bgauryy/octocode

**Evidence-first code research for AI agents and developers.**
Octocode researches **your local code and external code alike** (GitHub repositories, PRs, npm) with one toolset: ripgrep + AST search, trees, precise reads, and LSP. Use it as a **CLI** or **MCP server**, backed by a **Rust engine** for fast, token-efficient results across single files or mega-repos.
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Table of contents
- Quick start
- Why Octocode
- Built for research (benchmarks)
- Tools
- MCP
- CLI
- Configuration
- Authentication methods
- Security
- Language support
- Skills
- Architecture
- Documentation
- Troubleshooting
- Agent workflows
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Quick start
**Prerequisites:** Node.js 20.12+
**1. Run the Octocode CLI with npx**
**2. Authenticate with GitHub** - optional, but unlocks private repositories and higher API rate limits:
**3. Choose your interface.** Same tools and Rust engine on both. (Clone is on by
default in the CLI, opt-in for MCP.)
**🖥️ CLI** - research straight from your terminal:
**🤖 MCP** - one-click install:
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Show more install options (Windsurf, Kiro, Goose, LM Studio, Claude Code)
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**Claude Code:**
**Any other client:** npx octocode install
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Use it as an MCP server
Add to your MCP client config (or use a one-click install above):
Put a GitHub token and options under env (see Configuration).
Use it as an agentic-friendly CLI
Run npx octocode and agents figure out the rest. The bare command prints built-in usage and the full tool catalog, so any coding agent knows how to drive it out of the box, no MCP client or extra wiring required.
Every MCP tool is also a plain command: JSON in, token-efficient YAML out. Local paths route to local tools; owner/repo[/path] routes to GitHub.
Learn more at **octocode.ai**.
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Why Octocode
Agents code better from evidence than from guesses. Octocode researches **two worlds with one flow**, your **local code** and **external code** on GitHub and npm, and hands back compact, citable context before an agent changes, reviews, or explains code. *Code is truth; context is the map.*
Most tools do one slice (web search, or grep your repository) and hand back a fixed blob. Octocode covers the whole loop and lets the **agent decide what data it needs next**:
- **Agent-driven, efficient flows.** Instead of one-shot dumps, Octocode chains cheap steps into an optimized research flow: broad code search, then fetch only the **exact matched lines/region**, with **smart pagination** and **out-of-the-box minification** so the model never over-fetches. Every result carries **next-step hints** to the cheapest follow-up.
- **Scales to monorepos.** Spot a pattern in one repository, follow the PR that introduced it, then trace it across other repositories and your own files, without leaving the chat. Clone any repository and study it locally.
- **Smart GitHub flow.** Parallel bulk queries across code, PRs, commits, issues, and repositories, all with the same search-broad, read-narrow, trace-semantically discipline.
- **Works without GitHub.** Clone any repository and point the local tools (search, AST, LSP, content) at it, same evidence-first flow.
- **Reads shape, not noise.** On-the-fly minify/skeletonize across 70+ languages: a 100 KB file in a few hundred tokens, not walls of boilerplate.
- **Fast, self-contained.** Search, parsing, navigation, and redaction run in one prebuilt **Rust engine**: quick on a laptop or a mega-repo, nothing extra to install.
- **Safe by default.** Every byte to the model is scanned and secrets redacted first (see Security).
**What you can do** (whenever the next step needs proven context, not a guess):
| Need | Use Octocode to |
|------|-----------------|
| **Codebase questions** | Search local or GitHub code, read exact regions, browse trees, and carry file/line anchors into the answer. |
| **Implementation research** | Compare patterns across repositories, npm packages, pull requests, commits, and local files before changing code. |
| **Semantic navigation** | Resolve definitions, references, callers/callees, call hierarchy, hovers, symbols, diagnostics, and type relationships through LSP. |
| **Structural matching** | Run AST-shaped searches with patterns or YAML rules so comments and strings do not become false positives. |
| **Large-file context** | Minify, skeletonize, or paginate code so agents spend tokens on relevant structure instead of boilerplate. |
| **Agent workflows** | Same engine through MCP, CLI, and Agent Skills. |
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Built for research (benchmarks)