| Name | langgraph-checkpoint-aws JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | A LangChain checkpointer implementation that uses Bedrock Session Management Service to enable stateful and resumable LangGraph agents. |
| upload_time | 2025-10-21 21:24:54 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
aws
bedrock
langchain
langgraph
checkpointer
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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No coveralls.
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# LangGraph Checkpoint AWS
A custom LangChain checkpointer implementation that uses Bedrock AgentCore Memory to enable stateful and resumable LangGraph agents through efficient state persistence and retrieval.
## Overview
This package provides a custom checkpointing solution for LangGraph agents using AWS Bedrock AgentCore Memory Service. It enables:
1. Stateful conversations and interactions
2. Resumable agent sessions
3. Efficient state persistence and retrieval
4. Seamless integration with AWS Bedrock
## Installation
You can install the package using pip:
```bash
pip install langgraph-checkpoint-aws
```
## Requirements
```text
Python >=3.9
langgraph >=0.2.55
boto3 >=1.39.7
```
## Usage - Checkpointer
```python
# Import LangGraph and LangChain components
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent
# Import the AgentCoreMemory integrations
from langgraph_checkpoint_aws import AgentCoreMemorySaver
REGION = "us-west-2"
MEMORY_ID = "YOUR_MEMORY_ID"
MODEL_ID = "us.anthropic.claude-3-7-sonnet-20250219-v1:0"
# Initialize checkpointer for state persistence. No additional setup required.
# Sessions will be saved and persisted for actor_id/session_id combinations
checkpointer = AgentCoreMemorySaver(MEMORY_ID, region_name=REGION)
# Initialize chat model
model = init_chat_model(MODEL_ID, model_provider="bedrock_converse", region_name=REGION)
# Create a pre-built langgraph agent (configurations work for custom agents too)
graph = create_react_agent(
model=model,
tools=tools,
checkpointer=checkpointer, # AgentCoreMemorySaver we created above
)
# Specify config at runtime for ACTOR and SESSION
config = {
"configurable": {
"thread_id": "session-1", # REQUIRED: This maps to Bedrock AgentCore session_id under the hood
"actor_id": "react-agent-1", # REQUIRED: This maps to Bedrock AgentCore actor_id under the hood
}
}
# Invoke the agent
response = graph.invoke(
{"messages": [("human", "I like sushi with tuna. In general seafood is great.")]},
config=config
)
```
## Usage - Memory Store
```python
# Import LangGraph and LangChain components
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent
from langgraph_checkpoint_aws import (
AgentCoreMemoryStore
)
REGION = "us-west-2"
MEMORY_ID = "YOUR_MEMORY_ID"
MODEL_ID = "us.anthropic.claude-3-7-sonnet-20250219-v1:0"
# Initialize store for saving and searching over long term memories
# such as preferences and facts across sessions
store = AgentCoreMemoryStore(MEMORY_ID, region_name=REGION)
# Pre-model hook runs and saves messages of your choosing to AgentCore Memory
# for async processing and extraction
def pre_model_hook(state, config: RunnableConfig, *, store: BaseStore):
"""Hook that runs pre-model invocation to save the latest human message"""
actor_id = config["configurable"]["actor_id"]
thread_id = config["configurable"]["thread_id"]
# Saving the message to the actor and session combination that we get at runtime
namespace = (actor_id, thread_id)
messages = state.get("messages", [])
# Save the last human message we see before model invocation
for msg in reversed(messages):
if isinstance(msg, HumanMessage):
store.put(namespace, str(uuid.uuid4()), {"message": msg})
break
# OPTIONAL: Retrieve user preferences based on the last message and append to state
# user_preferences_namespace = ("preferences", actor_id)
# preferences = store.search(user_preferences_namespace, query=msg.content, limit=5)
# # Add to input messages as needed
return {"model_input_messages": messages}
# Initialize chat model
model = init_chat_model(MODEL_ID, model_provider="bedrock_converse", region_name=REGION)
