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Introduction to TrainLoop Evals

Welcome to TrainLoop Evals, a comprehensive framework for automating the collection and evaluation of Large Language Model (LLM) outputs. TrainLoop Evals simplifies the process of testing and improving your AI applications with a focus on developer experience and reliability.

What is TrainLoop Evals?​

TrainLoop Evals is an end-to-end evaluation framework that consists of:

  • 🤖 CLI Tool (trainloop command) - Python-based evaluation engine for managing evaluation workflows
  • 🎨 Studio UI - Next.js web interface for visualizing and analyzing evaluation results
  • 📚 Multi-language SDKs - Zero-touch instrumentation libraries for Python, TypeScript, and Go
  • 🔧 Registry System - Shareable metrics and evaluation suites for common use cases

Core Principles​

TrainLoop Evals is built around five key principles:

🎯 Simplicity First​

One environment variable, one function call, one folder of JSON files. No complex setup required.

🔄 Vendor Independence​

Everything is stored as newline-delimited JSON files. No databases, no vendor lock-in.

👥 Meet Developers Where They Are​

Accepts both simple declarative flows and existing bespoke evaluation loops.

🔒 Type-safe, In-code Tests​

All evaluation code lives in your codebase with full type safety.

🧩 Composable, Extensible System​

Helper generators follow proven patterns (similar to shadcn/ui) with trainloop add command.

Key Features​

📊 Automatic Data Collection​

  • Zero-touch instrumentation - Add one line to capture all LLM calls
  • Multi-language support - Works with Python, TypeScript/JavaScript, and Go
  • Flexible tagging - Tag specific calls for targeted evaluation
  • Buffering control - Configure immediate or batched data collection

🔍 Powerful Evaluation Engine​

  • Custom metrics - Write Python functions to evaluate any aspect of LLM output
  • Test suites - Group related evaluations into logical collections
  • LLM Judge - Built-in AI-powered evaluation for subjective metrics
  • Benchmarking - Compare multiple LLM providers on the same tasks

📈 Rich Visualization​

  • Interactive Studio UI - Explore evaluation results with charts and tables
  • DuckDB integration - Query evaluation data with SQL
  • Real-time updates - See evaluation results as they happen
  • Export capabilities - Share and analyze results outside the platform

🚀 Production Ready​

  • Scalable architecture - Handles large-scale evaluation workloads
  • Cloud storage support - Works with S3, GCS, and Azure
  • CI/CD integration - Automate evaluations in your development pipeline
  • Comprehensive testing - Extensively tested across all components

Use Cases​

TrainLoop Evals is perfect for:

  • 🔧 Development Testing - Continuously evaluate LLM outputs during development
  • 📊 A/B Testing - Compare different prompts, models, or configurations
  • 🔍 Quality Assurance - Ensure LLM outputs meet quality standards before deployment
  • 📈 Performance Monitoring - Track LLM performance over time in production
  • 🏆 Model Comparison - Benchmark different LLM providers and models
  • 🎯 Regression Testing - Detect when changes negatively impact LLM performance

How It Works​

Your App + SDK → Data Collection → CLI Evaluation → Studio Visualization
    ↓               ↓                 ↓              ↓
[LLM Calls]    [events/*.jsonl]   [results/*.json]  [Charts & Tables]
  1. 🔧 Instrument - Add TrainLoop SDK to your application with minimal code changes
  2. 📝 Collect - Automatically capture LLM requests and responses as JSONL files
  3. 📊 Evaluate - Define custom metrics and test suites to assess LLM performance
  4. 📈 Analyze - Use the Studio UI to visualize results and identify patterns
  5. 🔄 Iterate - Refine your prompts and models based on evaluation insights

Architecture Overview​

TrainLoop Evals Flow

TrainLoop Evals provides a complete workflow from data collection to analysis:

  • Multi-language SDKs automatically capture LLM interactions from your applications
  • Event storage preserves all request/response data in JSONL format
  • Evaluation engine applies custom metrics and suites to generate results
  • Studio UI provides interactive visualization and benchmarking capabilities

Getting Started​

Ready to start evaluating your LLM applications? Here's what you need to do:

  1. Install TrainLoop CLI - Get the command-line tool
  2. Follow the Quick Start Guide - Set up your first evaluation
  3. Explore the Guides - Learn advanced features and best practices
  4. Check the Reference - Detailed API documentation

Examples & Demos​

Want to see TrainLoop Evals in action?

📚 Working Examples​

Complete code examples in multiple languages:

🎮 Live Demos​

Community and Support​

Get started today and transform how you evaluate and improve your LLM applications!