Getting Started

System requirements

• Python 3.11+ (tested on 3.11, 3.12, 3.13)
• A C compiler and cmake (needed to build tree-sitter grammars)

Platform	Setup
macOS	xcode-select --install
Ubuntu/Debian	sudo apt install build-essential cmake
Fedora/RHEL	sudo dnf install gcc gcc-c++ cmake
Windows	Visual Studio Build Tools (C++ workload) + CMake

Install

CCE needs an embedding backend to index your code. Pick one:

Option	Install command	What it needs
Local (recommended)	uv tool install "code-context-engine[local]"	Nothing else. Includes fastembed + ONNX Runtime (~60 MB download on first run).
Ollama	uv tool install code-context-engine	Ollama running at localhost:11434 with nomic-embed-text pulled.

Using pipx instead of uv:

pipx install "code-context-engine[local]"

Caution

Installing without [local] and without Ollama running will cause cce init to fail with “No embedding backend available.” Always pick one of the two options above.

Initialize your project

cd /path/to/your/project
cce init

This does everything:

• Detects your embedding backend (fastembed or Ollama)
• Builds vector, FTS, and graph indexes
• Installs git hooks (auto-updates index on commit)
• Writes MCP config for detected editors
• Creates instruction files with output compression rules

Target a specific agent

cce init --agent claude     # Claude Code only
cce init --agent codex      # Codex CLI only
cce init --agent copilot    # VS Code / Copilot only
cce init --agent all        # Every supported editor

Verify it works

Restart your editor, then ask a question about your code. The agent will call context_search via MCP instead of reading files.

Check your savings:

cce savings

  my-project · 5 queries

  ⛁ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶  93% tokens saved

  Input savings   42.1k  tokens   $0.63
  Output savings  1.2k  tokens   $0.09
  ──────────────────────────────────────────
  Total saved   43.3k  tokens   $0.72

Embedding backends

CCE auto-detects the best available backend at init time:

1. fastembed (with [local] extra) — Uses BAAI/bge-small-en-v1.5. Works offline, no external services needed. ~60 MB model downloaded on first run.
2. Ollama — If running at localhost:11434 with nomic-embed-text pulled. Zero extra Python dependencies.

Force a specific backend with CCE_EMBED_BACKEND=fastembed or CCE_EMBED_BACKEND=ollama.

Next steps

• Multi-agent setup — Configure all your editors
• Configuration — Tune compression, embedding, and more
• CLI Reference — All available commands