, AGENTS.md, your notes folder, that growing pile of project READMEs. Stuff persists just fine.

The problem is how the agent consumes it. Every session starts by re-reading the whole pile from scratch — every Read, every Grep, scanning raw prose the agent has already processed dozens of times. Big context windows don't fix it. A 1M-token window is still paid per call, and still can't tell yesterday's stale note from today's relevant one.

Raw markdown is the wrong shape for memory. Not because it can't hold the words — it can — but because it forces the agent to pay full freight on every read.

remindb is a single SQLite file your agent treats as long-term memory. It parses your notes (Markdown, HTML, JSON, YAML, TOON) into a structured tree, hashes every node, encodes repetitive structures compactly when it saves tokens, and surfaces the whole thing through a tight MCP tool suite.

What you get

Each point is a summary — the full reasoning, with the tradeoffs, lives in docs/.

A tree the agent can index, not skim. One MemoryTree call returns a typed, labeled, token-counted hierarchy — ls -la for memory instead of ls-ing a folder and reading every file to orient. → The node tree

Hot vs. cold, like a real cache. Every node has a temperature that rises when it's read and decays over time. Hot nodes rank higher in search; cold ones stop crowding the top without ever being deleted. → Temperature

Summarization that happens when it should. When a node crosses the cold threshold the server nudges the agent to compact it in place — same anchor, same history, fewer tokens. No cron, no worker; it's driven by how the memory actually gets used. → Temperature

Git-style versioning, free. Every compile or write lands a snapshot with per-node diffs. A returning agent resyncs with MemoryDelta — a tiny payload instead of a whole-file re-read. → Versioning

TOON encoding where it pays off. Arrays of uniform objects store ~40% smaller in TOON than YAML or JSON. The parser tries both per node and keeps the win only when it's real; irregular prose stays plain text. The same ≥15% rule converts MathML in HTML to compact LaTeX. → TOON encoding · MathML → LaTeX

FTS5 search, not grep. Ranked anchors in milliseconds over a porter-tokenized index — no file rescans, no regex timeouts — trimmed to exactly the token budget you pass. → Search

Knowledge graph from lateral relations. Author [[Architecture; w=2.5]] in any payload and the compiler resolves a directed weighted edge; MemoryRelated traverses it up to 5 hops, ranked by path weight. Forward references self-heal. → Knowledge graph

Portable by design. The whole memory is one .db file. Copy it to another machine, hand it to another agent, commit it into a repo, sync it across devices. No server, no daemon, no external state — any MCP-capable agent can point serve at the same file and share the same knowledge.

Install

One-line install

Linux / macOS:

curl -fsSL https://raw.githubusercontent.com/radimsem/remindb/main/install.sh | bash

By default the binary lands at ~/.local/bin/remindb. Pick a different prefix:

curl -fsSL https://raw.githubusercontent.com/radimsem/remindb/main/install.sh | bash -s -- --prefix /usr/local

Windows (PowerShell 5.1+):

iwr -useb https://raw.githubusercontent.com/radimsem/remindb/main/install.ps1 | iex

Lands at %LOCALAPPDATA%\Programs\remindb\bin\remindb.exe. Override with -Prefix:

./install.ps1 -Prefix C:\tools\remindb

From source (Go 1.26+)

git clone https://github.com/radimsem/remindb.git
cd remindb
go build -o ~/.local/bin/remindb ./cmd/remindb

Verify:

remindb --version

Updating

Two moving parts: the binary (release tags) and the agent-side skills (remind, memoize — the markdown your agent loads to learn how to call the MCP tools). They iterate on different cadences, so they update independently.

Binary

remindb update

Reads the installed version, compares it against the latest GitHub release, and re-runs the install script only when they differ. dev-builds (from go build / go install) always proceed — there's no published version to compare against. Pass --force to reinstall regardless:

remindb update --force

Skills

The public skills live under skills/remind/ and skills/memoize/. They're refreshed by vercel-labs/skills.

First-time install (or after adding a new agent):

npx skills@latest add radimsem/remindb/skills -a claude-code
# -a codex | gemini-cli | opencode | openclaw | ...

Refresh later:

npx skills@latest update

Documentation

The README is the trailer. The manual is in docs/ — each page opens with the problem it solves, in plain language.

