Directory AI & ML PlatformsAI Governance & SafetyTraceloop

Traceloop

Open Source

About Traceloop

Traceloop provides a platform for tracking, evaluating, and monitoring LLM applications. The product turns LLM logs into insights. It provides visibility into prompts, responses, latency, and other data after a single line of code is added. It runs quality checks such as faithfulness, relevance, and safety using built-in metrics. Users can define custom quality criteria and train a custom evaluator. Traceloop can run standard and custom evaluations automatically as part of a pipeline.

Products

Hub: A native OpenTelemetry-based gateway for connecting LLMs across languages.

Used for

Tracking prompts, responses, and latency from LLM applications
Running quality checks like faithfulness, relevance, and safety on real data
Annotating examples and training custom evaluators
Running automated evaluations on every pull request or in real time

Who it is for

Built for developers.

From Traceloop's own website, read 2026-10-23. How we build listings.

How to evaluate AI Governance & Safety

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Traceloop. It comes from our AI Governance & Responsible AI buyer guide.

25%

AI Inventory & Lifecycle Governance

Automated discovery and a registry of every model, LLM/prompt, agent, and embedded-AI feature (including shadow and third-party AI); risk tiering by use case; intake-to-retirement workflow with approvals, deployment gates, and named owners; versioning and change control

25%

Regulatory Mapping & Compliance Evidence

Out-of-the-box, maintained policy packs for the EU AI Act, NIST AI RMF, ISO 42001, SR 11-7, and sector rules (e.g. NYC LL144); control mapping and gap analysis; one underlying assessment that satisfies many frameworks; audit-ready, exportable evidence and model cards

20%

Model Monitoring & Explainability

Production monitoring for drift, performance decay, and data quality; bias and fairness testing across protected groups; explainability (e.g. SHAP / feature attribution) for tabular and NLP models; the depth of this domain is what separates observability tools from pure GRC

15%

GenAI & Agentic Oversight

LLM evaluation and guardrails (hallucination, toxicity, PII leakage, prompt-injection and jailbreak defense); agent discovery, runtime policy enforcement, and a logged trail of agent actions and the authority behind them; red-teaming and continuous evaluation of agent traces

10%

Integration & ML-Stack Fit

Connectors to your model platforms (SageMaker, Vertex AI, Databricks, Dataiku, MLflow, Bedrock), CI/CD and registries; API and policy-as-code coverage; identity (SSO/RBAC); fit with existing GRC and data-security tooling rather than yet another silo

Human-in-the-Loop & Accountability

Cross-functional workflows that reach risk, legal, and business owners (not just data scientists); reviewer sign-off and attestations; issue and exception tracking; reporting that a board or regulator can read, with a defensible audit trail

Other AI Governance & Safety Vendors

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