Merge branch 'master' into fix-33684

Author: aroun-coumar

.github/scripts/check_diff.py

@@ -134,7 +134,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ == "libs/core":
         py_versions = ["3.10", "3.11", "3.12", "3.13", "3.14"]
     # custom logic for specific directories
-    elif dir_ in {"libs/partners/chroma", "libs/partners/nomic"}:
+    elif dir_ in {"libs/partners/chroma"}:
         py_versions = ["3.10", "3.13"]
     else:
         py_versions = ["3.10", "3.14"]

.github/workflows/_release.yml

@@ -77,7 +77,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v5
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -208,7 +208,7 @@ jobs:
     steps:
       - uses: actions/checkout@v5
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -258,7 +258,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -395,7 +395,7 @@ jobs:
       contents: read
     strategy:
       matrix:
-        partner: [openai, anthropic]
+        partner: [anthropic]
       fail-fast: false # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -428,7 +428,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         if: startsWith(inputs.working-directory, 'libs/core')
         with:
           name: dist
@@ -497,7 +497,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -537,7 +537,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/

.github/workflows/pr_lint.yml

@@ -30,7 +30,7 @@
 #   core, cli, langchain, langchain_v1, langchain-classic, standard-tests,
 #   text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq,
 #   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
-#   xai, infra
+#   xai, infra, deps
 #
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.

.gitignore

@@ -1,6 +1,8 @@
 .vs/
 .claude/
 .idea/
+#Emacs backup
+*~
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -34,17 +34,19 @@
   </a>
 </p>
 
-LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
+LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
 
 ```bash
 pip install langchain
 ```
 
+If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
+
 ---
 
 **Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview).
 
-If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
+**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
 
 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
@@ -58,21 +60,18 @@ Use LangChain for:
 - **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
 - **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
 
-## LangChain’s ecosystem
+## LangChain ecosystem
 
 While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
 
 To improve your LLM application development, pair LangChain with:
 
-- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
 - [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
-- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio).
+- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
 
 ## Additional resources
 
-- [Learn](https://docs.langchain.com/oss/python/learn): Use cases, conceptual overviews, and more.
-- [API Reference](https://reference.langchain.com/python): Detailed reference on
-navigating base packages and integrations for LangChain.
+- [API Reference](https://reference.langchain.com/python): Detailed reference on navigating base packages and integrations for LangChain.
+- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview): List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
 - [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues.
-- [LangChain Forum](https://forum.langchain.com): Connect with the community and share all of your technical questions, ideas, and feedback.
-- [Chat LangChain](https://chat.langchain.com): Ask questions & chat with our documentation.

libs/core/langchain_core/agents.py

@@ -5,12 +5,10 @@
 
 !!! warning
     New agents should be built using the
-    [langgraph library](https://github.com/langchain-ai/langgraph), which provides a
+    [`langchain` library](https://pypi.org/project/langchain/), which provides a
     simpler and more flexible way to define agents.
 
-    Please see the
-    [migration guide](https://python.langchain.com/docs/how_to/migrate_agent/) for
-    information on how to migrate existing agents to modern langgraph agents.
+    See docs on [building agents](https://docs.langchain.com/oss/python/langchain/agents).
 
 Agents use language models to choose a sequence of actions to take.
 

libs/core/langchain_core/chat_history.py

@@ -121,15 +121,15 @@ def add_user_message(self, message: HumanMessage | str) -> None:
         This method may be deprecated in a future release.
 
         Args:
-            message: The human message to add to the store.
+            message: The `HumanMessage` to add to the store.
         """
         if isinstance(message, HumanMessage):
             self.add_message(message)
         else:
             self.add_message(HumanMessage(content=message))
 
     def add_ai_message(self, message: AIMessage | str) -> None:
-        """Convenience method for adding an AI message string to the store.
+        """Convenience method for adding an `AIMessage` string to the store.
 
         !!! note
             This is a convenience method. Code should favor the bulk `add_messages`
@@ -138,7 +138,7 @@ def add_ai_message(self, message: AIMessage | str) -> None:
         This method may be deprecated in a future release.
 
