Pull request risk scoring
Scores every pull request for risk using LLMs, incident-trained machine learning, and code-graph analysis.
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Coding / product dossier
Scores pull-request risk, auto-approves low-risk changes and routes riskier ones to engineers.
Product brief
Most AI review tools flag issues. LiveReview promises production results: fewer incidents, fewer rollbacks, less senior reviewer load. Every PR is risk-scored. The lowest-risk 30-50% are safely auto-approved; the risky ones go straight to the engineers who should see them. The approach is inspired by Meta's highly successful internal AI review system, where production incidents fell 50x and rollbacks 3x. LLM + incident-trained ML + code-graph analysis. Free and self-hostable.
Why we selected it
Combines LLMs, incident-trained machine learning and code-graph analysis for risk-based review routing. Free self-hosting makes it worth evaluating, though the supplied material does not establish its claimed production-
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Capability scan
Only capabilities supported by the product information we collected are listed here.
Scores every pull request for risk using LLMs, incident-trained machine learning, and code-graph analysis.
Offers automatic approval for low-risk changes; the description claims the lowest-risk 30–50% of pull requests can be auto-approved.
Routes risky pull requests to the engineers who should review them.
Best-fit use cases
FAQ
It risk-scores every pull request using a combination of LLMs, incident-trained machine learning, and code-graph analysis.
LiveReview targets low-risk changes for automatic approval. Its description claims the lowest-risk 30–50% can be auto-approved; this is not an established outcome for every codebase.
They are routed to the engineers who should review them rather than being automatically approved.
Yes. The supplied description explicitly states that LiveReview is free and self-hostable.
No. LiveReview promises fewer incidents and rollbacks, but the supplied material does not establish those outcomes. The numerical reductions cited in its description refer to Meta's internal system, which inspired its approach.