> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Customize Deep Agents

> Learn how to customize Deep Agents with system prompts, tools, subagents, and more

`createDeepAgent` has the following configuration options:

* [Model](#model)
* [Tools](#tools)
* [System Prompt](#system-prompt)
* [Middleware](#middleware), including  [prebuilt middleware](#prebuilt-middleware) and [custom middleware](#custom-middleware)
* [Subagents](#subagents)
* [Backends (virtual filesystems)](#backends)
* [Human-in-the-loop](#human-in-the-loop)
* [Skills](#skills)
* [Memory](#memory)
* [Profiles](#profiles)

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const agent = createDeepAgent({
  backend?: AnyBackendProtocol | (config: __type) => AnyBackendProtocol,
  checkpointer?: boolean | BaseCheckpointSaver<number>,
  contextSchema?: ContextSchema,
  interruptOn?: Record<string, boolean | __type>,
  memory?: string[],
  middleware?: TMiddleware,
  model?: string | BaseLanguageModel<any, BaseLanguageModelCallOptions>,
  name?: string,
  permissions?: FilesystemPermission[],
  responseFormat?: TResponse,
  skills?: string[],
  store?: BaseStore,
  streamTransformers?: TStreamTransformers,
  subagents?: TSubagents,
  systemPrompt?: string | SystemMessage<MessageStructure<MessageToolSet>>,
  tools?: TTools | StructuredTool<ToolInputSchemaBase, any, any, any, unknown>[]
});
```

For the full parameter list, see the [`createDeepAgent`](https://reference.langchain.com/javascript/deepagents/types/CreateDeepAgentParams) API reference.

## Model

Pass a `model` string in `provider:model` format, or an initialized model instance. See [supported models](/oss/javascript/deepagents/models#supported-models) for all providers and [suggested models](/oss/javascript/deepagents/models#suggested-models) for tested recommendations.

<Tip>
  Use the `provider:model` format (for example `openai:gpt-5.4`) to quickly switch between models.
</Tip>

<Tabs>
  <Tab title="OpenAI">
    👉 Read the [OpenAI chat model integration docs](/oss/javascript/integrations/chat/openai/)

    <CodeGroup>
      ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      npm install @langchain/openai deepagents
      ```

      ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      pnpm install @langchain/openai deepagents
      ```

      ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      yarn add @langchain/openai deepagents
      ```

      ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      bun add @langchain/openai deepagents
      ```
    </CodeGroup>

    <CodeGroup>
      ```typescript default parameters theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { createDeepAgent } from "deepagents";

      process.env.OPENAI_API_KEY = "your-api-key";

      const agent = createDeepAgent({ model: "gpt-5.4" });
      // this calls initChatModel for the specified model with default parameters
      // to use specific model parameters, use initChatModel directly
      ```

      ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { initChatModel } from "langchain";
      import { createDeepAgent } from "deepagents";

      process.env.OPENAI_API_KEY = "your-api-key";

      const model = await initChatModel("gpt-5.4");
      const agent = createDeepAgent({
        model,
        temperature: 0,
      });
      ```

      ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { ChatOpenAI } from "@langchain/openai";
      import { createDeepAgent } from "deepagents";

      const agent = createDeepAgent({
        model: new ChatOpenAI({
          model: "gpt-5.4",
          apiKey: "your-api-key",
          temperature: 0,
        }),
      });
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Anthropic">
    👉 Read the [Anthropic chat model integration docs](/oss/javascript/integrations/chat/anthropic/)

    <CodeGroup>
      ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      npm install @langchain/anthropic deepagents
      ```

      ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      pnpm install @langchain/anthropic deepagents
      ```

      ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      yarn add @langchain/anthropic deepagents
      ```

      ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      bun add @langchain/anthropic deepagents
      ```
    </CodeGroup>

    <CodeGroup>
      ```typescript default parameters theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { createDeepAgent } from "deepagents";

      process.env.ANTHROPIC_API_KEY = "your-api-key";

      const agent = createDeepAgent({ model: "anthropic:claude-sonnet-4-6" });
      // this calls initChatModel for the specified model with default parameters
      // to use specific model parameters, use initChatModel directly
      ```

      ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { initChatModel } from "langchain";
      import { createDeepAgent } from "deepagents";

      process.env.ANTHROPIC_API_KEY = "your-api-key";

      const model = await initChatModel("claude-sonnet-4-6");
      const agent = createDeepAgent({
        model,
        temperature: 0,
      });
      ```

      ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { ChatAnthropic } from "@langchain/anthropic";
      import { createDeepAgent } from "deepagents";

      const agent = createDeepAgent({
        model: new ChatAnthropic({
          model: "claude-sonnet-4-6",
          apiKey: "your-api-key",
          temperature: 0,
        }),
      });
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Azure">
    👉 Read the [Azure chat model integration docs](/oss/javascript/integrations/chat/azure/)

    <CodeGroup>
      ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      npm install @langchain/azure deepagents
      ```

      ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      pnpm install @langchain/azure deepagents
      ```

      ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      yarn add @langchain/azure deepagents
      ```

      ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      bun add @langchain/azure deepagents
      ```
    </CodeGroup>

    <CodeGroup>
      ```typescript default parameters theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { createDeepAgent } from "deepagents";

      process.env.AZURE_OPENAI_API_KEY = "your-api-key";
      process.env.AZURE_OPENAI_ENDPOINT = "your-endpoint";
      process.env.OPENAI_API_VERSION = "your-api-version";

