AgentPack Docs
AgentPack is a local context engine for AI coding agents. It ranks relevant repository files and builds compact task-focused context packs for Claude Code, Codex, Cursor, Windsurf, Antigravity, MCP tools, CI jobs, and markdown-based LLM workflows.
Use these docs when you want local/offline repo analysis, MCP-first routing, CI-friendly context packs, and benchmarkable file-selection quality without hosted indexing or embeddings.
Get started
- Commands: CLI reference and common workflows.
- Configuration: config, scoring weights,
.agentignore, and git integration. - How AgentPack works: route, pack, retrieve, learn, and benchmark flow.
- Demo assets: generated README GIF/MP4 and regeneration command.
Core onboarding uses agentpack init, agentpack route, agentpack pack,
agentpack doctor, and agentpack benchmark. The rest of the CLI is advanced
workflow, release, learning, or diagnostic surface.
Agents and IDEs
- Integrations: setup paths for Claude Code, Codex, Cursor, Windsurf, Antigravity, and generic agents.
- Agent and IDE plugins: thin plugin/rule distribution layer for Codex, Cursor, Windsurf, Copilot, Cline, Kiro, OpenCode, and more.
- Codex plugin: packaged Codex plugin skeleton and
@agentpack-*commands. - Claude Code context engine: Claude Code setup and MCP-first context.
- Cursor context packing: Cursor setup and context workflows.
- MCP context engine: MCP tools for fresh task context.
- AgentPack for AI agents: short guide for agent maintainers.
Guides
- AI coding agent context packing: why ranked task context helps agent workflows.
- Reduce Claude Code token usage: token-focused usage guide.
- Agent behavior before and after: concrete cold-start examples.
Evidence
- Benchmarking: quality bar, release gate, sample fixtures, and public artifacts.
- Benchmark learnings: current tuning decisions and known bottlenecks.