feat(weave): add cache token fields to LLM usage and cost schemas#6483
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andrewtruong wants to merge 2 commits intomasterfrom
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feat(weave): add cache token fields to LLM usage and cost schemas#6483andrewtruong wants to merge 2 commits intomasterfrom
andrewtruong wants to merge 2 commits intomasterfrom
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Add cache_read_input_tokens and cache_write_input_tokens to LLMUsageSchema, LLMCostSchema, and LLMAggregatedUsage so the data model can represent cached token counts and their costs. Update usage_utils.py to extract, normalize (cache_creation_input_tokens → cache_write_input_tokens), merge, and aggregate the new fields through the call hierarchy. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
cache_read_input_tokensandcache_write_input_tokenstoLLMUsageSchemaTypedDictcache_read_input_tokens_total_cost,cache_write_input_tokens_total_cost,cache_read_input_token_cost,cache_write_input_token_costtoLLMCostSchemaLLMAggregatedUsageBaseModelusage_utils.pyto extract, normalize (cache_creation_input_tokens→cache_write_input_tokens), merge, and aggregate cache tokens through the call hierarchyContext
This is Layer 2 of the cached token tracking feature. It extends the data model so the system can represent cached token counts flowing from provider SDKs (L1) and enables downstream cost calculation (L5) and query support (L6/L7).
Test plan
LLMAggregatedUsageserializes/deserializes with new fields defaulting to 0/None_extract_call_usageand_merge_usage🤖 Generated with Claude Code