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feat: migrate to Kimi K2 thinking model with robust parsing and enhanced context (#12)
This PR successfully migrates the AI reviewer to use the Kimi K2 thinking model with comprehensive improvements to response handling, context management, and review quality. Key Changes: - Thinking model support with robust parsing of thinking tags and markdown blocks - Enhanced context: PR descriptions, commit messages, human comments - Smart context limits to optimize token usage - 64k token limit to prevent truncation while maintaining cost-effectiveness - Improved error handling and debug logging - Concise, importance-based review structure - Security improvements with DEBUG_MODE controls Cost Impact: /bin/bash.01-/bin/bash.05 per review even for large PRs with full context
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.github/workflows/ai-code-reviewer.yml

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jobs:
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comprehensive-review:
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name: 🤖 AI Code Review
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name: AI Code Review
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runs-on: ubuntu-latest
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if: github.event.action == 'labeled' && github.event.label.name == 'ai_code_review'
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permissions:
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
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AI_MODEL: ${{ vars.AI_MODEL || 'z-ai/glm-4.6' }}
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AI_MODEL: ${{ vars.AI_MODEL || 'moonshotai/kimi-k2-thinking' }}
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AI_TEMPERATURE: ${{ vars.AI_TEMPERATURE || '0.1' }}
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AI_MAX_TOKENS: ${{ vars.AI_MAX_TOKENS || '2000' }}
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AI_MAX_TOKENS: ${{ vars.AI_MAX_TOKENS || '64000' }}
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MAX_DIFF_SIZE: ${{ vars.MAX_DIFF_SIZE || '800000' }}
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EXCLUDE_FILE_PATTERNS: ${{ vars.EXCLUDE_FILE_PATTERNS || '*.lock,*.min.js,*.min.css,package-lock.json,yarn.lock' }}
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PR_NUMBER: ${{ github.event.pull_request.number }}
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echo "Running AI code review..."
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AI_RESPONSE=$(cat diff.txt | bash ai-reviewer.sh)
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# Save response for debugging (only in debug mode)
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# Output raw response for debugging (only in debug mode)
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if [ "$DEBUG_MODE" = "true" ]; then
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echo "Debug: AI response saved to ai_response.txt"
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echo "$AI_RESPONSE" > ai_response.txt
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echo "=== RAW AI RESPONSE FOR DEBUG ==="
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echo "$AI_RESPONSE"
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echo "=== END RAW AI RESPONSE ==="
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fi
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# Check if AI_RESPONSE is empty
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# Parse JSON response
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if ! echo "$AI_RESPONSE" | jq . >/dev/null 2>&1; then
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echo "⚠️ AI response is not valid JSON. Cannot process review."
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# Log raw response for debugging (redact sensitive info)
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echo "=== DEBUG: Raw AI Response (first 2000 chars) ==="
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echo "$AI_RESPONSE" | head -c 2000
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echo ""
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echo "=== END DEBUG ==="
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# Try to extract JSON from thinking model response
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if echo "$AI_RESPONSE" | grep -q '"content"'; then
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echo "=== DEBUG: Attempting to extract content from response ==="
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EXTRACTED_CONTENT=$(echo "$AI_RESPONSE" | jq -r '.choices[0].message.content // empty' 2>/dev/null || echo "")
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if [ -n "$EXTRACTED_CONTENT" ]; then
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echo "Extracted content (first 1000 chars):"
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echo "$EXTRACTED_CONTENT" | head -c 1000
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echo ""
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fi
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fi
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# Don't post raw response as it may contain sensitive data
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echo "AI response could not be processed. Please check the workflow logs." | gh pr comment ${{ github.event.pull_request.number }} --repo ${{ github.repository }} -F -
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echo "AI response could not be processed. Please check the workflow logs for debugging information." | gh pr comment ${{ github.event.pull_request.number }} --repo ${{ github.repository }} -F -
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exit 1
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fi
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README.md

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# Friendly AI Reviewer (FAIR) Setup Guide
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# Friendly AI Reviewer Setup Guide
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- Creates highly-customizable AI Reviews as PR comments.
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- Installation: Just 2 files copied to your repo and a open router API Key in your secrets.
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- Costs: $0.01 - $0.05 per review (depends highly on model)
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- ** Example output ** https://github.com/LearningCircuit/local-deep-research/pull/959#issuecomment-3445396169
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- Installation: Just 2 files copied to your repo and an OpenRouter API Key in your secrets.
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- Costs: $0.01 - $0.05 per review (even for large PRs with full context)
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- **Example output**: https://github.com/LearningCircuit/local-deep-research/pull/959#issuecomment-3445396169
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This guide explains how to set up the automated AI PR review system using OpenRouter to analyze pull requests with your choice of AI model.
