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docs: add competitive analysis and foundation principles
Analyze 21 code intelligence tools, rank codegraph #7/22, and establish 8 core principles (zero-infrastructure, dual engine, confidence scoring, incremental builds, embeddable-first, single registry, security defaults, scope boundaries).
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‎COMPETITIVE_ANALYSIS.md‎

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# Competitive Analysis — Code Graph / Code Intelligence Tools
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**Date:** 2026-02-22
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**Scope:** 21 code analysis tools compared against `@optave/codegraph`
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---
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## Overall Ranking
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Ranked by weighted score across 6 dimensions (each 1–5):
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| # | Score | Project | Stars | Lang | License | Summary |
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|---|-------|---------|-------|------|---------|---------|
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| 1 | 4.5 | [vitali87/code-graph-rag](https://github.com/vitali87/code-graph-rag) | 1,916 | Python | MIT | Graph RAG with Memgraph, multi-provider AI, code editing, semantic search, MCP |
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| 2 | 4.2 | [seatedro/glimpse](https://github.com/seatedro/glimpse) | 349 | Rust | MIT | Clipboard-first codebase-to-LLM tool with call graphs, token counting, LSP resolution |
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| 3 | 4.0 | [SimplyLiz/CodeMCP (CKB)](https://github.com/SimplyLiz/CodeMCP) | 59 | Go | Custom | SCIP-based indexing, compound operations (83% token savings), CODEOWNERS, secret scanning |
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| 4 | 3.9 | [harshkedia177/axon](https://github.com/harshkedia177/axon) | 29 | Python | None | 11-phase pipeline, KuzuDB, Leiden community detection, dead code, change coupling |
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| 5 | 3.8 | [anrgct/autodev-codebase](https://github.com/anrgct/autodev-codebase) | 111 | TypeScript | None | 40+ languages, 7 embedding providers, Cytoscape.js visualization, LLM reranking |
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| 6 | 3.7 | [Anandb71/arbor](https://github.com/Anandb71/arbor) | 85 | Rust | MIT | Native GUI, confidence scoring, architectural role classification, fuzzy search, MCP |
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| **7** | **3.6** | **[@optave/codegraph](https://github.com/optave/codegraph)** | — | **JS/Rust** | **Apache-2.0** | **Dual engine (native Rust + WASM), 11 languages, SQLite, MCP, semantic search, zero-cloud** |
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| 8 | 3.4 | [Durafen/Claude-code-memory](https://github.com/Durafen/Claude-code-memory) | 72 | Python | None | Memory Guard quality gate, persistent codebase memory, Voyage AI + Qdrant |
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| 9 | 3.3 | [NeuralRays/codexray](https://github.com/NeuralRays/codexray) | 2 | TypeScript | MIT | 16 MCP tools, TF-IDF semantic search (~50MB), dead code, complexity, path finding |
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| 10 | 3.2 | [al1-nasir/codegraph-cli](https://github.com/al1-nasir/codegraph-cli) | 11 | Python | MIT | CrewAI multi-agent system, 6 LLM providers, browser explorer, DOCX export |
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| 11 | 3.1 | [anasdayeh/claude-context-local](https://github.com/anasdayeh/claude-context-local) | 0 | Python | None | 100% local, Merkle DAG incremental indexing, sharded FAISS, hybrid BM25+vector, GPU accel |
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| 12 | 3.0 | [Vasu014/loregrep](https://github.com/Vasu014/loregrep) | 12 | Rust | Apache-2.0 | In-memory index library, Rust + Python bindings, AI-tool-ready schemas |
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| 13 | 2.9 | [rahulvgmail/CodeInteliMCP](https://github.com/rahulvgmail/CodeInteliMCP) | 8 | Python | None | DuckDB + ChromaDB (zero Docker), multi-repo, lightweight embedded DBs |
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| 14 | 2.8 | [Bikach/codeGraph](https://github.com/Bikach/codeGraph) | 6 | TypeScript | MIT | Neo4j graph, Claude Code slash commands, Kotlin support, 40-50% cost reduction |
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| 15 | 2.7 | [yumeiriowl/repo-graphrag-mcp](https://github.com/yumeiriowl/repo-graphrag-mcp) | 3 | Python | MIT | LightRAG + tree-sitter, entity merge (code ↔ docs), implementation planning tool |
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| 16 | 2.6 | [0xjcf/MCP_CodeAnalysis](https://github.com/0xjcf/MCP_CodeAnalysis) | 7 | Python/TS | None | Stateful tools (XState), Redis sessions, socio-technical analysis, dual language impl |
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| 17 | 2.5 | [RaheesAhmed/code-context-mcp](https://github.com/RaheesAhmed/code-context-mcp) | 0 | Python | MIT | Security pattern detection, auto architecture diagrams, code flow tracing |
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| 18 | 2.4 | [shantham/codegraph](https://github.com/shantham/codegraph) | 0 | TypeScript | MIT | Polished `npx` one-command installer, sqlite-vss, 7 MCP tools |