# Create a pre-built langgraph agent (configurations work for custom agents too)
graph = create_react_agent(
model=model,
tools=[],
pre_model_hook=pre_model_hook,
)
# Specify config at runtime for ACTOR and SESSION
config = {
"configurable": {
"thread_id": "session-1", # REQUIRED: This maps to Bedrock AgentCore session_id under the hood
"actor_id": "react-agent-1", # REQUIRED: This maps to Bedrock AgentCore actor_id under the hood
}
}
# Invoke the agent
response = graph.invoke(
{"messages": [("human", "I like sushi with tuna. In general seafood is great.")]},
config=config
)
```
## Development
Setting Up Development Environment
* Clone the repository:
```bash
git clone <repository-url>
cd libs/aws/langgraph-checkpoint-aws
```
* Install development dependencies:
```bash
make install_all
```
* Or install specific components:
```bash
make install_dev # Basic development tools
make install_test # Testing tools
make install_lint # Linting tools
make install_typing # Type checking tools
make install_codespell # Spell checking tools
```
## Running Tests
```bash
make tests # Run all tests
make test_watch # Run tests in watch mode
```
## Code Quality
```bash
make lint # Run linter
make format # Format code
make spell_check # Check spelling
```
## Clean Up
```bash
make clean # Remove all generated files
```
## AWS Configuration
Ensure you have AWS credentials configured using one of these methods:
1. Environment variables
2. AWS credentials file (~/.aws/credentials)
3. IAM roles
4. Direct credential injection via constructor parameters
## Required AWS permissions
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Statement1",
"Effect": "Allow",
"Action": [
"bedrock-agentcore:CreateEvent",
"bedrock-agentcore:ListEvents",
"bedrock-agentcore:GetEvent",
],
"Resource": [
"*"
]
}
]
}
```
## Bedrock Session Saver (Alternative Implementation)
This package also provides an alternative checkpointing solution using AWS Bedrock Session Management Service:
### Usage
```python
from langgraph.graph import StateGraph
from langgraph_checkpoint_aws.saver import BedrockSessionSaver
# Initialize the saver
session_saver = BedrockSessionSaver(
region_name="us-west-2", # Your AWS region
credentials_profile_name="default", # Optional: AWS credentials profile
)
# Create a session
session_id = session_saver.session_client.create_session().session_id
# Use with LangGraph
builder = StateGraph(int)
builder.add_node("add_one", lambda x: x + 1)
builder.set_entry_point("add_one")
builder.set_finish_point("add_one")
graph = builder.compile(checkpointer=session_saver)
config = {"configurable": {"thread_id": session_id}}
graph.invoke(1, config)
```
You can also invoke the graph asynchronously:
```python
from langgraph.graph import StateGraph
from langgraph_checkpoint_aws.async_saver import AsyncBedrockSessionSaver
# Initialize the saver
session_saver = AsyncBedrockSessionSaver(
region_name="us-west-2", # Your AWS region
credentials_profile_name="default", # Optional: AWS credentials profile
)
# Create a session
session_create_response = await session_saver.session_client.create_session()
session_id = session_create_response.session_id
# Use with LangGraph
builder = StateGraph(int)
builder.add_node("add_one", lambda x: x + 1)
builder.set_entry_point("add_one")
builder.set_finish_point("add_one")
graph = builder.compile(checkpointer=session_saver)
config = {"configurable": {"thread_id": session_id}}
await graph.ainvoke(1, config)
```
### Configuration Options
`BedrockSessionSaver` and `AsyncBedrockSessionSaver` accepts the following parameters:
```python
def __init__(
client: Optional[Any] = None,
session: Optional[boto3.Session] = None,
region_name: Optional[str] = None,
credentials_profile_name: Optional[str] = None,
aws_access_key_id: Optional[SecretStr] = None,
aws_secret_access_key: Optional[SecretStr] = None,
aws_session_token: Optional[SecretStr] = None,
endpoint_url: Optional[str] = None,
config: Optional[Config] = None,
)
```
* `client`: boto3 Bedrock runtime client (e.g. boto3.client("bedrock-agent-runtime"))
