Octocode - Agentic Research Platform

Evidence-first code research for AI agents and developers.

Octocode gives an agent the full context it needs to change, review, or explain code: real evidence from your local workspace and from external sources (GitHub repositories, pull requests, and npm packages). One toolset covers all of it: ripgrep and AST structural search, repository tree browsing, precise content fetching, LSP semantic navigation, and binary inspection.

Run it as a CLI or an MCP server. A Rust engine keeps every call fast and token-efficient, minifying and skeletonizing code so an agent reads the shape of a file instead of every byte, from a single file to a mega-repo. It is also the best tool for cross-repository research and exploration across millions of repositories.

Table of Contents

• Quick Start
• Why Octocode
• What You Can Do
• Tools
• MCP
• CLI
• Configuration
• Authentication Methods
• Security
• Language Support
• Skills
• Architecture
• Documentation
• Troubleshooting

Why Octocode

Agents write better code from evidence than from assumptions. Octocode turns guess-driven work into research-driven work. Before an agent changes, reviews, or explains code, it gathers real evidence from your local workspace and from GitHub repositories, pull requests, and npm packages, then hands it back as compact, citable context. Code is truth; context is the map.

Most tools cover one slice: searching the web, or grepping your repo. Octocode covers the whole research flow, end to end:

• Built for scale. In organizations with thousands of repositories and endless code, Octocode is the solution: spot a pattern in one repo, follow it through the pull request that introduced it, then trace the same shape across other repos and your own files without leaving the conversation. Clone any repo and study it locally, on any machine.
• Smart GitHub workflows. Parallel bulk queries and built-in next-step hints keep the agent on the cheapest path: search broadly, read narrowly, trace semantically. Each result points to the natural follow-up.
• No GitHub required. Even without GitHub, clone any repository locally and point Octocode's local tools (search, structural AST, LSP, content) at it for the same evidence-first research.
• Reads the shape, not the noise. Code is minified and skeletonized on the fly across 70+ languages, so an agent grasps a 100 KB file in a few hundred tokens instead of spending its context on boilerplate.
• Fast and self-contained. Search, parsing, semantic navigation, and redaction run in one prebuilt Rust engine: quick on a laptop or a mega-repo, with no extra toolchain to install.
• Safe by default. Every byte returned to the model is scanned and secrets redacted first (see Security).

What You Can Do

Octocode is useful whenever the next coding step depends on finding and proving context, not guessing it.

See Quick Start to install in your terminal or AI assistant.

Tools

Octocode ships 14 research tools; the same implementations run identically over MCP and the CLI. Local tools are enabled by default; ENABLE_LOCAL=false disables them. ghCloneRepo is opt-in for MCP (ENABLE_CLONE=true) and enabled by default for CLI. All flags: Configuration Reference.

Token knobs. concise:true returns path/title-only lists. minify controls file read density: symbols = skeleton with line numbers, standard = comments/blanks stripped (default), none = exact bytes.

GitHub Tools

Local Tools

Package Search

LSP

OQL Search

Full schemas, fields, and examples for every tool live in docs/OCTOCODE_TOOLS.md (linked under Documentation).

MCP

The MCP server exposes the Octocode tool catalog directly to your AI assistant over stdio.

https://github.com/user-attachments/assets/de8d14c0-2ead-46ed-895e-09144c9b5071

Manual Configuration

Add to your MCP client config. Pick the package that matches the version you want:

New Octocode (Rust-powered engine) — use @octocodeai/mcp:

{
  "octocode": {
    "command": "npx",
    "type": "stdio",
    "args": [
      "@octocodeai/mcp@latest"
    ]
  }
}

Classic octocode-mcp — use octocode-mcp:

{
  "octocode": {
    "command": "npx",
    "type": "stdio",
    "args": [
      "octocode-mcp@latest"
    ]
  }
}

Add a GitHub token and options under env — see Authentication and Configuration.

