Show HN: Polymcp and Ollama for Simple Local and Cloud LLM Execution

We’ve added first-class Ollama support to Polymcp to make running large language models easy—whether you’re working locally or deploying in the cloud. By using Ollama as a backend provider, Polymcp can coordinate MCP servers and models with minimal configuration. This lets you focus on building agents instead of wiring infrastructure. from polymcp.polyagent import PolyAgent, OllamaProvider agent = PolyAgent( llm_provider=OllamaProvider(model="gpt-oss:120b"), mcp_servers=["http://localhost:8000/mcp"] ) result = agent.run("What is the capital of France?") print(result) What this enables: • Clean orchestration: Polymcp manages MCP servers while Ollama handles model execution. • Same workflow, everywhere: Run the same setup on your laptop or in the cloud. • Flexible model choice: Works with models like gpt-oss:120b, Kimi K2, Nemotron, and others supported by Ollama. The goal is to provide a straightforward way to experiment with and deploy LLM-powered agents without extra glue code. Would love feedback or ideas on how you’d use this. Repo: https://github.com/poly-mcp/Polymcp https://github.com/poly-mcp/Polymcp