         Args:
-            message: The AI message to add.
+            message: The `AIMessage` to add.
         """
         if isinstance(message, AIMessage):
             self.add_message(message)
@@ -173,7 +173,7 @@ def add_messages(self, messages: Sequence[BaseMessage]) -> None:
         in an efficient manner to avoid unnecessary round-trips to the underlying store.
 
         Args:
-            messages: A sequence of BaseMessage objects to store.
+            messages: A sequence of `BaseMessage` objects to store.
         """
         for message in messages:
             self.add_message(message)
@@ -182,7 +182,7 @@ async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None:
         """Async add a list of messages.
 
         Args:
-            messages: A sequence of BaseMessage objects to store.
+            messages: A sequence of `BaseMessage` objects to store.
         """
         await run_in_executor(None, self.add_messages, messages)
 

libs/core/langchain_core/document_loaders/base.py

@@ -27,7 +27,7 @@ class BaseLoader(ABC):  # noqa: B024
     """Interface for Document Loader.
 
     Implementations should implement the lazy-loading method using generators
-    to avoid loading all Documents into memory at once.
+    to avoid loading all documents into memory at once.
 
     `load` is provided just for user convenience and should not be overridden.
     """
@@ -53,9 +53,11 @@ async def aload(self) -> list[Document]:
     def load_and_split(
         self, text_splitter: TextSplitter | None = None
     ) -> list[Document]:
-        """Load Documents and split into chunks. Chunks are returned as `Document`.
+        """Load `Document` and split into chunks. Chunks are returned as `Document`.
 
-        Do not override this method. It should be considered to be deprecated!
+        !!! danger
+
+            Do not override this method. It should be considered to be deprecated!
 
         Args:
             text_splitter: `TextSplitter` instance to use for splitting documents.
@@ -135,7 +137,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]:
         """
 
     def parse(self, blob: Blob) -> list[Document]:
-        """Eagerly parse the blob into a `Document` or `Document` objects.
+        """Eagerly parse the blob into a `Document` or list of `Document` objects.
 
         This is a convenience method for interactive development environment.
 

libs/core/langchain_core/document_loaders/blob_loaders.py

@@ -28,7 +28,7 @@ class BlobLoader(ABC):
     def yield_blobs(
         self,
     ) -> Iterable[Blob]:
-        """A lazy loader for raw data represented by LangChain's Blob object.
+        """A lazy loader for raw data represented by LangChain's `Blob` object.
 
         Returns:
             A generator over blobs

libs/core/langchain_core/document_loaders/langsmith.py

@@ -14,13 +14,13 @@
 
 
 class LangSmithLoader(BaseLoader):
-    """Load LangSmith Dataset examples as Documents.
+    """Load LangSmith Dataset examples as `Document` objects.
 
-    Loads the example inputs as the Document page content and places the entire example
-    into the Document metadata. This allows you to easily create few-shot example
-    retrievers from the loaded documents.
+    Loads the example inputs as the `Document` page content and places the entire
+    example into the `Document` metadata. This allows you to easily create few-shot
+    example retrievers from the loaded documents.
 
-    ??? note "Lazy load"
+    ??? note "Lazy loading example"
 
         ```python
         from langchain_core.document_loaders import LangSmithLoader
@@ -66,12 +66,11 @@ def __init__(
             format_content: Function for converting the content extracted from the example
                 inputs into a string. Defaults to JSON-encoding the contents.
             example_ids: The IDs of the examples to filter by.
-            as_of: The dataset version tag OR
-                timestamp to retrieve the examples as of.
-                Response examples will only be those that were present at the time
-                of the tagged (or timestamped) version.
+            as_of: The dataset version tag or timestamp to retrieve the examples as of.
+                Response examples will only be those that were present at the time of
+                the tagged (or timestamped) version.
             splits: A list of dataset splits, which are
-                divisions of your dataset such as 'train', 'test', or 'validation'.
+                divisions of your dataset such as `train`, `test`, or `validation`.
                 Returns examples only from the specified splits.
             inline_s3_urls: Whether to inline S3 URLs.
             offset: The offset to start from.