Terrain — prepares the ground so agents don't have to guess where to stand.
🔗 GitHub: https://github.com/sopaco/terrain
Sound Familiar?
You inherit a new project and open hundreds of files, blindly searching for architectural information. You ask an AI assistant to help understand the codebase, but it can only grep through the repository in real-time. After every refactoring, the Wiki documentation drifts from reality—and nobody notices.
Terrain was built to solve exactly these problems.
It's an engineering environment management platform for both human developers and AI coding assistants. Point it at a Git repository, and Terrain automatically scans the code, generates architectural documentation, packages source code indexes, and builds a knowledge system—so every user (human or AI) can understand the entire project in minutes instead of days.
Above: Terrain's desktop project list interface. Each project displays a freshness score, giving you instant visibility into whether your knowledge is current.
Three Core Pillars
| Pillar | Metaphor | What You Get |
|---|---|---|
| Knowledge Assets | 🗺️ Map | C4 architecture docs and Agent context auto-generated from code, always in sync |
| Agent Toolchain | 🛤️ Road | CodeGraph, RTK, Terrain CLI—one command to install everything |
| Conventions & Workflows | 📍 Signpost | Skills, AGENTS.md, four-phase SDD workflow—keeps AI working by the same rules |
One Codebase, Two Audiences
Terrain's core philosophy: the same knowledge serves both humans and AI.
| Audience | Path | Format |
|---|---|---|
| Human Developers | .terrain/human/ |
Narrative C4 docs with Mermaid diagrams |
| AI Coding Assistants | .terrain/agent/context.md |
Structured architecture overview (≤ 14 KiB) |
| AI Source Retrieval | .terrain/agent/repomix.md |
Repomix source packs—grep on demand, not preloaded |
| Business Knowledge | .terrain/knowledge/ |
Business glossary and internal conventions |
Knowledge Factory: Starting from a Git repository, through scanning, packing, context generation, and documentation output—producing dual-track knowledge assets.
Why Terrain?
- 🕐 Minutes-level onboarding — Register repo → run init → get full C4 docs and Agent-ready context. Compress days of familiarization into minutes.
- 🔄 Incremental updates — Tracks Git HEAD, regenerates only changed portions, not full rebuilds.
- 📊 Freshness scoring — Every knowledge asset carries a score. Agents automatically reduce the weight of stale information.
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🤝 Unified Agent contract — Claude Code, Codex, OpenCode, Cursor all access knowledge through the same
terrain toolsinterface. - ⚡ Native Rust performance — Single binary, no runtime, no database. scan/pack/search/freshness all run offline.
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🧩 One-click toolchain deployment —
terrain env applyinstalls CodeGraph, RTK, and preset Skills in dependency order.
Architecture at a Glance
Key Architecture Points:
-
terrain-coreis the domain core—no LLM execution. It handles scanning, packing, searching, freshness scoring, and other pure computation tasks. -
terrain-agentis the execution layer, orchestrating DeepWiki Q&A, knowledge generation, and SDD workflows. - Lightweight tasks go through native LLM (ADK Runner); heavy tool calls go through ACP subprocesses.
- All Rust types auto-generate TypeScript frontend types via ts-rs, ensuring zero drift in IPC contracts.
Get Started
# Pre-compiled installers (recommended)
# Download from GitHub Releases for macOS / Windows
# Or install CLI via npm
npm install -g @terrain-ai/cli
# Register and initialize a project
terrain init ./my-repo
# Query the knowledge base
terrain ask query "How does the authentication flow work?" --project my-repo
Community & Ecosystem
Terrain evolved from Litho/deepwiki-rs (1.7k★), battle-tested at scale. It extends Litho's documentation capabilities into a full engineering environment platform—adding environment standardization, workflow management, and Agent bridging.
MIT licensed. Contributions welcome!
"Terrain prepares the ground so agents don't have to guess where to stand."
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