Implementing An LLM Based ARC Reviewer For GitHub Actions Parity

Jul 26, 2025 by ADMIN 65 views

LLM-Based ARC Reviewer Implementation for GitHub Actions Parity

Introduction

In this article, we'll dive into the exciting world of implementing an LLM-based ARC Reviewer for GitHub Actions parity. This project aims to enhance our CI pipeline by leveraging the power of Large Language Models (LLMs) to automate and improve code reviews. We'll explore the scope, acceptance criteria, code readiness, pre-execution context, implementation notes, and Claude Code execution details.

Task Context

This endeavor falls under sprint-4.2 and represents Phase 2: Implementation within the ci-pipeline component. Our primary goal is to create an LLM-based ARC Reviewer that mirrors the behavior of GitHub Actions, ensuring a seamless transition and consistent code review process.

Scope Assessment

We've assessed the scope and determined that it's clear. The requirements are well-defined, allowing us to proceed directly with implementation. The scope was determined clear based on GitHub Actions workflow analysis.

Acceptance Criteria

To ensure the LLM-based ARC Reviewer meets our expectations, we've established a comprehensive set of acceptance criteria. These criteria focus on functionality, performance, and integration with our existing systems. Let's break down the key requirements:

Local ARC Reviewer using Claude API: Our reviewer must utilize the Claude API to precisely replicate the behavior of GitHub Actions, guaranteeing consistency in code review outcomes.
Tool Access Parity: The LLM-based ARC Reviewer needs to support the same tools as our GitHub workflow, including Bash, Read, Grep, and Glob. This ensures the LLM can effectively analyze code and identify potential issues.
Prompt Template Alignment: We'll use the identical prompt template from .github/workflows/claude-code-review.yml to maintain consistency and leverage our existing knowledge base.
YAML Output Format: The reviewer should produce YAML output in the exact same format as the GitHub Actions version, facilitating seamless integration with our CI pipeline.
API Key Configuration: The solution needs to support API key configuration via environment variables, providing flexibility and security.
Fallback Mechanism: We'll implement a fallback to rule-based review when the API is unavailable, ensuring continuous code review even in the face of external dependencies.
Backward Compatibility: The LLM-based ARC Reviewer must maintain backward compatibility with our existing CI pipeline integration, minimizing disruption to our workflow.
Functional Coverage and Security Checks: Coverage and security checks need to remain fully functional, ensuring the integrity and security of our codebase.
Acceptable Performance: The review process should complete within an acceptable timeframe, ideally less than 30 seconds for a typical PR review, to maintain developer productivity.

Claude Code Readiness Checklist

Before diving into implementation, we conducted a thorough readiness assessment to ensure a smooth development process. This checklist covers context URLs, file scope estimation, dependency mapping, test strategy definition, and breaking change assessment.

Context URLs Identified

We've identified the following context URLs to guide our development efforts:

GitHub Actions workflow: .github/workflows/claude-code-review.yml
Current implementation: src/agents/arc_reviewer.py
CI documentation: /docs/ci-*.md

These resources will provide valuable insights into the existing code review process and help us align our LLM-based ARC Reviewer with the desired behavior.

File Scope Estimated

We've estimated the file scope to be manageable, involving less than 4 files and fewer than 400 lines of code (LoC) for the new LLM-based ARC Reviewer specific logic. The primary file of focus is src/agents/arc_reviewer.py, which currently sits at approximately 600 LoC but will undergo refactoring to accommodate the LLM integration. We'll introduce a new file, src/agents/llm_reviewer.py, estimated to be around 300 LoC, to encapsulate the LLM-specific functionality. Additionally, we'll need to update requirements.txt to include the Anthropic SDK and .env.example to include the ANTHROPIC_API_KEY.

Dependencies Mapped

We've identified the following dependencies for our LLM-based ARC Reviewer:

Anthropic Python SDK: This library will enable us to interact with the Claude API.
Environment variable for API key: We'll rely on an environment variable (ANTHROPIC_API_KEY) to securely store and access the API key.
Existing subprocess/git integration: We'll leverage our existing infrastructure for interacting with subprocesses and Git.

Understanding these dependencies is crucial for ensuring a seamless integration and avoiding potential conflicts.

