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You can automate a first-pass GitHub pull request review by having a GitHub App send pull request webhooks to an HTTPS endpoint backed by AWS Lambda. The Lambda function fetches the pull request context, asks a model for structured findings using function calling, validates those findings in application code, and creates either a pending or submitted GitHub review. The model proposes; your code decides what is allowed and what gets published.
The important engineering work is not just prompting: it is limiting permissions and input, checking model output, mapping inline comments to real lines in the diff, and building TypeScript into JavaScript for Lambda. This guide lays out that workflow and the decisions that make it safer to operate.
How the review workflow fits together
Think of the integration as an event-driven pipeline with separate responsibilities for GitHub, Lambda, the model, and your application policy. Function calling sits inside the pipeline as a structured handoff, not as permission for the model to execute code or write to GitHub.
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- Lambda validates and filters it. Verify the webhook signature, handle duplicate deliveries idempotently, and check the event action before making API or model calls.
- The application fetches review context. Use the GitHub API to retrieve the pull request details and changed files. Limit the amount of content processed and omit generated, sensitive, or irrelevant files where appropriate.
- The model proposes structured findings. Send a focused prompt and a schema-defined tool. The model can return a tool call with candidate findings; it does not run the tool or publish comments itself.
- Application code validates and publishes. Check each finding against local policy and the current diff, then create a pending review or submit a review through GitHub’s API.
This is the function-calling control loop described in OpenAI’s Function calling guide: make a request, receive a tool call, execute the corresponding application logic, send the tool result back, and continue until the model returns a final response or another tool call. Treat every tool call as untrusted input, even when its arguments match the schema.
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How to use function calling for review findings
Make the tool describe findings, not unrestricted actions
A narrowly scoped tool can ask the model to return candidate review findings such as a changed-file path, a line reference, a severity, and an explanation. Keep the tool’s job distinct from the application’s job: the model identifies a possible issue; the application decides whether the finding is valid, in scope, and suitable to publish.
Avoid exposing a general-purpose capability such as “post any comment.” A safer design is to have the model return structured findings and let deterministic application code prepare the review only after checks pass. If you add a tool that prepares or stages a draft review, gate that operation in code as well.
Use strict schemas where supported, then validate locally
OpenAI recommends strict mode when tool arguments need to follow a JSON Schema. Under the guide’s strict-mode rules, each object must set additionalProperties to false, and every property must be required. Represent a conceptually optional value with a nullable type rather than omitting the property. Strictness improves argument shape; it does not establish that a finding is correct or authorize a GitHub write.
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For example, a findings tool could require a list of objects, each with a path, a line reference, and a rationale. Define the full schema for the API and model you select, then validate the returned arguments again in your handler. Reject unexpected paths, invalid locations, excessive lengths, unsupported severities, and findings that violate your review policy.
Keep the model interaction bounded
- Ask for actionable findings tied to the supplied changes, not a review of the entire repository.
- Set limits for files, diff size, finding count, and comment length in application code.
- Do not send secrets or unnecessary repository content to the model.
- Handle malformed arguments, tool errors, and model responses with no findings as normal outcomes; none should bypass the application’s checks.
Connect pull request events to GitHub’s API
Create an app with only the required access
GitHub’s App tutorial demonstrates receiving a pull request webhook and using the API to add a comment. Its example uses a pull request webhook and Pull requests read/write permission. Treat that as tutorial setup, not a blanket production permission recommendation: identify the exact endpoints and data your integration needs, then request the narrowest applicable repository permissions. Review creation requires Pull requests write permission for the documented fine-grained token types.
The tutorial lists Node.js 20 or greater and npm 6.12.0 or greater as prerequisites for its example. Those are the tutorial’s prerequisites, not a requirement that every production deployment use precisely those versions.
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Validate deliveries before doing work
At the HTTPS webhook endpoint, verify GitHub’s webhook signature before trusting the payload. Use the delivery identifier or equivalent durable state to make retries idempotent: GitHub may redeliver an event, and duplicate processing can otherwise produce duplicate reviews. Check the event action and ignore actions your workflow does not handle before fetching a diff or spending model tokens. The GitHub tutorial establishes the webhook URL and event pattern; signature validation, idempotency, and filtering are operational safeguards your implementation must provide.
