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Alternatives

Products that do what Pipevals – a visual pipeline builder for evaluation-driven AI does

Hey 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

  1. 1

    Evaluation pipelines for every LLM application

    Mar 2026

  2. 2PD

    We’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…

    Oct 2025 · github.com

  3. 3PL
  4. 4
    buildpipe116

    Compose, run and automate multi step AI developer workflows

    May 2026 · buildpipe.com

  5. 5

    Multi Model Stable Diffusion Pipelines

    2023

  6. 6
    pipe46

    Pipe coldpress datasets straight into your pipeline

    2024

  7. 7OP

    Hi HN, I’ve been working on an OCR pipeline specifically optimized for machine learning dataset preparation. It’s designed to process complex academic materials — including math formulas, tables, figures, and multilingual text — and output clean, structured formats like JSON and Markdown. Some features: • Multi-stage OCR combining DocLayout-YOLO, Google Vision, MathPix, and Gemini Pro Vision • Extracts and understands diagrams, tables, LaTeX-style math, and multilingual text (Japanese/Korean/English) • Highly tuned for ML training pipelines, including dataset generation and…

    2025 · github.com

  8. 8
    Flowise258

    Build AI agents, visually

    2025

  9. 9PB
  10. 10GB
  11. 11

    Accelerating open machine learning research with Cloud TPUs

    2017

  12. 12
    HFlow91

    Scalable multimodal data pipelines for robotics

    10d ago · github.com

  13. 13

    3D data prep made easy

    2024

  14. 14
    Pipecat66

    Build AI workflows and assistants for your business

    May 2026 · app.pipecat.in

  15. 15BD
  16. 16TZ

    2017 · gpestana.gitbooks.io

  17. 17DA

    I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…

    2023 · github.com

  18. 18PV

    Throughout my 30+ software development career, after spending many sleepless nights digging up through enormous codebases to understand logic or fix a bug, I was thinking: "There must be a better, visual way to represent program rather than text". However, no usable visual programming language popped up on horizon for the whole duration of 30+ years of my career. Therefore, I decided to take matters in my own hands, creating new visual programming language called "Pipe". A book about this language was published recently. The book is available for free on Amazon Kindle and Apple iBooks.…

    Oct 2025 · pipelang.com

  19. 19
    Seeknal57

    Data & AI/ML CLI for pipelines and NL queries

    Apr 2026 · seeknal.exe.xyz

  20. 20MA

    Hi HN, I built MOL, a domain-specific language for AI pipelines. The main idea: the pipe operator |> automatically generates execution traces — showing timing, types, and data at each step. No logging, no print debugging. Example: let index be doc |> chunk(512) |> embed("model-v1") |> store("kb") This auto-prints a trace table with each step's execution time and output type. Elixir and F# have |> but neither auto-traces. Other features: - 12 built-in domain types (Document, Chunk, Embedding, VectorStore, Thought, Memory, Node) - Guard assertions: `guard answer.confidence > 0.5 : "Too low"` -…

    Feb 2026 · github.com

  21. 21DP
  22. 22

    Node-based creative pipelines, now with real-time collab

    May 2026 · elevenlabs.io

  23. 23

    Realtime analytics database

    2014

  24. 24EA

    Hey HN, we’re Ross and Javier, co-founders of Engraph (www.engraph.ai). Our goal is to completely automate the process of building ETL pipelines, from ad hoc pipelines for question answering to fully fledged ETL pipelines within large organisations: For ad hoc pipelines, a question answering platform which enables users to ask questions in natural language about their organisation's data. Traditionally, access to data within organisations is limited to a handful of data-engineers. This means that if an employee needs access to some data, they have to go through a lengthy process of…

    2023

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