# and this is also supported
{"messages": [{"type": "human", "content": "message"}]}
```

Since the state updates are always deserialized into LangChain `Messages` when using `add_messages`, you should use dot notation to access message attributes, like `state["messages"][-1].content`. Below is an example of a graph that uses `add_messages` as its reducer function.

```python
from langchain_core.messages import AnyMessage
from langgraph.graph.message import add_messages
from typing import Annotated
from typing_extensions import TypedDict

class GraphState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]
```





#### MessagesState

Since having a list of messages in your state is so common, there exists a prebuilt state called `MessagesState` which makes it easy to use messages. `MessagesState` is defined with a single `messages` key which is a list of `AnyMessage` objects and uses the `add_messages` reducer. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like:

```python
from langgraph.graph import MessagesState

class State(MessagesState):
    documents: list[str]
```



## Nodes

In LangGraph, nodes are Python functions (either synchronous or asynchronous) that accept the following arguments:

1. `state`: The [state](#state) of the graph
2. `config`: A `RunnableConfig` object that contains configuration information like `thread_id` and tracing information like `tags`
3. `runtime`: A `Runtime` object that contains [runtime `context`](#runtime-context) and other information like `store` and `stream_writer`

Similar to `NetworkX`, you add these nodes to a graph using the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.StateGraph.add_node) method:

```python
from dataclasses import dataclass
from typing_extensions import TypedDict

from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph
from langgraph.runtime import Runtime

class State(TypedDict):
    input: str
    results: str

@dataclass
class Context:
    user_id: str

builder = StateGraph(State)

def plain_node(state: State):
    return state

def node_with_runtime(state: State, runtime: Runtime[Context]):
    print("In node: ", runtime.context.user_id)
    return {"results": f"Hello, {state['input']}!"}

def node_with_config(state: State, config: RunnableConfig):
    print("In node with thread_id: ", config["configurable"]["thread_id"])
    return {"results": f"Hello, {state['input']}!"}


builder.add_node("plain_node", plain_node)
builder.add_node("node_with_runtime", node_with_runtime)
builder.add_node("node_with_config", node_with_config)
...
```





Behind the scenes, functions are converted to [RunnableLambda](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html)s, which add batch and async support to your function, along with native tracing and debugging.

If you add a node to a graph without specifying a name, it will be given a default name equivalent to the function name.

```python
builder.add_node(my_node)