Free hosted GitHub App · Public repositories only · Apache-2.0 source

Open-source pull request review for public GitHub repositories.

LlamaPReview reviews a pull request at its exact head commit, explains findings against the code, and reports known gaps in the evidence.

A finding you can check against the code

Published review excerpt · Texarkanine/SumMem#10

LlamaPReview — Blocking issues found

Do not merge until the nap instruction names the runnable driver path, matching the activation scheme this PR ships; currently the printed instruction tells agents to run summem, not .summem/summem.

Exact-head CI remains unresolved (1 pending); no CI-dependent merge-safety claim is made.

In this public review, LlamaPReview flagged a mismatch between the command an agent was told to run and the executable path shipped by the PR. It also recorded unresolved CI separately.

One historical example, not a measure of overall accuracy. Read the full review or inspect another example.

A substantive review summary contains findings, evidence and suggested next steps. Zero or more inline comments appear where findings can be placed safely. An optional diagram can help explain the change. Known evidence gaps remain visible. Output contract →

Install, open a PR, read the review

Install the GitHub App, select a public repository, open a non-draft PR or mark a draft ready, then read the review on GitHub. Over-capacity requests are skipped, not queued.
On small screens, scroll the diagram. The steps are also listed below.
  1. Install the GitHub App and select the public repositories you want reviewed.
  2. Open a non-draft pull request, or mark a draft ready for review.
  3. Read the review on GitHub and check its findings against the code.

The app does not review every push. Reviews use GitHub's COMMENT state, not APPROVE or REQUEST_CHANGES, and are not a required merge check.

Hosted capacity and limits

Free hosted capacity: up to 3 admitted review attempts per repository per UTC day, subject to shared daily capacity and available model budget. These are admission limits, not a guaranteed allocation or a promise of a completed review. Over-capacity requests are skipped, not queued.

Capacity policy · Failure and skip messages

Reviews can miss issues or be wrong; they supplement your tests and review process. Evidence retrieval is bounded, and missing coverage remains a gap.

How one review is made

The model supplies engineering judgment. Code enforces evidence boundaries, output validation, safe placement and publication.

An admitted public PR is pinned to head H. Route and PFR guide bounded evidence selection with sources and explicit gaps. Deep judges risks and uncertainty; Final presents findings, actions and placement requests. Code validates and prepares an immutable request, rechecks head and lifecycle, and publishes a GitHub COMMENT review. Recovery reconciles the same request.
The reviewed head is checked again through the pipeline and before publication. Recovery reuses the saved request.

This repository contains the Webhook and Pipeline source used by the hosted service, under Apache-2.0. Inspect the evidence retrieval, output validation and publication path, or read the architecture documentation.

Self-host in your AWS account

Run the public-repository review pipeline in your own AWS account. Supply your own GitHub App and model-provider credentials, and pay your own AWS and provider costs.

The reference Terraform stack disables hosted quota counters by default and leaves one-time head succession enabled. New source installations default to an OpenRouter GPT-6 Luna max trial with documented qualification limits. The existing DeepSeek profile remains selectable. Direct OpenAI is also an explicit option with a stage profile and verified budget policy. These source choices are separate from the hosted service, which uses the OpenAI API directly.

Self-hosting guide → · Verify a release

Data and privacy

New private-repository events stop after signature verification and minimum visibility parsing, before durable product storage, provider calls or GitHub product API calls. For eligible public requests, selected evidence and prompts are sent directly to OpenAI; generated responses can be included in subsequent review-stage requests.

The Privacy Policy separates OpenAI processing from LlamaPReview’s AWS storage and dates the last verified retention settings. Neither prompt caching nor store=false is a zero-retention guarantee. Read the Privacy Policy and security model.

Work on the reviewer

Found a reproducible problem or have a public review worth discussing? Open an Issue, join Discussions, or read the contributing guide.

Install for a public repository