⚡ SigMap

SigMap is the deterministic, verifiable grounding layer for AI code work.

Try it now

No install required. Run instantly on any machine:

npx sigmap
npx sigmap ask "Where is auth handled?"

Zero config. Zero dependencies. Under 10 seconds.

What is SigMap?

SigMap builds a deterministic, auditable signature-and-evidence map of your codebase — no LLM calls, no embeddings, byte-stable output — so AI agents, CI, and reviewers can trust and verify which files and symbols are real before acting. Same repo in, same map out, every time.

That map is exactly what agentic grep is worst at: reproducible, auditable context an agent can consume without a copy-paste, and a grounding check that proves an AI answer is anchored to real signatures and line numbers. Token reduction comes for free — but trust is the point.

Model-agnostic. Works with:

• Cloud LLMs: Claude, GPT-4, Copilot, Gemini
• Open-source agents: OpenCode, Aider, OpenHands, Cline
• Local LLMs: Ollama, llama.cpp, vLLM (no API keys, full privacy)
• Any editor: VS Code, Cursor, Windsurf, Neovim, JetBrains
• Any model: Use what you want, no vendor lock-in

Why SigMap?

Deterministic and verifiable — the two things an agentic-grep loop can't give you:

• Deterministic — no LLM calls, no agent loop; the same repo always produces a byte-identical map you can diff, cache, and gate in CI.
• Auditable & grounded — every symbol in the AST and anchored-regex tiers carries a real :start-end line anchor; Tier-3 formats (Ruby, R, Lua, Elixir, C/C++, config formats) are listed without anchors, as KNOWN_LIMITATIONS.md states. sigmap verify flags any AI claim that isn't grounded.
• Zero dependencies — npx sigmap on any machine; no embeddings, no vector DB, no hosted service, fully offline.

Proof it pays off (full benchmark below):

• 80.4% hit@5 — right file in top 5 results (retrieval corpus, 18 repos)

• 92.8% vs 40.0% single-shot grep baseline — 2.32× measured lift on the honest corpus (125 tasks / 19 repos)

• 95.7% smaller than the full repository — the generated map vs every source file, averaged across 21 real repos. A map-size measurement, not a per-call cost saving: an agent's context footprint is not the whole repo

• 61.9% task-success proxy — modeled from retrieval tiers, not measured LLM sessions

• 1.58 prompts per task — down from 2.84 (44.4% fewer retries, modeled)

• 38 languages supported — TypeScript, Python, Go, Rust, Java, R, and more

• No vendor lock-in — works with any AI assistant or local LLM

• No API costs — use local models (Ollama, llama.cpp, vLLM) with zero token fees

• Full privacy — keep your code and context on your machine

🔒 sigmap verify — the grounding flagship

The one thing no agentic-grep loop, and no competitor, gives you: prove an AI answer is anchored to real signatures and line numbers before you trust it. Deterministic, offline, no LLM — SigMap indexes your repo plus the libraries actually installed here and flags every fabricated file, import, imported name, symbol, test, or npm script.

sigmap verify answer.md                 # ✓ grounded, or a line-by-line list of fabrications
sigmap verify answer.md --json          # machine-readable report; exits 1 if any issue (CI gate)
sigmap verify answer.md --report        # standalone red/amber/green HTML report