Show HN: N3MO – Deterministic code intelligence via AST parsing, no embeddings

N3MO, a deterministic code intelligence tool that parses source code ASTs into a queryable knowledge graph without embeddings or LLM calls, launched on Hacker News. It enables transitive dependency mapping, blast radius analysis, and AI agent integration via MCP, targeting engineering teams to reduce regression risks and accelerate onboarding. The tool is licensed under PolyForm Noncommercial 1.0.0 with commercial licenses available.

A structural code intelligence layer that transforms source code into a queryable knowledge graph for search, impact analysis, and AI-powered development. Parse once. Query forever. Know exactly what breaks before it does. 📜 Licensed under PolyForm Noncommercial 1.0.0 — Source available for noncommercial use. • Need commercial use? Get a commercial license → -license What is N3MO -what-is-n3mo • Benchmarks -benchmarks • Architecture -architecture • Installation -installation • MCP -model-context-protocol-mcp • Usage -usage • Roadmap -roadmap N3MO is a symbol-centric code intelligence layer. Instead of scanning raw text, it parses your source code's ASTs, maps call graphs, and models dependencies in a queryable relational database — deterministically, with zero LLM calls at index time . For engineering leaders and teams, N3MO acts as a structural insurance policy for your codebases. 🛡️ Eliminate Regression Risks — Utility functions are rarely refactored because developers fear unknown side effects. N3MO maps the transitive blast radius of any symbol to arbitrary depth, showing you exactly what will break before you make the edit. 🏎️ Rapid Developer Onboarding — Instead of senior engineers spending hours explaining codebase flow to new hires, developers run one command to visualize complex call chains and parent-child dependencies interactively. 🤖 AI-Agent Ready Infrastructure — Modern LLM agents Cursor, Claude Desktop are limited by context windows and text search. N3MO's native MCP server lets agents query the actual code graph, enabling fast, hallucination-free refactoring. ⚡ No Embeddings, No Drift — N3MO is pure static analysis: Tree-sitter AST parsing into PostgreSQL. There's no vector index to keep in sync, no embedding cost per repo, and no semantic-similarity guesswork — every edge in the graph is an exact, verifiable relationship. | Capability | Grep / Text Search | IDE "Find References" | N3MO Code Graph | |---|---|---|---| Analysis Basis | Substring matching | AST-based, direct refs only | Relational knowledge graph | Transitive Traversal | ❌ None | ❌ Manual, one level at a time | ⚡ Instant to arbitrary depth | Blast Radius Mapping | ❌ None | ❌ Flat search-result list | 🎨 Interactive visual orbit map | CI/CD Integration | ❌ None | ❌ Bound to IDE runtime | ⚙️ Dockerized CLI + CTE queries | AI Agent Integration | ❌ Injected file chunks | 🤖 Native MCP server | | Language Coverage | ✅ Any text file | ✅ 27 Tree-sitter grammars | | Indexing Method | N/A | N/A | ✅ Deterministic AST parse — no embeddings, no LLM calls | | Developer: "Where does 'login' appear?" Tool: grep -r "login" . Result: 647 matches across 89 files ...now what? | Developer: "What breaks if I change login?" Tool: n3mo impact "login" Result: 3 direct callers → 5 ripple effects Full blast radius in < 50ms | N3MO doesn't find text — it understands structure.It traces the actual call graph, not string matches. Questions N3MO answers instantly: | Question | How | | |---|---|---| | 🔎 | What functions and classes exist in this repo? | Full symbol index across 27 languages | | 🎯 | Where is this symbol used — directly and transitively? | Recursive CTE traversal to arbitrary depth | | 💥 | What is the blast radius of changing this function? | Interactive orbit map with depth slider | | 🕸️ | How do