feat: Add intelligent auto-router and enhanced integrations
- Add intelligent-router.sh hook for automatic agent routing - Add AUTO-TRIGGER-SUMMARY.md documentation - Add FINAL-INTEGRATION-SUMMARY.md documentation - Complete Prometheus integration (6 commands + 4 tools) - Complete Dexto integration (12 commands + 5 tools) - Enhanced Ralph with access to all agents - Fix /clawd command (removed disable-model-invocation) - Update hooks.json to v5 with intelligent routing - 291 total skills now available - All 21 commands with automatic routing 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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11
prometheus/docs/Evaluation-log.md
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prometheus/docs/Evaluation-log.md
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## This log tracks our evaluation results and associated costs.
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| Date | Executed by | Version | Dataset | #Instance | Model | Resolved Rate | API Cost | Notes |
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|------------|-------------|----------------------|-----------------------------------|-----------|----------------------|---------------|----------|------------------------------------|
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| 2025-07-08 | Yue Pan | v1.0 | SWE-Bench Lite | 300 | DeepSeek V3 | 28.67% | $70.05 | initial version |
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| 2025-07-18 | Yue Pan | v1.0 | SWE-Bench Multilingual | 300 | DeepSeek V3 | 13.67% | $113.6 | initial version |
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| 2025-07-31 | Yue Pan | v1.1 | SWE-Bench Lite | 300 | GPT-4o | 30.00% | $1569.73 | context retrieval improved version |
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| 2025-08-09 | Zhaoyang | v1.0 | SWE-Bench Verified | 500 | Devstral Medium 2507 | 33.00% | - | |
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| 2025-08-11 | Yue Pan | v1.1 | SWE-Bench Verified | 500 | Devstral Medium 2507 | 38.4% | - | |
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| 2025-11-06 | Yue Pan | v1.3(with Athena) | dcloud347/SWE-bench_verified_lite | 50 | GPT-5 + gpt-4o | 70.00% | $200.79 | |
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| 2025-11-06 | Yue Pan | v1.3(without Athena) | dcloud347/SWE-bench_verified_lite | 50 | GPT-5 + gpt-4o | 56.00% | $367.73 | |
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179
prometheus/docs/GitHub-Issue-Debug-Guide.md
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prometheus/docs/GitHub-Issue-Debug-Guide.md
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# GitHub Issue Auto Debug Script Usage Guide
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## Overview
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`prometheus/script/github_issue_debug.py` is an automated script for:
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1. Retrieving detailed information (title, body, comments, etc.) of a specified issue from the GitHub API.
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2. Automatically uploading the GitHub repository to Prometheus.
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3. Using Prometheus's AI analysis capabilities to debug the issue.
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4. Returning analysis results, fix patches, etc.
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## Prerequisites
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### 1. Start Prometheus Service
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Ensure the Prometheus service is running:
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```bash
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# Start using docker-compose
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docker-compose up --build
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```
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### 2. Obtain GitHub Personal Access Token
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1. Visit https://github.com/settings/tokens
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2. Click "Generate new token (classic)"
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3. Select the appropriate permission scope:
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- `repo` (access private repositories)
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- `public_repo` (access public repositories)
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4. Generate and save the token.
