Quick Start

Get from zero to semantic code search in under 60 seconds.

1. Initialize & Index

cd /path/to/your-project
codex init          # Creates .codex/ directory
codex index .       # Index the entire codebase
codex doctor        # Verify everything is healthy

2. Search Your Code

# Semantic search (natural language)
codex search "authentication middleware"

# Get JSON output for tooling
codex search "error handling" --json

3. Explore Symbols

# Explain a function or class
codex explain process_payment

# Get rich context
codex context process_payment

# Trace dependencies
codex deps src/api/handlers.py

4. AI-Powered Analysis

Requires LLM Configuration

Set up an LLM provider in .codex/config.json first. See Installation.

# Ask questions about your code
codex ask "How does the authentication flow work?"

# Multi-turn conversation
codex chat

# AI code review
codex review src/api/auth.py

# Autonomous investigation
codex investigate "Find all security vulnerabilities"

5. Quality & Metrics

# Analyze code quality
codex quality src/

# Find risky hotspots
codex hotspots

# Impact analysis (what breaks if I change this?)
codex impact

# Enforce quality gates in CI
codex gate

6. VS Code Integration

Add CodexA as a Copilot instruction source for your project:

mkdir -p .github

Create .github/copilot-instructions.md referencing CodexA commands. See the AI Agent Setup for the full integration guide.

Configure VS Code settings:

{
  "github.copilot.chat.codeGeneration.instructions": [
    { "file": ".github/copilot-instructions.md" }
  ]
}

Now Copilot Chat in Agent mode will automatically use CodexA for code understanding.

7. Start the Bridge Server (Optional)

For persistent IDE or agent connections:

codex serve --port 24842

Agents can then call http://127.0.0.1:24842/tools/invoke directly.

What's Next?

• CLI Reference — All 36 commands
• Architecture — How CodexA works internally
• Plugin System — Extend with custom hooks
• AI Workflows — Multi-turn chat, investigation chains