CodeSeek

Code intelligence CLI tool for Claude Code. AST-based call graph analysis + semantic search — right from your terminal.

Quick Start

# Install via npm (handles setup wizard + binary download automatically)
npm install -g codeseek
# First run — interactive setup wizard configures your embedding model
codeseek
# Index your project
codeseek init
# Search code by symbol name
codeseek search main --limit 10
# Query call graph
codeseek callers main
codeseek callees process_data
# Register with Claude Code / Codex as MCP tools
codeseek install
# Check status
codeseek status
# Auto-index on git commits
codeseek install-hooks

Natural Language Code Search example

╰─$ codeseek search 'how the code embedding work'
1. get_embedding (0.7973)
/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/embedding_service.rs:0
2. EmbeddingService (0.2855)
/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/embedding_service.rs:0
3. EmbeddingData (0.1449)
/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/embedding_service.rs:0
4. EmbeddingResponse (0.1304)
/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/embedding_service.rs:0
5. default_model (0.0450)
/home/do/ssd/iohub/dev/codeseek/rust-core/src/config.rs:0

Function Call Graph example

╰─$ codeseek callgraph apply_rerank
Call graph for'apply_rerank' (depth=1):
== Callers (upstream, depth=1) ==
search (/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/hybrid_search.rs:210)
== Callees (downstream, depth=1) ==
rerank (/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/reranker_service.rs:331)
config (/home/do/ssd/iohub/dev/codeseek/rust-core/src/services/hybrid_search.rs:325)

Install

npm

npm install -g codeseek

The npm package ships a lightweight JS wrapper that handles:

Step	Description
First-run wizard	Interactive CLI prompts for embedding API token, model, and base URL
Binary download	Automatically pulls the correct Rust binary for your platform from GitHub Releases
Pass-through	All commands (init, search, callers, etc.) are forwarded to the native binary

Supported platforms:

Platform	Architecture
macOS	arm64 (Apple Silicon), x64 (Intel)
Linux	x64

Homebrew

brew tap CodeBendKit/codeseek git@github.com:CodeBendKit/codeseek.git
brew install codeseek

From source

# install protoc# macos: brew install protobuf# ubuntu: sudo apt install protoc
git clone https://github.com/CodeBendKit/codeseek.git
cd codeseek
./build.sh --release

build.sh compiles both the TypeScript wrapper (dist/) and the Rust binary, then installs to ~/.codeseek/bin/.

Commands

Command	Description
codeseek	First-time setup wizard (configures embedding model interactively)
codeseek init	Build/update code index (full on first run, MD5-incremental thereafter)
codeseek status	Index statistics: functions, files, last update
codeseek search <query>	Symbol name search (falls back from vector → graph name match)
codeseek callers <symbol>	Find functions that call this symbol
codeseek callees <symbol>	Find functions this symbol calls
codeseek callgraph <symbol>	Query call graph with configurable depth (bi-directional)
codeseek list	List all indexed projects with paths
codeseek install	Register codeseek as MCP tools in Claude Code / Codex
codeseek uninstall	Remove MCP integration
codeseek uninit	Delete the current project index
codeseek install-hooks	Install git hooks (post-commit/post-merge → codeseek init)
codeseek serve --mcp	Start MCP server (stdio JSON-RPC, used by Claude Code internally)

All query commands support --json for machine-readable output.

Claude Code / Codex Integration

codeseek install

Writes MCP server config to:

Agent	Config file
Claude Code	~/.claude.json (global, all projects) or ./.mcp.json (project-local)
Codex CLI	~/.codex/config.toml

Claude Code auto-discovers these tools after restart:

Tool	Capability
codeseek_search	Find symbols by name
codeseek_callers	Trace upstream callers
codeseek_callees	Trace downstream callees
codeseek_callgraph	Query call graph with configurable depth (bi-directional)
codeseek_status	Check index health

Remove integration:

codeseek uninstall

How It Works

Index Building (codeseek init)

Source files
→ Tree-sitter AST parse (7 languages)
→ Extract functions / classes / methods
→ Batch embed via API (20 texts per call, SQLite cache)
→ Store vectors in LanceDB
→ Build BM25 index in Tantivy
→ Serialize call graph (PetCodeGraph)
→ Save to ~/.codeseek/<project_hash>/

Idempotent: first run is full build, subsequent runs compare MD5 hashes — only changed files are re-processed. Use codeseek install-hooks for automatic re-index on git commit/merge.

