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LangChain v1.0Welcome to the new LangChain documentation! If you encounter any issues or have feedback, please open an issue so we can improve. Archived v0 documentation can be found here.See the release notes and migration guide for a complete list of changes and instructions on how to upgrade your code.
Agents combine language models with tools to create systems that can reason about tasks, decide which tools to use, and iteratively work towards solutions. createAgent() provides a production-ready agent implementation. An LLM Agent runs tools in a loop to achieve a goal. An agent runs until a stop condition is met - i.e., when the model emits a final output or an iteration limit is reached.
createAgent() builds a graph-based agent runtime using LangGraph. A graph consists of nodes (steps) and edges (connections) that define how your agent processes information. The agent moves through this graph, executing nodes like the model node (which calls the model), the tools node (which executes tools), or middleware.Learn more about the Graph API.

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Core components

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Model

The model is the reasoning engine of your agent. It can be specified in multiple ways, supporting both static and dynamic model selection.

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Static model

Static models are configured once when creating the agent and remain unchanged throughout execution. This is the most common and straightforward approach. To initialize a static model from a : 
import { createAgent } from "langchain";

const agent = createAgent({
  model: "openai:gpt-5",
  tools: []
});
Model identifier strings use the format provider:model (e.g. "openai:gpt-5"). You may want more control over the model configuration, in which case you can initialize a model instance directly using the provider package: 
import { createAgent } from "langchain";
import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({
  model: "gpt-4o",
  temperature: 0.1,
  maxTokens: 1000,
  timeout: 30
});

const agent = createAgent({
  model,
  tools: []
});
Model instances give you complete control over configuration. Use them when you need to set specific parameters like temperature, max_tokens, timeouts, or configure API keys, base_url, and other provider-specific settings. Refer to the API reference to see available params and methods on your model.

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Dynamic model

Dynamic models are selected at based on the current and context. This enables sophisticated routing logic and cost optimization. To use a dynamic model, create middleware with wrapModelCall that modifies the model in the request: 
import { ChatOpenAI } from "@langchain/openai";
import { createAgent, createMiddleware } from "langchain";

const basicModel = new ChatOpenAI({ model: "gpt-4o-mini" });
const advancedModel = new ChatOpenAI({ model: "gpt-4o" });

const dynamicModelSelection = createMiddleware({
  name: "DynamicModelSelection",
  wrapModelCall: (request, handler) => {
    // Choose model based on conversation complexity
    const messageCount = request.messages.length;

    return handler({
        ...request,
        model: messageCount > 10 ? advancedModel : basicModel,
    });
  },
});

const agent = createAgent({
  model: "gpt-4o-mini", // Base model (used when messageCount ≤ 10)
  tools,
  middleware: [dynamicModelSelection] as const,
});
For more details on middleware and advanced patterns, see the middleware documentation.
For model configuration details, see Models. For dynamic model selection patterns, see Dynamic model in middleware.

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Tools

Tools give agents the ability to take actions. Agents go beyond simple model-only tool binding by facilitating:
  • Multiple tool calls in sequence (triggered by a single prompt)
  • Parallel tool calls when appropriate
  • Dynamic tool selection based on previous results
  • Tool retry logic and error handling
  • State persistence across tool calls

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Defining tools

Pass a list of tools to the agent. 
import * as z from "zod";
import { createAgent, tool } from "langchain";

const search = tool(
  ({ query }) => `Results for: ${query}`,
  {
    name: "search",
    description: "Search for information",
    schema: z.object({
      query: z.string().describe("The query to search for"),
    }),
  }
);

const getWeather = tool(
  ({ location }) => `Weather in ${location}: Sunny, 72°F`,
  {
    name: "get_weather",
    description: "Get weather information for a location",
    schema: z.object({
      location: z.string().describe("The location to get weather for"),
    }),
  }
);

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [search, getWeather],
});
If an empty tool list is provided, the agent will consist of a single LLM node without tool-calling capabilities.

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Tool error handling

To customize how tool errors are handled, use the wrapToolCall hook in a custom middleware: 
import { createAgent, createMiddleware, ToolMessage } from "langchain";

const handleToolErrors = createMiddleware({
  name: "HandleToolErrors",
  wrapToolCall: (request, handler) => {
    try {
      return handler(request);
    } catch (error) {
      // Return a custom error message to the model
      return new ToolMessage({
        content: `Tool error: Please check your input and try again. (${error})`,
        tool_call_id: request.toolCall.id!,
      });
    }
  },
});

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [
    /* ... */
  ],
  middleware: [handleToolErrors] as const,
});
The agent will return a @[ToolMessage] with the custom error message when a tool fails.
Learn more about tools in tools.

