Human readers: See HUMANS.md for a gentler introduction.
Aiome automates multi-scale knowledge infrastructure for codebases — so that any AI agent, at any moment, can load exactly the context it needs at exactly the right resolution. See docs/design-doc.md for the full architecture. See docs/vision.md for the foundational ideas.
The CLI is a REPL that echoes input. Everything below is direction, not implemented.
The next milestone is Phase 1 (Analyze): static + temporal coupling analysis producing an entanglement map and partition structure. No LLM needed. See the design doc’s "CLI" section for the phased build plan.
├── Cargo.toml # Rust 2024 edition, rustyline dependency
├── src/
│ └── main.rs # CLI entry point: REPL loop
├── docs/
│ ├── design-doc.md # Architecture: Analyze → Generate → Serve
│ └── vision.md # Foundational ideas: BRAIN pattern, stigmergy, scaffolding
├── HUMANS.md # Human-oriented overview
└── .github/workflows/
└── ci.yml # CI: fmt, clippy, test
- Rust 2024 edition.
cargo fmt,cargo clippy -- -D warnings,cargo testmust all pass. CI enforces this. - Small changes. Prefer focused commits over sweeping refactors.
- Coordination via artifacts. Code, tests, issues, and PRs over chatty handoffs.
- Honesty over aspiration. If this document drifts from the code, update the document.
cargo run # Start the REPL
cargo test # Run tests (none yet)
cargo fmt # Format code
cargo clippy --all-targets --all-features -- -D warnings # Lint (matches CI)
cargo install --path . # Install locally as `aiome`Phase 1 of the design doc: aiome init producing an entanglement map and partition structure from static analysis
(tree-sitter) and git history (git2). Key dependencies to add: tree-sitter, git2, petgraph, clap.