Hybrid Search Pipeline (codeseek search)

 ┌─────────────────────┐
User query ────────────→│ Embedding Model │──→ Query vector
└─────────────────────┘
│
┌───────────────────────┼───────────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌───────────────┐ ┌───────────────┐
│ Dense Search │ │ Sparse Search │ │ Graph Search │
│ (LanceDB ANN)│ │ (Tantivy BM25)│ │ (PetCodeGraph)│
└──────┬───────┘ └──────┬────────┘ └────────┬──────┘
│ │ │
└──────────────────────┼────────────────────────┘
▼
┌─────────────────┐
│ RRF Fusion │ ← Reciprocal Rank Fusion
│ (Top-20 candidates)│
└────────┬────────┘
│
▼
┌─────────────────┐
│ Reranker │ ← Cross-Encoder fine re-ranking
│ (Qwen3-Reranker)│ scores each (query, code) pair
└────────┬────────┘
│
▼
┌─────────────────┐
│ Final Results │ ← Top-5 (or Top-N)
└─────────────────┘

Stage	Technology	Role	Speed
Dense Search	LanceDB + Embedding Model	Semantic vector similarity	Fast
Sparse Search	Tantivy BM25	Keyword & token matching	Fast
RRF Fusion	Reciprocal Rank Fusion	Merge heterogeneous scores fairly	Instant
Reranker	Cross-Encoder (Qwen3-Reranker-4B)	Full-interaction precision scoring	~1-2s
Fallback	PetCodeGraph	Graph-based name search (no API needed)	Instant

If embedding/Reranker are unavailable, the pipeline falls back gracefully to graph-based name search.

Storage

• Config: ~/.codeseek/config.json (global, shared across all projects)
• Index: ~/.codeseek/<md5(project_root)>/
  • project.json — Project metadata
  • graph.bin — Serialized call graph
  • embeddings.lance/ — LanceDB vector data
  • tantivy_bm25/ — BM25 full-text index
  • file_hashes.json — MD5 incremental tracking

No daemon, no HTTP server. Every command is a standalone process.

Supported Languages

Language	Functions	Structs/Classes	Call Graph
Rust	✅	✅	✅
Python	✅	✅	✅
JavaScript	✅	✅	✅
TypeScript	✅	✅	✅
Go	✅	✅	✅
C/C++	✅	✅	✅
Java	✅	✅	✅

Configuration

~/.codeseek/config.json:

{
"embedding": {
"provider": "openai-compatible",
"model": "Qwen/Qwen3-Embedding-4B",
"api_token": "sk-...",
"api_base_url": "https://api.siliconflow.cn/v1",
"dimensions": 2560
},
"index": {
"min_code_block_length": 16,
"enable_reranker": true,
"hybrid": {
"enable_bm25": true,
"bm25_top_k": 20,
"vector_top_k": 20,
"rrf_k": 60,
"rrf_top_k": 20
},
"reranker": {
"enabled": true,
"model": "Qwen/Qwen3-Reranker-4B",
"api_token": "sk-...",
"api_base_url": "https://api.siliconflow.cn/v1/rerank",
"top_n": 5,
"candidate_multiplier": 5,
"timeout_secs": 60
}
},
"installed_hooks": {}
}

Model Roles

Model	Role	When
Qwen/Qwen3-Embedding-4B	Converts code → vectors for dense search	Index building
Qwen/Qwen3-Reranker-4B	Scores (query, code) pairs for precision	Search time

Set via the interactive wizard on first run, or create manually.

Development

cd rust-core
# Build
cargo build
# Build + install to ~/.codeseek/bin/cd .. && ./build.sh --release
# Run tests
cargo test# Compile TypeScript wrapper
npm run build

License

MIT

Built with: Tree-sitter · Petgraph · LanceDB · Tantivy · Tokio · Clap