I’m the creator of agentfab, a distributed agentic platform that features task decomposition, multi-agent orchestration, model heterogeneity with custom agentic fabrics, bounded review loops, and a bespoke self-curating memory system that enable shared context.

My background is in engineering at hyperscalers where I worked extensively with foundational distributed systems. I started agentfab because I wanted an agentic coding tool that could effectively decompose and parallelize work across different model providers and agent profiles. agentfab will run locally on your machine, on your VM fleet, on your K8s cluster, or any distributed compute environment.

The clear benefit over existing agentic coding tools is that agentfab is able to fan out work across many different agents from various model providers (OpenAI, OAI-compatible, Google, Anthropic) with shared context - it’s able to break down large scope tasks into subtasks and assign the appropriate agent for each; it handles bounded review loops, artifact streaming between agents, and uses OS-level sandboxing and secure communication between agents.

You are in full control of your agent "fabric" - simply define YAML agents and specify which tools, capabilities, and special knowledge they have and the platform will know how to make use of them during task decomposition. There are 4 default agents that cover most software development tasks.

agentfab has an interactive CLI with taskgraphs and progress tracking - you can prompt agentfab to execute on end-to-end projects or chat with any of the agents in the fabric to query against their knowledge.

If you want to read more about agentfab, check out this blog: https://razvanmaftei.me/article?slug=agentfab-stateful-multi...

Check out the GitHub repo if you want to try it out. Looking forward to hearing your thoughts.

Thanks!