Braintrust

Braintrust is an observability and evaluation platform for teams building AI agents and applications. It records prompts, responses, and tool calls so developers can investigate production behavior. Teams can turn those traces into datasets and compare changes with repeatable evaluations.

Key Highlights

• Agent traces with latency, cost, and quality views
• Evaluation datasets drawn from production examples
• LLM, code, and human scoring
• Prompt and model comparisons in playgrounds
• SDKs, a CLI, and an MCP server

What Makes It Different

Braintrust connects production investigation with evaluation work. Teams can take a problematic trace into a dataset, test a change, and compare the results. Its discovery tools identify recurring behavior across traces, while the built-in Loop agent assists with investigation and evaluation development.

Features & Capabilities

Instrument an application with supported providers or frameworks to send traces to Braintrust. Trace views expose the steps and tool calls behind a response. Custom annotation views let domain experts review outputs alongside engineering teams.

Versioned datasets and experiments support comparisons across prompts and models. Scorers can use code, model judgments, or human feedback. The CLI and MCP server give coding agents access to parts of the workflow. Useful evaluation criteria still need to reflect the application's actual task and failure modes.

User Ratings and Testimonials

Braintrust publishes customer stories from AI product teams. For a practical comparison, measure whether your team can reproduce a production failure and catch it with an evaluation. Inspect scorer disagreements and review how representative the dataset is. A high score on a narrow test set does not prove reliable behavior for every request.

Pricing & Value

• Starter: $0/month, with 1 GB of processed data, 10,000 scores, and 14-day retention.
• Pro: $249/month, with 5 GB of processed data, 50,000 scores, and 30-day retention.
• Enterprise: custom pricing and deployment options.

Data processing, scores, model usage, and storage can add charges beyond included allowances. Estimate these dimensions separately when forecasting costs.