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## Basic Usage
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### Simple Example
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```bash
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python github_issue_debug.py \
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--github-token "your_token_here" \
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--repo "owner/repository" \
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--issue-number 42
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```
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### Full Parameter Example
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```bash
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python github_issue_debug.py \
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--github-token "ghp_xxxxxxxxxxxxxxxxxxxx" \
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--repo "microsoft/vscode" \
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--issue-number 123 \
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--prometheus-url "http://localhost:9002/v1.2" \
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--output-file "debug_result.json" \
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--run-build \
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--run-test \
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--run-reproduction-test \
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--run-regression-test \
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--push-to-remote \
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--image-name "python:3.11-slim" \
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--workdir "/app" \
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--build-commands "pip install -r requirements.txt" "python setup.py build" \
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--test-commands "pytest tests/" \
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--candidate-patches 3
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```
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## Parameter Details
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### Required Parameters
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- `--github-token`: GitHub Personal Access Token
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- `--repo`: GitHub repository name in the format `owner/repo`
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- `--issue-number`: Issue number to process
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### Optional Parameters
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- `--prometheus-url`: Prometheus service address (default: http://localhost:8000)
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- `--output-file`: Path to the result output file (if not specified, output to console)
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### Validation Options
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- `--run-build`: Run build validation for the generated patch
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- `--run-test`: Run test validation for the generated patch
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- `--run-reproduction-test`: Run reproduction test to verify if the issue can be reproduced
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- `--run-regression-test`: Run regression test to ensure existing functionality is not broken
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- `--push-to-remote`: Push the fix to a remote Git branch
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### Docker Environment Configuration
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- `--dockerfile-content`: Specify Dockerfile content directly
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- `--image-name`: Use a predefined Docker image
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- `--workdir`: Working directory inside the container (default: /app)
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- `--build-commands`: List of build commands
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- `--test-commands`: List of test commands
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### Other Options
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- `--candidate-patches`: Number of candidate patches (default: 6)
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## Usage Scenarios
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### Scenario 1: Simple Bug Report Analysis
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```bash
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# Analyze a simple bug report without running any validation
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python github_issue_debug.py \
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--github-token "your_token" \
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--repo "pytorch/pytorch" \
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--issue-number 89123
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```
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### Scenario 2: Python Project with Test Validation
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```bash
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# Perform a complete debug for a Python project, including build and test validation
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python github_issue_debug.py \
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--github-token "your_token" \
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--repo "requests/requests" \
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--issue-number 5678 \
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--run-build \
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--run-test \
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--run-reproduction-test \
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--run-regression-test \
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--image-name "python:3.11-slim" \
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--build-commands "pip install -e ." \
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--test-commands "pytest tests/test_requests.py"
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```
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### Scenario 3: Node.js Project with Auto Push
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```bash
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# Process an issue for a Node.js project and automatically push the fix to a remote branch
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python github_issue_debug.py \
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--github-token "your_token" \
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--repo "facebook/react" \
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--issue-number 9876 \
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--run-build \
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--run-test \
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--run-reproduction-test \
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--run-regression-test \
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--push-to-remote \
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--image-name "node:18-slim" \
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--build-commands "npm ci" "npm run build" \
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--test-commands "npm test"
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```
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### Scenario 4: Custom Docker Environment
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```bash
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# Use a custom Dockerfile for debugging
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python github_issue_debug.py \
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--github-token "your_token" \
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--repo "tensorflow/tensorflow" \
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--issue-number 4321 \
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--run-build \
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--dockerfile-content "FROM tensorflow/tensorflow:latest-gpu
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WORKDIR /app
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COPY . /app
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RUN pip install -r requirements.txt" \
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--workdir "/app" \
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--build-commands "python setup.py build_ext --inplace" \
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--test-commands "python -m pytest tests/unit/"
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```
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## Output Result Explanation
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After execution, the script outputs results in JSON format, including the following fields:
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```json
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{
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"success": true,
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"issue_info": {
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"repo": "owner/repo",
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"number": 123,
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"title": "Issue Title",
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"url": "https://github.com/owner/repo/issues/123",
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"state": "open"
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},
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"prometheus_result": {
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"patch": "Generated code patch",
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"passed_reproducing_test": true,
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"passed_existing_test": false,
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"passed_regression_test": true,
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"passed_reproduction_test": true,
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"issue_response": "AI-generated issue response"
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},
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"created_branch_and_pushed": true,
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"branch_name": "fix-issue-123"
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}
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```
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### Result Field Description
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- `success`: Whether the process was successful
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- `issue_info`: Basic information about the GitHub issue
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- `prometheus_result.patch`: Code fix patch generated by Prometheus
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- `prometheus_result.passed_*`: Status of various validations
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- `prometheus_result.issue_response`: AI-generated issue analysis and response
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prometheus/docs/Multi-Agent-Architecture.md
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prometheus/docs/Multi-Agent-Architecture.md
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# Multi-Agent Architecture
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Prometheus uses a multi-agent system powered by LangGraph to intelligently process and resolve GitHub issues. Each agent is specialized for a specific task in the issue resolution pipeline.
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## Agent Overview
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### 1. Issue Classification Agent
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**Purpose**: Automatically classifies GitHub issues into categories (bug, question, feature, documentation).
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**Location**: `prometheus/lang_graph/subgraphs/issue_classification_subgraph.py`
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**Workflow**:
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- Retrieves relevant code context from the knowledge graph
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- Uses LLM to analyze issue content and classify type
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- Returns issue type for routing to appropriate handler
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**When Used**: When `issue_type == "auto"` in the issue request
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---
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### 2. Environment Build Agent
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**Status**: In Progress
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**Purpose**: Automatically sets up and configures the development environment for testing and building.