      const agent = createDeepAgent({ model: "azure_openai:gpt-5.4" });
      // this calls initChatModel for the specified model with default parameters
      // to use specific model parameters, use initChatModel directly
      ```

      ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { initChatModel } from "langchain";
      import { createDeepAgent } from "deepagents";

      process.env.AZURE_OPENAI_API_KEY = "your-api-key";
      process.env.AZURE_OPENAI_ENDPOINT = "your-endpoint";
      process.env.OPENAI_API_VERSION = "your-api-version";

      const model = await initChatModel("azure_openai:gpt-5.4");
      const agent = createDeepAgent({
        model,
        temperature: 0,
      });
      ```

      ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { AzureChatOpenAI } from "@langchain/openai";
      import { createDeepAgent } from "deepagents";

      const agent = createDeepAgent({
        model: new AzureChatOpenAI({
          model: "gpt-5.4",
          azureOpenAIApiKey: "your-api-key",
          azureOpenAIApiEndpoint: "your-endpoint",
          azureOpenAIApiVersion: "your-api-version",
          temperature: 0,
        }),
      });
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Google Gemini">
    👉 Read the [Google GenAI chat model integration docs](/oss/javascript/integrations/chat/google_generative_ai/)

    <CodeGroup>
      ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      npm install @langchain/google-genai deepagents
      ```

      ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      pnpm install @langchain/google-genai deepagents
      ```

      ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      yarn add @langchain/google-genai deepagents
      ```

      ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      bun add @langchain/google-genai deepagents
      ```
    </CodeGroup>

    <CodeGroup>
      ```typescript default parameters theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { createDeepAgent } from "deepagents";

      process.env.GOOGLE_API_KEY = "your-api-key";

      const agent = createDeepAgent({ model: "google-genai:gemini-3.1-pro-preview" });
      // this calls initChatModel for the specified model with default parameters
      // to use specific model parameters, use initChatModel directly
      ```

      ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { initChatModel } from "langchain";
      import { createDeepAgent } from "deepagents";

      process.env.GOOGLE_API_KEY = "your-api-key";

      const model = await initChatModel("google-genai:gemini-3.1-pro-preview");
      const agent = createDeepAgent({
        model,
        temperature: 0,
      });
      ```

      ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
      import { createDeepAgent } from "deepagents";

      const agent = createDeepAgent({
        model: new ChatGoogleGenerativeAI({
          model: "gemini-3.1-pro-preview",
          apiKey: "your-api-key",
          temperature: 0,
        }),
      });
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Bedrock Converse">
    👉 Read the [AWS Bedrock chat model integration docs](/oss/javascript/integrations/chat/bedrock_converse/)

    <CodeGroup>
      ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      npm install @langchain/aws deepagents
      ```

      ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      pnpm install @langchain/aws deepagents
      ```

      ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      yarn add @langchain/aws deepagents
      ```

      ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      bun add @langchain/aws deepagents
      ```
    </CodeGroup>

    <CodeGroup>
      ```typescript default parameters theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { createDeepAgent } from "deepagents";

      // Follow the steps here to configure your credentials:
      // https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

      const agent = createDeepAgent({ model: "bedrock:anthropic.claude-sonnet-4-6" });
      // this calls initChatModel for the specified model with default parameters
      // to use specific model parameters, use initChatModel directly
      ```

      ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { initChatModel } from "langchain";
      import { createDeepAgent } from "deepagents";

      // Follow the steps here to configure your credentials:
      // https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

      const model = await initChatModel("bedrock:anthropic.claude-sonnet-4-6");
      const agent = createDeepAgent({
        model,
        temperature: 0,
      });
      ```

      ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
      import { ChatBedrockConverse } from "@langchain/aws";
      import { createDeepAgent } from "deepagents";

      // Follow the steps here to configure your credentials:
      // https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

      const agent = createDeepAgent({
        model: new ChatBedrockConverse({
          model: "anthropic.claude-sonnet-4-6",
          region: "us-east-2",
          temperature: 0,
        }),
      });
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Other">
    Pass any [supported model string](/oss/javascript/deepagents/models#supported-models), or an initialized model instance:

    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { initChatModel } from "langchain";
    import { createDeepAgent } from "deepagents";

    const model = await initChatModel("provider:model-name");
    const agent = createDeepAgent({ model });
    ```
  </Tab>
</Tabs>

### Connection resilience

LangChain chat models automatically retry failed API requests with exponential backoff. By default, models retry up to **6 times** for network errors, rate limits (429), and server errors (5xx). Client errors like 401 (unauthorized) or 404 are not retried.

You can adjust the `maxRetries` parameter when creating a model to tune this behavior for your environment:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatAnthropic } from "@langchain/anthropic";
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
    model: new ChatAnthropic({
        model: "claude-sonnet-4-6",
        maxRetries: 10, // Increase for unreliable networks (default: 6)
        timeout: 120_000, // Increase timeout for slow connections
    }),
});
```

