Problem Statement
First of all, thank you for open-sourcing Open Code Review and for adding Codex skill/plugin support. The project is very useful, especially the deterministic diff handling and structured review workflow.
I would like to use Open Code Review from inside Codex via the installed skill/plugin. Today, the Codex integration invokes the local ocr CLI, and ocr still requires a separately configured LLM endpoint/API key. For users who already have a Codex subscription or token allowance, this means the Codex integration cannot use their existing Codex quota and requires another provider setup or extra API billing.
This is a bit confusing from a user perspective because the workflow is launched inside Codex, but the actual model calls are still managed outside Codex.
Proposed Solution
It would be great to have a Codex-native mode for the Codex integration.
Possible shape:
- A documented mode such as
ocr review --audience agent --llm-runner codex, or a dedicated Codex skill/plugin path.
- In this mode, Open Code Review could keep the deterministic parts of the pipeline, such as diff/file selection, grouping, rules, and output formatting.
- The LLM judgment/review step would be delegated to the current Codex session, so users can use their existing Codex quota instead of configuring
OCR_LLM_URL / OCR_LLM_TOKEN.
If direct access to the Codex model runner is not technically possible, another helpful option might be to expose an ocr command that only prepares the deterministic review context as JSON/Markdown. The Codex skill could then ask Codex itself to perform the review from that context.
Alternatives Considered
Current workarounds I found:
- Configure a separate OpenAI/Anthropic/OpenAI-compatible endpoint for
ocr.
- Use a local model through Ollama/LM Studio.
- Use Codex's native code review without Open Code Review.
These work, but they either require extra provider setup/cost, or they lose the Open Code Review pipeline that makes this project valuable.
Affected Area
Review Agent / LLM interaction
Additional Context
Related docs/PRs I looked at:
I completely understand there may be product/API limitations here. Even a short documentation note explaining the limitation and recommended Codex-native workaround would be very helpful. Thanks again for the great project!
Problem Statement
First of all, thank you for open-sourcing Open Code Review and for adding Codex skill/plugin support. The project is very useful, especially the deterministic diff handling and structured review workflow.
I would like to use Open Code Review from inside Codex via the installed skill/plugin. Today, the Codex integration invokes the local
ocrCLI, andocrstill requires a separately configured LLM endpoint/API key. For users who already have a Codex subscription or token allowance, this means the Codex integration cannot use their existing Codex quota and requires another provider setup or extra API billing.This is a bit confusing from a user perspective because the workflow is launched inside Codex, but the actual model calls are still managed outside Codex.
Proposed Solution
It would be great to have a Codex-native mode for the Codex integration.
Possible shape:
ocr review --audience agent --llm-runner codex, or a dedicated Codex skill/plugin path.OCR_LLM_URL/OCR_LLM_TOKEN.If direct access to the Codex model runner is not technically possible, another helpful option might be to expose an
ocrcommand that only prepares the deterministic review context as JSON/Markdown. The Codex skill could then ask Codex itself to perform the review from that context.Alternatives Considered
Current workarounds I found:
ocr.These work, but they either require extra provider setup/cost, or they lose the Open Code Review pipeline that makes this project valuable.
Affected Area
Review Agent / LLM interaction
Additional Context
Related docs/PRs I looked at:
ocr review --audience agent.I completely understand there may be product/API limitations here. Even a short documentation note explaining the limitation and recommended Codex-native workaround would be very helpful. Thanks again for the great project!