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| 19 | 2.3 | [0xd219b/codegraph](https://github.com/0xd219b/codegraph) | 0 | Rust | None | Pure Rust, HTTP server mode, Java + Go support |
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| 20 | 2.1 | [floydw1234/badger-graph](https://github.com/floydw1234/badger-graph) | 0 | Python | None | Dgraph backend (Docker), C struct field access tracking |
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| 21 | 2.0 | [khushil/code-graph-rag](https://github.com/khushil/code-graph-rag) | 0 | Python | MIT | Fork of vitali87/code-graph-rag with no modifications |
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| 22 | 1.8 | [m3et/CodeRAG](https://github.com/m3et/CodeRAG) | 0 | Python | None | Iterative RAG with self-reflection, ChromaDB, Azure OpenAI dependent |
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---
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## Scoring Breakdown
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| # | Project | Features | Analysis Depth | Deploy Simplicity | Lang Support | Code Quality | Community |
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|---|---------|----------|---------------|-------------------|-------------|-------------|-----------|
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| 1 | code-graph-rag | 5 | 4 | 3 | 4 | 4 | 5 |
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| 2 | glimpse | 4 | 4 | 5 | 3 | 5 | 5 |
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| 3 | CKB | 5 | 5 | 4 | 3 | 4 | 3 |
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| 4 | axon | 5 | 5 | 4 | 2 | 4 | 2 |
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| 5 | autodev-codebase | 5 | 3 | 3 | 5 | 3 | 4 |
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| 6 | arbor | 4 | 4 | 5 | 4 | 5 | 3 |
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| **7** | **codegraph (us)** | **3** | **3** | **5** | **4** | **4** | **2** |
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| 8 | Claude-code-memory | 4 | 3 | 3 | 3 | 4 | 3 |
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| 9 | codexray | 5 | 4 | 4 | 4 | 3 | 1 |
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| 10 | codegraph-cli | 5 | 3 | 3 | 2 | 3 | 2 |
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| 11 | claude-context-local | 4 | 3 | 3 | 4 | 4 | 1 |
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| 12 | loregrep | 3 | 3 | 4 | 3 | 5 | 2 |
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| 13 | CodeInteliMCP | 3 | 3 | 4 | 3 | 3 | 1 |
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| 14 | Bikach/codeGraph | 3 | 3 | 3 | 2 | 3 | 1 |
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| 15 | repo-graphrag-mcp | 3 | 3 | 3 | 4 | 3 | 1 |
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| 16 | MCP_CodeAnalysis | 4 | 3 | 3 | 2 | 3 | 1 |
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| 17 | code-context-mcp | 4 | 2 | 3 | 2 | 2 | 1 |
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| 18 | shantham/codegraph | 3 | 2 | 4 | 4 | 3 | 1 |
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| 19 | 0xd219b/codegraph | 2 | 3 | 4 | 1 | 4 | 1 |
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| 20 | badger-graph | 2 | 2 | 2 | 1 | 2 | 1 |
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| 21 | khushil/code-graph-rag | 5 | 4 | 3 | 4 | 4 | 1 |
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| 22 | CodeRAG | 3 | 2 | 2 | 1 | 2 | 1 |
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**Scoring criteria:**
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- **Features** (1-5): breadth of tools, MCP integration, search, visualization, export
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- **Analysis Depth** (1-5): how deep the code analysis goes (dead code, complexity, flow tracing, coupling)
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- **Deploy Simplicity** (1-5): ease of setup — zero Docker = 5, requires Docker = 3, complex multi-service = 1
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- **Lang Support** (1-5): number of well-supported programming languages
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- **Code Quality** (1-5): architecture, performance characteristics, engineering rigor
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- **Community** (1-5): stars, contributors, activity, documentation quality
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---
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## Where Codegraph Wins
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| Strength | Details |
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|----------|---------|
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| **Zero-dependency deployment** | `npm install` and done. No Docker, no cloud, no API keys needed. Most competitors require Docker (Memgraph, Neo4j, Dgraph, Qdrant) or cloud APIs |
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| **Dual engine architecture** | Only project with native Rust (napi-rs) + automatic WASM fallback. Others are pure Rust OR pure JS/Python — never both |
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| **Single-repo MCP isolation** | Security-conscious default: tools have no `repo` property unless `--multi-repo` is explicitly enabled. Most competitors default to exposing everything |
