GitNexus

⚠️ Important Notice:** GitNexus has NO official cryptocurrency, token, or coin. Any token/coin using the GitNexus name on Pump.fun or any other platform is not affiliated with, endorsed by, or created by this project or its maintainers. Do not purchase any cryptocurrency claiming association with GitNexus.

Building nervous system for agent context.

Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart tools so AI agents never miss code.

https://github.com/user-attachments/assets/172685ba-8e54-4ea7-9ad1-e31a3398da72

Like DeepWiki, but deeper. DeepWiki helps you understand code. GitNexus lets you analyze it — because a knowledge graph tracks every relationship, not just descriptions.

TL;DR: The Web UI is a quick way to chat with any repo. The CLI + MCP is how you make your AI agent actually reliable — it gives Cursor, Claude Code, Codex, and friends a deep architectural view of your codebase so they stop missing dependencies, breaking call chains, and shipping blind edits. Even smaller models get full architectural clarity, making it compete with goliath models.

Star History

Two Ways to Use GitNexus

Bridge mode: gitnexus serve connects the two — the web UI auto-detects the local server and can browse all your CLI-indexed repos without re-uploading or re-indexing.

Enterprise

GitNexus is available as an enterprise offering - either as a fully managed SaaS or a self-hosted deployment. Also available for commercial use of the OSS version with proper licensing.

Enterprise includes:

• PR Review - automated blast radius analysis on pull requests
• Auto-updating Code Wiki - always up-to-date documentation (Code Wiki is also available in OSS)
• Auto-reindexing - knowledge graph stays fresh automatically
• Multi-repo support - unified graph across repositories
• OCaml support - additional language coverage
• Priority feature/language support - request new languages or features

Upcoming:

• Auto regression forensics
• End-to-end test generation

👉 Learn more at akonlabs.com

💬 For commercial licensing or enterprise inquiries, ping us on Discord or drop an email at founders@akonlabs.com

Development

• ARCHITECTURE.md — packages, index → graph → MCP flow, where to change code
• RUNBOOK.md — analyze, embeddings, stale index, MCP recovery, CI snippets
• GUARDRAILS.md — safety rules and operational “Signs” for contributors and agents
• CONTRIBUTING.md — license, setup, commits, and pull requests
• TESTING.md — test commands for gitnexus and gitnexus-web

CLI + MCP (recommended)

The CLI indexes your repository and runs an MCP server that gives AI agents deep codebase awareness.

Quick Start

# Index your repo (run from repo root)
npx gitnexus analyze

That's it. This indexes the codebase, installs agent skills, registers Claude Code hooks, and creates AGENTS.md / CLAUDE.md context files — all in one command.

To configure MCP for your editor, run npx gitnexus setup once — or set it up manually below.

MCP Setup

gitnexus setup auto-detects your editors and writes the correct global MCP config. You only need to run it once.

Editor Support

Claude Code gets the deepest integration: MCP tools + agent skills + PreToolUse hooks that enrich searches with graph context + PostToolUse hooks that auto-reindex after commits.

Community Integrations

Built by the community — not officially maintained, but worth checking out.

Have a project built on GitNexus? Open a PR to add it here!

If you prefer manual configuration:

Claude Code (full support — MCP + skills + hooks):

# macOS / Linux
claude mcp add gitnexus -- npx -y gitnexus@latest mcp

# Windows
claude mcp add gitnexus -- cmd /c npx -y gitnexus@latest mcp

Codex (full support — MCP + skills):

codex mcp add gitnexus -- npx -y gitnexus@latest mcp

Cursor (~/.cursor/mcp.json — global, works for all projects):

{
  "mcpServers": {
    "gitnexus": {
      "command": "npx",
      "args": ["-y", "gitnexus@latest", "mcp"]
    }
  }
}

OpenCode (~/.config/opencode/config.json):

{
  "mcp": {
    "gitnexus": {
      "type": "local",
      "command": ["gitnexus", "mcp"]
    }
  }
}

Codex (~/.codex/config.toml for system scope, or .codex/config.toml for project scope):

