elstongun / leviathan

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.
## Quickstart
 [code] 
 [code] 
- `-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. 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 |
 [code] 
Any database works through its own CLI; Leviathan never holds credentials:
 [code] 
Builds are atomic and skipped when nothing changed; `upsert` and `delete` keep an index fresh. Full reference: docs/CONFIG.md.
## Plug it into your agent
**CLI + skill (recommended):** any agent with a shell can call it. Copy
`skills/leviathan` into `~/.claude/skills/` or
paste it into `AGENTS.md` / `.cursor/rules`. Costs 0 tokens until used.
`skills/leviathan-memory` 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).
**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 ca