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Tool use in the ReAct loop

Agents follow the ReAct (“Reasoning + Acting”) pattern, alternating between brief reasoning steps with targeted tool calls and feeding the resulting observations into subsequent decisions until they can deliver a final answer.
Prompt: Identify the current most popular wireless headphones and verify availability.
================================ Human Message =================================

Find the most popular wireless headphones right now and check if they're in stock
  • Reasoning: “Popularity is time-sensitive, I need to use the provided search tool.”
  • Acting: Call search_products("wireless headphones")
================================== Ai Message ==================================
Tool Calls:
  search_products (call_abc123)
 Call ID: call_abc123
  Args:
    query: wireless headphones

================================= Tool Message =================================

Found 5 products matching "wireless headphones". Top 5 results: WH-1000XM5, ...
  • Reasoning: “I need to confirm availability for the top-ranked item before answering.”
  • Acting: Call check_inventory("WH-1000XM5")
================================== Ai Message ==================================
Tool Calls:
  check_inventory (call_def456)
 Call ID: call_def456
  Args:
    product_id: WH-1000XM5

================================= Tool Message =================================

Product WH-1000XM5: 10 units in stock
  • Reasoning: “I have the most popular model and its stock status. I can now answer the user’s question.”
  • Acting: Produce final answer
================================== Ai Message ==================================

I found wireless headphones (model WH-1000XM5) with 10 units in stock...
To learn more about tools, see Tools.

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System prompt

You can shape how your agent approaches tasks by providing a prompt. The @[system_prompt] parameter can be provided as a string: 
const agent = createAgent({
  model,
  tools,
  systemPrompt: "You are a helpful assistant. Be concise and accurate.",
});
When no @[system_prompt] is provided, the agent will infer its task from the messages directly.

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Dynamic system prompt

For more advanced use cases where you need to modify the system prompt based on runtime context or agent state, you can use middleware. 
import * as z from "zod";
import { createAgent, dynamicSystemPromptMiddleware } from "langchain";

const contextSchema = z.object({
  userRole: z.enum(["expert", "beginner"]),
});

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [/* ... */],
  contextSchema,
  middleware: [
    dynamicSystemPromptMiddleware<z.infer<typeof contextSchema>>((state, runtime) => {
      const userRole = runtime.context.userRole || "user";
      const basePrompt = "You are a helpful assistant.";

      if (userRole === "expert") {
        return `${basePrompt} Provide detailed technical responses.`;
      } else if (userRole === "beginner") {
        return `${basePrompt} Explain concepts simply and avoid jargon.`;
      }
      return basePrompt;
    }),
  ],
});

// The system prompt will be set dynamically based on context
const result = await agent.invoke(
  { messages: [{ role: "user", content: "Explain machine learning" }] },
  { context: { userRole: "expert" } }
);
For more details on message types and formatting, see Messages. For comprehensive middleware documentation, see Middleware.

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Invocation

You can invoke an agent by passing an update to its state. All agents include a sequence of messages in their state; to invoke the agent, pass a new message: 
await agent.invoke({
  messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
For streaming steps and / or tokens from the agent, refer to the streaming guide. Otherwise, the agent follows the LangGraph Graph API and supports all associated methods.

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Advanced concepts

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Structured output

In some situations, you may want the agent to return an output in a specific format. LangChain provides a simple, universal way to do this with the responseFormat parameter. 
import * as z from "zod";
import { createAgent } from "langchain";

const ContactInfo = z.object({
  name: z.string(),
  email: z.string(),
  phone: z.string(),
});

const agent = createAgent({
  model: "openai:gpt-4o",
  responseFormat: ContactInfo,
});

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "Extract contact info from: John Doe, john@example.com, (555) 123-4567",
    },
  ],
});

console.log(result.structuredResponse);
// {
//   name: 'John Doe',
//   email: 'john@example.com',
//   phone: '(555) 123-4567'
// }
To learn about structured output, see Structured output.

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Memory

Agents maintain conversation history automatically through the message state. You can also configure the agent to use a custom state schema to remember additional information during the conversation. Information stored in the state can be thought of as the short-term memory of the agent: 
import * as z from "zod";
import { MessagesZodState } from "@langchain/langgraph";
import { createAgent, type BaseMessage } from "langchain";

const customAgentState = z.object({
  messages: MessagesZodState.shape.messages,
  userPreferences: z.record(z.string(), z.string()),
});

const CustomAgentState = createAgent({
  model: "openai:gpt-4o",
  tools: [],
  stateSchema: customAgentState,
});
To learn more about memory, see Memory. For information on implementing long-term memory that persists across sessions, see Long-term memory.

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Streaming

We’ve seen how the agent can be called with invoke to get a final response. If the agent executes multiple steps, this may take a while. To show intermediate progress, we can stream back messages as they occur. 
const stream = await agent.stream(
  {
    messages: [{
      role: "user",
      content: "Search for AI news and summarize the findings"
    }],
  },
  { streamMode: "values" }
);

for await (const chunk of stream) {
  // Each chunk contains the full state at that point
  const latestMessage = chunk.messages.at(-1);
  if (latestMessage?.content) {
    console.log(`Agent: ${latestMessage.content}`);
  } else if (latestMessage?.tool_calls) {
    const toolCallNames = latestMessage.tool_calls.map((tc) => tc.name);
    console.log(`Calling tools: ${toolCallNames.join(", ")}`);
  }
}
For more details on streaming, see Streaming.

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Middleware

Middleware provides powerful extensibility for customizing agent behavior at different stages of execution. You can use middleware to:
  • Process state before the model is called (e.g., message trimming, context injection)
  • Modify or validate the model’s response (e.g., guardrails, content filtering)
  • Handle tool execution errors with custom logic
  • Implement dynamic model selection based on state or context
  • Add custom logging, monitoring, or analytics
Middleware integrates seamlessly into the agent’s execution graph, allowing you to intercept and modify data flow at key points without changing the core agent logic.
For comprehensive middleware documentation including hooks like beforeModel, afterModel, and wrapToolCall, see Middleware.
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