openrabbit/CLAUDE.md

# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Overview

OpenRabbit is an enterprise-grade AI code review system for Gitea (and GitHub). It provides automated PR review, issue triage, interactive chat, and codebase analysis through a collection of specialized AI agents.

## Commands

### Development

```bash
# Run tests
pytest tests/ -v

# Run specific test file
pytest tests/test_ai_review.py -v

# Install dependencies
pip install -r tools/ai-review/requirements.txt

# Run a PR review locally
cd tools/ai-review
python main.py pr owner/repo 123

# Run issue triage
python main.py issue owner/repo 456

# Test chat functionality
python main.py chat owner/repo "How does authentication work?"

# Run with custom config
python main.py pr owner/repo 123 --config /path/to/config.yml
```

### Testing Workflows

```bash
# Validate workflow YAML syntax
python -c "import yaml; yaml.safe_load(open('.github/workflows/ai-review.yml'))"

# Test security scanner
python -c "from security.security_scanner import SecurityScanner; s = SecurityScanner(); print(list(s.scan_content('password = \"secret123\"', 'test.py')))"
```

## Architecture

### Agent System

The codebase uses an **agent-based architecture** where specialized agents handle different types of events:

1. **BaseAgent** (`agents/base_agent.py`) - Abstract base class providing:
   - Gitea API client integration
   - LLM client integration with rate limiting
   - Common comment management (upsert, find AI comments)
   - Prompt loading from `prompts/` directory
   - Standard execution flow with error handling

2. **Specialized Agents** - Each agent implements:
   - `can_handle(event_type, event_data)` - Determines if agent should process the event
   - `execute(context)` - Main execution logic
   - Returns `AgentResult` with success status, message, data, and actions taken

   - **PRAgent** - Reviews pull requests with inline comments and security scanning
   - **IssueAgent** - Triages issues and responds to @ai-bot commands
   - **CodebaseAgent** - Analyzes entire codebase health and tech debt
   - **ChatAgent** - Interactive assistant with tool calling (search_codebase, read_file, search_web)

3. **Dispatcher** (`dispatcher.py`) - Routes events to appropriate agents:
   - Registers agents at startup
   - Determines which agents can handle each event
   - Executes agents (supports concurrent execution)
   - Returns aggregated results

### Multi-Provider LLM Client

The `LLMClient` (`clients/llm_client.py`) provides a unified interface for multiple LLM providers:

- **OpenAI** - Primary provider (gpt-4.1-mini default)
- **OpenRouter** - Multi-provider access (claude-3.5-sonnet)
- **Ollama** - Self-hosted models (codellama:13b)

Key features:
- Tool/function calling support via `call_with_tools(messages, tools)`
- JSON response parsing with fallback extraction
- Provider-specific configuration via `config.yml`

### Platform Abstraction

The `GiteaClient` (`clients/gitea_client.py`) provides a unified REST API client for **Gitea** (also compatible with GitHub API):

- Issue operations (create, update, list, get, comments, labels)
- PR operations (get, diff, files, reviews)
- Repository operations (get repo, file contents, branches)

Environment variables:
- `AI_REVIEW_API_URL` - API base URL (e.g., `https://api.github.com` or `https://gitea.example.com/api/v1`)
- `AI_REVIEW_TOKEN` - Authentication token

### Security Scanner

The `SecurityScanner` (`security/security_scanner.py`) uses **pattern-based detection** with 17 built-in rules covering:

- OWASP Top 10 categories (A01-A10)
- Common vulnerabilities (SQL injection, XSS, hardcoded secrets, weak crypto)
- Returns `SecurityFinding` objects with severity (HIGH/MEDIUM/LOW), CWE references, and recommendations

Can scan:
- File content via `scan_content(content, filename)`
- Git diffs via `scan_diff(diff)` - only scans added lines

### Chat Agent Tool Calling

The `ChatAgent` implements an **iterative tool calling loop**:

1. Send user message + system prompt to LLM with available tools
2. If LLM returns tool calls, execute each tool and append results to conversation
3. Repeat until LLM returns a final response (max 5 iterations)

Available tools:
- `search_codebase` - Searches repository files and code patterns
- `read_file` - Reads specific file contents (truncated at 8KB)
- `search_web` - Queries SearXNG instance (requires `SEARXNG_URL`)

