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Documentation Index

Fetch the complete documentation index at: https://docs.getcodewolf.com/llms.txt

Use this file to discover all available pages before exploring further.

CodeWolf is built as a lightweight, event-driven GitHub App centered around webhook processing. At a high level, it listens to pull request events, processes code changes, analyzes them using an LLM, and posts structured feedback back to the PR.

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System Overview

The system consists of three main layers:
  1. GitHub Integration Layer
  2. Core Review Engine
  3. LLM Processing Layer
Each layer is modular and designed to be easily extendable.

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Event Flow

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1. Webhook Listener

The main server (app/server.js) listens for incoming GitHub webhook events. It handles pull request triggers such as:
  • opened
  • synchronize
When a PR event is received:
  • The request is parsed
  • Metadata is extracted:
    • Repository
    • Pull request number
    • Commit SHA

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2. GitHub Authentication

Authentication is handled via: app/github/githubClient.js
  • Uses the GitHub App’s private key
  • Generates an installation-scoped Octokit client
  • Ensures secure, scoped access to the repository

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3. Fetching Code Changes

Once authenticated, CodeWolf:
  • Fetches the list of changed files using the GitHub API
  • Retrieves:
    • File diffs (patch)
    • Full file contents
This provides both what changed and full context.

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4. Review Pipeline

The core processing happens in: app/core/reviewEngine.js

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File Filtering

Before sending data to the LLM, CodeWolf filters out:
  • Large files
  • Generated assets
  • Unsupported file types
  • Low-signal changes
This ensures only relevant code is analyzed.

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5. Prompt Construction

For each relevant file:
  • A structured prompt is created
  • Includes:
    • Code diff
    • Surrounding context
    • Review instructions
This ensures the LLM has enough context to generate meaningful feedback.

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6. LLM Processing

The prompt is sent to the configured LLM provider via: app/llm Example: app/llm/huggingFace.js Key characteristics:
  • BYOK (Bring Your Own Key)
  • Supports different providers via abstraction
  • Model can be swapped without changing core logic
The LLM returns structured insights such as:
  • Bugs and edge cases
  • Security vulnerabilities
  • Production risks
  • Code improvement suggestions

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7. Review Aggregation

  • Responses from the LLM are normalized
  • Results across files are aggregated
  • Structured into a readable format

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8. PR Commenting

Finally, CodeWolf:
  • Posts the review back to the pull request
  • Uses the GitHub API
  • Delivers feedback as a structured comment

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Design Principles

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Event-driven

CodeWolf reacts to GitHub events instead of polling, making it efficient and real-time.

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Modular

Each layer (GitHub, Core, LLM) is isolated, making it easy to extend or replace components.

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Self-hosted

All processing runs on your infrastructure. No code leaves your environment unless your configured LLM requires it.

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LLM-agnostic

Supports multiple providers through a unified interface, allowing flexibility and control.

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Extensibility

The architecture is designed to evolve with minimal changes. Future improvements can include:
  • Support for additional LLM providers
  • Smarter filtering and prioritization
  • Multi-file and cross-file analysis
  • Automated fixes and PR generation
  • Custom rule enforcement
CodeWolf is built to remain simple at its core while allowing powerful extensions as it evolves.