LayerLens
LayerLens is an independent AI model evaluation platform for understanding how models perform through verified results across benchmarks, prompt-level results, agentic benchmarks, and audit-ready comparisons across vendors. It helps teams compare more than 200 AI models side by side, with transparent benchmarks, model comparison tools, and consistent evaluation methods for accuracy, latency, behavior, and real-world applicability. LayerLens is built for deep model analysis through Spaces, where teams can group benchmarks and evaluations, explore task strengths, and track performance patterns in context. It supports continuous evaluation by running ongoing evals across model versions, prompt changes, judge updates, and live traces, helping teams detect quality regressions, drift, silent failures, contamination, and policy issues before they affect production.
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Langfuse
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Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications.
Observability: Instrument your app and start ingesting traces to Langfuse
Langfuse UI: Inspect and debug complex logs and user sessions
Prompts: Manage, version and deploy prompts from within Langfuse
Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports
Evals: Collect and calculate scores for your LLM completions
Experiments: Track and test app behavior before deploying a new version
Why Langfuse?
- Open source
- Model and framework agnostic
- Built for production
- Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents
- Use GET API to build downstream use cases and export data
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Galileo
Galileo is an AI observability and eval engineering platform that helps teams measure, protect, and improve AI systems in development and production. Now part of Cisco, the platform turns offline evaluations into production guardrails so teams can stop AI failures instead of only monitoring them. Galileo helps users build datasets from synthetic, development, and live production data, while capturing subject matter expert annotations as ground truth. The platform includes more than 20 out-of-the-box evals for RAG, agents, safety, security, and custom evaluation workflows. Its Luna models distill expensive LLM-as-judge evaluators into lower-cost, low-latency guardrails that can monitor production traffic. Built for enterprises and developers, Galileo helps teams debug failures, improve agent behavior, enforce AI policies, and ship AI applications with more confidence.
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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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