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The world's first DevToCode platform for high-performance React and Flutter apps.

The enterprise-grade stack for evaluating, logging, and refining AI applications with 10x developer velocity.

Braintrust (often referred to via its CLI 'bt') is a sophisticated infrastructure layer designed for the 2026 AI engineering lifecycle. It provides a vertically integrated solution for evaluating, logging, and optimizing large language model (LLM) applications. Unlike general observability tools, Braintrust focuses on the 'AI Engineering Loop'—allowing teams to run high-speed evaluations (Evals) across thousands of test cases in seconds. Its technical architecture centers around a high-performance proxy that handles request routing, automatic retries, and semantic caching, which significantly reduces API costs and latency. In the 2026 market, Braintrust has positioned itself as the 'Datadog for AI,' offering enterprise-grade security features like VPC deployment and SOC2 compliance. It bridges the gap between prompt engineering and production monitoring by allowing developers to use the same 'golden datasets' for both local testing and real-time production benchmarking, ensuring that performance improvements in the lab translate directly to user-facing reliability.
Braintrust (often referred to via its CLI 'bt') is a sophisticated infrastructure layer designed for the 2026 AI engineering lifecycle.
Explore all tools that specialize in prompt version control. This domain focus ensures Braintrust (bt) delivers optimized results for this specific requirement.
Uses vector embeddings to identify and serve cached responses for semantically similar queries.
Programmable scoring functions that use models like GPT-4o or Claude 3.5 to grade the quality of other model outputs.
Statistical comparison of current eval runs against historical baselines to flag performance drops.
A single endpoint that provides access to OpenAI, Anthropic, Gemini, and Llama with consistent logging.
Automatically captures production edge cases and routes them back into the evaluation dataset.
Browser-based environment to iterate on prompts using actual production data variables.
The entire Braintrust stack can be deployed inside a client's AWS/GCP account.
Install the Braintrust CLI using 'npm install -g braintrust' or 'pip install braintrust'.
Initialize your project workspace using 'bt init' to create a configuration file.
Securely authenticate by setting the BRAINTRUST_API_KEY environment variable.
Define a 'Golden Dataset' containing input-output pairs for your model's expected behavior.
Write a scoring function in Python or TypeScript to evaluate model outputs (e.g., Factuality, Tone).
Integrate the Braintrust Proxy into your application code to capture real-time production logs.
Run a local evaluation experiment using the 'bt eval' command to benchmark a new prompt version.
Review results in the Braintrust UI, analyzing delta improvements across specific test cases.
Enable semantic caching on the Braintrust Proxy to reduce costs for redundant queries.
Configure CI/CD triggers to run evaluations automatically on every pull request.
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