Agents as Micro-Services

Hey HN, I've been building SimplAI for the past several months — it's a platform for building, testing, and deploying LLM-powered agents and multi-step workflows. The problem I kept running into: spinning up an AI agent pipeline means stitching together prompt management, tool calling, memory, evals, and deployment — often from scratch every time. SimplAI tries to be the layer that handles all of that so you can focus on what your agent actually does. What it does: - Visual + code-first workflow builder for chaining LLM calls, tools, and APIs - Built-in prompt versioning and A/B testing…

n8n is built for humans dragging nodes on a canvas. That breaks down at B2B scale (embedding in a product, multi-tenant scalability, etc). n8n does have an MCP server so agents can create workflows too, but it outputs raw JSON. That's fine for n8n's engine, but painful for a coding agent (or a human reviewing its output) to read, write, diff, or debug. I'm building an alternative where workflows are authored by a coding agent in [a more dev-legible format] instead of JSON blobs, and execute it at scale.

Build Once and Deploy Anywhere

A fun, flexible, task manager for desktop web

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…

Hi HN! I’m excited to share ControlFlow, our new open-source framework for building agentic workflows. ControlFlow is built around a core opinion that LLMs perform really well on small, well-defined tasks and run off the rails otherwise. I know that may seem obvious, but the key insight is that if you compose enough of these small tasks into a structured workflow, you can recover the kind of complex behaviors we associate with autonomous AIs, without sacrificing control or observability at each step. It ends up feeling a lot like writing a traditional software workflow. With ControlFlow you:…