terrain-ai 2.0.0

Give your AI a complete map of any codebase — function signatures, call graphs, and semantic search

Terrain

English | Chinese / CN

Give your AI coding assistant a complete map of any codebase — function signatures, call graphs, and semantic search across every line of code.

The Problem

You drop a 500,000-line codebase in front of Claude Code. It reads what it can see. It guesses what it can't. You get answers that are almost right.

Terrain indexes the entire codebase once, then gives your AI a precise, queryable knowledge graph — so it stops guessing.

What This Looks Like

Ask Claude Code about an unfamiliar codebase:

"How does the authentication token get refreshed?"

Without Terrain, the AI skims files and makes educated guesses — possibly missing the real implementation buried three call levels deep.

With Terrain:

find_api("authentication token refresh")

→ refresh_access_token() in auth/token_manager.c:187
  Signature: int refresh_access_token(TokenCtx *ctx, const char *refresh_token)
  Called by: session_heartbeat() → event_loop_tick() → main()
  Calls:     http_post(), parse_jwt(), update_session_store()

Precise. Complete. Instant.

Quick Start

npx terrain-ai@latest --setup

The setup wizard installs the Python package, configures your LLM and embedding provider, and registers Terrain as a global MCP server for Claude Code. One command.

Then in Claude Code — point it at any repo and ask anything.

Index a Codebase

terrain index /path/to/your/repo

Takes a few minutes the first time. Incremental updates after that:

terrain index -i   # git-diff based, fast

What You Can Ask

You want to know...	What to ask
Where does X get initialized?	"find where X is initialized"
What calls this function?	"find callers of function_name"
How does feature Y work end-to-end?	"trace the call chain for Y"
What functions handle Z?	"find Z handler"

Supported Languages

C/C++, Python, JavaScript/TypeScript, Rust, Go, Java, Scala, C#, PHP, Lua

---

Reference

Install

# Recommended
npx terrain-ai@latest --setup

# Or via pip
pip install terrain-ai
terrain-mcp  # Start MCP server

Uninstall

npx terrain-ai@latest --uninstall

Removes: Claude MCP registration, Python package, workspace data.

MCP Client Configuration

Add to your MCP client config (Claude Code, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "terrain": {
      "command": "npx",
      "args": ["-y", "terrain-ai@latest", "--server"]
    }
  }
}

On Windows:

{
  "mcpServers": {
    "terrain": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "terrain-ai@latest", "--server"]
    }
  }
}

CLI Tool (`terrain`)

Workspace

terrain status              # Show active repository, workspace, LLM & embedding info
terrain list                # List all indexed repositories
terrain repo                # Interactively switch active repository
terrain config              # Interactive configuration wizard (LLM, embedding, workspace)
terrain link <path>         # Link a local repo to shared pre-built artifacts
terrain link <path> --db x  # Link to a specific artifact directory

Indexing

terrain index               # Index current directory (graph → api-docs → embeddings)
terrain index /path/to/repo # Index a specific path
terrain index -i            # Incremental update (git-diff based, fast)
terrain index --no-embed    # Skip embedding generation
terrain index --no-wiki     # Skip wiki generation only

Rebuild & Clean

terrain rebuild             # Rebuild all steps for active repository
terrain rebuild --step graph   # Rebuild only the graph
terrain rebuild --step api     # Rebuild only API docs
terrain rebuild --step embed   # Rebuild only embeddings
terrain rebuild --step wiki    # Rebuild only wiki

terrain clean               # Remove indexed data (interactive)
terrain clean repo_name     # Remove specific repository
terrain clean --all         # Remove all indexed repositories

Low-Level Commands

terrain scan /path          # Scan repo and build knowledge graph
  --backend kuzu|memgraph|memory
  --db-path ./graph.db
  --exclude "vendor,build"
  --language "c,python"
  --clean               # Clean DB before scanning
  -o graph.json         # Export graph to JSON

terrain query "MATCH (f:Function) RETURN f.name LIMIT 10"
  --format table|json

terrain export /path -o graph.json
  --build               # Build graph before exporting

terrain stats               # Show graph statistics (nodes, relationships)

Global Flags

terrain --version           # Show version
terrain -v ...              # Verbose/debug output
terrain --help              # Show help

MCP Tools

Core workflow for AI agents: initialize_repository → find_api → get_api_doc

Repository Management

Tool	Description
initialize_repository	Index a repo: graph + API docs + embeddings
get_repository_info	Active repo stats (node/relationship counts, service status)
list_repositories	All indexed repos with pipeline completion status
switch_repository	Switch active repo for queries
link_repository	Reuse existing index for a different repo path (no re-indexing)

Code Search & Documentation

Tool	Description
find_api	Hybrid semantic + keyword search with API doc (primary search tool)
list_api_docs	Browse L1 module index or L2 module details
get_api_doc	L3 function detail: signature, call tree, usage examples, source
generate_api_docs	Generate/update API docs (full / resume / enhance)

Call Graph Analysis

Tool	Description
find_callers	Find all functions that call a specific function (no LLM required)
trace_call_chain	BFS upward call chain trace with entry point discovery

Configuration & Maintenance

Tool	Description
get_config	Show server configuration and service availability
rebuild_embeddings	Build or rebuild vector embeddings

Pipeline

Step	What	Input	Output
1. graph-build	Tree-sitter AST parsing	Source code	Kuzu graph database
2. api-doc-gen	Query graph, render docs	Graph	3-level Markdown (index / module / function)
2b. desc-gen	LLM generates descriptions	Functions without docstrings	Descriptions in L3 Markdown
3. embed-gen	Vectorize function docs	L3 Markdown files	Vector store (pickle)

initialize_repository  ->  Steps 1-3 (full pipeline)
build_graph            ->  Step 1 only
generate_api_docs      ->  Step 2 + 2b (modes: full / resume / enhance)
rebuild_embeddings     ->  Step 3
generate_wiki          ->  Separate (not in main pipeline)

API Documentation Format

Generated docs are optimized for both AI agent reading and vector retrieval.

