fix(langchain): stop double-counting anthropic cache tokens in prompt totals

py/src/braintrust/integrations/langchain/callbacks.py

@@ -617,24 +617,6 @@ def _get_model_name_from_response(response: LLMResult) -> str | None:
     return model_name
 
 
-def _cache_tokens_are_separate_from_input_tokens(input_token_details: dict[str, Any]) -> bool:
-    # LangChain provider packages use different cache-token conventions:
-    # - OpenAI-style responses report cache reads as a subset of input_tokens.
-    # - Anthropic-style responses report cache reads/creation separately from input_tokens.
-    #
-    # Avoid provider-name checks here so any LangChain integration using the same
-    # "separate cache tokens" schema gets normalized, while providers that only
-    # expose cache_read as input-token detail do not get double-counted.
-    return any(
-        key in input_token_details
-        for key in (
-            "cache_creation",
-            "ephemeral_5m_input_tokens",
-            "ephemeral_1h_input_tokens",
-        )
-    )
-
-
 def _get_metrics_from_response(response: LLMResult):
     metrics = {}
 
@@ -685,15 +667,16 @@ def _get_metrics_from_response(response: LLMResult):
                 completion_tokens = metrics.get("completion_tokens")
                 total_tokens = metrics.get("total_tokens")
                 if prompt_tokens is not None and completion_tokens is not None:
-                    if (
-                        cache_tokens
-                        and total_tokens == prompt_tokens + completion_tokens
-                        and _cache_tokens_are_separate_from_input_tokens(input_token_details)
-                    ):
+                    # LangChain's UsageMetadata contract makes input_token_details a
+                    # breakdown of input_tokens, so cache tokens already count toward
+                    # the prompt total (langchain-anthropic >= 0.2.3, langchain-aws,
+                    # langchain-openai all comply). Cache tokens exceeding the prompt
+                    # total means the integration reported uncached input only — fold
+                    # cache tokens back in so prompt/total stay internally consistent.
+                    if cache_tokens > prompt_tokens and total_tokens == prompt_tokens + completion_tokens:
                         prompt_tokens += cache_tokens
                         metrics["prompt_tokens"] = prompt_tokens
-                        if total_tokens is not None:
-                            metrics["total_tokens"] = total_tokens + cache_tokens
+                        metrics["total_tokens"] = total_tokens + cache_tokens
                     metrics["tokens"] = prompt_tokens + completion_tokens
 
     if not metrics or not any(metrics.values()):

py/src/braintrust/integrations/langchain/test_callbacks.py

@@ -1098,6 +1098,12 @@ def test_prompt_caching_tokens(logger_memory_logger):
     assert first_metrics["prompt_tokens"] >= first_cache_creation_tokens
     assert first_metrics["tokens"] == first_metrics["prompt_tokens"] + first_metrics["completion_tokens"]
 
+    # langchain-anthropic already folds cache read/creation tokens into
+    # usage_metadata input_tokens; the callback must not add them again.
+    assert res.usage_metadata is not None
+    assert first_metrics["prompt_tokens"] == res.usage_metadata["input_tokens"]
+    assert first_metrics["total_tokens"] == res.usage_metadata["total_tokens"]
+
     second_metrics = None
     for attempt in range(3):
         res = model.invoke(
@@ -1134,6 +1140,10 @@ def test_prompt_caching_tokens(logger_memory_logger):
     assert second_metrics["prompt_tokens"] >= second_metrics["prompt_cached_tokens"]
     assert second_metrics["tokens"] == second_metrics["prompt_tokens"] + second_metrics["completion_tokens"]
 
+    assert res.usage_metadata is not None
+    assert second_metrics["prompt_tokens"] == res.usage_metadata["input_tokens"]
+    assert second_metrics["total_tokens"] == res.usage_metadata["total_tokens"]
+
 
 @pytest.mark.vcr
 def test_image_input(logger_memory_logger):