Practical OpenClaw Advice: Starting Small, Avoiding Common Pitfalls

A developer on r/openclaw shares practical lessons from their first OpenClaw project, moving from initial confusion to building a functional personal health tracker.

Project Evolution

The developer initially aimed for a marketing agent to scrape and rewrite content but found it too ambitious. Instead, they built a personal health tracker/coach to address a gap in web-based AI tools: persistent health tracking without context bleeding. The system logs both calories and estimated food costs from convenience stores. After discovering OpenClaw's image processing capability, they extended the workflow to automatically log data from photos.

Practical Advice for Beginners

• Narrow the Scope: Ignore grand ideas. Build something small, specialized, and iterate.
• Avoid the "Automated Company" Trap: These are not beginner projects. You must understand each agent's role like managing a team of human specialists.
• Use Deterministic Workflows: Offload repetitive tasks to Python scripts created by the agent. This reduces token usage and error rates.
• Stick to One LLM: Your workspace will implicitly optimize around it. Bouncing between models (Claude, ChatGPT, Gemini) resulted in bloated, inconsistent markdown files. Let the agent audit its own system periodically under supervision.

Model Observations

The developer's workspace is optimized for Gemini, which may skew results:

• ChatGPT: "Dumber than I thought" in an agent-first setup but polite compared to Gemini. Might be best for chatting.
• Gemini: Significantly more capable regarding cost-to-performance. Even the Flash model handles image recognition, generation, and Google search integration reliably. In one instance, ChatGPT failed to use its own tools and routed an image task to Gemini via API.

The developer considered downgrading to 2.5 flash after workspace stabilization but hasn't tried "latest-flash." Next steps include returning to the marketing agent project and experimenting with Hermes.