This monorepo provides LangChain and LangGraph components for various AWS services. It aims to replace and expand upon the existing LangChain AWS components found in the langchain-community package in the LangChain repository.

The following packages are hosted in this repository:

• langchain-aws (PyPI)
• langgraph-checkpoint-aws (PyPI)

• LLMs: Includes LLM classes for AWS services like Bedrock and SageMaker Endpoints, allowing you to leverage their language models within LangChain.
• VectorStores: Supports vectorstores for services like Amazon MemoryDB and Amazon S3 Vectors, providing efficient and scalable vector database for your applications.
• Retrievers: Supports retrievers for services like Amazon Kendra and KnowledgeBases for Amazon Bedrock, enabling efficient retrieval of relevant information in your RAG applications.
• Graphs: Provides components for working with AWS Neptune graphs within LangChain.
• Agents: Includes Runnables to support Amazon Bedrock Agents, allowing you to leverage Bedrock Agents within LangChain and LangGraph.
• Tools: Includes tools and toolkits to enable use of Amazon Bedrock AgentCore's built-in tools with LangChain and LangGraph agents.

• Checkpointers: Provides a custom checkpointing solution for LangGraph agents using either the AgentCore Memory Service or the AWS Bedrock Session Management Service.
• Memory Stores - Provides a memory store solution for saving, processing, and retrieving intelligent long term memories using the AgentCore Memory Service.

...and more to come. This repository will continue to expand and offer additional components for various AWS services as development progresses.

Note: This repository will replace all AWS integrations currently present in the langchain-community package. Users are encouraged to migrate to this repository as soon as possible.

You can install the langchain-aws package from PyPI.

pip install langchain-aws

The langgraph-checkpoint-aws package can also be installed from PyPI.

pip install langgraph-checkpoint-aws

Here's a simple example of how to use the langchain-aws package.

from langchain_aws import ChatBedrockConverse
# Initialize the Bedrock chat model
model = ChatBedrockConverse(
 model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"
)
# Invoke the model
response = model.invoke("Hello! How are you today?")
print(response)

For more detailed usage examples and documentation, please refer to the LangChain docs.

You can find usage examples for langgraph-checkpoint-aws in the README.

We welcome contributions to this repository! To get started, please follow the contribution guide for your specific project of interest:

• For langchain-aws, see langchain-aws/CONTRIBUTING.md.
• For langgraph-checkpointer-aws, see langgraph-checkpointer-aws/CONTRIBUTING.md.

Each guide provides detailed instructions on how to set up the project for development and guidance on how to contribute effectively.

This project is licensed under the MIT License.