Skip to main content

​
Overview

LangChain’s create_agent runs on LangGraph’s runtime under the hood. LangGraph exposes a Runtime object with the following information:
  1. Context: static information like user id, db connections, or other dependencies for an agent invocation
  2. Store: a BaseStore instance used for long-term memory
  3. Stream writer: an object used for streaming information via the "custom" stream mode
Runtime context provides dependency injection for your tools and middleware. Instead of hardcoding values or using global state, you can inject runtime dependencies (like database connections, user IDs, or configuration) when invoking your agent. This makes your tools more testable, reusable, and flexible.
You can access the runtime information within tools and middleware.

​
Access

When creating an agent with create_agent, you can specify a context_schema to define the structure of the context stored in the agent Runtime. When invoking the agent, pass the context argument with the relevant configuration for the run: 
from dataclasses import dataclass  from langchain.agents import create_agent   @dataclass class Context:  user_name: str  agent = create_agent(  model="gpt-5-nano",  tools=[...],  context_schema=Context  )  agent.invoke(  {"messages": [{"role": "user", "content": "What's my name?"}]},  context=Context(user_name="John Smith")  )

​
Inside tools

You can access the runtime information inside tools to:
  • Access the context
  • Read or write long-term memory
  • Write to the custom stream (ex, tool progress / updates)
Use the ToolRuntime parameter to access the Runtime object inside a tool. 
from dataclasses import dataclass from langchain.tools import tool, ToolRuntime   @dataclass class Context:  user_id: str  @tool def fetch_user_email_preferences(runtime: ToolRuntime[Context]) -> str:   """Fetch the user's email preferences from the store."""  user_id = runtime.context.user_id    preferences: str = "The user prefers you to write a brief and polite email."  if runtime.store:   if memory := runtime.store.get(("users",), user_id):   preferences = memory.value["preferences"]   return preferences

​
Inside middleware

You can access runtime information in middleware to create dynamic prompts, modify messages, or control agent behavior based on user context. Use request.runtime to access the Runtime object inside middleware decorators. The runtime object is available in the ModelRequest parameter passed to middleware functions. 
from dataclasses import dataclass  from langchain.messages import AnyMessage from langchain.agents import create_agent, AgentState from langchain.agents.middleware import dynamic_prompt, ModelRequest, before_model, after_model from langgraph.runtime import Runtime   @dataclass class Context:  user_name: str  # Dynamic prompts @dynamic_prompt def dynamic_system_prompt(request: ModelRequest) -> str:  user_name = request.runtime.context.user_name   system_prompt = f"You are a helpful assistant. Address the user as {user_name}."  return system_prompt  # Before model hook @before_model def log_before_model(state: AgentState, runtime: Runtime[Context]) -> dict | None:   print(f"Processing request for user: {runtime.context.user_name}")   return None  # After model hook @after_model def log_after_model(state: AgentState, runtime: Runtime[Context]) -> dict | None:   print(f"Completed request for user: {runtime.context.user_name}")   return None  agent = create_agent(  model="gpt-5-nano",  tools=[...],  middleware=[dynamic_system_prompt, log_before_model, log_after_model],   context_schema=Context )  agent.invoke(  {"messages": [{"role": "user", "content": "What's my name?"}]},  context=Context(user_name="John Smith") )
Edit the source of this page on GitHub.
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.