Braintrust

What is Braintrust?

Braintrust is an observability platform for teams building and running AI agents in production. It provides tools to trace agent behavior, evaluate output quality, and discover patterns across logs. The platform offers native SDKs for Python, TypeScript, Go, Ruby, and C#, and states it is framework agnostic with no vendor lock-in.

What are the features of Braintrust?

• Agent trace inspection

  Ingests and displays prompts, responses, and tool calls from production agents, tracking latency, cost, and quality metrics in real time.
• Automated and manual evaluation

  Scores agent outputs against versioned datasets using LLMs, code, or human reviewers to define expected quality before shipping.
• Pattern discovery (Patterns)

  An automation that identifies recurring issues and behaviors across production traces to surface problems worth investigating.
• AI-assisted optimization (Loop)

  Generates improved prompts, scorers, and datasets based on a natural language description of what the user wants to optimize.
• Trace-to-dataset conversion

  Converts production traces into evaluation datasets to build regression tests from real failures and edge cases.
• IDE integration via MCP

  Connects coding agents to the platform through an MCP server, allowing users to query logs, run evaluations, and update prompts directly from their IDE.

What are the use cases of Braintrust?

• Blocking regressions before release

  Running experiments against real datasets to compare prompts and models side-by-side, catching issues before they reach production.
• Diagnosing production failures

  Searching across millions of logs containing long prompts, model responses, and tool calls to debug agent behavior using the Brainstore database and Nitro query engine.
• Customizing review workflows

  Building task-specific annotation interfaces to review different outputs, such as support conversations versus code generation, without frontend development.