Hey all, thanks for the feedback and questions!

First things first: This is a solo project in a very early stage. Many parts are indeed either mocked or simplified for now just to see the bigger picture of how these evolving, self-building agents might work together. I’m currently developing a “system bus” to handle agent-to-agent communication in a more distributed, scalable way. Once that’s in place, I plan to circle back and strengthen all the fundamentals—agent evolution, storage (including vector DB), test coverage, governance checks, etc. My “production-grade” wording may have been too ambitious at this point, so consider it more of a vision statement than a claim of readiness.

A few clarifications based on the comments:

Agent Evolution
Right now, the evolution mechanism is, honestly, a naive “please improve yourself” prompt approach. I fully agree it’s rudimentary. The plan is to add more robust ways to retrain or adapt an agent’s underlying models (and store interim states) so that “evolution” actually reflects meaningful improvements rather than superficial prompt tweaks.

Why YAML?
I get the concerns—YAML can get bulky, and yes, there’s a joke about burying agents under a giant YAML file. I’m primarily using YAML for readability and version control; it’s simple to keep track of how agents communicate in a “single source of truth.” But nothing’s set in stone. If another structure or domain language works better, I’m open to exploring it.

Storage and Vector DB
Several folks suggested a vector database or similar. I actually do plan to integrate a vector DB for more efficient embedding lookups and advanced search. The current approach is just a placeholder while I validate the overall workflow.

“Production-Grade” and Benchmarks
Totally fair to question that term. I used it loosely to convey that I eventually want these agents to be robust enough for real-world scenarios, not just toy experiments. But I have a ways to go before calling it truly production-ready, and benchmarks will definitely be needed.

Governance
I’m taking governance seriously. Agents “autonomously” evolving can definitely raise eyebrows around compliance, safety, and unexpected behaviors. My approach is to provide a “firmware” layer of guardrails that define domain rules and boundaries. As it matures, I plan to integrate testing and compliance checks for each agent’s modifications, so it’s not just a runaway chain of self-modifications.

Service Bus & Distributed Systems
The upcoming feature is the “system bus,” which will support more robust service discovery and distributed task routing. That includes letting multiple agents run in different environments or infrastructure. Right now, everything is local and pretty simplistic.

Mocked Features
Some of you noticed placeholders or references to incomplete features. Absolutely. My approach is to wire up the architecture first—even with partial stubs—so I can see how everything fits together. Once I know the big pieces make sense, I’ll replace these mocks with actual implementations and refine them.

Graphics & Diagrams
I hear you: an actual flowchart or architecture diagram might be more useful than a generic AI-generated image. That’s on my to-do list, alongside better docs and examples. For now, I just wanted something to visually convey the concept in the README, but I’ll definitely add more meaningful visuals.

Collaboration
I appreciate the offers to join forces! I’d love to collaborate more closely once the fundamentals are stable. For now, if anyone wants to experiment, provide feedback, or open an issue/PR on GitHub, that’s fantastic.

Thanks again for all the feedback and for checking this out. I know it’s a bit rough around the edges, but the goal is to create a toolkit for truly autonomous, evolving, and self-governed AI systems—while staying within safe operational boundaries. I’m excited to keep iterating and improving, and I welcome any further thoughts or contributions!