A curated index of the tools developers actually use to build with AI. No models — just the tooling around them: frameworks, vector databases, inference, observability, agents and more.
Once an LLM feature is live you need to see what it is doing and know when a change makes it worse. These tools cover tracing, prompt management, experiment tracking and automated evals.
Open-source LLM observability and prompt management.
Langfuse records every trace of your LLM application — prompts, latencies, costs, scores — so you can debug and improve what you shipped. It also versions prompts and runs dataset-based evaluations, covering the loop from development to production.
Experiment tracking and LLM tracing for ML teams.
W&B is the industry standard for tracking ML experiments, and its Weave module extends that to LLM apps with tracing, prompt experiments and evals. One dashboard for datasets, runs, metrics, prompts and costs.
Test and evaluate prompts like code.
promptfoo runs your prompts across a matrix of models and test cases with assertions on the output — then fails your CI when something regresses. The quickest way to make prompt and model changes safe instead of a leap of faith.
LLM frameworks & SDKs →Inference & model routing →Vector databases →AI coding tools →Data & agent tooling →
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