## Configuration

### Primary Config File: `tools/ai-review/config.yml`

Critical settings:

```yaml
provider: openai  # openai | openrouter | ollama

model:
  openai: gpt-4.1-mini
  openrouter: anthropic/claude-3.5-sonnet
  ollama: codellama:13b

interaction:
  mention_prefix: "@codebot"  # Bot trigger name - update workflows too!
  commands:
    - explain      # Explain what the issue is about
    - suggest      # Suggest solutions or next steps
    - security     # Security analysis
    - summarize    # Summarize the issue
    - triage       # Full triage with labeling
  
review:
  fail_on_severity: HIGH  # Fail CI if HIGH severity issues found
  max_diff_lines: 800     # Skip review if diff too large
  
agents:
  chat:
    max_iterations: 5  # Tool calling loop limit
```

**Important**: When changing `mention_prefix`, also update all workflow files in `.gitea/workflows/`:
- `ai-comment-reply.yml`
- `ai-chat.yml`
- `ai-issue-triage.yml`

Look for: `if: contains(github.event.comment.body, '@codebot')` and update to your new bot name.

Current bot name: `@codebot`

### Environment Variables

Required:
- `AI_REVIEW_API_URL` - Platform API URL
- `AI_REVIEW_TOKEN` - Bot authentication token
- `OPENAI_API_KEY` - OpenAI API key (or provider-specific key)

Optional:
- `SEARXNG_URL` - SearXNG instance for web search
- `OPENROUTER_API_KEY` - OpenRouter API key
- `OLLAMA_HOST` - Ollama server URL

## Workflow Architecture

Workflows are located in `.gitea/workflows/`:

- **ai-review.yml** / **enterprise-ai-review.yml** - Triggered on PR open/sync
- **ai-issue-triage.yml** - Triggered on `@codebot triage` mention in issue comments
- **ai-comment-reply.yml** - Triggered on issue comments with @bot mentions
- **ai-chat.yml** - Triggered on issue comments for chat (non-command mentions)
- **ai-codebase-review.yml** - Scheduled weekly analysis

**Note**: Issue triage is now **opt-in** via `@codebot triage` command, not automatic on issue creation.

Key workflow pattern:
1. Checkout repository
2. Setup Python 3.11
3. Install dependencies (`pip install requests pyyaml`)
4. Set environment variables
5. Run `python main.py <command> <args>`

## Prompt Templates

Prompts are stored in `tools/ai-review/prompts/` as Markdown files:

- `base.md` - Base instructions for all reviews
- `issue_triage.md` - Issue classification template
- `issue_response.md` - Issue response template

**Important**: JSON examples in prompts must use **double curly braces** (`{{` and `}}`) to escape Python's `.format()` method. This is tested in `tests/test_ai_review.py::TestPromptFormatting`.

## Code Patterns

### Creating a New Agent

```python
from agents.base_agent import BaseAgent, AgentContext, AgentResult

class MyAgent(BaseAgent):
    def can_handle(self, event_type: str, event_data: dict) -> bool:
        # Check if agent is enabled in config
        if not self.config.get("agents", {}).get("my_agent", {}).get("enabled", True):
            return False
        return event_type == "my_event_type"
    
    def execute(self, context: AgentContext) -> AgentResult:
        # Load prompt template
        prompt = self.load_prompt("my_prompt")
        formatted = prompt.format(data=context.event_data.get("field"))
        
        # Call LLM with rate limiting
        response = self.call_llm(formatted)
        
        # Post comment to issue/PR
        self.upsert_comment(
            context.owner,
            context.repo,
            issue_index,
            response.content
        )
        
        return AgentResult(
            success=True,
            message="Agent completed",
            actions_taken=["Posted comment"]
        )
```

### Calling LLM with Tools

```python
messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Search for authentication code"}
]

tools = [{
    "type": "function",
    "function": {
        "name": "search_code",
        "description": "Search codebase",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"]
        }
    }
}]

response = self.llm.call_with_tools(messages, tools=tools)

if response.tool_calls:
    for tc in response.tool_calls:
        result = execute_tool(tc.name, tc.arguments)
        messages.append({
            "role": "tool",
            "tool_call_id": tc.id,
            "content": result
        })
```

