Why Batho?
Companies are reducing AI usage because token spend is getting pricey. Batho fixes cost and quality.
Lower Token Costs
Your agent traverses a graph instead of reading entire files. Feed the LLM only what it needs.
Fewer Hallucinations
Deterministic, tree-sitter-parsed relationships. No guessing, no embeddings — just facts.
More Use Cases
When cost and accuracy are solved, automation possibilities widen with imagination.
Works With Your Stack
Python, TypeScript, Rust, Go, Java, and more. Batho parses 40+ languages so your agent understands your entire codebase.
Learn more →Spend Less on Tokens
Batho compresses entire codebases into a graph your agent traverses — using a fraction of the tokens.
Learn more →Track Codebase Evolution
Versioned snapshots and incremental diffing let your agent understand what changed and why — across every commit.
Learn more →Zero-Copy Performance
Apache Arrow IPC storage means zero-copy, memory-mapped reads. Your agent queries the graph instantly.
Learn more →Reconstruct Anything
Reconstruct any file byte-for-byte from the graph. Cryptographic hash integrity means nothing is lost.
Learn more →Zero Config to Start
Batho runs with zero config. Customize with a single YAML file when you need control.
Learn more →How It Works
From source code to AI-ready graph in four steps
Build
Parse your entire codebase into a structured hypergraph with cross-file symbol resolution.
batho build --root .Patch
Apply incremental updates 10-100x faster than full re-indexing using native content hashing.
batho patch --root .Export
Export a transportable ZIP artifact by default, or use --json for LLM-optimized views.
batho export --root .Verify
Execute integrity checks and diagnostic routines to repair database corruption or inconsistencies.
batho fix --dry-runGet Started in Seconds
One command to give your AI agent a map of your entire codebase.
pip install bathoStop paying to dump your repo into an LLM.
Index your codebase in one command. Your AI agent gets the map — not the whole territory.