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drep

<img src="docs/images/drep.png" alt="drep logo" width="200" />

Documentation & Review Enhancement Platform

Automated code review and documentation improvement tool for Gitea, GitHub, and GitLab. Powered by your choice of LLM backend: local models (LM Studio, Ollama, llama.cpp), AWS Bedrock (Claude 4.5), or Anthropic's Claude API.

v1.1.0: Interactive configuration wizard with guided setup! Full support for Python repositories on all three major git platforms: Gitea, GitHub, and GitLab. Support for additional languages and direct Anthropic API provider coming soon.

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Features

Proactive Code Analysis

Unlike reactive tools, drep continuously monitors repositories and automatically:

• Detects bugs, security vulnerabilities, and best practice violations
• Opens issues with detailed findings and suggested fixes
• No manual intervention required

Docstring Intelligence

LLM-powered docstring analysis purpose-built for Python:

• Generates Google-style docstrings for public APIs
• Flags TODOs, placeholders, and low-signal docstrings
• Respects decorators (e.g., @property, @classmethod) and skips simple helpers

Automated PR/MR Reviews

Intelligent review workflow for Gitea pull requests:

• Parses diffs into structured hunks
• Generates inline comments tied to added lines
• Produces a high-level summary with approval signal

Flexible LLM Backends

Choose the right LLM backend for your needs:

• Local models: Complete privacy with Ollama, llama.cpp, LM Studio
• AWS Bedrock: Enterprise compliance with Claude 4.5 on AWS ✅ NEW
• Anthropic Direct: Latest Claude models with direct API access (planned)
• OpenAI-compatible: Works with any compatible endpoint

Platform Support & Roadmap

• Available now: Gitea, GitHub, GitLab + Python repositories
• Planned: Additional languages, advanced draft PR workflows

LLM-Powered Analysis

drep includes intelligent code analysis powered by local LLMs via OpenAI-compatible backends (LM Studio, Ollama, open-agent-sdk).

Features

• Code Quality Analysis: Detects bugs, security issues, and best practice violations
• Docstring Generation: Automatically generates Google-style docstrings
• PR Reviews: Context-aware code review comments
• Smart Caching: 80%+ cache hit rate on repeated scans
• Cost Tracking: Monitor token usage and estimated costs
• Circuit Breaker: Graceful degradation when LLM unavailable
• Progress Reporting: Real-time feedback during analysis

Quick Start

Step 1: Install drep

pip install drep-ai

Step 2: Initialize Configuration (Interactive Wizard) 🧙‍♂️

drep init

The interactive wizard guides you through:

1. Config Location: Choose between current directory or user config directory
2. Platform Selection: Gitea, GitHub, or GitLab
3. Enterprise Servers: Detect and configure GitHub Enterprise, self-hosted GitLab/Gitea
4. Repository Patterns: Use wildcards (owner/*) or specific repos (owner/repo)
5. LLM Backend: OpenAI-compatible (local), AWS Bedrock, or Anthropic
6. Documentation Settings: Enable markdown linting, custom dictionaries
7. Advanced Options: Database URL, LLM temperature, rate limits

Example Wizard Flow:

$ drep init

============================================================
Welcome to drep configuration setup!
============================================================

Where should the configuration be created?

  1. Current directory (./config.yaml)
     Use for project-specific configuration

  2. User config directory (/Users/you/Library/Application Support/drep/config.yaml)
     Use for system-wide configuration (recommended for pip/brew install)

Choose location (1, 2) [2]: 1

Step 1: Git Platform Configuration
------------------------------------------------------------
Which git platform are you using? (github, gitea, gitlab) [github]: github

GitHub Configuration:
Are you using GitHub Enterprise? [y/N]: n

Repository Configuration:
Examples: 'your-org/*' (all repos), 'owner/repo' (single repo)
Enter repositories (comma-separated) [your-org/*]: slb350/*

Step 2: LLM Configuration
------------------------------------------------------------
Enable LLM-powered code analysis? [Y/n]: y
Choose LLM provider (openai-compatible, bedrock, anthropic) [openai-compatible]: openai-compatible

OpenAI-Compatible Configuration:
API Endpoint [http://localhost:1234/v1]:
Model name [qwen3-30b-a3b]:
Require API key? [y/N]: n

... (more wizard steps) ...