<Tip>
  For long-running agent tasks on unreliable networks, consider increasing `max_retries` to 10–15 and pairing it with a [checkpointer](/oss/javascript/langgraph/persistence) so that progress is preserved across failures.
</Tip>

## Tools

In addition to [built-in tools](/oss/javascript/deepagents/overview#core-capabilities) for planning, file management, and subagent spawning, you can provide custom tools:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
import { z } from "zod";

const internetSearch = tool(
  async ({
    query,
    maxResults = 5,
    topic = "general",
    includeRawContent = false,
  }: {
    query: string;
    maxResults?: number;
    topic?: "general" | "news" | "finance";
    includeRawContent?: boolean;
  }) => {
    const tavilySearch = new TavilySearch({
      maxResults,
      tavilyApiKey: process.env.TAVILY_API_KEY,
      includeRawContent,
      topic,
    });
    return await tavilySearch._call({ query });
  },
  {
    name: "internet_search",
    description: "Run a web search",
    schema: z.object({
      query: z.string().describe("The search query"),
      maxResults: z.number().optional().default(5),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const agent = createDeepAgent({
  tools: [internetSearch],
});
```

## System prompt

Deep Agents come with a built-in system prompt. A deep agent's value comes from the orchestration layer the SDK provides on top of the model—planning, virtual-filesystem tools, and subagents—and the model needs to know those exist and when to reach for them. The built-in prompt teaches the agent how to use that scaffolding so you don't have to re-derive it for every project; tweak it through a [profile](/oss/javascript/deepagents/profiles#harness-profiles) or your own `system_prompt=` rather than copying it verbatim.

When middleware add special tools, like the filesystem tools, it appends them to the system prompt.

Each deep agent should also include a custom system prompt specific to its specific use case:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createDeepAgent } from "deepagents";

const researchInstructions = `You are an expert researcher. ` +
  `Your job is to conduct thorough research, and then ` +
  `write a polished report.`;

const agent = createDeepAgent({
  systemPrompt: researchInstructions,
});
```

### Prompt assembly

Deep Agents builds the system prompt from up to four named parts so that caller-supplied instructions, the SDK's built-in agent guidance, and any model-specific [profile](/oss/javascript/deepagents/profiles) overrides can coexist with predictable precedence. Without this layering, a profile suffix tuned for Claude (for example) could overwrite or be overwritten by your `system_prompt=` argument depending on call order; the named slots make the ordering explicit and stable.

In practice, most callers only encounter two slots: `USER` (your `system_prompt=`) and `BASE` (the SDK default). Selecting a model with a built-in profile—Anthropic or OpenAI today—adds a `SUFFIX`. The full four-part assembly is mainly relevant when you author a custom `HarnessProfile` or debug why a profile's text appears where it does.