CLI

Same research engine, no MCP client needed. Local paths route to local tools; owner/repo[/path] routes to GitHub.

npx octocode auth login   # authenticate once
npx octocode status       # verify setup
npx octocode --help       # full usage

Commands

Search

More commands

• GitHub & npm — npx octocode search <…> --target repositories|packages|pullRequests|commits|artifacts|diff
• LSP — npx octocode search <file> --op definition|references|callers|callees|hover|diagnostic|callHierarchy (--symbol, --line to narrow)
• Cache & clone — npx octocode clone, npx octocode unzip, npx octocode cache fetch|status|clear
• Skills — npx octocode skill --list | --name <skill> | --add <github-path> | --install-all
• Language servers — npx octocode lsp-server list|install|status|uninstall|clean
• Setup & introspection — npx octocode install, npx octocode auth, npx octocode status, npx octocode tools, npx octocode context

Full syntax, flags, and exit codes: Octocode CLI Guide

Authentication Methods

GitHub-backed tools require authentication. Any one method is enough. Full details: Authentication Setup.

Option 1: Octocode CLI (Recommended)

npx octocode auth login
npx octocode status       # verify the active token source

Interactive login lets you choose Octocode browser OAuth or gh auth login. Octocode OAuth credentials are stored encrypted on disk.

Option 2: GitHub CLI (also supported)

gh auth login

Octocode reads the gh token automatically — no further config needed.

Option 3: Personal Access Token (also supported)

Set OCTOCODE_TOKEN, GH_TOKEN, or GITHUB_TOKEN in your shell. Required scopes: repo, read:user, read:org.

Create a token at github.com/settings/tokens.

Security tip: Never commit tokens to version control. Use environment variables or secure secret management.

Security

Every byte that reaches the model is scanned and redacted first. All content (local files, GitHub and npm responses, error messages, and tool outputs) passes through the Rust engine's secret scanner on the way in (tool inputs) and on the way out (results), so secrets never reach the LLM. The same enforcement runs identically under MCP and the CLI.

• Secret redaction, in and out. 300+ provider credential patterns (AWS, Azure, GCP, GitHub, OpenAI, Anthropic, Stripe, Slack, 1Password, and more) plus generic JWTs, PEM/private keys, bearer tokens, database connection strings, and high-entropy strings. Masked values surface a redaction warning so the agent knows.
• Content sanitized at the source. Local reads (localGetFileContent, ripgrep, structural search, binary, file discovery, structure) and external fetches (GitHub code/files, npm) are scanned as they are read, not only at the boundary.
• Path safety. Relative inputs resolve from WORKSPACE_ROOT / config / cwd, then local reads are bounded to the engine's allowed roots (home by default, plus ALLOWED_PATHS and Octocode-registered roots). Symlinks are resolved and the real target is re-validated, so a link cannot escape into a blocked location.
• Sensitive files and directories are blocked by default. Octocode refuses to read known secret-bearing files and folders wherever they live, returning a redacted error instead of contents. Blocked patterns include:
  • Keys and certs: *.pem, *.key, *.crt/*.cer/*.csr, *.p12/*.pfx/*.jks/*.keystore, and SSH keys (id_rsa, *_ed25519, authorized_keys, known_hosts, .ssh/).
  • Credentials and tokens: .env / .env.*, .netrc, .npmrc, .pgpass, .git-credentials, *_token / .token, client_secret*.json, *service-account*.json, auth.json, .htpasswd.
  • Cloud and infra: .aws/, .azure/, .config/gcloud/, .kube/ / kubeconfig, .docker/, .terraform/ and *.tfstate.
  • OS and app secret stores: .git/, secrets/, private/, browser login data (Chrome/Firefox), OS keychains, password managers (*.kdbx), shell history files, and crypto wallets.
• Command safety. Normal local search runs in-process inside octocode-engine. External helpers are fixed per lane, command/argument allowlisted, and run via spawn with argument arrays: no shell strings, no injection.
• Schema validation runs before any tool executes; untrusted input size and shape are bounded.
• Credentials. GitHub auth via env tokens, AES-256-GCM-encrypted on-disk OAuth, or the gh CLI; tokens are never logged.

Full security model, pipeline, and threat coverage: SECURITY.md. Related: Authentication · Configuration · Credentials

Language Support

Four code-intelligence axes; three are native to the Rust engine and need no external tooling:

📋 Full support matrix: every extension with its exact AST, signature, LSP, and minify capability, machine-generated from the shipped binary, lives in the Full format support matrix (151 extensions: 61 AST, 47 signature, 32 LSP, 90 minify-only). Regenerate or verify with yarn workspace @octocodeai/octocode-benchmark matrix:check.

Skills

Agent Skills are a lightweight, open format for extending AI agent capabilities. Browse and install on skills.sh/bgauryy/octocode-mcp

These are the skills the Octocode team itself uses to build Octocode. 9 skills live under skills/; the table below is the full index. ⭐ Research is the recommended starting skill for technical research, code work, reviews, refactors, and repeated evidence loops.

Each skill folder includes a human README with purpose, features, workflow, developer notes, and npx octocode skill installation. SKILL.md stays the compact agent-facing router.

Install them with the Octocode CLI through npx octocode; no global install is required. Octocode refreshes the canonical source in ~/.octocode/skills/<skill> and links it into the platform location by default. Pick the platform your agent reads from, or use common for the shared ~/.agents/skills folder.

npx octocode skill --list                                      # browse available Octocode skills
npx octocode skill --name octocode-research                    # install to ~/.agents/skills (common)
npx octocode skill --name octocode-research --platform pi      # install for Pi
npx octocode skill --name octocode-research --platform all --dry-run  # preview before installing everywhere
npx octocode skill --add owner/repo/skills/my-skill            # install any GitHub skill folder
npx octocode skill --add owner/repo/skills                     # install every skill in a GitHub skills library
npx octocode skill --install-all                               # install every official Octocode skill to ~/.agents/skills
npx octocode skill --help                                      # read live flags

Platforms: common (default, ~/.agents/skills), cursor (~/.cursor/skills), claude (~/.claude/skills and ~/.claude-desktop/skills), codex (~/.agents/skills), opencode (~/.config/opencode/skills), pi (~/.pi/agent/skills), copilot (~/.copilot/skills), gemini (~/.gemini/skills), all · Modes: symlink (default), copy, hybrid

Architecture

A yarn-workspaces monorepo. The MCP server and the CLI are thin front-ends over one shared TypeScript tool core, which delegates every CPU-heavy path to a single Rust engine (compiled via napi-rs to prebuilt .node binaries). One tool catalog, one security layer, one response shaper, reached two ways.

graph LR
    CLI["octocode<br/>CLI"]
    MCP["octocode-mcp<br/>MCP server, stdio"]
    VSC["VS Code extension<br/>OAuth + install"]
    CORE["octocode-tools-core<br/>tools, GitHub client, auth, pagination, security bridge"]
    ENGINE["octocode-engine (Rust)<br/>secrets, minify, AST, signatures, ripgrep/diff/YAML, LSP"]
    EXT["GitHub API, local FS + ripgrep, language servers"]

    CLI --> CORE
    MCP --> CORE
    VSC -. starts .-> MCP
    CORE --> ENGINE
    CORE --> EXT
    ENGINE --> EXT

    style ENGINE fill:#1a1a2e,stroke:#e75d2a,color:#fff

Request flow is identical whether a call arrives over MCP or the CLI:

client → sanitize inputs (Rust) → run tool (GitHub / FS / LSP) → sanitize + YAML-serialize + paginate (Rust) → result + next-step hints

One Rust engine owns secret detection, sanitization, path/command validation, minification (70+ languages), signature extraction, structural AST search, ripgrep parsing, diff filtering, YAML serialization, and LSP, so the Node event loop stays unblocked and there is no duplicate native loader. It ships prebuilt for darwin (arm64/x64), linux (arm64/x64, gnu + musl), and win32-x64; no Rust toolchain is needed at runtime.

Packages

Documentation

Website: octocode.ai · Product docs: github.com/bgauryy/octocode/tree/main/docs. This section is the canonical documentation index; benchmark methodology, evals, and run artifacts live in packages/octocode-benchmark.

Agent Workflows

Recommended dev mode: Pi + Octocode

Pi is a fast, local-first coding agent whose stated philosophy is "CLI tools with READMEs (Skills) over MCP." Pairing it with Octocode gives a lean, evidence-driven dev loop — Pi edits, Octocode researches. Two routes, pick by how much surface you need:

• Skill route — recommended, leanest. Drop the octocode-research skill into Pi's global skills dir. It drives the Octocode CLI directly — no MCP transport, minimal token overhead — and Pi auto-discovers it:

  Copy & paste — that's itCopy

  npx octocode skill --name octocode-research --platform pi

• Adapter route — full tool surface. Install pi-mcp-adapter to expose all 14 Octocode MCP tools behind a single ~200-token proxy tool, so servers stay disconnected until a tool is actually called. Enable clone tools with ENABLE_CLONE=true.

