A Field Guide to Free-Tier AI Code Reviewers: Glossary, Decision Tree, and Worked Leaves

Description and use

Step‑by‑step process for integrating free AI code reviewers, covering decision criteria, MonkeyCode server setup, GitHub webhooks, and token tracking.

Problem

Teams want to use free AI models for code review but lack a systematic way to evaluate suitability and manage token costs.

Outcome

Teams can safely integrate free-tier AI code reviewers for triage purposes, monitor token usage, and adjust workflow based on decision tree outcomes.

Workflow

• Define decision tree criteria: code visibility, latency needs, binding status, false-positive tolerance.
• If all criteria met, clone MonkeyCode repo and obtain API key and server endpoint.
• Create a webhook to listen for GitHub pull-request events (opened or synchronize).
• Fetch the PR diff and send it to the MonkeyCode LLM endpoint with a prompt like "You are a conservative code reviewer."
• Post the LLM response as a PR comment prefixed with "🤖 bot review (unverified)."
• Track token usage per run to stay within the 10M-token allowance.
• If latency is an issue, schedule a daily cron job to batch PRs and post a digest.
• If non-binding, use the bot only for statistical reporting (flag density, error patterns).

Evidence

Free-tier LLMs have small context windows. A 500-line diff might be truncated before the final if statement.

Source

Rate limits will hit you during a sprint burst. If ten PRs land at once, your bot may silently skip some events.

Source