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| **Incremental builds** | File-hash-based skip of unchanged files. Some competitors re-index everything |
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| **Platform binaries** | Published `@optave/codegraph-{platform}-{arch}` optional packages — true npm-native distribution |
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| **Import resolution depth** | 6-level priority system with confidence scoring — more sophisticated than most competitors' resolution |
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---
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## Where Codegraph Loses
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### vs code-graph-rag (#1, 1916 stars)
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- **Graph query expressiveness**: Memgraph + Cypher enables arbitrary graph traversals; our SQL queries are more rigid
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- **AI-powered code editing**: they can surgically edit functions via AST targeting with visual diffs
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- **Provider flexibility**: they support Gemini/OpenAI/Claude/Ollama and can mix providers per task
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- **Community**: 1,916 stars — orders of magnitude more traction
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### vs glimpse (#2, 349 stars)
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- **LLM workflow optimization**: clipboard-first output + token counting + XML output mode — purpose-built for "code → LLM context"
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- **LSP-based call resolution**: compiler-grade accuracy vs our tree-sitter heuristic approach
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- **Web content processing**: can fetch URLs and convert HTML to markdown for context
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### vs CKB (#3, 59 stars)
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- **Indexing accuracy**: SCIP provides compiler-grade cross-file references (type-aware), fundamentally more accurate than tree-sitter for supported languages
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- **Compound operations**: `explore`/`understand`/`prepareChange` batch multiple queries into one call — 83% token reduction, 60-70% fewer tool calls
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- **CODEOWNERS + secret scanning**: enterprise features we lack entirely
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### vs axon (#4, 29 stars)
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- **Analysis depth**: their 11-phase pipeline includes community detection (Leiden), execution flow tracing, git change coupling, dead code detection — all features we lack
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- **Graph database**: KuzuDB with native Cypher is more expressive for complex graph queries than our SQLite
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- **Branch structural diff**: compares code structure between branches using git worktrees
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### vs autodev-codebase (#5, 111 stars)
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- **Language breadth**: 40+ languages vs our 11
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- **Interactive visualization**: Cytoscape.js call graph explorer in the browser — we only have static DOT/Mermaid
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- **LLM reranking**: secondary LLM pass to improve search relevance — more sophisticated retrieval pipeline
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### vs arbor (#6, 85 stars)
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- **Native GUI**: desktop app for interactive impact analysis (we're CLI/MCP only)
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- **Confidence scoring surfaced to users**: every result shows High/Medium/Low confidence
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- **Architectural role classification**: auto-tags symbols as Entry Point / Core Logic / Utility / Adapter
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- **Fuzzy symbol search**: typo tolerance with Jaro-Winkler matching
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---
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## Features to Adopt — Priority Roadmap
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### Tier 1: High impact, low effort
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| Feature | Inspired by | Why |
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|---------|------------|-----|
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| **Dead code detection** | axon, codexray, CKB | We have the graph — find nodes with zero incoming edges (minus entry points/exports). Agents constantly ask "is this used?" |
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| **Fuzzy symbol search** | arbor | Add Levenshtein/Jaro-Winkler to `fn` command. Currently requires exact match |
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| **Expose confidence scores** | arbor | Already computed internally in import resolution — just surface them |
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| **Shortest path A→B** | codexray, arbor | BFS on existing edges table. We have `fn` for single chains but no A→B pathfinding |