* `session`: boto3.Session for custom credentials
* `region_name`: AWS region where Bedrock is available
* `credentials_profile_name`: Name of AWS credentials profile to use
* `aws_access_key_id`: AWS access key ID for authentication
* `aws_secret_access_key`: AWS secret access key for authentication
* `aws_session_token`: AWS session token for temporary credentials
* `endpoint_url`: Custom endpoint URL for the Bedrock service
* `config`: Botocore configuration object
### Additional AWS permissions for Session Saver
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Statement1",
"Effect": "Allow",
"Action": [
"bedrock:CreateSession",
"bedrock:GetSession",
"bedrock:UpdateSession",
"bedrock:DeleteSession",
"bedrock:EndSession",
"bedrock:ListSessions",
"bedrock:CreateInvocation",
"bedrock:ListInvocations",
"bedrock:PutInvocationStep",
"bedrock:GetInvocationStep",
"bedrock:ListInvocationSteps"
],
"Resource": [
"*"
]
},
{
"Effect": "Allow",
"Action": [
"kms:Decrypt",
"kms:Encrypt",
"kms:GenerateDataKey",
"kms:DescribeKey"
],
"Resource": "arn:aws:kms:{region}:{account}:key/{kms-key-id}"
},
{
"Effect": "Allow",
"Action": [
"bedrock:TagResource",
"bedrock:UntagResource",
"bedrock:ListTagsForResource"
],
"Resource": "arn:aws:bedrock:{region}:{account}:session/*"
}
]
}
```
## Security Considerations
* Never commit AWS credentials
* Use environment variables or AWS IAM roles for authentication
* Follow AWS security best practices
* Use IAM roles and temporary credentials when possible
* Implement proper access controls for session management
## Contributing
* Fork the repository
* Create a feature branch
* Make your changes
* Run tests and linting
* Submit a pull request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
* LangChain team for the base LangGraph framework
* AWS Bedrock team for the session management service
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"description": "# LangGraph Checkpoint AWS\n\nA custom LangChain checkpointer implementation that uses Bedrock AgentCore Memory to enable stateful and resumable LangGraph agents through efficient state persistence and retrieval.\n\n## Overview\n\nThis package provides a custom checkpointing solution for LangGraph agents using AWS Bedrock AgentCore Memory Service. It enables:\n\n1. Stateful conversations and interactions\n2. Resumable agent sessions\n3. Efficient state persistence and retrieval\n4. Seamless integration with AWS Bedrock\n\n## Installation\n\nYou can install the package using pip:\n\n```bash\npip install langgraph-checkpoint-aws\n```\n\n## Requirements\n\n```text\nPython >=3.9\nlanggraph >=0.2.55\nboto3 >=1.39.7\n```\n\n## Usage - Checkpointer\n\n```python\n# Import LangGraph and LangChain components\nfrom langchain.chat_models import init_chat_model\nfrom langgraph.prebuilt import create_react_agent\n\n# Import the AgentCoreMemory integrations\nfrom langgraph_checkpoint_aws import AgentCoreMemorySaver\n\nREGION = \"us-west-2\"\nMEMORY_ID = \"YOUR_MEMORY_ID\"\nMODEL_ID = \"us.anthropic.claude-3-7-sonnet-20250219-v1:0\"\n\n# Initialize checkpointer for state persistence. No additional setup required.\n# Sessions will be saved and persisted for actor_id/session_id combinations\ncheckpointer = AgentCoreMemorySaver(MEMORY_ID, region_name=REGION)\n\n# Initialize chat model\nmodel = init_chat_model(MODEL_ID, model_provider=\"bedrock_converse\", region_name=REGION)\n\n# Create a pre-built langgraph agent (configurations work for custom agents too)\ngraph = create_react_agent(\n model=model,\n tools=tools,\n checkpointer=checkpointer, # AgentCoreMemorySaver we created above\n)\n\n# Specify config at runtime for ACTOR and SESSION\nconfig = {\n \"configurable\": {\n \"thread_id\": \"session-1\", # REQUIRED: This maps to Bedrock AgentCore session_id under the hood\n \"actor_id\": \"react-agent-1\", # REQUIRED: This maps to Bedrock AgentCore actor_id under the hood\n }\n}\n\n# Invoke the agent\nresponse = graph.invoke(\n {\"messages\": [(\"human\", \"I like sushi with tuna. In general seafood is great.