LLM Pull Request Review

Overview

DPsim ships an optional, non-blocking pull-request reviewer that runs a series of specialised passes over the diff of a pull request using a large language model and posts a single review comment. It is intended as an assistive first pass: it never requests changes and cannot block a merge, so a human review remains authoritative.

The reviewer lives under .github/llm-review/ (the prompts in prompts.py and a pure-standard-library runner in review.py) and is driven by two workflows: llm-review-collect.yml, which runs on the pull request itself, and llm-review.yml, which performs the review. The split is what makes reviewing pull requests from forks safe (see Fork pull requests). It communicates with any OpenAI-compatible chat endpoint, configured through the environment variables described below.

How it works

For each pull request the runner reads the base..head diff and sends it, in turn, to a set of focused review stages, each with its own prompt. The stages cover model equations and their derivation, MNA stamping and domain modeling, numerical correctness, task scheduling and attribute usage, real-time safety, C++ class design and reuse, naming and in-code documentation, logging discipline, the Python bindings, input parsing, the build system and dependencies, testing and component coverage, and licensing and pull-request hygiene. Each stage returns a strict JSON list of findings. A final synthesis pass deduplicates and prioritises them, and the runner posts them as one review, anchoring inline comments only to lines present in the diff.

The prompts encode DPsim’s documented conventions (see Guidelines) and the recurring points raised in past reviews, so the feedback stays specific to this project rather than generic.

Configuration

The workflow requires one repository secret:

• RWTH_LLM_TOKEN: the bearer API key for the chat endpoint.

The following repository Actions variables are optional and override the defaults baked into the workflow:

• LLM_BASE_URL: the OpenAI-compatible base URL.
• LLM_MODEL: the model identifier.
• LLM_CHAT_PATH: the chat path appended to the base URL (default /chat/completions).
• LLM_REVIEW_RUNNER: the runner label (default ubuntu-latest; see Runner selection).

The workflow’s baked-in defaults target an OpenAI-compatible deployment; override LLM_BASE_URL and LLM_MODEL to point at a different endpoint or model.

Obtaining an API key

Obtain a bearer API key from the chosen OpenAI-compatible provider and store it as the RWTH_LLM_TOKEN repository secret. The key is only exposed to workflow runs on pull requests from the repository itself, never from forks.

Before storing the secret, a single request confirms that the key reaches the model:

curl -sS -X POST "$LLM_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $RWTH_LLM_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{\"model\":\"$LLM_MODEL\",\"messages\":[{\"role\":\"user\",\"content\":\"ok\"}]}"

Running locally

The runner has a dry-run mode that executes the full pipeline and prints the assembled review instead of posting it. It needs no GitHub token and no Actions runner, only network access to the endpoint:

cd .github/llm-review
export LLM_BASE_URL='<openai-compatible-base-url>'
export LLM_MODEL='<model-identifier>'
export LLM_CHAT_PATH='/chat/completions'
export LLM_API_KEY="$RWTH_LLM_TOKEN"
export BASE_SHA=$(git rev-parse origin/main) HEAD_SHA=$(git rev-parse HEAD) PR_NUMBER=0
python3 review.py --dry-run

Runner selection

The workflow defaults to a GitHub-hosted ubuntu-latest runner. If the chosen endpoint is only reachable from within a particular network, set the LLM_REVIEW_RUNNER variable to a self-hosted runner label registered inside that network; no change to the workflow is required.

Fork pull requests

Reviewing pull requests from forks requires care, because a fork’s code is untrusted and must never gain access to the secret. The reviewer uses the workflow_run pattern for this, rather than pull_request_target, and splits the work into two workflows:

• llm-review-collect.yml runs on the pull_request event, including from forks. GitHub withholds secrets from fork pull_request runs, so this job has no key. It checks out nothing and runs no code from the pull request; it only records the PR number and commit SHAs, taken from trusted GitHub context, into an artifact.
• llm-review.yml runs on workflow_run, after the collect job completes, in the base repository context where the secret is available. It checks out the base repository’s own code, never the pull request’s, and reads the diff as data through the GitHub API. It never builds or executes anything from the pull request.

Two properties keep the key safe. First, the key is only ever sent as an Authorization header to the configured LLM endpoint, and is never placed in the model prompt, so a prompt-injection payload in the diff cannot reveal it. Second, the privileged job runs only trusted base-repository code, so untrusted pull request code never executes with the secret in scope. The PR metadata read from the collect artifact is validated (numeric PR number, hexadecimal SHAs) before use. For this to operate, both the workflows and the secret must reside on the repository the pull requests target.