I've been building an open-source coding agent called Zelyq.

One of the problems I kept running into with coding agents wasn't necessarily code generation — it was continuity.

They can understand a project extremely well during a session, but as the project grows, context gets compressed or disappears. Architectural decisions, previous reasoning, conventions and relationships between different parts of the system can eventually be lost.

I'm experimenting with a different approach in Zelyq: persistent project memory that lives beyond the current conversation.

I'm also separating the development process into different modes. Architect Mode focuses on requirements, architecture and planning before implementation, while Engineer Mode handles implementation with that context available.

The longer-term idea is that the agent should build an understanding of the project over time rather than repeatedly rediscovering it.

Zelyq is open source and still early, so I'm particularly interested in criticism from developers who already use coding agents heavily.

GitHub: [**https://github.com/CrowPus/Zelyq**](https://github.com/CrowPus/Zelyq)

I'm especially curious: **how would you want persistent memory in a coding agent to behave? What should it remember, and what should it deliberately forget?**

GitHub - CrowPus/Zelyq: Open-source AI app builder. Describe an app, watch an agent build it in a real workspace, and edit the result live