Test Strategy Defined

Our testing strategy encompasses a multi-faceted approach to ensure the quality and reliability of the LLM-based ARC Reviewer. We'll employ unit tests, integration tests, and edge case testing to cover various aspects of the implementation.

Unit Tests: We'll develop unit tests to isolate and verify the LLM integration, using a mocked API to simulate interactions with the Claude API. This allows us to test the core logic of the LLM-based ARC Reviewer without relying on external services.
Integration Tests: Integration tests will compare the output of the LLM-based ARC Reviewer with that of GitHub Actions. This ensures that our implementation behaves as expected and produces consistent results.
Edge Cases: We'll specifically target edge cases, such as API failures, timeout handling, and malformed responses, to ensure the robustness and resilience of our solution.

Breaking Change Assessment

We've assessed the potential for breaking changes and determined that our implementation will not introduce any. We'll add the LLM mode alongside the existing rule-based mode, providing a seamless transition and maintaining backward compatibility. This approach minimizes disruption to our existing CI pipeline and ensures a smooth adoption of the LLM-based ARC Reviewer.

Pre-Execution Context

Before embarking on the implementation phase, it's essential to establish a clear understanding of the pre-execution context. This involves identifying key files, external dependencies, configuration parameters, and related issues/PRs.

Key Files

The following files are central to the implementation of the LLM-based ARC Reviewer:

src/agents/arc_reviewer.py: This file contains the current implementation of the ARC reviewer and will be modified to integrate the LLM functionality.
.github/workflows/claude-code-review.yml: This file serves as the reference implementation, providing insights into the desired behavior of the LLM-based ARC Reviewer.
scripts/claude-ci.sh: This script represents the integration point within our CI pipeline and will need to be updated to utilize the LLM-based ARC Reviewer.

External Dependencies

The LLM-based ARC Reviewer relies on the following external dependencies:

Anthropic Claude API: This is the core dependency for leveraging LLM capabilities. It requires a valid API key for authentication and usage.
Existing git, pytest, pre-commit tools: These tools are integral to our development workflow and will be used in conjunction with the LLM-based ARC Reviewer.

Configuration

The behavior of the LLM-based ARC Reviewer can be configured through the following parameters:

ANTHROPIC_API_KEY environment variable: This variable stores the API key for accessing the Anthropic Claude API.
Optional: ARC_REVIEWER_MODE (llm|rule-based|hybrid): This variable allows us to specify the review mode, enabling us to switch between LLM-based, rule-based, or a hybrid approach.
Existing .coverage-config.json for thresholds: This file defines the coverage thresholds for our code, ensuring that our tests provide adequate coverage.

Related Issues/PRs

The following issues and PRs provide relevant context for this project:

Original CI migration PRs: #1290, #1291: These PRs provide background information on the migration of our CI pipeline.
CI documentation updates: Updates to the CI documentation may be necessary to reflect the changes introduced by the LLM-based ARC Reviewer.

Implementation Notes

Now, let's delve into the technical approach and implementation steps for creating our LLM-based ARC Reviewer. We'll cover everything from the core architecture to error handling and testing strategies.

Technical Approach

Our technical approach revolves around creating an LLMReviewer class that effectively leverages the Claude API. This class will handle communication with the API, tool execution, and response parsing. Here's a breakdown of the key components:

