PipevalsPipevalsGitHubDemoPipevals is the pipeline builder for evaluation-driven AI development.Evaluate any model, any prompt, any pipeline. Track quality over time.Get Started→Evaluate in-line, without changing your stack.Add a single API call after your existing LLM code. Your pipeline evaluates every response — no SDK, no wrapper, just an HTTP POST.pipevals_integrationPythonNode.jsYour LLM Callpythonfrom openai import OpenAI import os client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) prompt = "Explain quantum computing." response = client.responses.create( model="gpt-4.1", input=prompt ) output_text = response.output[0].content[0].text print(output_text)# No evaluation data captured+ Pipevals Evaluation+8 lines⏩ Skipfrom openai import OpenAI import requests import os client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) prompt = "Explain quantum computing." response = client.responses.create( model="gpt-4.1", input=prompt ) output_text = response.output[0].content[0].text # Trigger your evaluation pipeline requests.post( f"{PIPEVALS_URL}/api/pipelines/{ID}/runs", headers={"x-api-key": KEY}, json={ "prompt": prompt, "response": output_text, }, )from openai import OpenAI import requests import os client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) prompt = "Explain quantum computing." response = client.responses.create( model="gpt-4.1", input=prompt ) output_text = response.output[0].content[0].text|# Pipeline runs, metrics stream to your dashboardThe platform.01Visual Pipeline BuilderDrag steps onto a canvas and wire them together. Call models, reshape data, capture scores, or pause for human review — all without writing orchestration code.02Durable Execution EngineEvery run walks the full graph step by step. Model calls, transforms, scoring — with execution that survives failures. Inspect each step's input, output, and timing when it completes.03Metrics DashboardSee where quality stands and where it's headed. Trend charts, score distributions, step durations, and pass rates — all populated automatically from your pipeline runs.The Vibe CheckMost teams evaluate AI by eyeballing results. It works until it doesn't — and you won't know when it stops working.The Compound Error95% accuracy per step sounds great. Over 10 steps, that's 60% accuracy overall. The pipeline is only as good as its weakest link.The Eval GapEveryone agrees you need evaluation pipelines. Somehow, you're still expected to build them from scratch.Start in minutes, not sprints.AI-as-a-JudgeTrigger↓Generator↓Judge↓MetricsScore any model's output with an LLM judge.Model A/B ComparisonTrigger↓ ↓Model A Model B↓ ↓Collect Responses↓Judge → MetricsCompare two models head to head.PipevalsMIT LicenseCredits
