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Context

Context engineering is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.

Context includes any data outside the message list that can shape behavior. This can be:

  • Information passed at runtime, like a user_id or API credentials.
  • Internal state updated during a multi-step reasoning process.
  • Persistent memory or facts from previous interactions.

LangGraph provides three primary ways to supply context:

Type Description Mutable? Lifetime
Config data passed at the start of a run ❌ per run
Short-term memory (State) dynamic data that can change during execution ✅ per run or conversation
Long-term memory (Store) data that can be shared between conversations ✅ across conversations

Provide runtime context

Config (static context)

Config is for immutable data like user metadata or API keys. Use when you have values that don't change mid-run.

Specify configuration using a key called "configurable" which is reserved for this purpose:

graph.invoke( # (1)!
    {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
    config={"configurable": {"user_id": "user_123"}} # (3)!
)
  1. This is the invocation of the agent or graph. The invoke method runs the underlying graph with the provided input.
  2. This example uses messages as an input, which is common, but your application may use different input structures.
  3. This is where you pass the configuration data. The config parameter allows you to provide additional context that the agent can use during its execution.
from langchain_core.messages import AnyMessage
from langchain_core.runnables import RunnableConfig
from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.prebuilt import create_react_agent

def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
    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"]

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
    prompt=prompt
)

agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    config={"configurable": {"user_name": "John Smith"}}
)

from langchain_core.runnables import RunnableConfig

def node(state: State, config: RunnableConfig):
    user_name = config["configurable"].get("user_name")
    ...

from langchain_core.runnables import RunnableConfig

@tool
def get_user_info(config: RunnableConfig) -> str:
    """Retrieve user information based on user ID."""
    user_id = config["configurable"].get("user_id")
    return "User is John Smith" if user_id == "user_123" else "Unknown user"

See the tool calling guide for details.

Short-term memory (mutable context)

State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.

Example shows how to incorporate state into an agent prompt.

State can also be accessed by the agent's tools, which can read or update the state as needed. See tool calling guide for details.

from langchain_core.messages import AnyMessage
from langchain_core.runnables import RunnableConfig
from langgraph.prebuilt import create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState

class CustomState(AgentState): # (1)!
    user_name: str

def prompt(
    state: CustomState
) -> list[AnyMessage]:
    user_name = state["user_name"]
    system_msg = f"You are a helpful assistant. User's name is {user_name}"
    return [{"role": "system", "content": system_msg}] + state["messages"]

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[...],
    state_schema=CustomState, # (2)!
    prompt=prompt
)

agent.invoke({
    "messages": "hi!",
    "user_name": "John Smith"
})
  1. Define a custom state schema that extends AgentState or MessagesState.
  2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
from typing_extensions import TypedDict
from langchain_core.messages import AnyMessage
from langgraph.graph import StateGraph

class CustomState(TypedDict): # (1)!
    messages: list[AnyMessage]
    extra_field: int

def node(state: CustomState): # (2)!
    messages = state["messages"]
    ...
    return { # (3)!
        "extra_field": state["extra_field"] + 1
    }

builder = StateGraph(State)
builder.add_node(node)
builder.set_entry_point("node")
graph = builder.compile()
  1. Define a custom state
  2. Access the state in any node or tool
  3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.

Turning on memory

Please see the memory guide for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.

Long-term memory (cross-conversation context)

For context that spans across conversations or sessions, LangGraph allows access to long-term memory via a store. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions).

For more information, see the Memory guide.