Context overview

Context engineering is the practice of building dynamic systems that provide the right information and tools, in the right format, so that an AI application can accomplish a task. Context can be characterized along two key dimensions:

By mutability:
- Static context: Immutable data that doesn't change during execution (e.g., user metadata, database connections, tools)
- Dynamic context: Mutable data that evolves as the application runs (e.g., conversation history, intermediate results, tool call observations)
By lifetime:
- Runtime context: Data scoped to a single run or invocation
- Cross-conversation context: Data that persists across multiple conversations or sessions

Tip

Runtime context refers to local context: data and dependencies your code needs to run. It does not refer to:

The LLM context, which is the data passed into the LLM's prompt.
The "context window", which is the maximum number of tokens that can be passed to the LLM.

Runtime context is a form of dependency injection and can be used to optimize the LLM context. It lets you provide dependencies (like database connections, user IDs, or API clients) to your tools and nodes at runtime rather than hardcoding them. For example, you can use user metadata in the runtime context to fetch user preferences and feed them into the context window.

LangGraph provides three ways to manage context, which combines the mutability and lifetime dimensions:

Context type	Description	Mutability	Lifetime	Access method
Static runtime context	User metadata, tools, db connections passed at startup	Static	Single run	`context` argument to `invoke`/`stream`
Dynamic runtime context (state)	Mutable data that evolves during a single run	Dynamic	Single run	LangGraph state object
Dynamic cross-conversation context (store)	Persistent data shared across conversations	Dynamic	Cross-conversation	LangGraph store

Static runtime context

Static runtime context represents immutable data like user metadata, tools, and database connections that are passed to an application at the start of a run via the context argument to invoke/stream. This data does not change during execution.

@dataclass
class ContextSchema:
    user_name: str

graph.invoke(
    {"messages": [{"role": "user", "content": "hi!"}]},
    context={"user_name": "John Smith"}  # [!code highlight]
)

Agent prompt

Workflow node

In a tool

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest


@dataclass
class ContextSchema:
    user_name: str

@dynamic_prompt  # [!code highlight]
def personalized_prompt(request: ModelRequest) -> str:  # [!code highlight]
    user_name = request.runtime.context.user_name
    return f"You are a helpful assistant. Address the user as {user_name}."

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[get_weather],
    middleware=[personalized_prompt],
    context_schema=ContextSchema
)

agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    context=ContextSchema(user_name="John Smith")  # [!code highlight]
)

See Agents for details.

from langgraph.runtime import Runtime

def node(state: State, runtime: Runtime[ContextSchema]):  # [!code highlight]
    user_name = runtime.context.user_name
    ...

See the Graph API for details.

from langchain.tools import tool, ToolRuntime

@tool
def get_user_email(runtime: ToolRuntime[ContextSchema]) -> str:
    """Retrieve user information based on user ID."""
    # simulate fetching user info from a database
    email = get_user_email_from_db(runtime.context.user_name)  # [!code highlight]
    return email

See the tool calling guide for details.

Tip

The Runtime object can be used to access static context and other utilities like the active store and stream writer. See the Runtime documentation for details.

Dynamic runtime context

Dynamic runtime context represents mutable data that can evolve during a single run and is managed through the LangGraph state object. This includes conversation history, intermediate results, and values derived from tools or LLM outputs. In LangGraph, the state object acts as short-term memory during a run.

In an agent

In a workflow

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.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest
from langchain.agents import AgentState


class CustomState(AgentState):  # [!code highlight]
    user_name: str

@dynamic_prompt  # [!code highlight]
def personalized_prompt(request: ModelRequest) -> str:  # [!code highlight]
    user_name = request.state.get("user_name", "User")
    return f"You are a helpful assistant. User's name is {user_name}"

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[...],
    state_schema=CustomState,  # [!code highlight]
    middleware=[personalized_prompt],  # [!code highlight]
)

agent.invoke({
    "messages": "hi!",
    "user_name": "John Smith"
})

from typing_extensions import TypedDict
from langchain.messages import AnyMessage
from langgraph.graph import StateGraph

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

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

builder = StateGraph(State)
builder.add_node(node)
builder.set_entry_point("node")
graph = builder.compile()

Tip

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.

Dynamic cross-conversation context

Dynamic cross-conversation context represents persistent, mutable data that spans across multiple conversations or sessions and is managed through the LangGraph store. This includes user profiles, preferences, and historical interactions. The LangGraph store acts as long-term memory across multiple runs. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions).

Static runtime context

Dynamic runtime context

Dynamic cross-conversation context

Learn more