Pipevals

Pipevals is the pipeline builder for evaluation-driven AI development.

Evaluate any model, any prompt, any pipeline. Track quality over time.

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_integration

Your LLM Callpython

from 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

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

# 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 dashboard

The platform.

Visual Pipeline Builder

Drag steps onto a canvas and wire them together. Call models, reshape data, capture scores, or pause for human review — all without writing orchestration code.

Durable Execution Engine

Every 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.

Metrics Dashboard

See 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 Check

Most teams evaluate AI by eyeballing results. It works until it doesn't — and you won't know when it stops working.

The Compound Error

95% 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 Gap

Everyone agrees you need evaluation pipelines. Somehow, you're still expected to build them from scratch.

Start in minutes, not sprints.

AI-as-a-Judge

Trigger

↓

Generator

↓

Judge

↓

Metrics

Score any model's output with an LLM judge.

Model A/B Comparison

Trigger

↓ ↓

Model A Model B

↓ ↓

Collect Responses

↓

Judge → Metrics

Compare two models head to head.