Create LLMReviewer class: This class will serve as the central hub for interacting with the Claude API.
- Initialize with API key from the environment: The class will retrieve the API key from the ANTHROPIC_API_KEY environment variable.
- Implement tool execution framework for Claude's use: We'll create a framework that allows Claude to execute tools like Bash, Read, Grep, and Glob.
- Parse and validate YAML responses: The class will parse and validate the YAML responses received from the Claude API.
Tool Implementation: We'll implement the following tools for Claude to use:
```
tools = {
    "Bash": self._execute_bash_command,
    "Read": self._read_file,
    "Grep": self._grep_files,
    "Glob": self._glob_files
}
```
These tools will enable Claude to interact with the file system, execute commands, and search for patterns.
Prompt Engineering: We'll carefully craft the prompt that is sent to Claude to ensure it receives the necessary context and instructions.
- Extract exact prompt from GitHub Actions workflow: We'll extract the prompt from .github/workflows/claude-code-review.yml to maintain consistency.
- Include all review criteria and YAML schema: The prompt will include all the review criteria and the YAML schema to guide Claude's analysis.
- Pass git diff context and file contents: We'll provide the git diff context and the contents of the relevant files to Claude, enabling it to perform a comprehensive code review.
Integration Points: We'll modify the existing ARCReviewer to delegate to the LLMReviewer when available.
- Modify ARCReviewer to delegate to LLMReviewer when available: This will allow us to seamlessly integrate the LLM-based review process into our existing workflow.
- Keep existing rule-based checks as fallback: We'll retain the existing rule-based checks as a fallback mechanism, ensuring that code reviews can still be performed even if the LLM is unavailable.
- Maintain the same CLI interface for backward compatibility: We'll ensure that the CLI interface remains unchanged, minimizing disruption to our users.
Error Handling: We'll implement robust error handling to address potential issues like API rate limits, malformed responses, network failures, and token limits.
- API rate limits → exponential backoff: We'll use exponential backoff to handle API rate limits, preventing our requests from being throttled.
- Malformed responses → retry with clarification: If we receive a malformed response from the API, we'll retry the request with a clarification prompt.
- Network failures → fallback to rule-based: In the event of a network failure, we'll fallback to the rule-based review process.
- Token limits → chunk large PRs: For large PRs, we'll chunk the code into smaller pieces to avoid exceeding token limits.
Testing Strategy: Our testing strategy will involve a combination of unit tests, integration tests, and performance benchmarks.
- Mock Anthropic API for unit tests: We'll mock the Anthropic API for unit tests, allowing us to isolate and test the LLM integration logic.
- Record real API responses for replay tests: We'll record real API responses to create replay tests, ensuring that our implementation behaves as expected in real-world scenarios.
- Compare outputs between local and GitHub Actions: We'll compare the outputs of our local LLM-based ARC Reviewer with those of GitHub Actions, verifying that they produce consistent results.
- Benchmark performance vs rule-based approach: We'll benchmark the performance of the LLM-based ARC Reviewer against the rule-based approach to assess its efficiency.

Implementation Steps

Here's a step-by-step breakdown of the implementation process:

Add anthropic package to requirements.txt: We'll add the Anthropic Python SDK to our project dependencies.
Create LLMReviewer class with Claude integration: We'll create the LLMReviewer class and implement the logic for interacting with the Claude API.
Implement tool execution framework: We'll build the framework that allows Claude to execute tools like Bash, Read, Grep, and Glob.
Extract and format prompt from GitHub workflow: We'll extract the prompt from .github/workflows/claude-code-review.yml and format it for use with the Claude API.
Update ARCReviewer to use LLMReviewer when configured: We'll modify the ARCReviewer class to delegate to the LLMReviewer when the LLM mode is enabled.
Add comprehensive error handling and retries: We'll implement robust error handling and retry mechanisms to ensure the resilience of our solution.
Write unit and integration tests: We'll develop unit and integration tests to verify the functionality and performance of the LLM-based ARC Reviewer.
Update documentation and .env.example: We'll update the documentation and .env.example file to reflect the changes introduced by the LLM-based ARC Reviewer.
Test against recent PRs to ensure parity: We'll test the LLM-based ARC Reviewer against recent PRs to ensure that it produces results that are consistent with GitHub Actions.

Claude Code Execution

The Claude Code execution phase involves running the LLM-based ARC Reviewer and monitoring its progress. This section provides details on session start time, task template, token budget, and completion target.

Session Started: 
Task Template Created: 
Token Budget: 
Completion Target:

This issue will be updated during Claude Code execution with progress and results.

Conclusion

Implementing an LLM-based ARC Reviewer for GitHub Actions parity is a significant step towards automating and enhancing our code review process. By leveraging the power of Large Language Models, we can improve the efficiency and effectiveness of our CI pipeline, ultimately leading to higher quality code. This article has provided a comprehensive overview of the project, covering everything from scope and acceptance criteria to implementation notes and Claude Code execution. Stay tuned for further updates as we progress with the implementation and testing phases. We believe that this LLM-based ARC Reviewer will transform our code review process, providing more insightful feedback and ultimately contributing to a more robust and secure codebase. We're excited to see the positive impact it will have on our development workflow and the quality of our software.