Proval review
A self-hosted reviewer with broad forge coverage and flexible model endpoints.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Proval is a self-hosted AI code review agent for teams that want review infrastructure on their own systems. It connects with GitHub, GitLab, and Forgejo, receives repository events through webhooks, sends review context to a selected LLM endpoint, and posts findings or replies on pull requests and issues. The product is aimed at teams that need automatic review runs across more than one forge while keeping deployment and model choices under their control.
Its integration coverage is a central strength. Proval supports pull request and merge request reviews, inline comments on affected code lines, threaded replies, and issue comment replies. The published integration list includes GitLab, Forgejo, GitHub, OpenAI-compatible APIs, the Anthropic Messages API, OpenRouter, llama.cpp, Ollama, and vLLM. A dashboard covers review runs, models, and repositories, giving teams a single web interface for the supported workflow. This makes Proval a practical fit for mixed-forge environments, especially where local, on-premises, or compatible model endpoints matter.
Deployment is deliberately self-hosted, with Docker Compose listed as the deployment path. That suits teams prepared to operate the review service and manage its model endpoint, but it is less appropriate for buyers seeking a hosted product with vendor-managed infrastructure. Proval stays focused on automated code-review activity: its listed capabilities cover review comments, replies, dashboards, and repository events rather than a wider application-security suite. Choose it when multi-forge support and deployment control are priorities; consider an alternative if you need a hosted workflow or broader verified security-analysis features.
Proval pros and cons
- Where it wins
- Automatic webhook-driven reviews for pull and merge requests
- Supports GitHub, GitLab, and Forgejo with inline comments
- Works with local and OpenAI-compatible LLM endpoints
- Where it doesn't
- Self-hosted deployment puts infrastructure responsibility on the team
- Model selection and endpoint configuration remain team responsibilities
- Focuses on review workflows rather than verified security analysis or fixes
Proval fact sheet, pricing and score →
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