### Adding Security Rules

Edit `tools/ai-review/security/security_scanner.py` or create `security/security_rules.yml`:

```yaml
rules:
  - id: SEC018
    name: Custom Rule Name
    pattern: 'regex_pattern_here'
    severity: HIGH  # HIGH, MEDIUM, LOW
    category: A03:2021 Injection
    cwe: CWE-XXX
    description: What this detects
    recommendation: How to fix it
```

## Testing

The test suite (`tests/test_ai_review.py`) covers:

1. **Prompt Formatting** - Ensures prompts don't have unescaped `{}` that break `.format()`
2. **Module Imports** - Verifies all modules can be imported
3. **Security Scanner** - Tests pattern detection and false positive rate
4. **Agent Context** - Tests dataclass creation and validation
5. **Metrics** - Tests enterprise metrics collection

Run specific test classes:
```bash
pytest tests/test_ai_review.py::TestPromptFormatting -v
pytest tests/test_ai_review.py::TestSecurityScanner -v
```

## Common Development Tasks

### Adding a New Command to @codebot

1. Add command to `config.yml` under `interaction.commands`
2. Add handler method in `IssueAgent` (e.g., `_command_yourcommand()`)
3. Update `_handle_command()` to route the command to your handler
4. Update README.md with command documentation
5. Add tests in `tests/test_ai_review.py`

Example commands:
- `@codebot help` - Show all available commands with examples
- `@codebot triage` - Full issue triage with labeling
- `@codebot explain` - Explain the issue
- `@codebot suggest` - Suggest solutions
- `@codebot setup-labels` - Automatic label setup (built-in, not in config)

### Changing the Bot Name

1. Edit `config.yml`: `interaction.mention_prefix: "@newname"`
2. Update all Gitea workflow files in `.gitea/workflows/` (search for `contains(github.event.comment.body`)
3. Update README.md and documentation

### Supporting a New LLM Provider

1. Create provider class in `clients/llm_client.py` inheriting from `BaseLLMProvider`
2. Implement `call()` and optionally `call_with_tools()`
3. Register in `LLMClient.PROVIDERS` dict
4. Add model config to `config.yml`
5. Document in README.md

## Repository Labels

### Automatic Label Setup (Recommended)

Use the `@codebot setup-labels` command to automatically configure labels. This command:

**For repositories with existing labels:**
- Detects naming patterns: `Kind/Bug`, `Priority - High`, `type: bug`
- Maps existing labels to OpenRabbit schema using aliases
- Creates only missing labels following detected pattern
- Zero duplicate labels

**For fresh repositories:**
- Creates OpenRabbit's default label set
- Uses standard naming: `type:`, `priority:`, status labels

**Example with existing `Kind/` and `Priority -` labels:**
```
@codebot setup-labels

✅ Found 18 existing labels with pattern: prefix_slash

Proposed Mapping:
| OpenRabbit Expected | Your Existing Label | Status |
|---------------------|---------------------|--------|
| type: bug          | Kind/Bug            | ✅ Map |
| type: feature      | Kind/Feature        | ✅ Map |
| priority: high     | Priority - High     | ✅ Map |
| ai-reviewed        | (missing)           | ⚠️ Create |

✅ Created Kind/Question
✅ Created Status - AI Reviewed

Setup Complete! Auto-labeling will use your existing label schema.
```

### Manual Label Setup

The system expects these labels to exist in repositories for auto-labeling:

- `priority: critical`, `priority: high`, `priority: medium`, `priority: low`
- `type: bug`, `type: feature`, `type: question`, `type: documentation`, `type: security`, `type: testing`
- `ai-approved`, `ai-changes-required`, `ai-reviewed`

Labels are mapped in `config.yml` under the `labels` section.

### Label Configuration Format

Labels support two formats for backwards compatibility:

**New format (with colors and aliases):**
```yaml
labels:
  type:
    bug:
      name: "type: bug"
      color: "d73a4a"  # Red
      description: "Something isn't working"
      aliases: ["Kind/Bug", "bug", "Type: Bug"]  # For auto-detection
```

**Old format (strings only):**
```yaml
labels:
  type:
    bug: "type: bug"  # Still works, uses default blue color
```

### Label Pattern Detection

The `setup-labels` command detects these patterns (configured in `label_patterns`):

1. **prefix_slash**: `Kind/Bug`, `Type/Feature`, `Category/X`
2. **prefix_dash**: `Priority - High`, `Status - Blocked`
3. **colon**: `type: bug`, `priority: high`

When creating missing labels, the bot follows the detected pattern to maintain consistency.