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**Planned Features**:
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- Auto-detect project type (Python, Node.js, Java, etc.)
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- Install dependencies
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- Configure build tools
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- Validate environment setup
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---
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### 3. Bug Reproduction Agent
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**Purpose**: Attempts to reproduce reported bugs by writing and executing reproduction tests.
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**Location**: `prometheus/lang_graph/subgraphs/bug_reproduction_subgraph.py`
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**Workflow**:
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1. Retrieves bug-related code context from knowledge graph
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2. Generates reproduction test code using LLM
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3. Edits necessary files to create the test
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4. Executes the test in a Docker container
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5. Evaluates whether the bug was successfully reproduced
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6. Retries with feedback if reproduction fails
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**Output**:
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- `reproduced_bug`: Boolean indicating success
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- `reproduced_bug_file`: Path to reproduction test
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- `reproduced_bug_commands`: Commands to reproduce
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- `reproduced_bug_patch`: Git patch with changes
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**Key Features**:
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- Iterative refinement with retry loops
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- Docker-isolated execution
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- Feedback-driven improvement
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---
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### 4. Context Retrieval Agent
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**Purpose**: Retrieves relevant code and documentation context from the Neo4j knowledge graph.
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**Location**: `prometheus/lang_graph/subgraphs/context_retrieval_subgraph.py`
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**Workflow**:
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1. Converts natural language query to knowledge graph query
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2. Uses LLM with graph traversal tools to find relevant context
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3. Selects and extracts useful code snippets
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4. Optionally refines query and retries if context is insufficient
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5. Returns structured context (code, AST nodes, documentation)
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**Key Features**:
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- Iterative query refinement (2-4 loops)
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- Tool-augmented LLM with Neo4j access
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- Traverses file hierarchy, AST structure, and text chunks
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**Used By**: All other agents for context gathering
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---
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### 5. Issue Resolution Agent
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**Purpose**: Generates and validates bug fix patches for verified bugs.
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**Location**: `prometheus/lang_graph/subgraphs/issue_verified_bug_subgraph.py`
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**Workflow**:
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1. Retrieves fix-relevant code context
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2. Analyzes bug root cause using LLM
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3. Generates code patch to fix the bug
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4. Applies patch and creates git diff
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5. Validates patch against:
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- Reproduction test (must pass)
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- Regression tests (optional)
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- Existing test suite (optional)
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6. Generates multiple candidate patches
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7. Selects best patch based on test results
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8. Retries with error feedback if tests fail
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**Output**:
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- `edit_patch`: Final selected fix patch
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- Test pass/fail results
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**Key Features**:
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- Multi-candidate patch generation
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- Multi-level validation (reproduction, regression, existing tests)
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- Feedback-driven iteration
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- Best patch selection using LLM
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---
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## Agent Coordination
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### Main Issue Processing Flow
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```
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User Issue -> Issue Classification Agent
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[Route by issue type]
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+-----+-----+
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BUG QUESTION
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v v
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Bug Pipeline Question Pipeline
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```
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### Bug Resolution Pipeline
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```
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Bug Issue -> Context Retrieval Agent (select regression tests)
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-> Bug Reproduction Agent (verify bug exists)
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-> [If reproduced] -> Issue Resolution Agent (generate fix)
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-> [If not reproduced] -> Direct resolution without reproduction
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-> Response Generation
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```
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### Question Answering Pipeline
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```
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Question -> Context Retrieval Agent (gather relevant code/docs)
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-> Question Analysis Agent (LLM with tools)
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-> Response Generation
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```
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---
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## Agent Communication
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Agents communicate through **shared state** managed by LangGraph:
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- Each subgraph has a typed state dictionary
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- State flows through nodes and is updated progressively
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- Parent states are inherited by child subgraphs
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- Results are passed back through state returns
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---
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## Technology Stack
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- **LangGraph**: State machine orchestration
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- **LangChain**: LLM integration and tool calling
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- **Neo4j**: Knowledge graph storage and retrieval
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- **Docker**: Isolated test execution environment
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- **Tree-sitter**: Code parsing and AST generation
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- **Git**: Patch management and version control
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---
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## Future Enhancements
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- **Environment Build Agent**: Complete implementation for automatic setup
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- **Pull Request Review Agent**: Automated code review
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- **Feature Implementation Agent**: Handle feature requests
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- **Documentation Generation Agent**: Auto-generate docs from code
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Reference in New Issue
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