The four named parts (each may be absent):

| Name     | Source                                                                                        | Notes                                                     |
| -------- | --------------------------------------------------------------------------------------------- | --------------------------------------------------------- |
| `USER`   | `system_prompt=` argument to `create_deep_agent`                                              | `str` or `SystemMessage`; omitted when unset.             |
| `BASE`   | The SDK default (`BASE_AGENT_PROMPT`)                                                         | Always present unless replaced by a profile's `CUSTOM`.   |
| `CUSTOM` | [`HarnessProfile.base_system_prompt`](/oss/javascript/deepagents/profiles#harness-profiles)   | Replaces `BASE` outright when a matching profile sets it. |
| `SUFFIX` | [`HarnessProfile.system_prompt_suffix`](/oss/javascript/deepagents/profiles#harness-profiles) | Appended last when a matching profile sets it.            |

The order is always **`USER` -> (`BASE` or `CUSTOM`) -> `SUFFIX`**, joined by blank lines (`\n\n`). Two invariants follow:

1. **`USER` is always at the front.** The caller's text precedes any SDK or profile content, so persona/instructions take precedence regardless of which model is selected.
2. **`SUFFIX` is always at the end.** Profile suffixes sit closest to the conversation history, where model-tuning guidance lands most reliably.

Assembled shapes (✓ = field is set, - = field is unset):

| `system_prompt=` | profile `base_system_prompt` (`CUSTOM`) | profile `system_prompt_suffix` (`SUFFIX`) | Final assembled system prompt |
| ---------------- | :-------------------------------------: | :---------------------------------------: | ----------------------------- |
| `None`           |                    -                    |                     -                     | `BASE`                        |
| `None`           |                    -                    |                     ✓                     | `BASE` + `SUFFIX`             |
| `None`           |                    ✓                    |                     -                     | `CUSTOM`                      |
| `None`           |                    ✓                    |                     ✓                     | `CUSTOM` + `SUFFIX`           |
| `str`            |                    -                    |                     -                     | `USER` + `BASE`               |
| `str`            |                    -                    |                     ✓                     | `USER` + `BASE` + `SUFFIX`    |
| `str`            |                    ✓                    |                     -                     | `USER` + `CUSTOM`             |
| `str`            |                    ✓                    |                     ✓                     | `USER` + `CUSTOM` + `SUFFIX`  |

