Deep memory for agents over large datasets.
Index any table, export or log once; your agent gets the few records that answer the question.
Leviathan is a single binary that turns records (JSONL, JSON, CSV/TSV,
SQLite, or any database CLI's output) into a ranked full-text index. Agents
ask in plain words and get short, cited result cards: ~450 tokens per answer
at any dataset size, instead of grepping and reading raw history.
| At 1M records (678 MB) | Leviathan | best grep strategy |
|---|---:|---:|
| Median tokens per question | **436** | 107,122 (245×) |
| Relevant record returned | **99.0%** top 5 · 98.5% rank 1 | 96.0% within a 30K-char output |
| Worst case (1,200 questions) | **602 tokens** | 9.7M tokens |
| Median latency | **33 ms** | 92 ms |
Measured on a synthetic maintenance log (one example dataset; nothing in
Leviathan is specific to it). Methodology, all six scales and caveats:
[docs/BENCHMARKS.md](docs/BENCHMARKS.md).
## Quickstart
```bash
cargo install leviathan-index # or: cargo install --git https://github.com/elstongun/leviathan
cd examples/tickets && leviathan index
leviathan search -g acme "sso login loop after password reset"
```
```text
leviathan search · customer C-ACME "Acme Corp" (7 tickets) · query "sso login loop after password reset" · shown 3 of 3 · 24 tickets indexed
[1] T-1001 · 2024-01-08 09:12 · rel 16.9
Login loops back to sign-in page after password reset
status: closed · priority: high
resolution: Cleared stale session cookies on password reset; shipped in 4.2.1. Workaround: clear site data.
match: Users who reset their password get redirected to the sign-in page again in an endless loop.
...
```
- `-g` accepts a key, name or partial name. Ambiguous or unknown groups list candidates and exit 3, never guess.
- No match in the group falls back to other groups, labeled `OTHER CUSTOMER`.
- Combine `--where field=value`, `--since`/`--until`, `"phrases"` and `-exclusions`.
Prebuilt binaries are on [Releases](https://github.com/elstongun/leviathan/releases). No runtime dependencies; the index is one SQLite file.
## Map your data
Fields are paths (`a.b`, `items[].name`); only `id` is required.
| Field | Enables |
|---|---|
| `id` | `get`, `upsert`, `delete`, citations |
| `title` / `text` | Card headline (weighted 2×) / searched text (default: all strings) |
| `group` / `group_name` | `-g` scoped search, name resolution, labeled fallback |
| `date` | `--since`, `--until`, `recent` |
| `filters` / `display` | `--where` facets / fields shown on cards |
| `empty_values` / `rank.boost` | Placeholders treated as missing / favor complete records |
```bash
leviathan init ./export # infer a commented leviathan.toml
leviathan index tickets.csv --id "Ticket ID" --group customer_id --date created_at # or flags
leviathan index -c tickets.toml # or a config your agent wrote from the schema
leviathan describe # fields, groups, filter values, example calls
```
Any database works through its own CLI; Leviathan never holds credentials:
```bash
psql "$DATABASE_URL" -At -c "SELECT row_to_json(t) FROM tickets t" | leviathan index - -c tickets.toml
leviathan index app.db --sql "SELECT * FROM tickets" -c tickets.toml
duckdb -json -c "SELECT * FROM 'events/*.parquet'" | leviathan index - -c events.toml
```
Builds are atomic and skipped when nothing changed; `upsert` and `delete` keep an index fresh. Full reference: [docs/CONFIG.md](docs/CONFIG.md).
## Plug it into your agent
**CLI + skill (recommended):** any agent with a shell can call it. Copy
[`skills/leviathan`](skills/leviathan/SKILL.md) into `~/.claude/skills/` or
paste it into `AGENTS.md` / `.cursor/rules`. Costs 0 tokens until used.
[`skills/leviathan-memory`](skills/leviathan-memory/SKILL.md) does the same
for memory.
