Prompt Engineering

Building an Agentic Team for an Open Source Project with Claude Code

A core engineer on MemMachine — the one who owned the Semantic Memory subsystem — left the project. The codebase didn’t grow any less complex overnight, but the human attention available to maintain it did. That’s a familiar shape of problem in any open source project, and it’s the exact shape where a well-designed Claude Code agent team earns its keep.

This post documents what I built: a 22-agent maintenance team that lives entirely inside MemMachine’s repository, coordinates via Claude Code’s experimental Agent Teams runtime, and operates under a design I can reproduce for any existing repository with real code. The agents don’t push code, don’t sign commits, don’t merge pull requests, and don’t cut releases — humans still gatekeep every consequential action. What the agents do do is the tedious and error-prone middle of software maintenance: triage, spec drafting, implementation, QA, security review, docs, dependency and upstream tracking.

An Introduction to Generative Prompt Engineeering

Introduction

Over the past few years, there has been a significant explosion in the use and development of large language models (LLMs). An LLM is a language model consisting of a neural network with many parameters (commonly multi-billions of weights), trained on large quantities of text. Some of the most popular large language models are: GPT-3 (Generative Pretrained Transformer 3) – developed by OpenAI ; BERT (Bidirectional Encoder Representations from Transformers) – developed by Google; RoBERTa (Robustly Optimized BERT Approach) – developed by Facebook AI; T5 (Text-to-Text Transfer Transformer) – developed by Google. Many others exist and continue to emerge. These language models are designed to understand and generate natural language text, allowing for a wide range of applications such as chatbots, content creation, language translation, and more.