\")]},\n config=config\n)\n```\n\n## Usage - Memory Store\n\n```python\n# Import LangGraph and LangChain components\nfrom langchain.chat_models import init_chat_model\nfrom langgraph.prebuilt import create_react_agent\n\nfrom langgraph_checkpoint_aws import (\n AgentCoreMemoryStore\n)\n\nREGION = \"us-west-2\"\nMEMORY_ID = \"YOUR_MEMORY_ID\"\nMODEL_ID = \"us.anthropic.claude-3-7-sonnet-20250219-v1:0\"\n\n# Initialize store for saving and searching over long term memories\n# such as preferences and facts across sessions\nstore = AgentCoreMemoryStore(MEMORY_ID, region_name=REGION)\n\n# Pre-model hook runs and saves messages of your choosing to AgentCore Memory\n# for async processing and extraction\ndef pre_model_hook(state, config: RunnableConfig, *, store: BaseStore):\n \"\"\"Hook that runs pre-model invocation to save the latest human message\"\"\"\n actor_id = config[\"configurable\"][\"actor_id\"]\n thread_id = config[\"configurable\"][\"thread_id\"]\n \n # Saving the message to the actor and session combination that we get at runtime\n namespace = (actor_id, thread_id)\n \n messages = state.get(\"messages\", [])\n # Save the last human message we see before model invocation\n for msg in reversed(messages):\n if isinstance(msg, HumanMessage):\n store.put(namespace, str(uuid.uuid4()), {\"message\": msg})\n break\n \n # OPTIONAL: Retrieve user preferences based on the last message and append to state\n # user_preferences_namespace = (\"preferences\", actor_id)\n # preferences = store.search(user_preferences_namespace, query=msg.content, limit=5)\n # # Add to input messages as needed\n \n return {\"model_input_messages\": messages}\n\n# Initialize chat model\nmodel = init_chat_model(MODEL_ID, model_provider=\"bedrock_converse\", region_name=REGION)\n\n# Create a pre-built langgraph agent (configurations work for custom agents too)\ngraph = create_react_agent(\n model=model,\n tools=[],\n pre_model_hook=pre_model_hook,\n)\n\n# Specify config at runtime for ACTOR and SESSION\nconfig = {\n \"configurable\": {\n \"thread_id\": \"session-1\", # REQUIRED: This maps to Bedrock AgentCore session_id under the hood\n \"actor_id\": \"react-agent-1\", # REQUIRED: This maps to Bedrock AgentCore actor_id under the hood\n }\n}\n\n# Invoke the agent\nresponse = graph.invoke(\n {\"messages\": [(\"human\", \"I like sushi with tuna. In general seafood is great.\")]},\n config=config\n)\n```\n\n## Development\n\nSetting Up Development Environment\n\n* Clone the repository:\n\n```bash\ngit clone <repository-url>\ncd libs/aws/langgraph-checkpoint-aws\n```\n\n* Install development dependencies:\n\n```bash\nmake install_all\n```\n\n* Or install specific components:\n\n```bash\nmake install_dev # Basic development tools\nmake install_test # Testing tools\nmake install_lint # Linting tools\nmake install_typing # Type checking tools\nmake install_codespell # Spell checking tools\n```\n\n## Running Tests\n\n```bash\nmake tests # Run all tests\nmake test_watch # Run tests in watch mode\n\n```\n\n## Code Quality\n\n```bash\nmake lint # Run linter\nmake format # Format code\nmake spell_check # Check spelling\n```\n\n## Clean Up\n\n```bash\nmake clean # Remove all generated files\n```\n\n## AWS Configuration\n\nEnsure you have AWS credentials configured using one of these methods:\n\n1. Environment variables\n2. AWS credentials file (~/.aws/credentials)\n3. IAM roles\n4. Direct credential injection via constructor parameters\n\n## Required AWS permissions\n\n```json\n{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Sid\": \"Statement1\",\n \"Effect\": \"Allow\",\n \"Action\": [\n \"bedrock-agentcore:CreateEvent\",\n \"bedrock-agentcore:ListEvents\",\n \"bedrock-agentcore:GetEvent\",\n ],\n \"Resource\": [\n \"*\"\n ]\n }\n ]\n}\n```\n\n## Bedrock Session Saver (Alternative Implementation)\n\nThis package also provides an alternative checkpointing solution using AWS Bedrock Session Management Service:\n\n### Usage\n\n```python\nfrom langgraph.graph import StateGraph\nfrom langgraph_checkpoint_aws.saver import BedrockSessionSaver\n\n# Initialize the saver\nsession_saver = BedrockSessionSaver(\n region_name=\"us-west-2\", # Your