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Route LLM requests across providers and orchestrate MCP tools with local privacy.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-llm-router" yet — see the docs or source repo.
Use Claude for long-document summarization, a local model for sensitive rewriting, and GPT for final polishing. Provide a routing configuration approach based on mcp-llm-router.
A multi-model routing plan showing how different tasks are assigned to providers and models.
Design a workflow that first gathers information with a search MCP tool, then summarizes it with an LLM, and finally stores it in local memory. Explain how to chain these steps with mcp-llm-router.
An MCP orchestration flow with step order, tool interactions, and data movement between stages.
I want embeddings and long-term memory to stay local, while only non-sensitive requests go to cloud models. Based on mcp-llm-router, provide an architecture recommendation and security boundaries.
A privacy-first architecture recommendation that clarifies which data and tasks stay local versus go to the cloud.
Aggregate MCP servers and route tools intelligently for efficient parallel work.
Delegate low-risk tasks to a cheaper model with main-agent review.
Access multiple LLM providers through one encrypted OpenAI-compatible gateway.
Build, debug, and manage software tasks with natural language across LLMs.
Manage router settings and network control through natural language commands.
Offload non-critical LLM tasks to your own model to save premium quota.