Code intelligence for AI coding agents. Your AI sees dependencies before making changes.
Alpha (0.1.x) — works on real projects, used daily by the author. Expect rough edges.
AI assistants edit your code one file at a time. They don't see dependencies. They grep, read, guess — and break things three files away.
LensPR builds a dependency graph of your codebase and gives your AI structural understanding before it changes anything.
Quick Start
Requires Python 3.11+, macOS or Linux. For TypeScript/JS projects, also Node.js 18+.
pip install 'lenspr[all]'
lenspr init .
lenspr setup .
Restart your IDE. Done. Your AI now has lens_* tools.
Add .lens/ to your .gitignore — the graph is local and rebuilt from source.
What It Does
"What depends on this function?"
One call returns source code, who calls it, what it calls, and related tests:
> lens_context("auth.login_handler")
source: 42 lines
callers: auth_routes.create_routes, test_auth.test_login_success
callees: db.get_user, crypto.verify_password, jwt.create_token
tests: test_login_success, test_login_wrong_password
Without LensPR your AI makes 5-7 grep/read calls and still misses things. With LensPR — one call, full picture.
"What breaks if I change this?"
Before any modification, the AI sees the blast radius:
> lens_check_impact("models.User")
severity: CRITICAL
direct_dependents: 15
indirect_dependents: 23
affected_modules: auth, payments, notifications
tests_covering: 3
The AI warns you, changes its approach, or asks for confirmation. No more blind edits.
"How healthy is this codebase?"
> lens_vibecheck()
score: 86/100 (B)
test_coverage: 17/25 — 67% functions tested
dead_code: 20/20 — 0% dead code
circular_imports: 15/15 — 0 cycles
architecture: 12/15 — 1 violation
documentation: 8/10 — 81% documented
graph_confidence: 14/15 — 94% edges resolved
Track whether the codebase is improving or degrading over time.
Cross-language visibility
LensPR connects frontend and backend into a single graph:
LoginModal.tsx → fetch("/api/auth/login")
↓ CALLS_API
Backend: @router.post("/login") → login_handler()
→ db.query(User) [reads: users]
→ verify_password()
→ create_jwt_token()
Also tracks: database tables, Docker services, env vars, CI/CD workflows, SQL migrations.
Works With
| IDE | Setup |
|-----|-------|
| Claude Code | lenspr setup . — automatic |
| Cursor | Copy .mcp.json to .cursor/mcp.json |
| Any MCP client | lenspr serve <path> |
Languages: Python (95%+ resolution via Jedi/Pyright) and TypeScript/JavaScript (85-95% via tree-sitter + TS Compiler API).
Infrastructure: .sql files, Dockerfiles, docker-compose.yml, GitHub Actions workflows, .env files — all parsed into the same graph.
Everything runs locally. Your code never leaves your machine.
Key Features
| | |
|---|---|
| Impact Analysis | Severity (LOW → CRITICAL) before any change |
| One-Call Context | Source + callers + callees + tests in one request |
| Cross-Language | Frontend HTTP → backend routes, DB tables, Docker, env vars, CI/CD |
| Surgical Edits | Targeted find/replace within a function — no full file rewrites |
| Dead Code | Find unreachable functions (Django, FastAPI, Celery entry points) |
| Architecture Rules | Enforce layer boundaries — violations warn before changes apply |
| Git per Function | Blame, history, commit scope at function level |
| Session Memory | AI picks up where it left off across context resets |
| Auto-Sync | File watcher updates graph on every save |
| Health Score | 0-100 score across 6 dimensions — t
... View full README on GitHub