L3 Function Detail (embedding unit)

# parse_btype

> Parse base type declaration including struct/union/enum specifiers.

- Signature: `int parse_btype(CType *type, AttributeDef *ad, int ignore_label)`
- Return: `int`
- Visibility: static | Header: tccgen.h
- Location: tccgen.c:139-280
- Module: tinycc.tccgen --C code generator

## Call Tree

parse_btype
|-- expr_const           [static]
|-- parse_btype_qualify   [static]
|-- struct_decl           [static]
|   |-- expect
|   `-- next
`-- parse_attribute       [static]

## Called by (5)

- type_decl (tinycc.tccgen) -> tccgen.c:1200
- post_type (tinycc.tccgen) -> tccgen.c:1350

C/C++ Specific Features

• Extracts // and /* */ comments above functions as descriptions
• Struct/union/enum members displayed with types
• Macro definitions in dedicated section
• Static/public/extern visibility classification
• Memory ownership inference from signatures
• Header/implementation file split
• Cross-file function call resolution via #include header mapping
• Function pointer tracking and indirect call resolution
• GB2312/GBK encoding support for source files

Supported Languages (detail)

Language	Functions	Classes/Structs	Calls	Imports	Types
C / C++	Yes	struct, union, enum, typedef, macro	Yes	#include	Yes
Python	Yes	Yes	Yes	Yes	-
JavaScript / TypeScript	Yes	Yes	Yes	Yes	-
Rust	Yes	struct, enum, trait, impl	Yes	Yes	-
Go	Yes	struct, interface	Yes	Yes	-
Java	Yes	class, interface, enum	Yes	Yes	-
Scala	Yes	class, object	Yes	Yes	-
C#	Yes	class, namespace	Yes	-	-
PHP	Yes	class	Yes	-	-
Lua	Yes	-	Yes	-	-

Graph Schema

Nodes: Project, Package, Module, File, Folder, Class, Function, Method, Type, Enum, Union

Relationships: CONTAINS_*, DEFINES, DEFINES_METHOD, CALLS, INHERITS, IMPLEMENTS, IMPORTS, OVERRIDES

Properties: qualified_name (PK), name, path, start_line, end_line, signature, return_type, visibility, parameters, kind, docstring

Architecture

The project follows a 5-layer harness architecture:

L4  entrypoints/         MCP server, CLI
L3  domains/upper/       apidoc, rag, guidance, calltrace
L2  domains/core/        graph, embedding, search
L1  foundation/          parsers, services, utils
L0  foundation/types/    constants, models, type definitions

Environment Variables

LLM (first match wins)

Variable	Purpose	Default
LLM_API_KEY	Generic LLM key (highest priority)	-
LLM_BASE_URL	API endpoint	https://api.openai.com/v1
LLM_MODEL	Model name	gpt-4o
OPENAI_API_KEY	OpenAI or compatible	-
MOONSHOT_API_KEY	Moonshot / Kimi (legacy)	-

Embedding

Variable	Purpose	Default
DASHSCOPE_API_KEY	DashScope (Qwen3 Embedding)	-
DASHSCOPE_BASE_URL	DashScope endpoint	https://dashscope.aliyuncs.com/api/v1

System

Variable	Purpose	Default
TERRAIN_WORKSPACE	Workspace directory	~/.terrain

Installation Options

Install from PyPI

# Core (includes C/C++, Python, JS/TS grammars)
pip install terrain-ai

# With all language grammars (Rust, Go, Java, Scala, Lua)
pip install "terrain-ai[treesitter-full]"

Install from local source

git clone https://github.com/JeremyJiao01/CodeGraphWiki.git
cd CodeGraphWiki

# Install with all language grammars
pip install ".[treesitter-full]"

# Or install in editable mode for development
pip install -e ".[treesitter-full]"

Build and install from wheel

git clone https://github.com/JeremyJiao01/CodeGraphWiki.git
cd CodeGraphWiki

python3 -m build
pip install dist/terrain_ai-*.whl

Development

git clone https://github.com/JeremyJiao01/CodeGraphWiki.git
cd CodeGraphWiki
pip install -e ".[treesitter-full]"

python3 -m pytest tests/ -v

# Integration tests (requires tinycc repo at ../tinycc)
python3 -m pytest tests/domains/core/test_graph_build.py -v      # ~3 min
python3 -m pytest tests/domains/upper/test_api_docs.py -v        # ~3 min
python3 -m pytest tests/domains/core/test_step3_embedding.py -v  # ~27 min (API calls)
python3 -m pytest tests/domains/upper/test_api_find_integration.py -v  # ~47 min (full pipeline)

License

MIT