============================================================
✓ Configuration created successfully!
============================================================

Config location: config.yaml

Next steps:
1. Set the GITHUB_TOKEN environment variable:
   export GITHUB_TOKEN='your-api-token-here'

2. Validate your configuration:
   drep validate

3. Start scanning repositories:
   drep scan owner/repo

Manual Configuration (Alternative)

For advanced users who prefer YAML editing:

Option 1: Local Models (LM Studio)

1. Install LM Studio: https://lmstudio.ai/
2. Download a model (Qwen3-30B-A3B recommended)
3. Create config.yaml:

llm:
  enabled: true
  endpoint: http://localhost:1234/v1  # LM Studio / OpenAI-compatible API (also works with open-agent-sdk)
  model: qwen3-30b-a3b
  temperature: 0.2
  max_tokens: 8000

  # Rate limiting
  max_concurrent_global: 5
  requests_per_minute: 60

  # Caching
  cache:
    enabled: true
    ttl_days: 30

Option 2: AWS Bedrock (Claude 4.5)

1. Enable Bedrock model access in AWS Console
2. Configure AWS credentials (aws configure or ~/.aws/credentials)
3. Configure drep:

llm:
  enabled: true
  provider: bedrock  # Required for AWS Bedrock

  bedrock:
    region: us-east-1
    model: anthropic.claude-sonnet-4-5-20250929-v1:0  # Or Haiku 4.5

  temperature: 0.2
  max_tokens: 4000

  # Caching
  cache:
    enabled: true
    ttl_days: 30

See docs/llm-setup.md for detailed setup instructions and troubleshooting.

Run Analysis

drep scan owner/repo --show-progress --show-metrics

View Metrics

# Show detailed usage statistics
drep metrics --detailed

# Export to JSON
drep metrics --export metrics.json

# Last 7 days only
drep metrics --days 7

Example output:

===== LLM Usage Report =====
Session duration: 0h 5m 32s
Total requests: 127 (115 successful, 12 failed, 95 cached)
Success rate: 90.6%
Cache hit rate: 74.8%

Tokens used: 45,230 prompt + 12,560 completion = 57,790 total
Estimated cost: $0.29 USD (or $0 with LM Studio)

Performance:
  Average latency: 1250ms
  Min/Max: 450ms / 3200ms

By Analyzer:
  code_quality: 45 requests (12,345 tokens)
  docstring: 38 requests (8,901 tokens)
  pr_review: 44 requests (36,544 tokens)

Quick Start

Installation

Via Homebrew (macOS/Linux)

brew tap slb350/drep
brew install drep-ai

Via pip

pip install drep-ai

Note: The PyPI package is named drep-ai (the name drep was already taken). After installation, the command-line tool is still drep.

From source

git clone https://github.com/slb350/drep.git
cd drep
pip install -e ".[dev]"

Via Docker

docker pull ghcr.io/slb350/drep:latest

Configuration

drep supports GitHub, Gitea, and GitLab. The init command will ask which platform you're using and generate the correct configuration.

Step 1: Initialize Configuration

drep init

You'll be prompted to choose where to store the configuration and which platform to use:

Where should the configuration be created?

  1. Current directory (./config.yaml)
     Use for project-specific configuration

  2. User config directory (~/.config/drep/config.yaml)
     Use for system-wide configuration (recommended for pip/brew install)

Choose location (1, 2) [2]: 2

Step 1: Platform Configuration
------------------------------------------------------------
Which git platform are you using?
Choose platform (github, gitea, gitlab) [github]: github

✓ Configuration created successfully!
------------------------------------------------------------
Config location: /Users/yourname/.config/drep/config.yaml

Next steps:
1. Set the GITHUB_TOKEN environment variable:
   export GITHUB_TOKEN='your-api-token-here'

Config File Discovery: drep automatically finds your config file in this order:

1. Explicit --config path (if provided)
2. DREP_CONFIG environment variable
3. ./config.yaml (project-specific)
4. ~/.config/drep/config.yaml (user config)

This means you can run drep scan owner/repo without specifying --config - it will automatically find your configuration!

Step 2: Set Your API Token

Create an API token from your platform:

For GitHub:

1. Go to Settings → Developer settings → Personal access tokens → Tokens (classic)
2. Generate new token with repo scope
3. Set the environment variable:

export GITHUB_TOKEN="ghp_your_token_here"

For Gitea:

1. Go to Settings → Applications → Generate New Token
2. Set the environment variable:

export GITEA_TOKEN="your_token_here"

For GitLab:

1. Go to User Settings → Access Tokens
2. Create token with api scope
3. Set the environment variable:

export GITLAB_TOKEN="your_token_here"

Step 3: Configure Repositories (Optional)

Edit your config file (location shown in drep init output) to specify which repositories to monitor: