Pipevals
Evaluation pipelines for every LLM application
Pipevals launched on March 30, 2026 with 2 votes, #5,166 of 23,380 launches that month.
What it does
Evaluating LLM output by eyeballing it works... until it doesn’t. Pipevals is an open-source pipeline builder for AI evaluation. Trigger it with a single HTTP POST from your existing code, piping data through AI judges, scoring, and human review. Every run executes durably, with step-by-step results. Dashboards automatically track trends, distributions, and pass rates. Compare models, test prompts, and catch regressions. Self-hosted. MIT-licensed.
Systematically benchmark, evaluate, and monitor AI systems with visual evaluation pipelines.
Pipevals is the pipeline builder for evaluation-driven AI development. Evaluate any model, any prompt, any pipeline. Track quality over time. Add a single API call after your existing LLM code. Your pipeline evaluates every response — no SDK, no wrapper, just an HTTP POST. 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 ⏩ Skip from openai import OpenAI import requests import os client = OpenAI…from pipevals.com
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Pipevals – a visual pipeline builder for evaluation-driven AIMar 2026 · github.com · ▲6Hey HN! Pipevals is early and rough (this is a learning project), but usable. It currently lets you: - build evaluation pipelines as graphs - run them against datasets - track how output quality changes over time
Pipelex – Declarative language for repeatable AI workflowsOct 2025 · github.com · ▲122We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
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