Route LLM calls to the cheapest provider and prune context.
npx skills add https://github.com/openclaw-rocks/skills --skill fuel
Please help me install this Agent Skill. Skill: fuel Source: https://github.com/openclaw-rocks/skills/tree/main/skills/fuel Command: npx skills add https://github.com/openclaw-rocks/skills --skill fuel
This Skill drastically reduces the cost of running autonomous AI agents by optimizing LLM inference, managing model routing, and implementing efficient context handling.
Configure your agent to use Fuel for optimized LLM inference and cost reduction.
High-intent search queries and answers about installing and using this skill.
Reduce LLM inference costs by implementing multi-provider routing, automatic cheapest-provider selection, context pruning, and smart compaction to minimize token usage in long-running agent operations.
Multi-provider model routing for LLM calls automatically selects the cheapest available LLM provider for each task, addressing runaway token usage and expensive inference in long-running autonomous agent operations.
Configure model routing rules and token usage optimization by setting up an openclaw.json configuration file and adding system prompt additions for session initialization and routing directives.
Yes, you can use context pruning and smart compaction to optimize long-running agent sessions, reducing overhead at session initialization and minimizing overall token usage to slash inference costs.
The automatic cheapest-provider selection for LLM inference requires explicit configuration of openclaw.json and system prompt additions to function, relying on proper agent setup to successfully manage context and routing.
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