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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.
Route prompts across LLM providers with policy-based orchestration and verification.
Aggregate MCP servers and route tools intelligently for efficient parallel work.
Route LLM tasks locally first to keep sensitive data private and controlled.
Build, debug, and manage software tasks with natural language across LLMs.
Access multiple AI providers in one terminal for generation, search, and comparison.
Offload non-critical LLM tasks to your own model to save premium quota.