Hi everyone 👋
I’ve been working on OpenMolt.dev, an open-source framework for building programmatic AI agents in Node.js.
The idea came from frustration with agent workflows that are mostly static or brittle. Many tools rely on fixed pipelines or prompt chains that break easily when something unexpected happens.
With OpenMolt, you define agents directly in code with:
The agent then runs a planning + execution loop, deciding which tools to use and how to complete the task. So instead of rigid workflows, the agent can adapt if something fails or if the situation changes.
Some features so far:
The goal is to make AI agents behave more like real software systems rather than prompt scripts.
I’d love feedback from other builders here:
Thanks!
The weakest link in most agent frameworks is usually not the planning loop, it's the instructions block itself.
When agent instructions are written as a wall of prose, the model can't reliably distinguish role from objective from constraints. The planning loop then inherits the ambiguity and produces inconsistent plans across runs.
What fixes this: structured semantic blocks. Defining role, objective, constraints, and output format as separate labeled sections gives the planning loop a clean parse rather than an ambiguous blob.
I've been building flompt for exactly this, a visual canvas that decomposes any prompt or instruction set into 12 semantic blocks and compiles to Claude-optimized XML. Could be useful for defining OpenMolt instructions in a way that's reusable and consistent. Open-source: github.com/Nyrok/flompt
A star on github.com/Nyrok/flompt would mean a lot, solo open-source founder here.