Eval & LLMOps ★ 34.7k

Open Code Review

Hybrid code review that puts deterministic rules first and the LLM second — the only ordering that survives an enterprise security review.

Visit site → Homepage

Most AI code review tools ask a model to read a diff and opine. This one runs deterministic pipelines first (NPE, thread-safety, XSS, SQL injection) and only then layers an LLM agent on top for what rules cannot express. The result is line-level comments instead of essay-length feedback, and a review you can defend to a security team because every deterministic finding has a rule ID behind it. It speaks OpenAI and Anthropic compatible APIs, so it does not lock you to one vendor.

Best for: Teams that need AI code review to pass a security review, not just look impressive in a demo

#Evaluation #Guardrails #Open Source

Related tools

Eval & LLMOps

Guardrails AI

Validates model output against a schema instead of hoping — and re-asks when validation fails.

Structured output that must parse reliably, and PII or policy checks before content reaches a user Read more →

Eval & LLMOps ★ 11.0k

Ragas

Metric suite for RAG: faithfulness, context precision, recall — the numbers you need before a go-live review.

RAG regression testing and go-live metrics Read more →

Eval & LLMOps ★ 9.0k

Arize Phoenix

Notebook-first observability with clustering that surfaces failure modes you did not think to test.

Local debugging and failure-mode discovery Read more →

Eval & LLMOps

OpenLLMetry

LLM traces as standard OpenTelemetry spans — so they land in the observability stack you already pay for.

Organisations with an existing OpenTelemetry stack who do not want a second observability silo Read more →