How does OpenClaw evolution work?
Definitive Answer
OpenClaw evolution means the agent continuously learns from every interaction. It tracks which responses were helpful (via explicit feedback and implicit signals like follow-up questions), builds a preference model for each user, refines its understanding of your business domain, and adjusts its behavior accordingly. The longer you use OpenClaw, the more accurately it serves you — unlike a standard LLM that is static.
Step-by-Step Guide
- 1Deploy your OpenClaw agent with memory and evolution enabled on FetchOpenClaws.
- 2Use the agent normally — evolution happens automatically from real interactions.
- 3Provide explicit feedback when responses are particularly good or need improvement.
- 4OpenClaw updates user preference models and domain knowledge from each interaction.
- 5Review evolution insights in the dashboard: which topics improved, common misunderstandings, user satisfaction trends.
- 6Periodically review and curate evolved knowledge to ensure accuracy.
Example Prompt
I want my sales assistant agent to learn which product recommendations convert best for each customer segment. Enable evolution tracking and connect it to my CRM to correlate recommendations with purchase outcomes.
Common Pitfalls
- Expecting immediate improvement — evolution is gradual and requires real interaction data
- Not providing feedback — evolution accelerates when users signal good and bad responses
- Ignoring evolution dashboard insights — periodic review prevents drift toward incorrect patterns
- Mixing too many use cases in one agent — focused deployments evolve faster and more accurately
FAQ
User Feedback
Startup CTO
“The answer guides helped me choose the right deployment strategy and get our agent live in under an hour.”
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“The pitfalls list saved me from common misconfigurations that would have caused production outages.”
Agency Director
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