feat: add caching support for Anthropic models via Databricks#6320
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feat: add caching support for Anthropic models via Databricks#6320
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Add cache_control fields to requests for Claude models to enable prompt caching: - System message gets cache_control - Last two user messages get cache_control (for incremental caching) - Last tool definition gets cache_control (so all tools are cached as prefix) This reduces costs when using Claude models via Databricks by caching frequently repeated content like system prompts and tool definitions.
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Pull request overview
This PR adds prompt caching support for Claude models accessed through Databricks by implementing Anthropic-style cache_control fields in API requests. This optimization reduces costs by caching frequently repeated content like system prompts, conversation history, and tool definitions.
Key changes:
- Adds
is_claude_model()function to detect Claude models by name - Implements
apply_cache_control_for_claude()to add cache control markers to system messages, the last two user messages, and the last tool definition - Integrates cache control application into the
create_request()flow for Claude models
michaelneale
reviewed
Jan 2, 2026
| "cache_control": { "type": "ephemeral" } | ||
| }]); | ||
| } else if let Some(content_array) = content.as_array_mut() { | ||
| // Content is already an array, add cache_control to the last element |
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probably can drop little comments like this (thanks claude opus!) but others I found helpful (assuming they are true!)
michaelneale
approved these changes
Jan 2, 2026
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michaelneale
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nice (comments helpful - just the odd one liner one) but this is pretty cool, wouldn't really know where to start to work all this out.
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Add cache_control fields to requests for Claude models to enable prompt caching:
This reduces costs when using Claude models via Databricks by caching frequently repeated content like system prompts and tool definitions.