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llm_response_parsing
observability-llm-response-parsing-when-using-anthropic-reasoning-models-e-g-claude-h-a59c8c4a
IFWhen using Anthropic reasoning models (e.g., claude-haiku-4-5-20251001) with 'thinking' enabled, the LLM response contains a content array with objects of type 'thinking' and 'text', which Langfuse's generation parser fails to handle.
THENModify the response extraction logic to iterate over the content array, concatenate all 'text' blocks into the assistant message, and optionally capture 'thinking' blocks as a separate field. Ensure the parser can handle 'thinking', 'text', 'tool_use', and 'tool_result' block types without breaking experiments or evaluators. This fix enables correct logging of reasoning models' outputs.
Tier 170%
llm_response_parsing
ai-agents-llm-response-parsing-when-using-an-llm-integration-like-litellm-with-re-505497fd
IFWhen using an LLM integration (like LiteLLM) with response_format='json_object' on Ollama, the response parser crashes with KeyError: 'name' because it assumes all JSON responses contain a function call.
THENModify the response transformation to check for the presence of a tool_call or function_call key before accessing its fields. If absent, treat the response content as direct JSON text. This prevents false errors when the model outputs only a JSON object without a function call wrapper.
Tier 170%
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