AutoDev Remote Agent

🤖 Automated GitHub issue analysis using AI-powered code analysis. This action automatically analyzes GitHub issues when they are created or updated, providing intelligent insights and recommendations.

Features

• 🔍 Intelligent Issue Analysis: AI-powered analysis of GitHub issues with code context
• 💬 Automated Comments: Automatically add analysis results as comments to issues
• 🏷️ Smart Labeling: Automatically apply relevant labels based on analysis
• 🌐 Webhook Support: Standalone webhook server for real-time issue processing
• ⚙️ Configurable: Flexible configuration options for different workflows
• 🔗 Integration Ready: Built on top of the proven AutoDev GitHub Agent
• 🔍 Process Transparency: Detailed analysis process information and file filtering insights
• 🛠️ Diagnostic Information: Clear explanations when analysis results are limited
• 🍽️ Dogfooding: We use our own action to analyze issues in this repository!

Quick Start

Prerequisites

Before using this action, you need:

1. A GitHub repository
2. An API key from one of the supported LLM providers (OpenAI, DeepSeek, or GLM)

Setup Steps

1. Add API Key Secret:

   • Go to your repository's Settings → Secrets and variables → Actions
   • Click New repository secret
   • Add DEEPSEEK_TOKEN (or OPENAI_API_KEY/GLM_TOKEN) with your API key

2. Create Workflow File:

   • Create .github/workflows/issue-analysis.yml in your repository

3. Configure the Action:

   • Use the configuration examples below

GitHub Actions Usage

Add this action to your workflow file (e.g., .github/workflows/issue-analysis.yml):

name: Analyze Issues
on:
  issues:
    types: [opened, edited, reopened]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4

      - name: Analyze Issue
        uses: unit-mesh/autodev-remote-agent-action@v0.3.1
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}  # or use openai-api-key/glm-token
          analysis-depth: medium
          auto-comment: true
          auto-label: true

Standalone Webhook Server

# Install dependencies
npm install

# Set environment variables
export GITHUB_TOKEN="your-github-token"
export WEBHOOK_SECRET="your-webhook-secret"
export DEEPSEEK_TOKEN="your-deepseek-token"  # or OPENAI_API_KEY/GLM_TOKEN

# Start the server
npx autodev-github-action server --port 3000

CLI Usage

# Analyze a specific issue
npx autodev-github-action analyze \
  --owner unit-mesh \
  --repo autodev-workbench \
  --issue 81 \
  --depth deep

# Start webhook server
npx autodev-github-action server --port 3000

# Validate configuration
npx autodev-github-action validate

Configuration

Action Inputs

Input	Description	Default	Required
github-token	GitHub token for API access	${{ github.token }}	Yes
deepseek-token	DeepSeek API token for LLM analysis	``	No*
openai-api-key	OpenAI API key for LLM analysis	``	No*
glm-token	GLM API token for LLM analysis	``	No*
workspace-path	Path to repository workspace	${{ github.workspace }}	No
analysis-depth	Analysis depth (shallow/medium/deep)	medium	No
auto-comment	Add analysis comment to issues	true	No
auto-label	Add labels based on analysis	true	No
trigger-events	Events that trigger analysis	opened,edited,reopened	No
exclude-labels	Labels to exclude from analysis	``	No
include-labels	Labels to include for analysis	``	No
include-config-files	Include configuration files in analysis	true	No
include-test-files	Include test files in analysis	true	No
include-patterns	File patterns to force include	``	No
exclude-patterns	File patterns to exclude	``	No
force-include-files	Specific files to always include	``	No
webhook-secret	Secret for webhook verification	``	No

*At least one LLM API key is required for AI analysis

Environment Variables

Variable	Description	Default
GITHUB_TOKEN	GitHub personal access token	Required
DEEPSEEK_TOKEN	DeepSeek API token for LLM analysis	Optional*
OPENAI_API_KEY	OpenAI API key for LLM analysis	Optional*
GLM_TOKEN	GLM API token for LLM analysis	Optional*
WEBHOOK_SECRET	Secret for webhook verification	Optional
WORKSPACE_PATH	Repository workspace path	process.cwd()
AUTO_COMMENT	Auto-add comments	true
AUTO_LABEL	Auto-add labels	true
ANALYSIS_DEPTH	Analysis depth	medium
TRIGGER_EVENTS	Trigger events	opened,edited,reopened
EXCLUDE_LABELS	Exclude labels	``
INCLUDE_LABELS	Include labels	``

*At least one LLM API key is required for AI analysis

LLM Configuration

This action supports multiple LLM providers. You need to configure at least one API key:

DeepSeek (Recommended)

- uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}

OpenAI

- uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    openai-api-key: ${{ secrets.OPENAI_API_KEY }}

GLM (ChatGLM)

- uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    glm-token: ${{ secrets.GLM_TOKEN }}