AWS region\n credentials_profile_name=\"default\", # Optional: AWS credentials profile\n)\n\n# Create a session\nsession_id = session_saver.session_client.create_session().session_id\n\n# Use with LangGraph\nbuilder = StateGraph(int)\nbuilder.add_node(\"add_one\", lambda x: x + 1)\nbuilder.set_entry_point(\"add_one\")\nbuilder.set_finish_point(\"add_one\")\n\ngraph = builder.compile(checkpointer=session_saver)\nconfig = {\"configurable\": {\"thread_id\": session_id}}\ngraph.invoke(1, config)\n```\n\nYou can also invoke the graph asynchronously:\n\n```python\nfrom langgraph.graph import StateGraph\nfrom langgraph_checkpoint_aws.async_saver import AsyncBedrockSessionSaver\n\n# Initialize the saver\nsession_saver = AsyncBedrockSessionSaver(\n region_name=\"us-west-2\", # Your AWS region\n credentials_profile_name=\"default\", # Optional: AWS credentials profile\n)\n\n# Create a session\nsession_create_response = await session_saver.session_client.create_session()\nsession_id = session_create_response.session_id\n\n# Use with LangGraph\nbuilder = StateGraph(int)\nbuilder.add_node(\"add_one\", lambda x: x + 1)\nbuilder.set_entry_point(\"add_one\")\nbuilder.set_finish_point(\"add_one\")\n\ngraph = builder.compile(checkpointer=session_saver)\nconfig = {\"configurable\": {\"thread_id\": session_id}}\nawait graph.ainvoke(1, config)\n```\n\n### Configuration Options\n\n`BedrockSessionSaver` and `AsyncBedrockSessionSaver` accepts the following parameters:\n\n```python\ndef __init__(\n client: Optional[Any] = None,\n session: Optional[boto3.Session] = None,\n region_name: Optional[str] = None,\n credentials_profile_name: Optional[str] = None,\n aws_access_key_id: Optional[SecretStr] = None,\n aws_secret_access_key: Optional[SecretStr] = None,\n aws_session_token: Optional[SecretStr] = None,\n endpoint_url: Optional[str] = None,\n config: Optional[Config] = None,\n)\n```\n\n* `client`: boto3 Bedrock runtime client (e.g. boto3.client(\"bedrock-agent-runtime\"))\n* `session`: boto3.Session for custom credentials\n* `region_name`: AWS region where Bedrock is available\n* `credentials_profile_name`: Name of AWS credentials profile to use\n* `aws_access_key_id`: AWS access key ID for authentication\n* `aws_secret_access_key`: AWS secret access key for authentication\n* `aws_session_token`: AWS session token for temporary credentials\n* `endpoint_url`: Custom endpoint URL for the Bedrock service\n* `config`: Botocore configuration object\n\n### Additional AWS permissions for Session Saver\n\n```json\n{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Sid\": \"Statement1\",\n \"Effect\": \"Allow\",\n \"Action\": [\n \"bedrock:CreateSession\",\n \"bedrock:GetSession\",\n \"bedrock:UpdateSession\",\n \"bedrock:DeleteSession\",\n \"bedrock:EndSession\",\n \"bedrock:ListSessions\",\n \"bedrock:CreateInvocation\",\n \"bedrock:ListInvocations\",\n \"bedrock:PutInvocationStep\",\n \"bedrock:GetInvocationStep\",\n \"bedrock:ListInvocationSteps\"\n ],\n \"Resource\": [\n \"*\"\n ]\n },\n {\n \"Effect\": \"Allow\",\n \"Action\": [\n \"kms:Decrypt\",\n \"kms:Encrypt\",\n \"kms:GenerateDataKey\",\n \"kms:DescribeKey\"\n ],\n \"Resource\": \"arn:aws:kms:{region}:{account}:key/{kms-key-id}\"\n },\n {\n \"Effect\": \"Allow\",\n \"Action\": [\n \"bedrock:TagResource\",\n \"bedrock:UntagResource\",\n \"bedrock:ListTagsForResource\"\n ],\n \"Resource\": \"arn:aws:bedrock:{region}:{account}:session/*\"\n }\n ]\n}\n```\n\n## Security Considerations\n\n* Never commit AWS credentials\n\n* Use environment variables or AWS IAM roles for authentication\n* Follow AWS security best practices\n* Use IAM roles and temporary credentials when possible\n* Implement proper access controls for session management\n\n## Contributing\n\n* Fork the repository\n\n* Create a feature branch\n* Make your changes\n* Run tests and linting\n* Submit a pull request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n* LangChain team for the base LangGraph framework\n\n* AWS Bedrock team for the session management service\n",
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