**MCP (optional):** `leviathan mcp` serves four read-only stdio tools
(`search`, `resolve_group`, `get`, `describe`) whose descriptions include a
summary of your dataset (~640 tokens per session). `leviathan wrap `
prints the config for 24 agents (Claude Code, Codex, Cursor, VS Code /
Copilot, Gemini CLI, Windsurf, Cline, Zed, JetBrains and more) and
`--apply` writes it; `leviathan wrap` lists them all
([docs/AGENTS.md](docs/AGENTS.md)).
**Remote (optional):** `leviathan serve --http` serves the same tools as
Streamable HTTP MCP, REST and OpenAPI, with a bearer token or OAuth 2.1, so
claude.ai, ChatGPT, the Anthropic and OpenAI APIs and agent frameworks can
connect ([docs/REMOTE.md](docs/REMOTE.md)).
## Memory
Optional read/write memory for agents, in its own SQLite file. The agent
`remember`s one claim at a time; anything that can change has a `key`, so the
next write replaces the old value instead of contradicting it. Sessions start
with a short briefing, and `recall` returns only what fits a token budget.
```bash
leviathan wrap claude --memory --hooks --rules --apply # tools, session-start briefing, always-on rule
leviathan memory remember "Prefers pnpm over npm" --kind preference -s josh -k package_manager
leviathan memory recall package manager
leviathan memory import ~/.claude/projects//memory/MEMORY.md # bring an existing markdown memory
```
| After 300 sessions | Leviathan memory | MEMORY.md, whole file | MEMORY.md, first 200 lines |
|---|---:|---:|---:|
| Tokens at session start | **736** | 33,313 | 2,588 |
| Answer in context | **100%** | 100% | 18% |
| Stale value in context | **0%** | 73.8% | 36.4% |
Writes are explicit (no model runs inside Leviathan), secrets are refused,
and history stays queryable (`as_of`, `history`). Guide, formats and
benchmark: [docs/MEMORY.md](docs/MEMORY.md).
## Commands
| Command | Does |
|---|---|
| `init` / `index` / `upsert` / `delete` | Propose a mapping / build / update / remove records |
| `search [-g G] [words]` | Ranked records (`--scope`, `--where`, `--since`, `--until`, `-n`, `--offset`) |
| `recent [-g G]` · `resolve ` · `get ` · `describe` | Newest · group candidates · full records · index summary |
| `memory remember` / `recall` / `forget` / `briefing` | Write / ranked read / retire / session-start briefing (`--memory=PATH`) |
| `memory list` / `history` / `stats` / `import` / `export` / `prune` | Browse / slot versions / counts / markdown or JSONL in / backup / clean up |
| `mcp [--memory] [--remote URL]` · `serve --http ADDR` | stdio MCP server or bridge · HTTP MCP + REST server |
| `wrap [agent]` | Agent config (`--memory`, `--hooks`, `--rules`, `--apply`); no agent lists targets |
Global: `--index PATH`, `--json`, `--max-chars N`. Exit codes: `0` ok (zero hits included), `1` error, `2` bad request, `3` group unknown/ambiguous.
## How it works
Records stream into one SQLite file with an FTS5 index. A search resolves the
group (exact → name → contains → fuzzy), runs one FTS5 match where group and
filter values are indexed tokens (no post-filtering), ranks by BM25 × boosts,
and decodes only the top N into capped cards. A record's own group name is
excluded from scoped matching, placeholders count as missing, and every
answer reports `shown N of M` so agents can tell "no match" from "no data".
## Reproduce the benchmark
```bash
python3 -m venv bench/.venv && bench/.venv/bin/pip install -r bench/requirements.txt
bench/.venv/bin/python bench/run_bench.py && bench/.venv/bin/python bench/report.py # ~30 min, ~4 GB
```
## Contributing, security, license
See [CONTRIBUTING.md](CONTRIBUTING.md) (ranking changes need before/after
benchmark numbers) and [SECURITY.md](SECURITY.md) (indexes read-only,
offline unless you serve or connect over HTTP, `unsafe`-free; report
privately). Licensed under [Apache-2.0](LICENSE);
dependencies in [THIRD_PARTY.md](THIRD_PARTY.md).