Setting up API Keys

1. Go to your repository's Settings → Secrets and variables → Actions
2. Click New repository secret
3. Add one of the following secrets:
   • DEEPSEEK_TOKEN: Your DeepSeek API token from DeepSeek Platform
   • OPENAI_API_KEY: Your OpenAI API key from OpenAI Platform
   • GLM_TOKEN: Your GLM API token from GLM Platform

Analysis Depths

Shallow

• Quick analysis focusing on obvious patterns
• Fast execution (< 30 seconds)
• Basic code references
• Suitable for high-volume repositories

Medium (Default)

• Balanced analysis with meaningful insights
• Moderate execution time (30-60 seconds)
• Comprehensive code exploration
• Good for most use cases

Deep

• In-depth analysis including dependencies
• Longer execution time (60-120 seconds)
• Architectural pattern analysis
• Best for complex issues

Examples

Basic Issue Analysis

- name: Analyze Issues
  uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}

Advanced Configuration

- name: Advanced Issue Analysis
  uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}
    analysis-depth: deep
    auto-comment: true
    auto-label: true
    exclude-labels: 'wontfix,duplicate'
    include-labels: 'bug,enhancement'

File Filtering Configuration

Control which files are included in the analysis:

- name: Custom File Filtering
  uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}
    # File inclusion settings
    include-config-files: true    # Include jest.config.js, rollup.config.mjs, etc.
    include-test-files: true      # Include __tests__/, *.test.js, etc.
    # Custom patterns (comma-separated)
    include-patterns: "*.config.js,*.config.ts,docker-compose.yml"
    exclude-patterns: "*.min.js,*.bundle.js"
    force-include-files: "important-config.js,critical-setup.ts"

Transparency and Diagnostics

The action now provides detailed process information in comments:

- name: Analysis with Full Transparency
  uses: unit-mesh/autodev-remote-agent-action@v0.3.1
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}
    auto-comment: true  # Comments will include process details
    analysis-depth: medium

What you'll see in analysis comments:

• 📊 Process Summary: Files scanned vs. analyzed vs. filtered
• ⚠️ Filtered Files: Specific files that were excluded and why
• 🔄 Analysis Steps: Each step's success/failure with timing
• 🧠 LLM Calls: AI service calls and any errors
• 💡 Improvement Suggestions: How to get better analysis results

Webhook Server Setup

const { startWebhookServer } = require('@autodev/remote-agent-action');

const server = await startWebhookServer({
  port: 3000,
  webhookSecret: process.env.WEBHOOK_SECRET,
  githubToken: process.env.GITHUB_TOKEN
});

API Reference

GitHubActionService

Main service class for processing issues.

const service = new GitHubActionService({
  githubToken: 'your-token',
  workspacePath: '/path/to/repo',
  autoComment: true,
  autoLabel: true
});

const result = await service.processIssue({
  owner: 'unit-mesh',
  repo: 'autodev-workbench',
  issueNumber: 81
});

IssueAnalyzer

Core analysis engine.

const analyzer = new IssueAnalyzer(context);
const result = await analyzer.analyzeIssue({
  depth: 'medium',
  includeCodeSearch: true,
  includeSymbolAnalysis: true
});

WebhookHandler

Webhook server for real-time processing.

const handler = new WebhookHandler(actionService, {
  port: 3000,
  secret: 'webhook-secret',
  onIssueOpened: async (payload) => {
    console.log('Issue opened:', payload.issue.number);
  }
});

await handler.start();

Development

Setup

# Clone the repository
git clone https://github.com/unit-mesh/autodev-remote-agent-action.git
cd autodev-remote-agent-action

# Install dependencies (using pnpm for better dependency management)
pnpm install
# or use npm
npm install

# Build the package
npm run build

# Run tests
npm test

# Test locally
node dist/index.js --help

Build Process

# Clean build (removes dist/ and rebuilds)
npm run build:clean

# Development build with watch mode
npm run dev

# Lint code
npm run lint

# Test locally
node dist/index.js --help

Project Structure

autodev-remote-agent-action/
├── src/                   # Source code (TypeScript)
│   ├── action.ts           # Main action service
│   ├── issue-analyzer.ts   # Issue analysis logic
│   ├── webhook-handler.ts  # Webhook server
│   ├── types/             # Type definitions
│   └── index.ts           # Main entry point
├── dist/                  # Build output (committed to git)
├── bin/
│   └── action.js          # CLI entry point
├── action.yml             # GitHub Action definition
├── package.json
└── README.md

Release Process

1. Make your changes and test locally
2. Run npm run build:clean to ensure clean build
3. Test the built action: node dist/index.js
4. Update version in package.json
5. Commit all changes including dist/ files
6. Create a git tag: git tag v0.3.3 && git push origin v0.3.3
7. The new version will be available for use in GitHub Actions

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE for details.

Related Projects

• AutoDev GitHub Agent - Core analysis engine
• AutoDev Context Worker - Code context analysis
• AutoDev Worker Core - Core utilities

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions