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Build LangChain Applications on AWS
| Date | Stars |
|---|---|
| 2026-07-31 | 335 |
| 2026-08-01 | 336 |
| 2026-08-02 | 336 |
| 2026-08-03 | 336 |
| 2026-08-04 | 336 |
| 2026-08-05 | 337 |
| 2026-08-06 | 337 |
Today
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Momentum
35.0
growth rate 0.00%/day
# 🦜️🔗 LangChain 🤝 Amazon Web Services (AWS)
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](https://pypi.org/project/langchain-aws/))
- `langgraph-checkpoint-aws` ([PyPI](https://pypi.org/project/langgraph-checkpoint-aws/))
- `langchain-agentcore-codeinterpreter` ([PyPI](https://pypi.org/project/langchain-agentcore-codeinterpreter/))
## Features
### LangChain
- **LLMs**: Includes LLM classes for AWS services like [Bedrock](https://aws.amazon.com/bedrock) and [SageMaker Endpoints](https://aws.amazon.com/sagemaker/deploy/), allowing you to leverage their language models within LangChain.
- **VectorStores**: Supports vectorstores for services like [Amazon MemoryDB](https://aws.amazon.com/memorydb/), [Amazon S3 Vectors](https://aws.amazon.com/s3/features/vectors/), and [AWS ElastiCache for Valkey](https://aws.amazon.com/elasticache/), providing efficient and scalable vector database for your applications.
- **Retrievers**: Supports retrievers for services like [Amazon Kendra](https://aws.amazon.com/kendra/) and [KnowledgeBases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/), enabling efficient retrieval of relevant information in your RAG applications.
- **Graphs**: Provides components for working with [AWS Neptune](https://aws.amazon.com/neptune/) graphs within LangChain.
- **Agents**: Includes Runnables to support [Amazon Bedrock Agents](https://aws.amazon.com/bedrock/agents/), allowing you to leverage Bedrock Agents within LangChain and LangGraph.
- **Tools**: Includes tools and toolkits to enable use of [Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/)'s built-in tools with LangChain and LangGraph agents.
### LangGraph
- **Checkpointers**: Provides custom checkpointing solutions for LangGraph agents using several AWS services, including [Bedrock AgentCore Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html), [Bedrock Session Management](https://docs.aws.amazon.com/bedrock/latest/userguide/sessions.html), [DynamoDB](https://aws.amazon.com/dynamodb/), and [ElastiCache Valkey](https://aws.amazon.com/elasticache/).
- **Memory Stores** - Provides memory store solutions for saving, processing, and retrieving intelligent long term memories using services like [Bedrock AgentCore Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html) and [ElastiCache Valkey](https://aws.amazon.com/elasticache/).
### Deep Agents
- **Sandboxes**: Provides an [Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/) Code Interpreter sandbox backend for [Deep Agents](https://github.com/langchain-ai/deepagents), enabling secure code execution in isolated MicroVM environments.
...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.
## Installation
You can install the `langchain-aws` package from PyPI.
```bash
pip install langchain-aws
```
The `langgraph-checkpoint-aws` package can also be installed from PyPI.
```bash
pip install langgraph-checkpoint-aws
```
The `langchain-agentcore-codeinterpreter` package can also be installed from PyPI.
```bash
pip install langchain-agentcore-codeinterpreter
```
## Usage
### `langchain-aws`
Here's a simple example of how to use the `langchain-aws` package.
```python
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
responsExcerpt of 6,056 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f8eff95ed8ef39aa, llm:Repository topics: aws, generative-ai, langchain, langchain-python; description: 'Build LangChain Applications on AWS' — integration of LangChain (agent/chain/orchestration) with AWS services for building generative AI apps.
matched fp:f8eff95ed8ef39aa, llm:Repository topics: aws, generative-ai, langchain, langchain-python; description: 'Build LangChain Applications on AWS' — integration of LangChain (agent/chain/orchestration) with AWS services for building generative AI apps.
matched fp:f8eff95ed8ef39aa, llm:Repository topics: aws, generative-ai, langchain, langchain-python; description: 'Build LangChain Applications on AWS' — integration of LangChain (agent/chain/orchestration) with AWS services for building generative AI apps.