Directory AI & ML PlatformsAI Governance & SafetyBraintrust

Braintrust

About Braintrust

Braintrust's product lets teams discover patterns in production, turn them into evals, and improve quality over releases. It traces prompts, responses, and tool calls in real time. It allows scoring of outputs using LLMs, code, or humans. It includes a built-in agent called Loop that generates prompts, scorers, and datasets.

Products

Observe: Provides visibility into agent traces, tool calls, latency, cost, and quality in production.
Evaluate: Runs experiments against datasets and compares prompts and models.
Patterns: An automation that identifies recurring issues and opportunities across production traces.

Used for

Inspecting agent traces in real time
Tracing LLM calls including prompts, responses, tool calls, and retrieved context
Turning production traces into eval datasets
Setting alerts on metrics such as score drops, latency spikes, or error rate changes
Building custom annotation interfaces for reviewing traces

Who it is for

Used by engineering and product teams within an organization.

From Braintrust'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 Braintrust. 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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