MCP tools

A Memory* tool suite, registered once, surfaced to any MCP-capable agent (Claude Code, Codex, Gemini CLI, OpenCode, OpenClaw, …). The read path is documented in the remind skill, the write path in memoize.

Agent integrations

Five plugin folders ship with the repo, one per supported coding agent. Each has a manifest matching that agent's spec, an MCP stanza, and a README with install commands, env-var conventions, and a worked example that compiles the agent's own memory folder into remindb.

[!TIP] Pair the plugin with the two companion skills — remind (read path) and memoize (write path). They teach the agent the MCP tool suite so you don't re-explain it each session. Per-agent install instructions live in skills/README.md.

For any other MCP-capable agent, add this to its MCP config by hand. Stdio (the default — one server per client process):

{
  "mcpServers": {
    "remindb": {
      "type": "stdio",
      "command": "remindb",
      "args": ["serve"],
      "env": {
        "REMINDB_DB": "/absolute/path/to/memory.db",
        "REMINDB_SOURCE": "/absolute/path/to/notes"
      }
    }
  }
}

Every serve flag has a REMINDB_* environment equivalent — REMINDB_DB, REMINDB_SOURCE, REMINDB_RESCAN_INTERVAL, REMINDB_TRANSPORT, REMINDB_LISTEN — so pass them via args, the env block above, or a committed .remindb/config.json. Precedence is explicit flag → .remindb/config.json → env → built-in default; see Configuration.

Or HTTP, when you want one long-running server that multiple agent sessions (a local IDE, a CI worker, a hosted session) share. Start remindb serve --transport http --db ... --source ... once, then point each client at the listen URL:

{
  "mcpServers": {
    "remindb": {
      "type": "http",
      "url": "http://127.0.0.1:7474"
    }
  }
}

On startup the agent sees the full Memory* tool suite alongside its usual toolbox. A reasonable first prompt:

/remind Call MemoryTree to orient. Then call MemorySearch for "<topic>" with budget 1000
and MemoryFetch on the top hit. Explain what you learned and which files it came from.

Benchmarks

Token counts are measured against the naive baseline an agent falls back to without a memory layer: list the directory, read every matching file, grep through it. Numbers come from ./scripts/bench-agents.sh over the five plugin fixtures in testdata/, plus a one-off compile of a real Obsidian vault (~450 markdown files across AI concepts, market briefs, security notes, and MOCs — ~3.3M naive tokens end-to-end).

The scenario suite (tree · 3 searches · fetch · delta) rolls up into three workflow categories:

• context window — a single MemoryTree orientation call.
• context gathering — 3 × MemorySearch + MemoryFetch + MemoryDelta, token-weighted.
• total session — sum of both.

[!NOTE] Corpus size moves the numbers in remindb's favour. The plugin fixtures are ~3k–20k tokens each; the vault is ~3.3M. As the corpus grows, the naive baseline scales linearly (more files to list, more bytes to grep, more prose to re-read), while remindb's answers stay bounded by the token budget you pass. That's why the vault's context-gathering row hits 99.8% — every search still returns ~1k tokens, but the baseline is now ~100× larger.

The scenario list is also intentionally short. A real 30-minute agent session does dozens of orient/search/fetch/write/re-orient cycles, and the same search often fires three or four times as the agent loops on a problem. Each of those calls compounds toward 90%+ full-session savings on realistic corpora.

_{The obsidian vault row is a real vault: ~450 markdown files, ~3.3M naive tokens.}

Reproduce the table yourself:

./scripts/bench-agents.sh

Contributing

This is a project I maintain between classes — patches, bug reports, and ideas are genuinely welcome, and an extra pair of eyes goes a long way. The full guide lives in CONTRIBUTING.md: branch naming, the pre-PR checklist, the doc-update map, and how the AI-assisted workflow is wired up. If you want to start small, the "First-time contributors" section there has good entry points.

License

MIT — see LICENSE.

Support

I'm a college student building agentic AI tooling in the evenings and weekends between classes. remindb is free, MIT-licensed, and will stay that way — no telemetry.

If this saved you tokens (or saved you from reading the same 100 files for the hundredth time), even a small tip helps a lot.

Or send BTC to bc1qwyxsx7sledl4pru8y5ykd54fevsklytrv95ual.

Thanks for reading this far. If you end up using remindb in anger, I'd love to hear what you built — open an issue with a short story, or drop a star. Both matter more than you'd think.