Braintrust

Overview

Braintrust is an LLMOps platform designed to support the development and refinement of large language models (LLMs) and their applications. Established in 2022, the platform provides infrastructure for developers to log data, conduct evaluations, and manage datasets throughout the LLM lifecycle. Its primary utility lies in enabling data-driven iteration for prompt engineering and model optimization. The platform supports use cases ranging from initial prompt experimentation to continuous monitoring of deployed LLMs Braintrust documentation overview.

Developers use Braintrust to programmatically log inputs, outputs, and metadata for their LLM calls. This logged data forms the basis for automated and human-in-the-loop evaluations. For instance, a developer fine-tuning a chatbot might log various prompt templates and the corresponding model responses, then use Braintrust to compare the quality of these responses against a defined set of metrics or human judgments. The platform facilitates the creation of evaluation datasets and integrates with existing MLOps tools to streamline development workflows.

Braintrust is particularly beneficial for teams engaged in iterative prompt engineering, where small changes to prompts or model parameters can significantly impact output quality. By providing tools for versioning prompts and datasets, it helps maintain an auditable history of experiments, making it possible to revert to previous versions or compare performance across different iterations. The platform also offers features for human-in-the-loop annotation, allowing human experts to score or correct model outputs, which in turn can be used to improve future model performance or evaluation metrics. This approach aligns with broader industry practices for improving AI systems through human feedback, as discussed in various research on aligning LLMs with human preferences Aligning Language Models with Human Preferences via Reinforcement Learning from Human Feedback.

The platform is suitable for individual developers experimenting with LLMs as well as larger teams requiring collaborative evaluation frameworks and compliance standards like SOC 2 Type II. Its SDKs for Python and TypeScript allow direct integration into existing codebases, making it accessible for developers working across different development environments.

Key features

• LLM Evaluation Platform: Provides tools for defining evaluation metrics, running automated tests, and orchestrating human feedback loops to assess LLM performance and output quality.
• Prompt Playground: An interactive environment for experimenting with different prompts and model configurations, allowing for quick iteration and comparison of generated responses.
• Dataset Management: Features for creating, versioning, and managing datasets used in LLM training, fine-tuning, and evaluation. This includes tracking changes to data over time.
• Data Logging and Versioning: Capabilities to log all inputs, outputs, and metadata associated with LLM calls, providing a comprehensive audit trail and enabling reproducible experiments.
• Human-in-the-Loop Annotation: Tools for integrating human feedback into the evaluation process, allowing expert annotators to score, correct, or refine LLM outputs.
• SDKs for Integration: Python and TypeScript SDKs facilitate embedding Braintrust's capabilities directly into existing LLM development workflows and applications Braintrust SDK references.
• Experiment Tracking: Functionality to track various experiments, their parameters, and results, aiding in direct comparisons between different prompt strategies or model versions.

Pricing

Braintrust offers a tiered pricing structure, including a free plan for individuals and paid plans for teams and enterprises. Details are current as of May 2026 Braintrust pricing page.

Common integrations

• LangChain: Integration with LangChain allows developers to log data directly from LangChain-based applications into Braintrust for evaluation and experiment tracking Braintrust LangChain integration guide.
• OpenAI API: Direct logging of calls made to the OpenAI API, enabling evaluation of models like GPT-4 or GPT-3.5-turbo.
• Anthropic API: Supports logging and evaluation of responses from Anthropic models, such as Claude.
• Hugging Face Transformers: Compatibility with models and pipelines from the Hugging Face ecosystem for logging and performance analysis.
• Custom LLMs: Designed to integrate with any custom or self-hosted LLM by providing a flexible API for data ingestion.

Alternatives

• Weights & Biases: Offers MLOps tools including experiment tracking, model versioning, and dataset management, often used for broader machine learning workflows Weights & Biases platform details.
• Arize AI: Focuses on LLM observability and performance monitoring in production environments, providing tools for detecting drift and analyzing model behavior.
• LangChain: A framework for developing LLM applications, which includes components for evaluation and feedback loops, though not a dedicated evaluation platform in itself.
• MLflow: An open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, reproducibility, and model deployment.
• DeepEval: An open-source LLM evaluation framework that allows developers to test and evaluate LLMs and RAG systems directly within their codebases.

Getting started

To begin using Braintrust, you typically install the Python SDK, configure your API key, and then use the logging functions to track your LLM calls and evaluation results. The following example demonstrates how to log a simple LLM interaction and evaluate it within a Braintrust project.

import braintrust as bt

# Configure Braintrust with your API key
# bt.init(api_key="YOUR_BRAINTRUST_API_KEY") # Uncomment and replace with your key

# Define a project and experiment
project_name = "MyLLMEvaluationProject"
experiment_name = "InitialPromptTest"

# Create a Braintrust experiment session
with bt.Experiment(project_name=project_name, experiment_name=experiment_name) as experiment:
    # Simulate an LLM call
    user_input = "What is the capital of France?"
    llm_response = "Paris"

    # Log the interaction
    experiment.log(
        input=user_input,
        output=llm_response,
        metadata={
            "model_name": "gpt-3.5-turbo",
            "temperature": 0.7
        },
        expected="Paris", # Optional: provide expected output for direct comparison
        scores={
            "correctness": 1.0, # Example score from an automated check or human review
            "fluency": 0.9
        }
    )

    # Log another interaction for comparison
    user_input_2 = "Tell me about the Eiffel Tower."
    llm_response_2 = "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France."
    experiment.log(
        input=user_input_2,
        output=llm_response_2,
        metadata={
            "model_name": "gpt-3.5-turbo",
            "temperature": 0.7
        },
        scores={
            "correctness": 1.0,
            "detail": 0.8
        }
    )

print(f"Logged data to Braintrust project '{project_name}' under experiment '{experiment_name}'.")
print("View your experiment results on the Braintrust web UI for analysis and deeper evaluation.")

This code snippet initializes a Braintrust experiment and logs two simulated LLM interactions. The experiment.log() method records the input, output, metadata about the LLM call, and optional expected outputs or scores. This data is then available in the Braintrust UI for visualization, comparison, and further evaluation. Developers can define custom scoring functions or integrate human review processes to assign scores for metrics like correctness, relevance, or style. The Braintrust documentation provides further examples for integrating with various LLM frameworks and advanced evaluation techniques Braintrust getting started guide.