Tune Pi's behavior with an APPEND_SYSTEM.md packaged in @octocodeai/pi-extension. The full walkthrough — package install, setup commands, skills, optional MCP setup, and custom models — is in the Pi package README.

Octocode Harness

The Octocode harness is the recommended agent environment for research-driven development: Pi supplies the local coding loop, npx octocode supplies structured code research, and Octocode Skills encode the workflows agents should follow before they edit.

Docs: Pi package README · APPEND_SYSTEM starter

It is deliberately research-oriented because most agent failures start before implementation: the agent guesses the owner of a behavior, trusts a snippet without reading the exact source, or edits before proving blast radius. The harness pushes the agent through a cheaper loop first: orient with trees and discovery output, search with Octocode, read exact evidence, use AST/LSP when identity matters, then patch and verify.

That shape keeps the editing surface small while preserving context for what matters: file anchors, symbols, call paths, PR/history evidence, package sources, and the verification command that proves the change. In short, Pi is the hands, Octocode is the map, and the skills/system prompt make the habit repeatable.

The Manifest

"Code is Truth, but Context is the Map." Read the Manifest of Octocode for Research Driven Development to understand the philosophy behind Octocode.

Quick Start

1. Run the Octocode CLI with npx

npx octocode --help

2. Authenticate with GitHub — optional, but unlocks private repositories and higher API rate limits:

npx octocode auth login
npx octocode status       # verify the active token source

3. Choose your interface. The same engine and 14 tools run identically either way.

🖥️ CLI — research straight from your terminal:

npx octocode

🤖 MCP — one-click install:

• <img src="https://cursor.com/deeplink/mcp-install-dark.svg" alt="Install in Cursor">
• <img src="https://img.shields.io/badge/VS_Code-Install_Server-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white" alt="Install in VS Code">
• <img src="https://img.shields.io/badge/VS_Code_Insiders-Install_Server-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white" alt="Install in VS Code Insiders">
• <img src="https://img.shields.io/badge/Windsurf-Install_Server-1a1a1a?style=flat-square&logoColor=white" alt="Install in Windsurf">
• <img src="https://kiro.dev/images/add-to-kiro.svg" alt="Install in Kiro">
• <img src="https://goose-docs.ai/img/extension-install-dark.svg" alt="Install in Goose">
• <img src="https://files.lmstudio.ai/deeplink/mcp-install-light.svg" alt="Install in LM Studio">

Claude Code:

claude mcp add-json octocode --scope user '{"command":"npx","type":"stdio","args":["@octocodeai/mcp@latest"]}'

Any other client: npx octocode install

➡️ Learn more at octocode.ai.

Configuration

Everything is optional; Octocode runs on sensible defaults. Settings resolve from three sources, in priority order:

environment variables  >  <octocode-home>/.octocoderc  >  built-in defaults

1. MCP / environment variables (highest): per client or per project, set in your MCP config env or your shell.
2. Global config: <octocode-home>/.octocoderc, machine-wide defaults read by both the CLI and the MCP server.
3. Built-in defaults: used when neither is set.

Octocode home (<octocode-home>) holds the global config, encrypted credentials, sessions, stats, and tmp materialization caches. It defaults by platform and can be overridden with OCTOCODE_HOME:

Set values as MCP env entries (per client; these win over .octocoderc) or globally in <octocode-home>/.octocoderc (JSON with comments). Tokens never go in .octocoderc — use env or npx octocode auth login.

Common settings

The Scope column shows where a setting applies: Both, or MCP (the CLI ignores it).

Local defaults on; clone differs by surface. Both CLI and MCP default local tools on; set ENABLE_LOCAL=false to disable them. The CLI defaults clone on, while the MCP server requires ENABLE_CLONE=true.

Per-project overrides and custom LSP servers live in a workspace .octocode/ folder. For the full .octocoderc schema, a ready-to-copy example, clone-cache tuning, GitHub Enterprise setup, and precedence details, see the Configuration Reference.

Troubleshooting

Having Node.js issues? Run:

npx node-doctor check --json

Read the output and fix accordingly.