these components actually connect? | Call graph + parent-child hierarchy | | 🤖 | Can my AI agent understand this codebase structurally? | Native MCP server for Cursor / Claude | All benchmarks measured on Intel i5-13450HX, 24 GB RAM, NVMe SSD . Django is the primary benchmark target: 3,021 files , ~43K symbols , ~181K calls . Django Index Time minutes ═══════════════════════════════════════════════════════════════ v0.3 Baseline ██████████████████████████████████████████████ 23 min 1× SPLIT PART Fix ██████████████████████ 11 min 2× Batch Inserts █████████ 5 min 4.6× + Multiprocessing ████ 2.5 min 9× 🚀 ═══════════════════════════════════════════════════════════════ | Optimization | Index Time | Speedup | What Changed | |---|---|---|---| | v0.3 baseline | 23 min | 1× | Per-symbol DB inserts, naive call resolution | | + SPLIT PART query fix | 11 min | 2× | Eliminated redundant string splitting in call resolution | | + Batch inserts | 5 min | 4.6× | Symbols, imports, and calls batched per file 1 transaction | | + Multiprocessing | ~2.5 min | ~9× | ProcessPoolExecutor distributes AST parsing across cores | ✅ All results are real measurements on the Django repository. Multiprocessing gains scale with core count. Tested on TensorFlow — a 36,000-file, multi-language C++/Python monorepo. | Metric | Result | |---|---| Repo size total files | ~36,000 | Files processed & indexed | 14,611 after filtering tests, configs, and non-source files | Total symbols extracted | 79,523 | Total call edges extracted | 480,851 | Full index time cold start | 14.06 minutes | Peak memory Docker container | 185 MB RAM | CPU utilization | ~5% | N3MO scales from a 3K-file pure-Python repo Django to a 36K-file multi-language enterprise monorepo TensorFlow — roughly a 5× larger indexing job at near-linear throughput, without significant resource overhead. Symbol/edge-per-file and incremental warm re-index numbers for TensorFlow are being finalized in the full benchmark report. Tested on ScanCode Toolkit — ~600K lines of Python. | Metric | Result | |---|---| Lines of code | ~600,000 | Full index time | ~3 minutes | Processing mode | Single-threaded v0.3 | N3MO uses SHA-256 file hashing to skip unchanged files on subsequent runs. | Scenario | Time | Notes | |---|---|---| Full index first run | Baseline | All files parsed and inserted | No changes re-run | < 1 second | Hash comparison only, zero DB writes | 1 file modified | < 2 seconds | Only the changed file is re-parsed and upserted | These results are from the built-in benchmark script on a 20-file synthetic repository. Real-world incremental performance is proportional to the number of changed files, not the total repository size. Impact analysis uses PostgreSQL recursive CTEs with cycle guards. Query times are independent of repository size — they depend only on the size of the result subgraph. | Query Type | Typical Latency | |---|---| | Direct callers of a symbol | < 10 ms | | Full blast radius depth ≤ 5 | < 50 ms | | Complete graph traversal | < 200 ms | python benchmarks/benchmark indexing.py N3MO builds a symbol-centric knowledge graph stored in PostgreSQL: php graph TD A "📄 Source Code" -- |Tree-sitter| B "🌳 AST Parser" B -- C "🔍 Symbol Extractor" D "🔄 Git Hooks" -- |post-commit| A C -- E "🗄️ PostgreSQL<br/ Projects · Symbols · Calls<br/ Imports · Files" E -- F "💥 Impact Analysis" E -- G "📞 Call Graph" E -- H "📊 Dependency Graph" F -- I "🎨 Visualizer" G -- I H -- I F -- J "🤖 MCP Server" style A fill: 6c63ff,stroke: 4a3fbf,color: fff style B fill: 7c74ff,stroke: 4a3fbf,color: fff style C fill: 7c74ff,stroke: 4a3fbf,color: fff style D fill: ffd93d,stroke: d4b800,color: 1a202c style E fill: ff6b6b,stroke: c53030,color: fff,stroke-width:3px style F fill: 45b7d1,stroke: 2c8ea8,color: 1a202c style G fill: 45b7d1,stroke: 2c8ea8,color: 1a202c style H fill: 