# OpenRabbit

Enterprise-grade AI code review system for **GitHub** and **Gitea** with automated PR review, issue triage, interactive chat (Bartender), and codebase analysis.

---

## Features

| Feature | Description |
|---------|-------------|
| **PR Review** | Inline comments, security scanning, severity-based CI failure |
| **Issue Triage** | Auto-classification, labeling, priority assignment |
| **Chat (Bartender)** | Interactive AI chat with codebase search and web search tools |
| **@ai-bot Commands** | `@ai-bot summarize`, `explain`, `suggest` in issue comments |
| **Codebase Analysis** | Health scores, tech debt tracking, weekly reports |
| **Security Scanner** | 17 OWASP-aligned rules for vulnerability detection |
| **Enterprise Ready** | Audit logging, metrics, Prometheus export |
| **Multi-Platform** | Works with both GitHub and Gitea |

---

## Quick Start

### 1. Set Repository/Organization Secrets

```
OPENAI_API_KEY      - OpenAI API key (or use OpenRouter/Ollama)
SEARXNG_URL         - (Optional) SearXNG instance URL for web search
```

**For Gitea:**
```
AI_REVIEW_TOKEN     - Bot token with repo + issue permissions
```

**For GitHub:**
The built-in `GITHUB_TOKEN` is used automatically.

### 2. Add Workflows to Repository

Workflows are provided for both platforms:

| Platform | Location |
|----------|----------|
| GitHub | `.github/workflows/` |
| Gitea | `.gitea/workflows/` |

#### GitHub Example

```yaml
# .github/workflows/ai-review.yml
name: AI PR Review
on: [pull_request]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - run: pip install requests pyyaml

      - name: Run AI Review
        env:
          AI_REVIEW_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          AI_REVIEW_REPO: ${{ github.repository }}
          AI_REVIEW_API_URL: https://api.github.com
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: |
          cd tools/ai-review
          python main.py pr ${{ github.repository }} ${{ github.event.pull_request.number }}
```

#### Gitea Example

```yaml
# .gitea/workflows/ai-review.yml
name: AI PR Review
on: [pull_request]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: actions/checkout@v4
        with:
          repository: YourOrg/OpenRabbit
          path: .ai-review
          token: ${{ secrets.AI_REVIEW_TOKEN }}

      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - run: pip install requests pyyaml

      - name: Run AI Review
        env:
          AI_REVIEW_TOKEN: ${{ secrets.AI_REVIEW_TOKEN }}
          AI_REVIEW_REPO: ${{ gitea.repository }}
          AI_REVIEW_API_URL: https://your-gitea.example.com/api/v1
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: |
          cd .ai-review/tools/ai-review
          python main.py pr ${{ gitea.repository }} ${{ gitea.event.pull_request.number }}
```

For full workflow examples, see [Workflows Documentation](docs/workflows.md).

### 3. Create Labels

Create these labels in your repository for auto-labeling:
- `priority: high`, `priority: medium`, `priority: low`
- `type: bug`, `type: feature`, `type: question`
- `ai-approved`, `ai-changes-required`

---

## Project Structure

```
tools/ai-review/
├── agents/                 # Agent implementations
│   ├── base_agent.py       # Abstract base agent
│   ├── issue_agent.py      # Issue triage & @ai-bot commands
│   ├── pr_agent.py         # PR review with security scan
│   ├── codebase_agent.py   # Codebase health analysis
│   └── chat_agent.py       # Bartender chat with tool calling
├── clients/                # API clients
│   ├── gitea_client.py     # Gitea REST API wrapper
│   └── llm_client.py       # Multi-provider LLM client with tool support
├── security/               # Security scanning
│   └── security_scanner.py # 17 OWASP-aligned rules
├── enterprise/             # Enterprise features
│   ├── audit_logger.py     # JSONL audit logging
│   └── metrics.py          # Prometheus-compatible metrics
├── prompts/                # AI prompt templates
├── main.py                 # CLI entry point
└── config.yml              # Configuration

.github/workflows/          # GitHub Actions workflows
├── ai-review.yml           # PR review workflow
├── ai-issue-triage.yml     # Issue triage workflow
├── ai-codebase-review.yml  # Codebase analysis
├── ai-comment-reply.yml    # @ai-bot command responses
└── ai-chat.yml             # Bartender chat

.gitea/workflows/           # Gitea Actions workflows
├── enterprise-ai-review.yml
├── ai-issue-triage.yml
├── ai-codebase-review.yml
├── ai-comment-reply.yml
└── ai-chat.yml
```

