AI · alternatives · 2026
24 alternatives to Build Live AI and RAG Pipelines in Minutes with YAML Templates
Hello everyone! I am Jan, CTO and one of the creators of Pathway, the real-time data processing framework. I’m excited to share Pathway’s ready-to-use AI Pipelines, configurable with just YAML! These frameworks offer out-of-the-box solutions for AI search, RAG, and more—optimized for real-time indexing and in-memory processing. What makes it simple? YAML templates! The pipeline templates are fully customizable using YAMLs to fit your needs, from changing the data sources to the choice of the LLM model, all without touching Pathway’s Python code. Thanks to the Pathway data processing engine,…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Build Live AI and RAG Pipelines in Minutes with YAML Templates launched in 2024; newer entries below may have overtaken it.
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Hi HN data folks, I am excited to share Pathway, a Python data processing framework we built for ETL and RAG pipelines. https://github.com/pathwaycom/pathway We started Pathway to solve event processing for IoT and geospatial indexing. Think freight train operations in unmapped depots bringing key merchandise from China to Europe. This was not something we could use Flink or Elastic for. Then we added more connectors for streaming ETL (Kafka, Postgres CDC…), data indexing (yay vectors!), and LLM wrappers for RAG. Today Pathway provides a data indexing layer for live data…
2024 · github.com · its alternatives →
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io · its alternatives →
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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 · its alternatives →
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I've been exploring the (not so=) amazing potential of AI in coding and have compiled a list of tools. From AI-powered IDEs to code generators, this resource is my contribution to the community. I'm still on the fence about including txt2sql projects, as their functionality seems too basic to me. And I'm personally maintaining this, so your feedback is wellcome.
2025 · aicode.danvoronov.com · its alternatives →
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Heym▲83Self-hosted AI workflow automation with agents, RAG, and MCP
Apr 2026 · heym.run · its alternatives →
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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 · its alternatives →
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- Discovering the most effective RAG pipeline for your specific data and use case can be daunting. It requires experimenting with various RAG modules and configurations, which are both time-consuming and complex. - AutoRAG addresses this challenge by automatically evaluating different combinations of RAG modules and their parameters. You don't need to write implementation code yourself; everything is set up through a single YAML file. - Our aim is to save you the hassle of continuously adapting to new RAG modules and configurations. Instead, you can focus on developing robust data for your…
2024 · github.com · its alternatives →
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ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io. - zenml-io/zenml
2020 · github.com · its alternatives →
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The open source LLM router that connects your AI agents and harnesses to any provider in seconds. Subscriptions, pay-per-token, local models, and custom providers.
2024 · manifest.build · its alternatives →
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2020 · starsheep.io · its alternatives →
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Developer-first automation runner - pipelines as YAML, extended with Python, executed from the CLI or a local scheduler.
Jun 2026 · rorlikowski.github.io · its alternatives →
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Hi HN, I am Jan, CTO and co-founder of Pathway.com. We’ve built a LLM microservice that answers questions about a corpus of documents, while automatically reacting to additions of new docs. The single, self-contained service fully replaces a complex multi-system pipeline that scans in real-time for new documents, indexes them into a specialized database and queries it to generate answers. Everyone can have their own real-time vector now. Github: https://github.com/pathwaycom/llm-app Demo video: https://youtu.be/kcrJSk00duw I am eager to hear your thoughts…
2023 · github.com · its alternatives →
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Hey HN, About a year ago I shared a first MVP of a visual node-based system for CI/CD pipelines that I've been very passionate about. I've been building on it since, and it's now live. I've always liked building pipelines and workflows, but I've never liked writing YAML for anything more than simple linear tasks. Branching, conditions, loops, or trying to just run certain things in parallel always gets messy. So I built Actionforge, a visual node system to tackle some of these pain points. Instead of writing YAML yourself, you build workflows as graphs. While Actionforge still uses YAML…
Feb 2026 · actionforge.dev · its alternatives →
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Hi HN — I built Elf0, a command-line tool to define and run AI agent workflows in YAML. It helps you iterate on small multi-step "agents" without scaffolding a whole codebase. The agent patterns described in Anthropic's article was an inspiration: https://www.anthropic.com/engineering/building-effective-age... I then used Nvidia's AgentIQ YAML spec as inspiration. Why: I keep bumping into tasks where a single prompt isn’t enough (e.g., extracting quote data from an insurance PDF). Defining the workflow in YAML makes it easy to version prompts, parameters and logic, and to…
2025 · elf0.com · its alternatives →
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- 18SW
Hey HN, We’re Basia, Fokke, and Geno from Liquidmetal AI, and we built something we wish we had a long time ago: SmartBuckets. We’ve spent a lot of time building RAG and AI systems, and honestly, the infrastructure side has always been a pain. Every project turned into a mess of vector databases, graph databases, and endless custom pipelines before you could even get to the AI part. SmartBuckets is our take on fixing that. It works like an object store, but under the hood it handles the messy stuff — vector search, graph relationships, metadata indexing — the kind of infrastructure you'd…
2025 · its alternatives →
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YamlQL▲7query yaml files with sql and natural language powered by AI
2025 · github.com · its alternatives →
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Hey HN, I’m the author. I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language. How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config. Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).…
Dec 2025 · github.com · its alternatives →
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Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…
2024 · github.com · its alternatives →
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Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com · its alternatives →
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🧙 Build, run, and manage data pipelines for integrating and transforming data. - mage-ai/mage-ai
2022 · github.com · its alternatives →
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