How do I evaluate an LLM application?
Build a small set of real failure cases before reaching for a framework. Twenty examples drawn from actual production mistakes catch more regressions than a thousand synthetic ones, and you can read the results.
Surveyed 10 August 2026
Evaluate an LLM application
Open in Tools →- 01Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, prompt management, evaluation, and experimentation tools for AI applications. It helps teams trace every LLM call, monitor cost and latency, run evaluations, and continuously improve their AI products from prototype to production. It integrates with 100+ frameworks and model providers with no vendor lock-in.
Developer Tools - 02Ragas
Ragas is an open-source evaluation framework specialized for RAG pipelines, scoring retrieval and generation with metrics like faithfulness, answer relevancy, and context precision.
Developer Tools - 03Scenario
Scenario is an agent testing framework that uses LLM-powered user simulators to run end-to-end simulations of AI agents across diverse scenarios and edge cases. It enables developers to validate tool calling, multi-turn conversations, and agent behavior without requiring pre-built datasets. Works with LangGraph, CrewAI, Pydantic AI, and any other agent framework.
Developer Tools - 04OpenAI Evals
OpenAI's framework for evaluating LLMs and LLM systems, with an open-source registry of benchmarks the community can extend.
Developer Tools - 05LangSmith
LangSmith is a platform for LLM observability, tracing, evaluation, and monitoring from the LangChain team — framework-agnostic, works with or without LangChain.
Developer Tools - 06OpenJudge
Unified framework for holistic LLM evaluation and quality rewards, with reward models and grader skills for RLHF and agent alignment.
AI Tools - 07Anthropic Evals
Public evaluation suite from Anthropic. Reference tasks and frameworks for benchmarking Claude and other models.
AI Tools - 08UltraEval
Open-source framework for evaluating foundation models — modular benchmarks for LLM capability assessment (ACL 2024 Demo).
AI Tools
Ordered by the VibeLeaderboard index, re-surveyed each edition. Something missing?
What to look for
- 01Does it support both deterministic checks and model-graded scoring? You need the first wherever it is possible.
- 02Can it run in CI on every change, or only as a manual batch?
- 03Does it version the test set with the prompt? A score is meaningless without knowing what produced it.
Common questions
- How many examples does an eval set need?
- Start at twenty drawn from real failures. Small and real beats large and synthetic — you can actually inspect the results, which is where the useful signal is early on.
- Is using a model as a judge reliable?
- For relative comparisons and clear rubrics, reasonably. It is biased toward length and its own style, so calibrate against human labels before trusting the absolute number.
More in Understand the AI landscape
- Find AI benchmarksBrowse the evaluations used to compare models, agents, coding, and cost.
- Observe an LLM applicationTrace calls, inspect failures, and monitor latency, quality, and spend.
- Run models locallyUse local inference runtimes and model managers on your own hardware.
- Add vector searchStore embeddings and retrieve relevant context for AI applications.