Runtime

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
4. Execution info: identity and retry information for the current execution (thread ID, run ID, attempt number)
5. Server info: server-specific metadata when running on LangGraph Server (assistant ID, graph ID, authenticated user)

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.

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-5.5",
  tools: [
    /* ... */
  ],
  contextSchema,
});

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

Execution info and server info inside tools

Access execution identity (thread ID, run ID) via runtime.executionInfo, and server-specific metadata (assistant ID, authenticated user) via runtime.serverInfo when running on LangGraph Server:

import { tool } from "langchain";
import * as z from "zod";

const contextAwareTool = tool(
  async (_input, runtime) => {
    // Access thread and run IDs
    const info = runtime.executionInfo;
    console.log(`Thread: ${info.threadId}, Run: ${info.runId}`);

    // Access server info (only available on LangGraph Server)
    const server = runtime.serverInfo;
    if (server != null) {
      console.log(`Assistant: ${server.assistantId}`);
      if (server.user != null) {
        console.log(`User: ${server.user.identity}`);
      }
    }

    return "done";
  },
  {
    name: "context_aware_tool",
    description: "A tool that uses execution and server info.",
    schema: z.object({}),
  }
);

serverInfo is null when not running on LangGraph Server (e.g., during local development).

Requires deepagents>=1.9.0 (or @langchain/langgraph>=1.2.8) for runtime.executionInfo and runtime.serverInfo.

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";

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

// Dynamic prompt middleware
const dynamicPromptMiddleware = createMiddleware({
  name: "DynamicPrompt",
  contextSchema,
  beforeModel: (state, runtime) => {
    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-5.5",
  tools: [
    /* ... */
  ],
  middleware: [dynamicPromptMiddleware, loggingMiddleware],
  contextSchema,
});

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

Execution info and server info inside middleware

Middleware hooks can also access runtime.executionInfo and runtime.serverInfo:

import { createMiddleware } from "langchain";

const authGate = createMiddleware({
  name: "AuthGate",
  beforeModel: (state, runtime) => {
    const server = runtime.serverInfo;
    if (server != null && server.user == null) {
      throw new Error("Authentication required");
    }
    console.log(`Thread: ${runtime.executionInfo.threadId}`);
    return;
  },
});

Requires deepagents>=1.9.0 (or @langchain/langgraph>=1.2.8).

---

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.