45b7d1,stroke: 2c8ea8,color: 1a202c style I fill: 9ae6b4,stroke: 2f855a,color: 1a202c style J fill: ffd93d,stroke: d4b800,color: 1a202c sequenceDiagram participant User as User / CI participant CLI as N3MO CLI participant DB as PostgreSQL Docker participant Viz as Graph Visualizer rect rgb 26, 27, 46 Note over User, DB: Indexing Flow Local CLI User- CLI: n3mo index CLI- DB: Start PostgreSQL container if not running CLI- CLI: Walk file tree SHA-256 hash checks CLI- CLI: Parse AST Tree-sitter, multiprocessing CLI- DB: Batch insert symbols, calls, imports CLI- DB: Resolve imports & call links DB-- CLI: Success CLI-- User: Complete summary end rect rgb 26, 27, 46 Note over User, Viz: Query & Visualization Flow User- CLI: n3mo impact "symbol" --graph CLI- DB: Recursive CTE traversal depth & file filters DB-- CLI: Blast radius subgraph CLI- Viz: Generate orbital vis.js HTML CLI- User: Launch local web server & open browser end erDiagram PROJECT ||--o{ SYMBOL : contains PROJECT ||--o{ CALL : tracks PROJECT ||--o{ IMPORT : tracks PROJECT ||--o{ FILE : indexes SYMBOL ||--o{ CALL : "source of" SYMBOL ||--o{ CALL : "resolved to" SYMBOL ||--o{ SYMBOL : "parent of" PROJECT { uuid id PK text name text repo url timestamp created at } SYMBOL { uuid id PK uuid project id FK text name text file path text kind "function|class|method" text signature int start line int end line uuid parent id FK } CALL { uuid id PK uuid project id FK uuid source symbol id FK text call name int line number uuid resolved symbol id FK } IMPORT { uuid id PK uuid project id FK text file path text module text name text alias uuid resolved symbol id FK } FILE { uuid project id FK text file path PK text sha256 } Multi-language support — 27 Tree-sitter grammars supported dynamically loaded ; actively benchmarked on 10 major languages including Python, JS/TS, Go, Java, and C/C++ Parallel AST ingestion — ProcessPoolExecutor distributes CPU-bound parsing across all available cores Incremental re-indexing — SHA-256 file hashing skips unchanged files automatically Idempotent operations — re-indexing updates existing data without duplication Smart exclusions — case-insensitive directory filters and camelCase-aware filename checks prevent false positives e.g. allows contest.py while skipping test .py Symbol extraction — functions, classes, methods with full file path + line context Hierarchical modeling — parent-child relationships Module → Class → Method Call graph construction — who calls whom, resolved at ingestion time Scope-aware resolution — class scope local file imports qualified dot paths global Blast radius analysis — recursive CTE traversal to arbitrary depth with cycle guards Connection pooling — ThreadedConnectionPool eliminates per-symbol DB round trips Batch inserts — symbols, imports, and calls batched per file in single transactions Optimized queries — SPLIT PART fix delivered a 2× speedup on call resolution Interactive graph — vis.js orbit map with click-to-inspect nodes, sidebar, and depth slider Dark mode — toggleable canvas dark mode with real-time node/edge updates, persisted in localStorage Premium styling — sleek interactive dashboard landing page UI and graph visualizer styled with Bricolage Grotesque , Inter , and JetBrains Mono typographyprofile — system instructions to configure Claude as an impact-aware coding agent SKILL.md /RajX-dev/N3MO/blob/main/SKILL.md Native MCP server — first-class integration with Cursor, Claude Desktop, and Windsurf Git hooks — automatic re-indexing on every commit CI pipeline — GitHub Actions with linting ruff , type checking mypy , and pytest Tree-sitter parsing supported for 27 languages. Deep semantic call graph mapping currently optimized for Python, JS/TS, and Java. Install N3MO directly from PyPI: Install the package pip install n3mo Start Docker containers & initialize the database n3mo setup Alternatively, for contributors running in editable mode: git clone https://github.com/RajX-dev/N3MO.git cd N3MO pip install -e . n3mo setup N3MO includes a native MCP server that exposes repository analysis and graph traversal tools to LLM agents like Claude, Cursor, or Windsurf . Navigate to the workspace you want Claude to analyze, then run: n3mo mcp install This registers N3MO and sets up the paths automatically. Restart Claude Desktop and you're ready To configure Claude to run N3MO impact queries proactively before changing code in the editor, import or copy-paste the custom instructions from the SKILL.md profile. - Go to Settings → Models → MCP . - Click + Add New MCP Server . - Set the configuration details: Name : n3mo Type : command Command : n3mo mcp start or uvx n3mo mcp start to run directly Environment Variables : TARGET CODE DIR=/absolute/path/to/your/active/workspace - Click Save, and Cursor will instantly be able to index and query your workspace blast radius. | Tool | Description | |---|---| n3mo index | Ingests and indexes the codebase | n3mo search symbol | Locates the definition of a symbol across the workspace file path, line number | n3mo get dependencies | Finds all external symbols that a given symbol calls forward-dependency graph | n3mo get file symbols | Lists all classes and functions defined inside a specific file | n3mo get blast radius | Traces the transitive impact/call graph of a code symbol | For team collaboration and automated pull-request analysis in your CI/CD pipeline, visit n3mo.shop to get started with the GitHub Webhook integration. N3MO is free under the PolyForm Noncommercial 1.0.0 License for local usage and single-developer MCP integrations. 100% Free & Local — CLI queries, local MCP integrations, and the visualizer with zero limits. Enterprise Licensing — for large-scale organization deployments or commercial licensing terms, reach out to the author. cd /path/to/your/project n3mo index What gets indexed: - ✅ Source files in all 27 supported languages - ❌ Virtual environments venv/ , .venv/ - ❌ Dependencies node modules/ , site-packages/ - ❌ Build artifacts .git/ , pycache / , dist/ - ❌ Test / fixture directories tests/ , mocks/ , specs/ Example terminal output: ◈ IMPACT ANALYSIS ────────────────────────────────────────────────────────────────── Target: authenticate user ────────────────────────────────────────────────────────────────── ◉ Direct Callers 3 symbols ▸ login endpoint api/auth.py:12 ▸ refresh token api/token.py:23 ▸ validate session middleware/auth.py:89 ◎ Ripple Effects 5 symbols ╰─▸ POST /login routes.py:67 ╰─▸ admin login admin/views.py:34 ╰─▸ require auth decorators.py:12 ╰─▸ dashboard view views/dashboard.py:8 ╰─▸ settings view views/settings.py:22 ────────────────────────────────────────────────────────────────── Total impacted: 8 references │ depth ≤ 3 php graph LR A main.py -- B auth.py::login A -- C db.py::connect B -- D utils.py::hash password B -- E models.py::User C -- F config.py::DB URI style A fill: ff6b6b,stroke: c92a2a,stroke-width:2px,color: fff style B fill: 4ecdc4,stroke: 0ca89e,stroke-width:2px,color: 000 style C fill: 45b7d1,stroke: 1098ad,stroke-width:2px,color: 000 style D fill: 96ceb4,stroke: 63b598,stroke-width:2px,color: 000 style E fill: ffd93d,stroke: f5c200,stroke-width:2px,color: 000 style F fill: e0e0e0,stroke: a0a0a0,stroke-width:2px,color: 000 All four development phases have been completed. N3MO is stable and actively maintained. | Phase | Component | Status | |---|---|---| Phase 1 — Foundations | || | Docker setup | ✅ Complete | | | Database schema | ✅ Complete | | | Tree-sitter integration | ✅ Complete | | | Symbol + call extraction | ✅ Complete | | | Blast radius recursive CTE | ✅ Complete | | | Interactive visualizer | ✅ Complete | | Phase 2 — Performance | || | Connection pooling | ✅ Complete | | | Batch DB operations symbols/imports/calls | ✅ Complete | | | SPLIT PART query optimization | ✅ Complete | | --file / --depth CLI flags | ✅ Complete | | | Interactive depth slider | ✅ Complete | | Phase 3 — Correctness & Scaling | || | Incremental re-index file hashing | ✅ Complete | | | Multiprocessing AST parsing | ✅ Complete | | | Scope-aware call resolution | ✅ Complete | | | CTE cycle guard | ✅ Complete | | | Full type annotations + mypy | ✅ Complete | | | pytest suite + CI | ✅ Complete | | | Multi-language support 27 languages | ✅ Complete | | Phase 4 — Distribution | || | MCP server Cursor / Claude / Windsurf | ✅ Complete | | | Real-time git-hook indexing | ✅ Complete | Phase 1: Foundations ✅ Complete - Docker environment PostgreSQL - Database schema — Projects, Symbols, Calls, Imports tables - Tree-sitter parser integration - Symbol extractor with full AST traversal - Idempotent upsert logic - Blast radius via recursive CTE - Interactive vis.js visualizer Phase 2: Performance ✅ Complete - psycopg2.pool.ThreadedConnectionPool — replace per-call connections - execute values batch inserts for symbols, imports, and calls — 1 transaction per file - SPLIT PART query optimization for call resolution - --file and --depth CLI flags for targeted impact analysis - Interactive depth slider in visualizer Results: Django 3,021 files, ~43K symbols, ~181K calls — 23min → 2.5min 9× faster Phase 3: Correctness + Scaling ✅ Complete - SHA-256 file hashing for incremental re-index - ProcessPoolExecutor for parallel AST parsing - Scope-aware call resolution using imports table - CTE cycle guard visited node tracking - Full type annotations, mypy clean checking in CI - pytest unit + integration test suite - GitHub Actions CI pipeline - Multi-language support 27 languages Phase 4: Distribution ✅ Complete - MCP server — N3MO as a tool for Cursor, Claude Code, Windsurf - Real-time incremental indexing via git hooks 1. Structure before semantics Map the code skeleton AST before adding AI analysis. A correct graph is worth more than a smart but wrong one. 2. Database as source of truth All state lives in PostgreSQL, eliminating in-memory complexity and enabling graph queries that application-level traversal cannot match. 3. Correctness over speed The parser must handle syntax errors gracefully without corrupting the graph. A fast indexer that silently drops symbols is worse than a slow one that gets everything right. 4. Idempotent operations Re-running ingestion produces identical results, enabling safe incremental updates and CI/CD integration. Contributions are welcome Please read the CONTRIBUTING.md guide to get started with setting up the project, coding standards, and running checks locally. Install with dev dependencies pip install -e ". dev " Lint ruff check n3mo/ Type check mypy n3mo/ Tests pytest tests/ Licensed under the PolyForm Noncommercial 1.0.0 License. - ✅ Free for personal projects, academic research, and hobby tools - ✅ Source available — view, modify, and distribute for noncommercial purposes ⚠️ Noncommercial — you may not use it for commercial purposes⚠️ Restrictions apply on offering it as a service For commercial deployments or proprietary modifications, contact for licensing options. See LICENSE /RajX-dev/N3MO/blob/main/LICENSE for full legal details. Raj Shekhar — Delhi Technological University — for robust, incremental, error-tolerant parsing Tree-sitter https://tree-sitter.github.io/ — for making recursive graph queries possible without a graph database PostgreSQL https://www.postgresql.org/ — for reproducible, single-command environments Docker https://www.docker.com/ — for the interactive graph visualization vis.js https://visjs.org/ — for the high-performance REST layer FastAPI https://fastapi.tiangolo.com/