Radar / AI infrastructure / Leviathan
Leviathan: deep memory for agents over large datasets
A single static Rust binary that turns records (JSONL, CSV, SQLite, anything a database CLI can export) into a ranked full-text index that agents can query.
Why it matters
Agent memory without infrastructure: one binary, no database server, no embeddings service, ranked retrieval over large local datasets for coding agents.
What you could build with it
A developer running local coding agents could index a large codebase's logs, docs, and ticket history with Leviathan so agents retrieve relevant context from a single binary instead of re-reading files.
Does it hold up?
Too early to judge. Released Oct 5; the single-binary pitch is simple to verify, but no community benchmarks or integration reports exist yet.
Learn more
First spotted on github: source.
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