Awesome-list-of-Haskell-mentors VS swarm

The second one is contributing to open source projects with mentoring or without.

You may be interested in this resource: https://github.com/willbasky/Awesome-list-of-Haskell-mentors

There are many examples of this out in the wild already working, eg. https://github.com/openai/swarm

Swarm (made by OpenAI, experimental at the time of writing) emphasizes collaborative agent behavior, allowing many agents to work together towards a common goal.

It's like object oriented programming for prompts.

1. https://github.com/openai/swarm/tree/main

Developers know all too well the arduous process of building, testing, and deploying applications — a journey that can take weeks or even months. Enter AI orchestration frameworks like LangChain (LangGraph), CrewAI, OpenAI (Swarm), and Haystack, which have drastically reduced this timeline. Now, tasks that once took months can be completed in days — or even hours in some cases.

!pip install git+https://github.com/openai/swarm.git --quiet !pip install ollama openai --quiet

OpenAI recently released Swarm. Swarm is a stateless framework that enables the creation and management of multiple AI agents, allowing them to collaborate and solve complex tasks by breaking them down into smaller, more manageable subtasks.

It's worth mentioning that there are already frameworks designed to make this kind of orchestration much easier. Langchain and Langgraph come to mind, both of which are fantastic for streamlining agent interactions and orchestration. Recently, I also came across an educational framework from OpenAI around swarm orchestration that was a big inspiration for kicking off this project (OpenAI Swarm). But, to be honest, I want to be in the weeds. I want to feel the mistakes and learn from them firsthand. With 🤖 AI tooling becoming more commonplace, I think the "under the hood" knowledge might become harder to acquire if we always rely on ready-made solutions. So, I’m diving in headfirst, hoping that my path will be full of lessons—mistakes and all—that will make this journey interesting and engaging.

Let's start off with creating an AI-powered refund agent using OpenAI's lightweight multi-agent orchestration framework, Swarm. This agent contains two functions: process_refund to help a user to return an item, and apply_discount to apply a discount to the user's future purchases. These functions may be invoked based on OpenAI LLM's output. In addition, the process_refund function invokes several sub-steps (refund_step) which simulates a complex refund workflow.

However, you won't learn until you experiment (or even fail sometimes 😣). The examples above give only a glimpse of what Ell does. What you use it for is a completely different story. Just as learning a new programming framework doesn't make sense if you don't use it. So, look for things that you can optimize and automate with AI Agents. Maybe port your old project you made with LangChain or Swarm (a new agent framework from OpenAI)?