[mcp_servers.gitnexus]
command = "npx"
args = ["-y", "gitnexus@latest", "mcp"]

CLI Commands

gitnexus setup                   # Configure MCP for your editors (one-time)
gitnexus analyze [path]          # Index a repository (or update stale index)
gitnexus analyze --force         # Force full re-index
gitnexus analyze --skills        # Generate repo-specific skill files from detected communities
gitnexus analyze --skip-embeddings  # Skip embedding generation (faster)
gitnexus analyze --skip-agents-md  # Preserve custom AGENTS.md/CLAUDE.md gitnexus section edits
gitnexus analyze --embeddings    # Enable embedding generation (slower, better search)
gitnexus analyze --verbose       # Log skipped files when parsers are unavailable
gitnexus mcp                     # Start MCP server (stdio) — serves all indexed repos
gitnexus serve                   # Start local HTTP server (multi-repo) for web UI connection
gitnexus list                    # List all indexed repositories
gitnexus status                  # Show index status for current repo
gitnexus clean                   # Delete index for current repo
gitnexus clean --all --force     # Delete all indexes
gitnexus wiki [path]             # Generate repository wiki from knowledge graph
gitnexus wiki --model <model>    # Wiki with custom LLM model (default: gpt-4o-mini)
gitnexus wiki --base-url <url>   # Wiki with custom LLM API base URL

What Your AI Agent Gets

7 tools exposed via MCP:

When only one repo is indexed, the repo parameter is optional. With multiple repos, specify which one: query({query: "auth", repo: "my-app"}).

Resources for instant context:

2 MCP prompts for guided workflows:

4 agent skills installed to .claude/skills/ automatically:

• Exploring — Navigate unfamiliar code using the knowledge graph
• Debugging — Trace bugs through call chains
• Impact Analysis — Analyze blast radius before changes
• Refactoring — Plan safe refactors using dependency mapping

Repo-specific skills generated with --skills:

When you run gitnexus analyze --skills, GitNexus detects the functional areas of your codebase (via Leiden community detection) and generates a SKILL.md file for each one under .claude/skills/generated/. Each skill describes a module's key files, entry points, execution flows, and cross-area connections — so your AI agent gets targeted context for the exact area of code you're working in. Skills are regenerated on each --skills run to stay current with the codebase.

Multi-Repo MCP Architecture

GitNexus uses a global registry so one MCP server can serve multiple indexed repos. No per-project MCP config needed — set it up once and it works everywhere.

flowchart TD
    subgraph CLI [CLI Commands]
        Setup["gitnexus setup"]
        Analyze["gitnexus analyze"]
        Clean["gitnexus clean"]
        List["gitnexus list"]
    end

    subgraph Registry ["~/.gitnexus/"]
        RegFile["registry.json"]
    end

    subgraph Repos [Project Repos]
        RepoA[".gitnexus/ in repo A"]
        RepoB[".gitnexus/ in repo B"]
    end

    subgraph MCP [MCP Server]
        Server["server.ts"]
        Backend["LocalBackend"]
        Pool["Connection Pool"]
        ConnA["LadybugDB conn A"]
        ConnB["LadybugDB conn B"]
    end

    Setup -->|"writes global MCP config"| CursorConfig["~/.cursor/mcp.json"]
    Analyze -->|"registers repo"| RegFile
    Analyze -->|"stores index"| RepoA
    Clean -->|"unregisters repo"| RegFile
    List -->|"reads"| RegFile
    Server -->|"reads registry"| RegFile
    Server --> Backend
    Backend --> Pool
    Pool -->|"lazy open"| ConnA
    Pool -->|"lazy open"| ConnB
    ConnA -->|"queries"| RepoA
    ConnB -->|"queries"| RepoB

How it works: Each gitnexus analyze stores the index in .gitnexus/ inside the repo (portable, gitignored) and registers a pointer in ~/.gitnexus/registry.json. When an AI agent starts, the MCP server reads the registry and can serve any indexed repo. LadybugDB connections are opened lazily on first query and evicted after 5 minutes of inactivity (max 5 concurrent). If only one repo is indexed, the repo parameter is optional on all tools — agents don't need to change anything.

