Quick Start

Run your first AI code review in three steps.

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1. Install nitpik

Download the latest binary for your platform:

# Linux (x86_64)
curl -sSfL https://github.com/nsrosenqvist/nitpik/releases/latest/download/nitpik-x86_64-unknown-linux-gnu.tar.gz | sudo tar xz -C /usr/local/bin

See Installation for macOS, Docker, and other options.

2. Connect an LLM Provider

Set two environment variables — a provider name and the corresponding API key:

export NITPIK_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-...

nitpik supports Anthropic, OpenAI, Gemini, Cohere, DeepSeek, xAI, Groq, Perplexity, and any OpenAI-compatible endpoint. See LLM Providers for the full list.

3. Run a Review

From your repository, diff against a branch and review:

nitpik review --diff-base main

nitpik diffs your current branch against main, picks a reviewer profile, and prints findings:

nitpik · Free for personal & open-source use. Commercial use requires a license.

✔ w/handler.rs done

 ✖ error in handler.rs:21
   Backend crashes due to unhandled file I/O and parsing errors — The
   `load_users` function uses `unwrap()` for file reading and parsing,
   and accesses array elements without bounds checking.
   → Implement robust error handling (e.g., using `Result` and propagating
     errors) instead of `unwrap()`. Add bounds checking for array access.

 ⚠ warning in handler.rs:36
   N+1 query in `get_users_by_ids` — Calling `get_user` in a loop for
   each ID results in an N+1 query pattern, leading to significant
   performance degradation for large ID lists.
   → Consider implementing a batch fetch mechanism that retrieves all
     users in a single operation.

───────────────────────────────────
 2 findings: 1 errors, 1 warnings, 0 infos

Each finding includes:

• Severity — error (confirmed bug), warning (likely problem), or info (suggestion)
• Location — file and line number
• Title — one-line summary
• Message — detailed explanation
• Suggestion — recommended fix

What's Next?

• Run multiple reviewers — add --profile backend,security to get specialist perspectives. See Reviewer Profiles.
• Set up CI — output findings as GitHub annotations, GitLab Code Quality, or Bitbucket Code Insights. See CI/CD Integration.
• Enable secret scanning — add --scan-secrets to detect and redact secrets before they reach the LLM. See Secret Scanning.
• Explore agentic mode — add --agent to let the LLM read files and search your codebase for deeper analysis. See Agentic Mode.
• Create team config — drop a .nitpik.toml in your repo root. See Configuration.

Related Pages

• Installation — all install methods
• LLM Providers — provider setup details
• Diff Inputs — all the ways to feed code to nitpik