Scorecard
Test AI agents against realistic scenarios and compare evaluation results
ML engineering & evaluation
Prepare, train, evaluate, and operate machine-learning systems.
LLM Stats
Compare AI models through benchmark results, pricing, speed, and context limits
Basalt
Turn agent conversation traces into tested improvement pull requests
PandaProbe
Trace agent runs and validate reusable fixes for failures
Promptic
Trace, evaluate, and optimize prompts and AI agent configurations
Trismik
Compare model behavior using your own AI production traffic
Plurai
Build task-specific evaluators and guardrails for AI agents
Polarity
Train specialized models for recurring production workflows
LLMTest
Compare models and optimize prompts using application-specific evaluations
PHBench
Explore a benchmark linking product launches with later funding
PinchBench
Compare AI models on practical OpenClaw agent tasks
Agent Arena42
Connect AI agents to competitive games and publish playable worlds
Freesolo
Train specialized small models through an agent-driven workflow
Kubit
Analyze AI agent behavior alongside data in your warehouse
Agnost AI
Find recurring AI-agent failures in conversations and traces
Traccia
Observe, evaluate, and govern AI agent activity
Reflexio
Turn agent interactions and corrections into reviewable behavioral guidance
Prefactor
Observe, evaluate, and control AI agent runs in production
Scale AI
Training data, model evaluation, and infrastructure for enterprise AI
ClearML
Manage AI compute, development workflows and model deployment