Reorganize langgraph-checkpoint-aws README

libs/langgraph-checkpoint-aws/README.md

@@ -1,8 +1,8 @@
 # LangGraph Checkpoint AWS
-A custom LangChain checkpointer implementation that uses Bedrock Session Management Service to enable stateful and resumable LangGraph agents through efficient state persistence and retrieval.
+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 Session Management Service. It enables:
+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
@@ -22,12 +22,195 @@ poetry add langgraph-checkpoint-aws
 ## Requirements
 ```text
 Python >=3.9
-langgraph-checkpoint >=2.0.0
 langgraph >=0.2.55
-boto3 >=1.37.3
+boto3 >=1.39.7
 ```
 
-## Usage
+## 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 LLM
+llm = 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=llm,
+    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-LLM 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 LLM 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 {"llm_input_messages": messages}
+
+# Initialize LLM
+llm = 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=llm,
+    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
@@ -53,7 +236,6 @@ config = {"configurable": {"thread_id": session_id}}
 graph.invoke(1, config)
 ```
 
-
 You can also invoke the graph asynchronously:
 
 ```python
@@ -81,9 +263,9 @@ config = {"configurable": {"thread_id": session_id}}
 await graph.ainvoke(1, config)
 ```
 
-## Configuration Options
+### Configuration Options
 
-`BedrockSessionSaver` and `AsyncBedrockSessionSaver`  accepts the following parameters:
+`BedrockSessionSaver` and `AsyncBedrockSessionSaver` accepts the following parameters:
 
 ```python
 def __init__(
@@ -108,55 +290,8 @@ def __init__(
 - `aws_session_token`: AWS session token for temporary credentials
 - `endpoint_url`: Custom endpoint URL for the Bedrock service
 - `config`: Botocore configuration object
-## 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:
+### Additional AWS permissions for Session Saver:
 
 ```json
 {