Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/unit-mesh/autodev-remote-agent-action

🤖 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.
https://github.com/unit-mesh/autodev-remote-agent-action

Last synced: about 1 year ago
JSON representation

🤖 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.

Awesome Lists containing this project

README 

          
# 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`):



```yaml

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



```bash

# 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



```bash

# 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)

```yaml

- uses: unit-mesh/autodev-remote-agent-action@v0.3.1

  with:

    github-token: ${{ secrets.GITHUB_TOKEN }}

    deepseek-token: ${{ secrets.DEEPSEEK_TOKEN }}

```



### OpenAI

```yaml

- 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)

```yaml

- 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](https://platform.deepseek.com/)

   - `OPENAI_API_KEY`: Your OpenAI API key from [OpenAI Platform](https://platform.openai.com/api-keys)

   - `GLM_TOKEN`: Your GLM API token from [GLM Platform](https://open.bigmodel.cn/)



## 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



## 🔍 Analysis Transparency



This action provides detailed information about the analysis process to help you understand what happened:



### Process Information

- **Files Scanned**: Total number of files found in your repository

- **Files Analyzed**: Number of files the AI determined were relevant

- **Files Filtered**: Number of files excluded from analysis and why

- **Analysis Steps**: Detailed breakdown of each analysis phase

- **LLM Calls**: Information about AI service calls and their success/failure



### When Analysis Results Are Limited

If the analysis produces limited results, the action will provide:

- **Diagnostic Information**: Explanation of what might have gone wrong

- **File Filtering Details**: Which types of files were excluded and why

- **Improvement Suggestions**: How to write better issue descriptions for more comprehensive analysis

- **Troubleshooting Tips**: Common issues and how to resolve them



### Common File Filtering Scenarios

The AI may filter out files that seem unrelated to your issue:

- Configuration files (jest.config.js, rollup.config.mjs, etc.) unless specifically mentioned

- Test setup files unless the issue is about testing

- Build and package files unless the issue mentions dependencies or build problems

- Documentation files unless the issue is about documentation



💡 **Tips for Better Analysis Results**:



1. **Mention specific files**: "Error in `jest.config.js`" vs "Configuration problem"

2. **Include error messages**: Copy-paste actual error text

3. **Reference functions/classes**: "Issue in `getUserData()` function"

4. **Specify file types**: "Test configuration issue" includes test files

5. **Use keywords**: "build", "config", "test", "dependency" help include relevant files



### Understanding Analysis Comments



When the action analyzes your issue, it will add a comment with sections like:



```markdown

## 🤖 Automated Issue Analysis



### 🔍 Analysis Process

- Files scanned: 45

- Files analyzed: 8

- Files filtered: 37 (see details below)

- Analysis steps: 5



### ⚠️ Files Filtered from Analysis

37 files were not included in the analysis. This may include:

- jest.config.js - Configuration files often filtered by LLM as not directly relevant

- rollup.config.mjs - Build configuration files typically excluded from issue analysis

- __tests__/setup.ts - Test setup files may be filtered if issue doesn't mention testing



💡 Suggestions to include these files:

- Mention "configuration", "config files", or specific config file names if your issue relates to build/setup

- Include "test", "testing", or "test failure" if your issue involves test problems

```



This transparency helps you understand why certain files weren't considered and how to improve your issue description for better analysis.



## Examples



### Basic Issue Analysis



```yaml

- 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



```yaml

- 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:



```yaml

- 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:



```yaml

- 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



```javascript

const { startWebhookServer } = require('@autodev/github-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.



```typescript

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.



```typescript

const analyzer = new IssueAnalyzer(context);

const result = await analyzer.analyzeIssue({

  depth: 'medium',

  includeCodeSearch: true,

  includeSymbolAnalysis: true

});

```



### WebhookHandler



Webhook server for real-time processing.



```typescript

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



```bash

# 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



```bash

# 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](LICENSE) for details.



## Related Projects



- [AutoDev GitHub Agent](https://github.com/unit-mesh/autodev) - Core analysis engine

- [AutoDev Context Worker](https://github.com/unit-mesh/autodev) - Code context analysis

- [AutoDev Worker Core](https://github.com/unit-mesh/autodev) - Core utilities



## Support



- 📖 [Documentation](https://github.com/unit-mesh/autodev-remote-agent-action)

- 🐛 [Issue Tracker](https://github.com/unit-mesh/autodev-remote-agent-action/issues)

- 💬 [Discussions](https://github.com/unit-mesh/autodev-remote-agent-action/discussions)