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### Tier 2: High impact, medium effort
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| Feature | Inspired by | Why |
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|---------|------------|-----|
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| **Compound MCP tools** | CKB | `explore`/`understand` meta-tools that batch deps + fn + map into single responses. Biggest token-savings opportunity |
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| **Token counting on responses** | glimpse, arbor | tiktoken-based counts so agents know context budget consumed |
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| **Node classification** | arbor | Auto-tag Entry Point / Core / Utility / Adapter from in-degree/out-degree patterns |
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| **TF-IDF lightweight search** | codexray | SQLite FTS5 + TF-IDF as a middle tier (~50MB) between "no search" and full transformers (~500MB) |
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### Tier 3: High impact, high effort
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| Feature | Inspired by | Why |
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|---------|------------|-----|
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| **Interactive HTML visualization** | autodev-codebase, codegraph-cli | `codegraph viz` → opens interactive vis.js/Cytoscape.js graph in browser |
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| **Git change coupling** | axon | Analyze git history for files that always change together — enhances `diff-impact` |
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| **Community detection** | axon | Leiden algorithm to discover natural module boundaries vs actual file organization |
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| **Execution flow tracing** | axon, code-context-mcp | Framework-aware entry point detection + BFS flow tracing |
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| **Security pattern scanning** | CKB, code-context-mcp | Detect hardcoded secrets, SQL injection patterns, XSS in parsed code |
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### Not worth copying
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| Feature | Why skip |
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|---------|----------|
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| Memgraph/Neo4j/KuzuDB | Our SQLite = zero Docker, simpler deployment. Query gap matters less than simplicity |
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| Multi-provider AI | We're deliberately cloud-free — that's a feature, not a limitation |
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| SCIP indexing | Would require maintaining SCIP toolchains per language. Tree-sitter + native Rust is the right bet |
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| CrewAI multi-agent | Overengineered for a code analysis tool. Keep the scope focused |
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| Clipboard/LLM-dump mode | Different product category (glimpse). We're a graph tool, not a context-packer |
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---
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## Irrelevant Repos (excluded from ranking)
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These repos from the initial list were not code analysis / graph tools:
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| Repo | What it actually is |
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|------|-------------------|
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| [susliko/tla.nvim](https://github.com/susliko/tla.nvim) | TLA+/PlusCal Neovim plugin for formal verification |
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| [akaash-nigam/AxionApps](https://github.com/akaash-nigam/AxionApps) | Portfolio of 17 Indian social impact mobile apps |
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| [jasonjckn/tree-sitter-clojure](https://github.com/jasonjckn/tree-sitter-clojure) | Fork of Clojure tree-sitter grammar, inactive since 2022 |
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| [omkargade04/sentinel-agent](https://github.com/omkargade04/sentinel-agent) | AI-powered GitHub PR reviewer agent |
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| [rupurt/tree-sitter-graph-nix](https://github.com/rupurt/tree-sitter-graph-nix) | Nix flake packaging for tree-sitter-graph (1.8KB of Nix) |
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| [shandianchengzi/tree_sitter_DataExtractor](https://github.com/shandianchengzi/tree_sitter_DataExtractor) | Academic research on program graph representations for GNNs |
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| [hasssanezzz/GoTypeGraph](https://github.com/hasssanezzz/GoTypeGraph) | Go-only struct/interface relationship visualizer |
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| [romiras/py-cmm-parser](https://github.com/romiras/py-cmm-parser) | Python-only canonical metadata parser with Pyright LSP |
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| [OrkeeAI/orkee](https://github.com/OrkeeAI/orkee) | AI agent orchestration platform (CLI/TUI/Web/Desktop) — adjacent but different category |

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