Web UI (browser-based)

A fully client-side graph explorer and AI chat. No server, no install — your code never leaves the browser.

Try it now: gitnexus.vercel.app — drag & drop a ZIP and start exploring.

Or run locally:

git clone https://github.com/abhigyanpatwari/gitnexus.git
cd gitnexus/gitnexus-shared && npm install && npm run build
cd ../gitnexus-web && npm install
npm run dev

The web UI uses the same indexing pipeline as the CLI but runs entirely in WebAssembly (Tree-sitter WASM, LadybugDB WASM, in-browser embeddings). It's great for quick exploration but limited by browser memory for larger repos.

Local Backend Mode: Run gitnexus serve and open the web UI locally — it auto-detects the server and shows all your indexed repos, with full AI chat support. No need to re-upload or re-index. The agent's tools (Cypher queries, search, code navigation) route through the backend HTTP API automatically.

The Problem GitNexus Solves

Tools like Cursor, Claude Code, Codex, Cline, Roo Code, and Windsurf are powerful — but they don't truly know your codebase structure.

What happens:

1. AI edits UserService.validate()
2. Doesn't know 47 functions depend on its return type
3. Breaking changes ship

Traditional Graph RAG vs GitNexus

Traditional approaches give the LLM raw graph edges and hope it explores enough. GitNexus precomputes structure at index time — clustering, tracing, scoring — so tools return complete context in one call:

flowchart TB
    subgraph Traditional["Traditional Graph RAG"]
        direction TB
        U1["User: What depends on UserService?"]
        U1 --> LLM1["LLM receives raw graph"]
        LLM1 --> Q1["Query 1: Find callers"]
        Q1 --> Q2["Query 2: What files?"]
        Q2 --> Q3["Query 3: Filter tests?"]
        Q3 --> Q4["Query 4: High-risk?"]
        Q4 --> OUT1["Answer after 4+ queries"]
    end

    subgraph GN["GitNexus Smart Tools"]
        direction TB
        U2["User: What depends on UserService?"]
        U2 --> TOOL["impact UserService upstream"]
        TOOL --> PRECOMP["Pre-structured response:
        8 callers, 3 clusters, all 90%+ confidence"]
        PRECOMP --> OUT2["Complete answer, 1 query"]
    end

Core innovation: Precomputed Relational Intelligence

• Reliability — LLM can't miss context, it's already in the tool response
• Token efficiency — No 10-query chains to understand one function
• Model democratization — Smaller LLMs work because tools do the heavy lifting

How It Works

GitNexus builds a complete knowledge graph of your codebase through a multi-phase indexing pipeline:

1. Structure — Walks the file tree and maps folder/file relationships
2. Parsing — Extracts functions, classes, methods, and interfaces using Tree-sitter ASTs
3. Resolution — Resolves imports, function calls, heritage, constructor inference, and self/this receiver types across files with language-aware logic
4. Clustering — Groups related symbols into functional communities
5. Processes — Traces execution flows from entry points through call chains
6. Search — Builds hybrid search indexes for fast retrieval

Supported Languages

Imports — cross-file import resolution · Named Bindings — import { X as Y } / re-export tracking · Exports — public/exported symbol detection · Heritage — class inheritance, interfaces, mixins · Type Annotations — explicit type extraction for receiver resolution · Constructor Inference — infer receiver type from constructor calls (self/this resolution included for all languages) · Config — language toolchain config parsing (tsconfig, go.mod, etc.) · Frameworks — AST-based framework pattern detection · Entry Points — entry point scoring heuristics

Tool Examples

Impact Analysis

impact({target: "UserService", direction: "upstream", minConfidence: 0.8})

TARGET: Class UserService (src/services/user.ts)