---

## CLI Commands

```bash
# Review a pull request
python main.py pr owner/repo 123

# Triage an issue
python main.py issue owner/repo 456

# Respond to @ai-bot command
python main.py comment owner/repo 456 "@ai-bot explain"

# Analyze codebase
python main.py codebase owner/repo

# Chat with Bartender
python main.py chat owner/repo "How does authentication work?"
python main.py chat owner/repo "Find all API endpoints" --issue 789
```

---

## @ai-bot Commands

In any issue comment:

| Command | Description |
|---------|-------------|
| `@ai-bot summarize` | Summarize the issue in 2-3 sentences |
| `@ai-bot explain` | Explain what the issue is about |
| `@ai-bot suggest` | Suggest solutions or next steps |
| `@ai-bot` (any question) | Chat with Bartender using codebase/web search |

---

## Bartender Chat

Bartender is an interactive AI assistant with tool-calling capabilities:

**Tools Available:**
- `search_codebase` - Search repository files and code
- `read_file` - Read specific files
- `search_web` - Search the web via SearXNG

**Example:**
```
@ai-bot How do I configure rate limiting in this project?
```

Bartender will search the codebase, read relevant files, and provide a comprehensive answer.

---

## Configuration

Edit `tools/ai-review/config.yml`:

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

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

agents:
  issue:
    enabled: true
    auto_label: true
  pr:
    enabled: true
    inline_comments: true
    security_scan: true
  codebase:
    enabled: true
  chat:
    enabled: true
    name: "Bartender"
    searxng_url: ""  # Or set SEARXNG_URL env var

interaction:
  respond_to_mentions: true
  mention_prefix: "@ai-bot"  # Customize your bot name here!
  commands:
    - summarize
    - explain
    - suggest
```

---

## Customizing the Bot Name

You can change the bot's mention trigger from `@ai-bot` to any name you prefer:

**Step 1:** Edit `tools/ai-review/config.yml`:
```yaml
interaction:
  mention_prefix: "@bartender"  # or "@uni", "@joey", "@codebot", etc.
```

**Step 2:** Update the workflow files to match:

For GitHub (`.github/workflows/ai-comment-reply.yml` and `ai-chat.yml`):
```yaml
if: contains(github.event.comment.body, '@bartender')
```

For Gitea (`.gitea/workflows/ai-comment-reply.yml` and `ai-chat.yml`):
```yaml
if: contains(github.event.comment.body, '@bartender')
```

**Example bot names:**
| Name | Use Case |
|------|----------|
| `@bartender` | Friendly, conversational |
| `@uni` | Short, quick to type |
| `@joey` | Personal assistant feel |
| `@codebot` | Technical, code-focused |
| `@reviewer` | Review-focused |

---

## Security Scanning

17 rules covering OWASP Top 10:

| Category | Examples |
|----------|----------|
| Injection | SQL injection, command injection, XSS |
| Access Control | Hardcoded secrets, private keys |
| Crypto Failures | Weak hashing (MD5/SHA1), insecure random |
| Misconfiguration | Debug mode, CORS wildcard, SSL bypass |

---

## Documentation

| Document | Description |
|----------|-------------|
| [Getting Started](docs/getting-started.md) | Quick setup guide |
| [Configuration](docs/configuration.md) | All options explained |
| [Agents](docs/agents.md) | Agent documentation |
| [Security](docs/security.md) | Security rules reference |
| [Workflows](docs/workflows.md) | GitHub & Gitea workflow examples |
| [API Reference](docs/api-reference.md) | Client and agent APIs |
| [Enterprise](docs/enterprise.md) | Audit logging, metrics |
| [Troubleshooting](docs/troubleshooting.md) | Common issues |

---

## LLM Providers

| Provider | Model | Use Case |
|----------|-------|----------|
| OpenAI | gpt-4.1-mini | Fast, reliable |
| OpenRouter | claude-3.5-sonnet | Multi-provider access |
| Ollama | codellama:13b | Self-hosted, private |

---

## Enterprise Features

- **Audit Logging**: JSONL logs with daily rotation
- **Metrics**: Prometheus-compatible export
- **Rate Limiting**: Configurable request limits
- **Custom Security Rules**: Define your own patterns via YAML
- **Tool Calling**: LLM function calling for interactive chat

---

## License

MIT