Show HN: LLM-use – orchestrate LLMs for AI agents like OpenClaw, cut costs

I built llm-use, an open-source tool to run AI agent workflows across multiple LLMs with routing and cost optimization. Repo: https://github.com/llm-use/llm-use https://github.com/llm-use/llm-use OpenClaw-style agents are powerful but get expensive if every step runs on a single high-end model. llm-use helps by: • using a strong model only for planning and final synthesis • running most steps on cheaper or local models • mixing local and cloud models in the same workflow Example: python3 cli.py exec \ --orchestrator anthropic:claude-4-5-sonnet \ --worker ollama:llama3.1:8b \ --task "Monitor sources and produce a daily summary" This setup keeps long-running agents predictable in cost while preserving quality where it matters. Feedback welcome.