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## What's New
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**Latest Updates:**
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- **Thinking Model Support**: Now supports advanced reasoning models like Kimi K2 that use `<thinking>` tags
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- **Rich Context**: Includes PR descriptions, commit messages, and human comments for comprehensive reviews
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- **Higher Token Limits**: Default 64k tokens for complete reviews without truncation
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- **Smart Context Management**: Only fetches most recent AI review to save tokens
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- **Enhanced Error Handling**: Robust parsing of various AI response formats
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## Overview
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The AI Code Reviewer provides automated, comprehensive code reviews covering:
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### 3. Configure Workflow (Optional)
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The workflow is pre-configured with sensible defaults, but you can customize it by editing `.github/workflows/ai-code-reviewer.yml`:
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The workflow is pre-configured with sensible defaults, but you can customize it by setting repository variables in **Settings****Secrets and variables****Actions****Variables**:
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- **AI_MODEL**: Change the AI model (see [OpenRouter models](https://openrouter.ai/models))
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- **AI_MODEL**: Change the AI model (default: `moonshotai/kimi-k2-thinking`)
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- See [OpenRouter models](https://openrouter.ai/models) for options
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- Recommended: Models with reasoning capabilities (Kimi K2, o1, etc.)
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- **AI_TEMPERATURE**: Adjust randomness (default: `0.1` for consistent reviews)
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- **AI_MAX_TOKENS**: Maximum response length (default: `2000`)
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- **AI_MAX_TOKENS**: Maximum response length (default: `64000`)
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- High limit ensures comprehensive reviews without truncation
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- For large PRs with thinking models, this prevents cut-off responses
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- Adjust lower for cost savings on smaller PRs
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- **MAX_DIFF_SIZE**: Maximum diff size in bytes (default: `800000` / 800KB)
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- **DEBUG_MODE**: Enable debug logging (default: `false`)
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- ⚠️ Warning: Exposes code diff in workflow logs when enabled
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- Only enable temporarily for troubleshooting
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## Usage
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## Cost Estimation
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Costs vary by model, but most code-focused models on OpenRouter are very affordable:
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- Typical small PR (< 1000 lines): $0.001 - $0.01
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- Large PR (1000-5000 lines): $0.01 - $0.05
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Costs with the default Kimi K2 thinking model are very affordable. Based on real usage data:
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**Typical Costs:**
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- Small PR (< 1000 lines): $0.01 - $0.02
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- Medium PR (1000-3000 lines): $0.02 - $0.04
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- Large PR (3000+ lines): $0.04 - $0.06
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**Example from a 20-commit PR with full context:**
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- Input: ~5,000-9,000 tokens (diff + PR description + commits + human comments)
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- Output: ~2,000-6,000 tokens (comprehensive review)
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- **Total cost: $0.01 - $0.05 per review**
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**Why So Affordable:**
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- Kimi K2 has competitive pricing (~$0.001-$0.003 per 1k tokens)
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- Smart context management (only most recent AI review, limited commit history)
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- Most PRs are smaller than you think in token count
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- The 64k token limit is a ceiling, not typical usage
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**Cost varies based on:**
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- PR size (larger diffs = more input tokens)
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- Review complexity (detailed reviews = more output tokens)
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- Number of human comments and commit messages included
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- OpenRouter provider routing (prices vary slightly by provider)
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Check [OpenRouter pricing](https://openrouter.ai/models) for specific model costs.
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Check [OpenRouter pricing](https://openrouter.ai/models) for current Kimi K2 rates.
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## Customization
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The workflow fetches and sends these repository elements to the AI:
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1. **Code Changes**: Full diff of modified files
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2. **Labels**: All repository labels with descriptions and colors
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3. **Previous Comments**: All previous AI review comments from the PR
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4. **CI/CD Status**: GitHub Actions check runs and build statuses
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5. **PR Metadata**: Pull request details, head SHA, repository information
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6. **Files**: May include sensitive configuration files, keys, or credentials
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2. **PR Description**: Title and description text from the pull request
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3. **Commit Messages**: Up to 15 most recent commit messages (excluding merges)
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4. **Human Comments**: All comments from human reviewers on the PR
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5. **Labels**: All repository labels with descriptions and colors
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6. **Previous AI Review**: Most recent AI review comment only (limited to 10k chars)
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7. **CI/CD Status**: GitHub Actions check runs and build statuses
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8. **PR Metadata**: Pull request details, head SHA, repository information
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9. **Files**: May include sensitive configuration files, keys, or credentials
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### Recommended Mitigations
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