Braintrust

Describe a quality issue in plain English (e.g., 'responses are too formal') and Loop analyzes your production traces to generate 12 candidate prompt variations targeting that specific problem. The agent learns from evaluation outcomes, so each cycle improves on the last rather than starting from scratch. This is the core differentiator versus every other observability tool in our directory.

Captures every LLM call with full input/output, latency, token costs, and metadata across OpenAI, Anthropic, Google, and 20+ providers. Traces are searchable and filterable, and become the raw material the Loop agent uses for optimization. Free tier supports 1K eval rows/month with 14-day retention; Pro is unlimited with 30-day retention.

Define automated quality scorers — accuracy, helpfulness, tone, factuality, custom business metrics — that run on every production trace or eval batch. Scorers can be LLM-as-judge, code-based, or human-rated, and feed back into Loop for targeted optimization. Catches regressions before they reach users and quantifies prompt changes objectively.

Curate evaluation datasets directly from production traces, marking real user interactions as test cases rather than relying on synthetic examples. Datasets version automatically and integrate with CI/CD pipelines for regression testing. This grounds evaluation in real user behavior and edge cases that synthetic tests typically miss.

Run multiple prompt variations or model providers in parallel and compare results across all your scorers in a single dashboard. Useful for vendor selection (OpenAI vs Anthropic vs Google), prompt iteration, and cost/quality trade-off decisions. Outputs are diff-able at the row level so you can see exactly where two configurations diverge.