UPSTREAM (what depends on this):
  Depth 1 (WILL BREAK):
    handleLogin [CALLS 90%] -> src/api/auth.ts:45
    handleRegister [CALLS 90%] -> src/api/auth.ts:78
    UserController [CALLS 85%] -> src/controllers/user.ts:12
  Depth 2 (LIKELY AFFECTED):
    authRouter [IMPORTS] -> src/routes/auth.ts

Options: maxDepth, minConfidence, relationTypes (CALLS, IMPORTS, EXTENDS, IMPLEMENTS), includeTests

Process-Grouped Search

query({query: "authentication middleware"})

processes:
  - summary: "LoginFlow"
    priority: 0.042
    symbol_count: 4
    process_type: cross_community
    step_count: 7

process_symbols:
  - name: validateUser
    type: Function
    filePath: src/auth/validate.ts
    process_id: proc_login
    step_index: 2

definitions:
  - name: AuthConfig
    type: Interface
    filePath: src/types/auth.ts

Context (360-degree Symbol View)

context({name: "validateUser"})

symbol:
  uid: "Function:validateUser"
  kind: Function
  filePath: src/auth/validate.ts
  startLine: 15

incoming:
  calls: [handleLogin, handleRegister, UserController]
  imports: [authRouter]

outgoing:
  calls: [checkPassword, createSession]

processes:
  - name: LoginFlow (step 2/7)
  - name: RegistrationFlow (step 3/5)

Detect Changes (Pre-Commit)

detect_changes({scope: "all"})

summary:
  changed_count: 12
  affected_count: 3
  changed_files: 4
  risk_level: medium

changed_symbols: [validateUser, AuthService, ...]
affected_processes: [LoginFlow, RegistrationFlow, ...]

Rename (Multi-File)

rename({symbol_name: "validateUser", new_name: "verifyUser", dry_run: true})

status: success
files_affected: 5
total_edits: 8
graph_edits: 6     (high confidence)
text_search_edits: 2  (review carefully)
changes: [...]

Cypher Queries

-- Find what calls auth functions with high confidence
MATCH (c:Community {heuristicLabel: 'Authentication'})<-[:CodeRelation {type: 'MEMBER_OF'}]-(fn)
MATCH (caller)-[r:CodeRelation {type: 'CALLS'}]->(fn)
WHERE r.confidence > 0.8
RETURN caller.name, fn.name, r.confidence
ORDER BY r.confidence DESC

Wiki Generation

Generate LLM-powered documentation from your knowledge graph:

# Requires an LLM API key (OPENAI_API_KEY, etc.)
gitnexus wiki

# Use a custom model or provider
gitnexus wiki --model gpt-4o
gitnexus wiki --base-url https://api.anthropic.com/v1

# Force full regeneration
gitnexus wiki --force

The wiki generator reads the indexed graph structure, groups files into modules via LLM, generates per-module documentation pages, and creates an overview page — all with cross-references to the knowledge graph.

Tech Stack

Roadmap

Actively Building

• LLM Cluster Enrichment — Semantic cluster names via LLM API
• AST Decorator Detection — Parse @Controller, @Get, etc.
• Incremental Indexing — Only re-index changed files

Recently Completed

• Constructor-Inferred Type Resolution, self/this Receiver Mapping
• Wiki Generation, Multi-File Rename, Git-Diff Impact Analysis
• Process-Grouped Search, 360-Degree Context, Claude Code Hooks
• Multi-Repo MCP, Zero-Config Setup, 14 Language Support
• Community Detection, Process Detection, Confidence Scoring
• Hybrid Search, Vector Index

Security & Privacy

• CLI: Everything runs locally on your machine. No network calls. Index stored in .gitnexus/ (gitignored). Global registry at ~/.gitnexus/ stores only paths and metadata.
• Web: Everything runs in your browser. No code uploaded to any server. API keys stored in localStorage only.
• Open source — audit the code yourself.

Acknowledgments

• Tree-sitter — AST parsing
• LadybugDB — Embedded graph database with vector support (formerly KuzuDB)
• Sigma.js — WebGL graph rendering
• transformers.js — Browser ML
• Graphology — Graph data structures
• MCP — Model Context Protocol