diff --git a/docs/docs/agents/agents.md b/docs/docs/agents/agents.md
index acf2aac92..73935c650 100644
--- a/docs/docs/agents/agents.md
+++ b/docs/docs/agents/agents.md
@@ -112,7 +112,7 @@ from langgraph.prebuilt import create_react_agent
 
 # highlight-next-line
 def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
-    user_name = config.get("configurable", {}).get("user_name")
+    user_name = config["configurable"].get("user_name")
     system_msg = f"You are a helpful assistant. Address the user as {user_name}."
     return [{"role": "system", "content": system_msg}] + state["messages"]
 
diff --git a/docs/docs/agents/context.md b/docs/docs/agents/context.md
index 2415854c0..cd74ab297 100644
--- a/docs/docs/agents/context.md
+++ b/docs/docs/agents/context.md
@@ -107,7 +107,7 @@ Common use cases:
         config: RunnableConfig,
     ) -> list[AnyMessage]:
         # highlight-next-line
-        user_name = config.get("configurable", {}).get("user_name")
+        user_name = config["configurable"].get("user_name")
         system_msg = f"You are a helpful assistant. User's name is {user_name}"
         return [{"role": "system", "content": system_msg}] + state["messages"]
 
@@ -162,7 +162,7 @@ Common use cases:
     })
     ```
 
-## Tools
+## Accessing Context in Tools
 
 Tools can access context through special parameter **annotations**.
 
@@ -183,7 +183,7 @@ Tools can access context through special parameter **annotations**.
     ) -> str:
         """Look up user info."""
         # highlight-next-line
-        user_id = config.get("configurable", {}).get("user_id")
+        user_id = config["configurable"].get("user_id")
         return "User is John Smith" if user_id == "user_123" else "Unknown user"
 
     agent = create_react_agent(
@@ -231,66 +231,6 @@ Tools can access context through special parameter **annotations**.
     })
     ```
 
+### Update Context from Tools
 
-## Update context from tools
-
-Tools can modify the agent's state during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
-
-```python
-from typing import Annotated
-from langchain_core.tools import InjectedToolCallId
-from langchain_core.messages import ToolMessage
-from langgraph.prebuilt import InjectedState
-from langgraph.types import Command
-
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-def get_user_info(
-    # highlight-next-line
-    tool_call_id: Annotated[str, InjectedToolCallId],
-    # highlight-next-line
-    config: RunnableConfig
-) -> Command:
-    """Look up user info."""
-    # highlight-next-line
-    user_id = config.get("configurable", {}).get("user_id")
-    name = "John Smith" if user_id == "user_123" else "Unknown user"
-    return Command(update={
-        # highlight-next-line
-        "user_name": name,
-        # update the message history
-        # highlight-next-line
-        "messages": [
-            ToolMessage(
-                "Successfully looked up user information",
-                # highlight-next-line
-                tool_call_id=tool_call_id
-            )
-        ]
-    })
-
-def greet(
-    # highlight-next-line
-    state: Annotated[CustomState, InjectedState]
-) -> str:
-    """Use this to greet the user once you found their info."""
-    user_name = state["user_name"]
-    return f"Hello {user_name}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info, greet],
-    # highlight-next-line
-    state_schema=CustomState
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "greet the user"}]},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
\ No newline at end of file
+Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](./memory.md#read-short-term) guide for more information.
\ No newline at end of file
diff --git a/docs/docs/agents/memory.md b/docs/docs/agents/memory.md
index 81f3801ec..9b9b33144 100644
--- a/docs/docs/agents/memory.md
+++ b/docs/docs/agents/memory.md
@@ -149,6 +149,104 @@ agent = create_react_agent(
 
 To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)
 
+### Read in tools { #read-short-term }
+
+LangGraph allows agent to access its short-term memory (state) inside the tools.
+
+```python
+from typing import Annotated
+from langgraph.prebuilt import InjectedState, create_react_agent
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_id: str
+
+def get_user_info(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Look up user info."""
+    # highlight-next-line
+    user_id = state["user_id"]
+    return "User is John Smith" if user_id == "user_123" else "Unknown user"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info],
+    # highlight-next-line
+    state_schema=CustomState,
+)
+
+agent.invoke({
+    "messages": "look up user information",
+    # highlight-next-line
+    "user_id": "user_123"
+})
+```
+
+See the [Context](./context.md#__tabbed_2_2) guide for more information.
+
+### Write from tools { #write-short-term }
+
+To modify the agent's short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
+
+```python
+from typing import Annotated
+from langchain_core.tools import InjectedToolCallId
+from langchain_core.runnables import RunnableConfig
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.types import Command
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_name: str
+
+def update_user_info(
+    tool_call_id: Annotated[str, InjectedToolCallId],
+    config: RunnableConfig
+) -> Command:
+    """Look up and update user info."""
+    user_id = config["configurable"].get("user_id")
+    name = "John Smith" if user_id == "user_123" else "Unknown user"
+    # highlight-next-line
+    return Command(update={
+        # highlight-next-line
+        "user_name": name,
+        # update the message history
+        "messages": [
+            ToolMessage(
+                "Successfully looked up user information",
+                tool_call_id=tool_call_id
+            )
+        ]
+    })
+
+def greet(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Use this to greet the user once you found their info."""
+    user_name = state["user_name"]
+    return f"Hello {user_name}!"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info, greet],
+    # highlight-next-line
+    state_schema=CustomState
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "greet the user"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
+
 ## Long-term memory
 
 Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
@@ -158,9 +256,10 @@ To use long-term memory, you need to:
 1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
 2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
 
-### Reading
+### Read { #read-long-term }
 
 ```python title="A tool the agent can use to look up user information"
+from langchain_core.runnables import RunnableConfig
 from langgraph.config import get_store
 from langgraph.prebuilt import create_react_agent
 from langgraph.store.memory import InMemoryStore
@@ -183,7 +282,7 @@ def get_user_info(config: RunnableConfig) -> str:
     # Same as that provided to `create_react_agent`
     # highlight-next-line
     store = get_store() # (6)!
-    user_id = config.get("configurable", {}).get("user_id")
+    user_id = config["configurable"].get("user_id")
     # highlight-next-line
     user_info = store.get(("users",), user_id) # (7)!
     return str(user_info.value) if user_info else "Unknown user"
@@ -212,7 +311,7 @@ agent.invoke(
 7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
 8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.
 
-### Writing
+### Write { #write-long-term }
 
 ```python title="Example of a tool that updates user information"
 from typing_extensions import TypedDict
@@ -231,7 +330,7 @@ def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
     # Same as that provided to `create_react_agent`
     # highlight-next-line
     store = get_store() # (4)!
-    user_id = config.get("configurable", {}).get("user_id")
+    user_id = config["configurable"].get("user_id")
     # highlight-next-line
     store.put(("users",), user_id, user_info) # (5)!
     return "Successfully saved user info."
diff --git a/docs/docs/agents/tools.md b/docs/docs/agents/tools.md
index 623e6396a..71279f5cb 100644
--- a/docs/docs/agents/tools.md
+++ b/docs/docs/agents/tools.md
@@ -271,6 +271,13 @@ By default, the agent will catch all exceptions raised during tool calls and wil
 
 See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
 
+## Working with memory
+
+LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
+
+* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
+* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
+
 ## Prebuilt tools
 
 LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.