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You can add AI feedback to GitHub pull requests with a workflow that prepares the changes, sends them to a model, and posts the response as a comment. “Free-tier” applies only to a provider-specific inference option—not a promise of unlimited free reviews. The example below follows the documented OpenAI Codex Action pattern; check current provider eligibility, limits, and terms before relying on any free route.
What the workflow needs
An AI pull-request reviewer connects four pieces: a GitHub event to start the run, the pull request’s changed files as context, a route to a model, and a way to publish the model’s feedback. GitHub Actions execution and model inference are separate services, each with its own limits. An API key stored as a GitHub Actions secret supplies model access in the Codex Action example; a GitHub token alone does not provide arbitrary model inference.
The OpenAI example uses pull_request with the opened activity type, checks out the pull request merge ref, fetches the base and head refs, runs openai/codex-action@v1, and uses the action’s final message as a pull-request comment. It grants contents: read for the job and uses a workspace permission profile. Treat this as a documented configuration pattern, not as a guarantee that it will work unchanged in every repository or as an independently verified test. Check the current action version, its inputs and outputs, and required permissions in the Codex Action README before adopting it.
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- Choose when reviews run. The example runs when a pull request is opened. You can select other relevant pull-request activity types if your review policy calls for them, but avoid triggering duplicate reviews on every update unless that behavior is intentional.
- Make the change available. Prepare the pull-request merge ref and fetch the base and head refs so the review can identify the changes. The Codex example sets
persist-credentials: falseduring checkout; retain controls like this unless you have a specific need for persisted credentials. - Keep the provider credential in a secret. Add the API key as a GitHub Actions secret, then pass it to the action through the workflow environment. Do not hard-code it in YAML, expose it in logs, or assume an outside contributor’s run will receive it.
- Grant only necessary GitHub permissions. Start with the narrow permissions required for the chosen checkout and comment method. The example grants
contents: read; publishing a comment may require additional permission depending on the action and implementation. Verify the current permission requirements rather than granting broad repository access by default. - Label the result. Make clear in the comment that it is AI-generated. Ask for concise, change-specific findings and treat them as suggestions for maintainers, not as approval or a substitute for human review.
If Codex must build or run repository code, the example notes that dependencies should be downloaded before the Codex step: its recommended workspace profile does not grant network access. Review each command in the workflow carefully. In particular, do not execute untrusted pull-request files or scripts while a secret is available.
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What “free-tier” can mean
A Marketplace listing for AI Code Reviewer Pro advertises support for OpenAI-compatible APIs, OpenAI, Anthropic, OpenRouter, and local Ollama; it labels one OpenRouter model route as free-tier and Ollama as local/free. These are claims made by the listing, not independent confirmation that a provider’s free access is currently available or suitable for your usage. Check the provider’s current terms and rate limits before configuring a route.
| Inference route | What it involves | Important limitation |
|---|---|---|
| Hosted model | Use an eligible provider account and credential; pull-request content is sent to that external service. | Eligibility, rate limits, terms, and availability depend on the provider and can change. |
| Local model | Connect the workflow to a reachable model host with adequate local compute. | “Local/free” does not remove the need to operate and secure the host or provide sufficient compute. |
The Codex Action example specifically uses an API key passed through a GitHub Actions secret. GitHub Actions run limits and model-provider limits are distinct; the available sources do not establish a universal monthly quota or price for a repository’s chosen provider.
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Handle fork pull requests without exposing secrets
Under GitHub’s documented fork policy, workflows from fork pull requests receive a read-only GITHUB_TOKEN and have no access to repository secrets. GitHub states: “Run workflows from fork pull requests – Allows users to run workflows from fork pull requests, using a GITHUB_TOKEN with read-only permission, and with no access to secrets.” See GitHub Docs on managing repository Actions settings for the applicable settings and policies. Repository settings may also require a maintainer to approve workflow runs from external contributors.
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That policy means a workflow that needs a repository-stored model API key cannot assume it can review outside fork contributions. Do not switch casually to pull_request_target to work around the limitation: it runs in the base-repository context with secrets and broader permissions. If a workflow then runs attacker-controlled pull-request code, it can expose those secrets. Use the safer pull_request event for untrusted contributions, keep job permissions narrow, and avoid running contributor-controlled scripts while secrets are present. GitHub also warns that user-controlled workflow code on self-hosted runners can threaten runner infrastructure; do not route arbitrary outside contributions to a self-hosted machine without a deliberate isolation and approval model. For more guidance, see the GitHub Marketplace security guidance for Codex AI Code Review.
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Choose review behavior that helps maintainers
A 2025 study of 16 AI code-review actions across 178 repositories and more than 22,000 review comments found wide variation in effectiveness. In that study’s sample, concise comments with code snippets and manually triggered, hunk-level feedback were more likely to lead to code changes. That is an association in the study, not a guarantee that a particular action or configuration will improve a repository’s code. The study is available as “Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions”.
- Prefer specific findings tied to changed lines over broad, repetitive summaries.
- Consider making a detailed review manually requested rather than posting one automatically on every event.
- Use the AI response to focus human attention; keep maintainers responsible for decisions and merges.
Pin actions and review updates
Using a version tag such as @v1 is convenient, but a tag can move. The Marketplace security guidance recommends pinning third-party actions to full commit SHAs for stronger protection against action changes. If you choose SHA pins, establish a process to review and update them deliberately. Also recheck action permissions, provider terms, and the workflow’s handling of untrusted code when updating the configuration.
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