feat(ai): langchain cached and reasoning tokens #258
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PR Summary
Enhanced LangChain callback token tracking by adding comprehensive monitoring for reasoning and cache operations in the PostHog Python SDK.
- Added cache token tracking in
posthog/ai/langchain/callbacks.pyto monitor both read and write operations across different LLM providers - Implemented reasoning token tracking with support for models like o1-mini and structured handling through a new ModelUsage dataclass
- Added detailed test cases in
test_callbacks.pycovering OpenAI and Anthropic token tracking scenarios - Updated minimum version requirements for LangChain dependencies in
pyproject.tomlto support new token tracking features
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sortafreel
approved these changes
Jun 12, 2025
| else captured_count | ||
| ) # For Bedrock, the token count is a list when streamed | ||
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| parsed_usage[type_key] = final_count |
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Nitpick (not related to the change): Could captured_count (in captured_count = usage[model_key]) be None in any case? Should we add a null check here to prevent potential exceptions?
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It can be None. The code won't crash even though it's None, or did I miss the spot?
k11kirky
approved these changes
Jun 12, 2025
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Problem
The LangChain callback doesn't count reasoning and cache write/read tokens.
Changes