Elf0 – Build and run AI agent workflows in YAML (CLI)
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…
What it does
In the maker’s words, at launch
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 feed the whole spec back to an LLM to improve. What it does: - Run single- or multi-step agents from a YAML spec. - Reference files or directories in prompts with @file / @dir. - Improve a workflow with a built-in command. - Use OpenAI or Anthropic APIs, or run locally via Ollama. - Optional MCP server example (YouTube transcript analyser). - Interactive mode for step-by-step runs. Anyway it's been a lot of fun and really useful, let me know any ideas you might have to improve it?
Does the same job
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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,…
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