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Fetch only the context needed for the review
Use the GitHub API to obtain the pull request’s changed files and relevant metadata. Bound the number of files and amount of diff text processed. Exclude generated output, vendored code, or sensitive paths according to repository policy. If a pull request is too large or its changed-file data cannot be fetched reliably, fail closed or produce a clearly limited summary rather than pretending the review covered every change.
Turn findings into useful GitHub feedback
Choose between inline comments and a review summary
Inline comments are actionable when a finding maps cleanly to a changed line. A review body can explain a broader concern that does not belong to one line. Inline placement is correctness-sensitive: GitHub anchors review comments to positions in the pull request diff, not simply to arbitrary source-file line numbers. Reject findings that cannot be mapped unambiguously; do not guess a nearby line.
| Feedback form | Best use | Trade-off |
|---|---|---|
| Inline diff comment | A specific issue on a valid changed line | More actionable, but line mapping can fail or become stale when the diff or commit changes. |
| Review summary | Broad observations, review scope, or context that does not belong to one line | Less dependent on line mapping, but readers must locate the relevant code themselves. |
Pin the review to the commit that was actually examined where appropriate. If the pull request changes while the model is reviewing it, discard or repeat work whose line references or commit context no longer match.
Decide whether to submit immediately or stage a pending review
GitHub supports creating a pending review and submitting it later. A submitted review can carry an event of COMMENT, APPROVE, or REQUEST_CHANGES; omitting the event when creating a review leaves it pending. A pending review is useful when a person must inspect AI-generated feedback before publication. Immediate submission reduces delay but puts more weight on your filters and validation.
| Publication choice | Workflow effect | Main consideration |
|---|---|---|
| Submit the review immediately | Feedback appears as soon as the application creates it. | Fast, but noisy or incorrect findings are published without a human staging step. |
| Create a pending review | Feedback is staged for later submission. | Allows human inspection before publication, at the cost of an additional review or submission step. |
For a first deployment, a pending review is a practical way to observe quality and tune policy before allowing automatic publication. That is a workflow recommendation, not a guarantee that pending reviews eliminate incorrect feedback.
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Build and package the TypeScript Lambda
AWS states that Node.js does not run TypeScript natively in Lambda: TypeScript must first be transpiled to JavaScript. The handler you deploy therefore needs a build step that produces JavaScript compatible with the selected Lambda runtime.
Choose a TypeScript build path
| Build approach | Type checking | Trade-offs |
|---|---|---|
tsc compilation |
The TypeScript compiler can type-check while compiling, depending on configuration. | A direct compiler workflow; configure its output and target for the chosen Lambda runtime. |
esbuild plus separate tsc checking |
Run tsc --noEmit (or configure noEmit) separately; esbuild does not perform type checks. |
Separates fast transpilation from type checking, but requires both steps to be configured and run in the build pipeline. |
Whichever path you use, set the JavaScript target to match the Lambda Node.js runtime you select, and make type checking an explicit build or CI step. A successful esbuild bundle alone does not prove the TypeScript types are valid.
Select and monitor a supported runtime
AWS runtime availability and lifecycle dates change. The AWS Lambda documentation reviewed on October 7, 2026 listed Node.js 26, 24, and 22 runtimes, with lifecycle dates for some listed runtimes. Check AWS’s live runtime table when choosing a runtime and plan to update the deployment as support changes; do not treat a listed version as a permanent recommendation.
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Operational checks before enabling automatic reviews
- Webhook safety: Verify signatures, filter event actions, and make retries idempotent.
- Least privilege: Restrict the GitHub App’s repository permissions to the endpoints and data the workflow needs.
- Input control: Limit diff and file volume, and exclude content that should not be sent for review.
- Output control: Validate every proposed path, line, severity, and comment before creating a GitHub review.
- Stale context: Ensure findings still match the commit and diff being reviewed before publishing.
- Build correctness: Transpile TypeScript, run type checking separately when using esbuild, and target the selected Lambda runtime.
- Failure behavior: Make API errors, oversized pull requests, invalid locations, and empty model results visible in logs or application status without turning them into fabricated findings.
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