A solution that breaks through the limitations of the Claude Code model
With the Any LLM in Claude Code tool, the following steps can be followed to realize the free switching of multiple models:
- environmental preparation: using Python 3.8+ environment, automated dependency management via uv tools
- Configuration file modification: Configure two sets of key parameters in the .env file:
- BIG_MODEL family of variables to control complex task models
- SMALL_MODEL family of variables to control lightweight task models
- Model Routing Policy::
- High-performance requirements: configure Vertex AI with gemini-1.5-pro and other large models to handle sonnet tasks
- Daily requirements: processing haiku tasks using OpenAI's gpt-4o-mini and other small models
- API compliant processing: LiteLLM automatically converts different vendors' API formats without manual intervention.
Realization effect: users who could only use Claude's fixed model can now access different levels of models from multiple AI service providers at the same time, intelligently allocating computing resources according to the type of task.
This answer comes from the articleAny LLM in Claude Code: An Open Source Agent for Calling Multilingual Models for Claude CodeThe
May not be reproduced without permission:AI productivity tools " How to solve the limitation of Claude Code usage for free switching of multiple models?
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