fix(langchain): alias Bedrock providers in summarization token check (#37453)

libs/langchain_v1/langchain/agents/middleware/summarization.py

@@ -77,6 +77,23 @@ _DEFAULT_MESSAGES_TO_KEEP = 20
 _DEFAULT_TRIM_TOKEN_LIMIT = 4000
 _DEFAULT_FALLBACK_MESSAGE_COUNT = 15
 
+# Some providers tag emitted messages with a `model_provider` string that differs from
+# their LangSmith `ls_provider`. The reported-token check below compares the two, so we
+# accept known aliases per `ls_provider`.
+_LS_PROVIDER_ALIASES: dict[str, frozenset[str]] = {
+    "amazon_bedrock": frozenset({"bedrock", "bedrock_converse"}),
+}
+
+
+def _provider_matches(message_provider: str, model_ls_provider: str | None) -> bool:
+    if model_ls_provider is None:
+        return False
+    if message_provider == model_ls_provider:
+        return True
+    aliases = _LS_PROVIDER_ALIASES.get(model_ls_provider)
+    return aliases is not None and message_provider in aliases
+
+
 ContextFraction = tuple[Literal["fraction"], float]
 """Fraction of model's maximum input tokens.
 
@@ -379,7 +396,10 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
             and (reported_tokens := last_ai_message.usage_metadata.get("total_tokens", -1))
             and reported_tokens >= threshold
             and (message_provider := last_ai_message.response_metadata.get("model_provider"))
-            and message_provider == self.model._get_ls_params().get("ls_provider")  # noqa: SLF001
+            and _provider_matches(
+                message_provider,
+                self.model._get_ls_params().get("ls_provider"),  # noqa: SLF001
+            )
         ):
             return True
         return False

libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_summarization.py

@@ -6,6 +6,7 @@ import pytest
 from langchain_core.callbacks import AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun
 from langchain_core.language_models import ModelProfile
 from langchain_core.language_models.base import (
+    LangSmithParams,
     LanguageModelInput,
 )
 from langchain_core.language_models.chat_models import BaseChatModel
@@ -27,7 +28,10 @@ from pydantic import Field
 from typing_extensions import override
 
 from langchain.agents import AgentState
-from langchain.agents.middleware.summarization import SummarizationMiddleware
+from langchain.agents.middleware.summarization import (
+    SummarizationMiddleware,
+    _provider_matches,
+)
 from langchain.chat_models import init_chat_model
 from tests.unit_tests.agents.model import FakeToolCallingModel
 
@@ -1219,6 +1223,84 @@ def test_usage_metadata_trigger() -> None:
     assert not middleware._should_summarize(messages, 0)
 
 
+def test_provider_matches() -> None:
+    """Direct equality matches, plus Bedrock aliases under amazon_bedrock."""
+    assert _provider_matches("anthropic", "anthropic")
+    assert _provider_matches("openai", "openai")
+    # Bedrock chat models tag messages with model_provider="bedrock" or
+    # "bedrock_converse" but trace under ls_provider="amazon_bedrock".
+    assert _provider_matches("bedrock", "amazon_bedrock")
+    assert _provider_matches("bedrock_converse", "amazon_bedrock")
+    # Non-matches
+    assert not _provider_matches("openai", "anthropic")
+    assert not _provider_matches("bedrock", "anthropic")
+    assert not _provider_matches("anthropic", None)
+
+
+class _MockBedrockChatModel(BaseChatModel):
+    """Mock model that mimics ChatBedrockConverse's ls_provider for tracing."""
+
+    @override
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        return ChatResult(generations=[ChatGeneration(message=AIMessage(content="Summary"))])
+
+    @property
+    def _llm_type(self) -> str:
+        return "amazon_bedrock_converse_chat"
+
+    @override
+    def _get_ls_params(self, stop: list[str] | None = None, **kwargs: Any) -> LangSmithParams:
+        return LangSmithParams(ls_provider="amazon_bedrock", ls_model_type="chat")
+
+
+def test_reported_tokens_trigger_for_bedrock_converse() -> None:
+    """Bedrock messages should satisfy the reported-token check.
+
+    Despite the model_provider/ls_provider mismatch (bedrock_converse vs.
+    amazon_bedrock), the reported-token check should still trigger summarization.
+    """
+    middleware = SummarizationMiddleware(
+        model=_MockBedrockChatModel(),
+        trigger=("tokens", 10_000),
+        keep=("messages", 4),
+    )
+    messages: list[AnyMessage] = [
+        HumanMessage(content="msg1"),
+        AIMessage(
+            content="msg2",
+            response_metadata={"model_provider": "bedrock_converse"},
+            usage_metadata={
+                "input_tokens": 7500,
+                "output_tokens": 2501,
+                "total_tokens": 10_001,
+            },
+        ),
+    ]
+    # reported token count (10_001) should override the supplied count of 0
+    assert middleware._should_summarize(messages, 0)
+
+    # mismatched provider should not engage
+    messages_other_provider: list[AnyMessage] = [
+        HumanMessage(content="msg1"),
+        AIMessage(
+            content="msg2",
+            response_metadata={"model_provider": "anthropic"},
+            usage_metadata={
+                "input_tokens": 7500,
+                "output_tokens": 2501,
+                "total_tokens": 10_001,
+            },
+        ),
+    ]
+    assert not middleware._should_summarize(messages_other_provider, 0)
+
+
 class ConfigCapturingModel(BaseChatModel):
     """Mock model that captures the config passed to invoke/ainvoke."""