Alternatives
Products that do what RunStack does
Build, test, and run LLM workflows — visually.
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So, it feels like this should exist. But I couldn't find it. So I tried to build it. Agentflow lets you run complex LLM workflows from a simple JSON file. This can be as little as a list of tasks. Tasks can include variables, so you can reuse workflows for different outputs by providing different variable values. They can also include custom functions, so you can go beyond text generation to do anything you want to write a function for. Someone might say: "Why not just use ChatGPT?" Among other reasons, I'd say that you can't template a workflow with ChatGPT, trigger it with different…
2023 · github.com
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Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
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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
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Recently featured in a LangChain blog https://blog.langchain.dev/empowering-development-with-flowt... , use LLMs to construct an API first runnable workflow with an IDE experience.
2024 · github.com
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Write a task in plain English. An AI agent runs it on a simulator on your Mac and tells you if a real user could complete it. Save the successful run as a regression check you can replay later.
23d ago · app.deltix.ai
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I am working on a modular open source framework called Griptape that allows Python developers to create LLM pipelines and DAGs for complex workflows that use rules and memory. Griptape can be thought of as "Airflow for LLMs," providing an alternative to the agent-based LangChain approach. Developers can also build reusable LLM tools with explicit JSON schemas that can be executed in any environment (local, containerized, cloud, etc.) and integrated into Griptape workflows. They can also be easily converted into ChatGPT Plugin APIs and LangChain tools via adapters. Tools can be thought of as…
2023 · github.com
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Hey HN! Pretty excited to show Flows— something that we’ve been working on for past few weeks! What is Flows? Flows lets you create multi-step AI workflows in minutes by chaining modular blocks like: - LLM calls - API calls, - Code execution - RAG - Document Parsing …and 50+ more tools. Why You'll Love Flows - Test Flows on your datasets—with thousands of rows effortlessly! - Deploy workflows to production and scale your AI applications. - Collaborate with your team or share workflows publicly. Which workflow would you build with Flows? Share your ideas below!
2025 · app.athina.ai
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Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use (https://news.ycombinator.com/item?id=43173378), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these…
2025 · github.com
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