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Overview

LangChain’s createAgent 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
The runtime context is how you thread data through your agent. Rather than storing things in global state, you can attach values — like a database connection, user session, or configuration — to the context and access them inside tools and middleware. This keeps things stateless, testable, and reusable.
You can access the runtime information within tools and middleware.

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Access

When creating an agent with createAgent, you can specify a contextSchema 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: 
import * as z from "zod";
import { createAgent } from "langchain";

const contextSchema = z.object({ 
  userName: z.string(), 
}); 

const agent = createAgent({
  model: "gpt-4o",
  tools: [
    /* ... */
  ],
  contextSchema, 
});

const result = await agent.invoke(
  { messages: [{ role: "user", content: "What's my name?" }] },
  { context: { userName: "John Smith" } } 
);

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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 runtime parameter to access the Runtime object inside a tool. 
import * as z from "zod";
import { tool } from "langchain";
import { type ToolRuntime } from "@langchain/core/tools"; 

const contextSchema = z.object({
  userName: z.string(),
});

const fetchUserEmailPreferences = tool(
  async (_, runtime: ToolRuntime<any, typeof contextSchema>) => { 
    const userName = runtime.context?.userName; 
    if (!userName) {
      throw new Error("userName is required");
    }

    let preferences = "The user prefers you to write a brief and polite email.";
    if (runtime.store) { 
      const memory = await runtime.store?.get(["users"], userName); 
      if (memory) {
        preferences = memory.value.preferences;
      }
    }
    return preferences;
  },
  {
    name: "fetch_user_email_preferences",
    description: "Fetch the user's email preferences.",
    schema: z.object({}),
  }
);

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Inside middleware

You can access runtime information in middleware to create dynamic prompts, modify messages, or control agent behavior based on user context. Use the runtime parameter to access the Runtime object inside middleware. 
import * as z from "zod";
import { createAgent, createMiddleware, SystemMessage } from "langchain";
import { type Runtime } from "@langchain/langgraph"; 

const contextSchema = z.object({
  userName: z.string(),
});

// Dynamic prompt middleware
const dynamicPromptMiddleware = createMiddleware({
  name: "DynamicPrompt",
  contextSchema
  beforeModel: (state, runtime: Runtime<z.infer<typeof contextSchema>>) => {  
    const userName = runtime.context?.userName;  
    if (!userName) {
      throw new Error("userName is required");
    }

    const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
    return {
      messages: [new SystemMessage(systemMsg), ...state.messages]
    };
  }
});

// Logging middleware
const loggingMiddleware = createMiddleware({
  name: "Logging",
  contextSchema,
  beforeModel: (state, runtime) => {  
    console.log(`Processing request for user: ${runtime.context?.userName}`);  
    return;
  },
  afterModel: (state, runtime) => {  
    console.log(`Completed request for user: ${runtime.context?.userName}`);  
    return;
  }
});

const agent = createAgent({
  model: "gpt-4o",
  tools: [
    /* ... */
  ],
  middleware: [dynamicPromptMiddleware, loggingMiddleware],  
  contextSchema,
});

const result = await agent.invoke(
  { messages: [{ role: "user", content: "What's my name?" }] },
  { context: { userName: "John Smith" } }
);
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