Hey all, I compared two off-the-shelf agent libraries (Awesome LLM and Agent SDK Go) for their pro's and con's and thought it might be worth sharing the results. These agents are built to be plug and play. There is a bit of technical expertise required, but all instructions are in the Github readme or you can ping me if you need help. TL;DR Awesome LLM → best for quick demos and experimentation. Agent SDK Go → best for structured, scalable agent development in Go. Awesome LLM apps The awesome llm apps repo is a lightweight collection of ready made examples for experimenting with AI agents, RAG setups, and LLM apps in Python, JS, and TS. Simple to use, you clone the repo, install requirements, and run an example. Ideal for quick learning, testing, and exploring concepts without much setup or coding structure. Agent Go SDK (ingenimax) The Agent Go SDK by ingenimax repo is a full Go framework for building production ready AI agents with support for multiple LLMs, tools, memory, and configuration. You install it as a Go module (need experience in this). The setup is more formal, but the framework offers more power and structure for serious projects at enterprise level. Overview This walkthrough compares two open-source frameworks for building or experimenting with AI agents: Awesome LLM Apps and Agent Go SDK. It outlines their setup, ease of use, and best-fit scenarios so you can decide which suits your workflow, whether for quick experiments or production-grade systems. How does this help? Helps agency founders and developers pick the right framework for their goals — quick demos or scalable systems. Saves time by clarifying setup complexity, use cases, and strengths of each framework before diving in. ⚙️ Apps and tools [ ] GitHub [ ] Python / JavaScript / TypeScript [ ] Go (v1.23+) [ ] Redis (optional for Go SDK) Main Steps — Comparing Awesome LLM Apps and Agent Go SDK Step 1 — Installation and Setup Awesome LLM Apps offers a lightweight, ready-to-run experience: