firekeeper

Agentic AI code reviewer CLI

Parallel review, custom rules, agent skills, run anywhere

⚠️ Early Development: This project is in early development phase. APIs may change frequently.

Features

• Privacy-first: Bring your own LLM API key and model—works with any OpenAI-compatible endpoint
• Agentic review: Uses an agentic loop with tools to intelligently investigate code changes, not just one-shot LLM calls
• Custom rules: Define project-specific review rules in firekeeper.toml with detailed instructions for the AI agent
• Flexible scope: Review uncommitted changes, specific commits, date ranges, or entire repositories
• Parallel execution: Splits review tasks across multiple workers for speed and focus, with configurable file batching
• Structured output: JSON output and markdown trace files for integration with CI/CD and debugging
• Context engineering: Include files, shell command outputs, and Agent Skills as context for reviews

Installation

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/firekeeper-ai/firekeeper/releases/latest/download/firekeeper-installer.sh | sh

powershell -ExecutionPolicy Bypass -c "irm https://github.com/firekeeper-ai/firekeeper/releases/latest/download/firekeeper-installer.ps1 | iex"

npm install -g @firekeeper.ai/firekeeper

Getting Started

Init a config file firekeeper.toml:

firekeeper init

Set LLM API key (OpenRouter by default):

export FIREKEEPER_LLM_API_KEY=sk-xxxxxxxxxxxxxx

Review uncommitted changes or the last commit:

firekeeper review

To add or update rules, just ask your coding agent to modify the config:

add a rule to firekeeper.toml: update CHANGELOG.md when Rust files change

Review uncommitted changes only, suitable for git hooks or coding agent hooks:

firekeeper review --base HEAD

Review changes from 1 day ago with structured output, suitable for CI/CD pipelines:

firekeeper review --base "@{1.day.ago}" --output /tmp/report.json --trace /tmp/trace.md

Review all files (ensure you have sufficient LLM token budget):

firekeeper review --base ROOT

Prek Hook

[[repos]]
repo = "https://github.com/firekeeper-ai/firekeeper"
rev = "v0.5.0"
hooks = [
  { id = "pre-commit" },
]

Pre-commit Hook

repos:
  - repo: https://github.com/firekeeper-ai/firekeeper
    rev: v0.5.0
    hooks:
      - id: pre-commit

FAQ

Why use a dedicated AI code reviewer instead of coding agents with MCP/Skills?

• Cost efficiency: Reviewers need less coding capability than code generators, so you can use cheaper models (Gemini Flash vs Pro, Claude Haiku vs Opus)
• Integration: CLI design fits naturally into git hooks and CI/CD pipelines
• Specialized tooling: Reviewer agents can have a different, optimized tool set
• Performance at scale: Parallel execution with filtered scopes keeps reviews fast and focused, preventing quality degradation on large codebases

Why doesn't this tool fix bugs after review?

Fixing bugs requires high-quality output (passing compilation and tests), which coding agents already handle well. To avoid duplicate responsibility, firekeeper focuses solely on code review.

Recommended workflow: Integrate firekeeper in pre-commit git hooks → coding agent triggers the hook → sees review results → auto-optimizes the code.

What should I review with this tool?

Don't use for: Issues caught by static analysis tools (formatters, linters, compilers, static analyzers). They're faster, more accurate, and cheaper.

Do use for: Semantic rules and conventions that traditional tools can't detect:

• Documentation updates after code changes
• Error logging after exception handling
• Code duplication that should be extracted into modules
• Project-specific conventions and patterns

This tool is designed for user-defined rules, not built-in nitpicking.

CHANGELOG