By 9000000
Code intelligence powered by a knowledge graph. Provides execution flow tracing, blast radius analysis, and augmented search across your codebase.
Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"
Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"
Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"
Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"
Executes bash commands
Hook triggers when Bash tool is used
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⚠️ 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.
The 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 MCP tools so AI agents never miss code.
💬 Discord · 🌐 Web UI · 🏢 Enterprise (SaaS & self-hosted)
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 — a knowledge graph tracks every relationship, not just descriptions.
TL;DR: The CLI + MCP makes your AI agent reliable — it gives Cursor, Claude Code, Antigravity, 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. The Web UI is a quick way to chat with any repo in the browser.
# 1. Index your repo (run from repo root)
npx gitnexus analyze
# 2. Connect your editors (one-time, auto-detects Claude Code, Cursor, Codex, …)
npx gitnexus setup
That's it. analyze indexes the codebase, installs agent skills, registers Claude Code hooks, and creates AGENTS.md / CLAUDE.md context files — all in one command. setup writes the MCP config so your AI agent can use the graph.
On npm 11.x?
npxcan crash during install withCannot destructure property 'package' of 'node.target'(an npm/arborist bug, before GitNexus runs). Use pnpm instead — it builds the native deps explicitly:pnpm --allow-build=@ladybugdb/core --allow-build=gitnexus --allow-build=tree-sitter dlx gitnexus@latest analyzeOr install globally (
npm install -g gitnexus@latest) and rungitnexus analyze. See #1939.
Fastest MCP startup: install globally (
npm i -g gitnexus) before runninggitnexus setup— this writes an absolute-path MCP config that bypassesnpxentirely. On a cold cache, annpx-based MCP install can exceed Claude Code'sMCP_TIMEOUTdefault (~30s).
No C++ toolchain? Set
GITNEXUS_SKIP_OPTIONAL_GRAMMARS=1beforenpm install -g gitnexusto skip the vendored grammar materialize/build fortree-sitter-dart,tree-sitter-proto,tree-sitter-swift, andtree-sitter-kotlin— those four languages won't be parsed, but install completes in seconds withoutpython3/make/g++. Strict=1only — any other value falls through to the rebuild.
npx claudepluginhub 9000000/gitnexus --plugin gitnexusPermanent coding companion for Claude Code — survives any update. MCP-based terminal pet with ASCII art, stats, reactions, and personality.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
v9.52.0 - Reliability wave: tangle contextual review correction loop with hard round ceiling, progress-supervised review rounds (per-agent stall watch, descendant-tree kills), council diversity and agy pin fixes, marketplace generator source-of-truth fix, provider troubleshooting runbook and cost-expectations docs. Run /octo:setup.
Enterprise AI agent orchestration plugin with 150+ commands, 74+ specialized agents, SPARC methodology, swarm coordination, GitHub integration, and neural training capabilities
Production-grade vibe coding suite. Structured workflows from discovery to deployment: start, build, ship, fix, refactor.