Braintrust

Captures execution traces from AI applications via native SDKs (Python, TypeScript, Go, Ruby, C#) and stores them in Braintrust's proprietary Brainstore database optimized for nested, large AI traces. Enables real-time inspection of prompts, responses, tool calls, latency, and cost metrics with full-text search across millions of traces. Implements scalable trace ingestion with custom column definitions and saved table views without requiring frontend engineering.

Provides a framework for evaluating AI outputs against datasets using three scoring methods: LLM-as-judge (using configurable LLM models), code-based scorers (custom Python/TypeScript functions), and human annotation. Runs evaluations across production traces or custom datasets, compares results across prompt/model variants, and generates comparison reports. Integrates with CI/CD pipelines to block releases when quality metrics regress below thresholds.

Implements tiered data retention policies with automatic archival to S3 for long-term storage. Starter tier retains traces for 14 days, Pro tier for 30 days, Enterprise tier with custom retention. Enables export of traces and datasets to S3 for external analysis, compliance archival, or migration to other platforms. Supports per-project retention policies on Enterprise tier.

Implements full-text search across all trace data with optimized indexing for AI-specific structures (prompts, responses, tool calls). Provides 'Topics' feature for automatic pattern discovery and classification of similar traces without manual rule definition. Enables deep search across millions of traces with low latency, supporting complex queries across custom dimensions and metadata.

Provides SOC 2 Type II, GDPR, and HIPAA compliance certifications with Business Associate Agreement (BAA) available on Enterprise tier. Implements data governance controls including encryption, access logging, and data residency options. Supports on-premises or hosted deployment for Enterprise customers requiring data sovereignty.

Provides a prompt playground and version control system for managing prompt iterations with automatic versioning, comparison, and A/B testing capabilities. Stores prompts in Braintrust with full history, enables side-by-side comparison of prompt variants, and supports running experiments to measure performance differences across versions. Integrates with IDE via MCP (Model Context Protocol) for prompt updates without leaving the editor.

Enables creation and management of evaluation datasets with automatic conversion from production traces. Allows teams to capture real-world examples from production, label them with expected outputs or quality criteria, and build evaluation datasets without manual data collection. Supports dataset versioning, filtering, and export for use in evaluations and experiments.

Monitors AI application quality metrics in production and automatically detects regressions when performance drops below configured thresholds. Implements pattern discovery via 'Topics' feature to classify and group similar traces, enabling identification of systematic issues. Supports custom alerts and automations triggered by quality degradation, latency increases, or cost anomalies. Integrates with CI/CD to block releases when regressions are detected.

Enables creation of custom dashboards with user-defined charts and metrics without requiring frontend engineering. Supports custom columns in trace tables, saved table views, and dashboard widgets that visualize aggregated metrics across traces. Implements real-time updates as new traces arrive, allowing teams to monitor application health without building custom monitoring UIs.

Provides a Model Context Protocol (MCP) server that enables developers to query logs, run evaluations, and update prompts directly from their IDE (VS Code, JetBrains, etc.). Allows AI coding agents to access Braintrust data and operations without leaving the editor, enabling prompt updates, evaluation runs, and log inspection as part of the development workflow.

Provides native SDKs for Python, TypeScript, Go, Ruby, C#, and additional languages that integrate with any AI framework or stack without requiring framework-specific adapters. SDKs implement automatic trace capture with minimal code changes, supporting both synchronous and asynchronous execution patterns. Designed to avoid vendor lock-in by using standard trace formats and enabling data export to S3.

Provides an AI-powered agent (Loop Agent) that autonomously iterates on prompts by running evaluations, generating test cases, and optimizing prompt variations without manual intervention. Analyzes evaluation results to identify improvement opportunities and proposes prompt changes. Available on Pro and Enterprise tiers only.

Provides role-based access control (RBAC) for managing team permissions and data access. Pro tier includes basic roles, Enterprise tier supports custom roles. Enables teams to collaborate on evaluations, prompts, and datasets with granular permission controls. Supports SSO/SAML integration for identity management and MFA for account security.