Audience
Professional users interested in a tool to evaluate, test, and optimize their LLM applications
About DeepEval
DeepEval is a simple-to-use, open source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence. The framework supports synthetic dataset generation with advanced evolution techniques and integrates seamlessly with popular frameworks, allowing for efficient benchmarking and optimization of LLM systems.
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Maxim
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Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production.
Features:
Agent Simulation
Agent Evaluation
Prompt Playground
Logging/Tracing Workflows
Custom Evaluators- AI, Programmatic and Statistical
Dataset Curation
Human-in-the-loop
Use Case:
Simulate and test AI agents
Evals for agentic workflows: pre and post-release
Tracing and debugging multi-agent workflows
Real-time alerts on performance and quality
Creating robust datasets for evals and fine-tuning
Human-in-the-loop workflows
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evalship
evalship helps engineering teams test LLM-powered software with a review of their evals at setup and a report on every pull request. The review checks scorers, judges and prompts for flaws, such as a judge that scores 1.0 when its call fails, and gives each flaw a fix with code references. Setup is a prompt for your coding agent: it configures GitHub Actions and opens a setup pull request. On later pull requests, evalship compares results with the default branch, reports the evals a change really broke and the likely cause, sets flaky evals aside, and flags changed prompts or LLM calls that no eval exercises. Its check can block a merge that breaks an eval. It reads results from pytest, Vitest, Jest, promptfoo, Langfuse, Braintrust and LangSmith, with no SDK to add. Evals run in the customer's own GitHub Actions with their own model API keys; evalship only reads the results. Teams get a dashboard with each eval's history and a weekly report by email. Public repositories are free.
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Pricing
Starting Price:
Free
Free Version:
Free Version available.
Integrations
Company Information
Confident AI
United States
docs.confident-ai.com
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Product Details
Platforms Supported
Cloud
Training
Documentation
Support
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