Braintrust is an observability and evaluation platform for AI agents, with the core goal of helping teams ship high-quality agents at scale.
Purpose
According to the material, agents fail differently from ordinary software and require proactive observability to monitor and fix. AI can drift and degrade silently, and Braintrust automatically surfaces patterns so teams can evaluate against expectations and continuously iterate. The platform is designed for the whole team, from engineering to product, to collaborate within the same platform.
Key Capabilities
- Production tracing and observability: See what actually happens in production, inspect each agent's trajectory and tool calls, search across millions of logs, and track latency, cost, and quality in real time.
- Evaluation and experimentation: Define the standard for "good" before release, run experiments on real datasets, compare prompts and models side by side, and score outputs with LLMs, code, or humans.
- Behavior understanding and quality improvement: Apply intelligent analysis to trajectories, identify important behaviors, validate evidence, test improvements, and measure results in the same system.
- Brainstore database: A database built for large-scale AI data, paired with the Nitro query engine, helping investigate long prompts, model responses, and tool calls faster so production agents can be debugged.
Security and Compliance
Braintrust claims to be secure by default and compliant out of the box. Specifically, this includes: SOC 2 Type II certification; GDPR compliance; SSO, RBAC, HIPAA compliance, and hybrid deployment options available out of the box; independently audited security controls verified annually; integration with identity providers for seamless authentication; full HIPAA compliance to protect PII; full compliance with EU data protection regulations; fine-grained access control at the project and resource levels; and the ability to deploy the Brainstore data plane on your own infrastructure.
Use Cases
The material notes that the platform is built for teams running agents in production environments, covering everything from first release to enterprise scale. The page cites evaluations from multiple practitioners, involving roles such as Agent Experience Manager, Software Engineer, Product Management, CTO, and AI Engineering Lead, emphasizing values such as iterating during development and shipping directly to production, serving as the single source of truth for all datasets, and discovering patterns that are difficult to identify manually.
Limitations
The material does not provide specific pricing information, free trial details, a list of supported programming languages or frameworks, or detailed usage steps or installation commands. This information is not currently provided.