Worked example — built-in profiles (Anthropic, OpenAI) ship only a `system_prompt_suffix`, so a typical call lands in the `str` + `-` + `✓` row:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    system_prompt="You are a customer-support agent for ACME Corp.",
)
# Final = USER + BASE + SUFFIX
#       = "You are a customer-support agent for ACME Corp."
#         + "\n\n"
#         + BASE_AGENT_PROMPT
#         + "\n\n"
#         + <Claude-specific guidance>
```

<Note>
  Passing a `SystemMessage` (rather than a string) triggers a different concatenation path: the right-hand assembly (`BASE`-or-`CUSTOM` plus any `SUFFIX`) is appended as an additional text content block onto the message's existing `content_blocks`. The same logical ordering applies (caller blocks first), and any `cache_control` markers on the caller's blocks are preserved — useful for placing explicit Anthropic prompt-cache breakpoints.
</Note>

<AccordionGroup>
  <Accordion title="Subagent prompts">
    The same overlay rules apply to declarative [subagents](/oss/javascript/deepagents/subagents) — each subagent re-runs profile resolution against **its own model**, then applies the resolved profile's `base_system_prompt` / `system_prompt_suffix` to its authored `system_prompt`. The subagent's `system_prompt` plays the `BASE` role; `CUSTOM` and `SUFFIX` come from the profile that matches the subagent's model (which may differ from the main agent's profile).

    | `spec["system_prompt"]` | profile `base_system_prompt` (`CUSTOM`) | profile `system_prompt_suffix` (`SUFFIX`) | Final subagent system prompt |
    | ----------------------- | :-------------------------------------: | :---------------------------------------: | ---------------------------- |
    | authored                |                    -                    |                     -                     | authored                     |
    | authored                |                    -                    |                     ✓                     | authored + `SUFFIX`          |
    | authored                |                    ✓                    |                     -                     | `CUSTOM`                     |
    | authored                |                    ✓                    |                     ✓                     | `CUSTOM` + `SUFFIX`          |

    There is no `USER` segment for subagents — the spec's authored `system_prompt` is the closest analog and stays in the `BASE` slot. A profile that ships only a `system_prompt_suffix` (the common case for built-in Anthropic / OpenAI profiles) just appends to whatever the subagent author wrote; a profile that sets `base_system_prompt` will *replace* the authored prompt outright, so reach for that field deliberately.
  </Accordion>

  <Accordion title="General-purpose subagent prompt">
    The auto-added [general-purpose subagent](/oss/javascript/deepagents/subagents#the-general-purpose-subagent) follows the same overlay rules with one extra layer: the GP base prompt is resolved as **`general_purpose_subagent.system_prompt` (if set) -> `HarnessProfile.base_system_prompt` (if set) -> SDK GP default**. The profile suffix layers on top either way.

    The two override fields can both carry a base-prompt replacement, but they are not interchangeable. `general_purpose_subagent.system_prompt` is GP-specific configuration; `base_system_prompt` is a global override that primarily targets the main agent. When both are set, the **GP-specific intent wins for the GP subagent** so a user tuning both fields never sees their GP override silently dropped:

    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    register_harness_profile(
        "anthropic",
        HarnessProfile(
            base_system_prompt="You are ACME's support orchestrator.",  # main agent
            general_purpose_subagent=GeneralPurposeSubagentProfile(
                system_prompt="You are a research subagent. Cite sources.",  # GP subagent
            ),
            system_prompt_suffix="Always think step by step.",
        ),
    )
    ```

    | Stack       | Final system prompt                                     |
    | ----------- | ------------------------------------------------------- |
    | Main agent  | `"You are ACME's support orchestrator." + SUFFIX`       |
    | GP subagent | `"You are a research subagent. Cite sources." + SUFFIX` |

    If `general_purpose_subagent.system_prompt` is unset, the GP subagent falls back to `base_system_prompt` (when set) and finally to the SDK GP default.
  </Accordion>
</AccordionGroup>

## Middleware

Deep Agents support any [middleware](/oss/javascript/langchain/middleware/overview), including the built-in middleware listed below, prebuilt middleware from LangChain, provider-specific middleware, and custom middleware you write yourself. Pass middleware to the `middleware` argument of `create_deep_agent`.

By default, Deep Agents have access to the following middleware:

* [`TodoListMiddleware`](https://reference.langchain.com/javascript/langchain/index/todoListMiddleware): Tracks and manages todo lists for organizing agent tasks and work
* [`FilesystemMiddleware`](https://reference.langchain.com/javascript/deepagents/middleware/createFilesystemMiddleware): Handles file system operations such as reading, writing, and navigating directories
* [`SubAgentMiddleware`](https://reference.langchain.com/javascript/deepagents/middleware/createSubAgentMiddleware): Spawns and coordinates subagents for delegating tasks to specialized agents
* [`SummarizationMiddleware`](https://reference.langchain.com/javascript/langchain/index/summarizationMiddleware): Condenses message history to stay within context limits when conversations grow long
* [`AnthropicPromptCachingMiddleware`](https://reference.langchain.com/javascript/langchain/index/anthropicPromptCachingMiddleware): Automatic reduction of redundant token processing when using Anthropic models
* [`PatchToolCallsMiddleware`](https://reference.langchain.com/javascript/deepagents/middleware/createPatchToolCallsMiddleware): Automatic message history fixes when tool calls are interrupted or cancelled before receiving results

If you are using memory, skills, or human-in-the-loop, the following middleware is also included:

* [`MemoryMiddleware`](https://reference.langchain.com/javascript/deepagents/middleware/createMemoryMiddleware): Persists and retrieves conversation context across sessions when the `memory` argument is provided
* [`SkillsMiddleware`](https://reference.langchain.com/javascript/deepagents/middleware/createSkillsMiddleware): Enables custom skills when the `skills` argument is provided
* `HumanInTheLoopMiddleware`: Pauses for human approval or input at specified points when the `interruptOn` argument is provided

### Prebuilt middleware

LangChain exposes additional prebuilt middleware that let you add-on various features, such as retries, fallbacks, or PII detection. See [Prebuilt middleware](/oss/javascript/langchain/middleware/built-in) for more.

The `deepagents` package also exposes [`createSummarizationMiddleware`](https://reference.langchain.com/javascript/deepagents/middleware/createSummarizationMiddleware) for the same workflow. For more detail, see [Summarization](/oss/javascript/deepagents/context-engineering#summarization).

### Provider-specific middleware

For provider-specific middleware that is optimized for specific LLM providers, see [Official integrations](/oss/javascript/integrations/middleware#official-integrations) and [Community integrations](/oss/javascript/integrations/middleware#community-integrations).

### Custom middleware

You can provide additional middleware to extend functionality, add tools, or implement custom hooks:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool, createMiddleware } from "langchain";
import { createDeepAgent } from "deepagents";
import * as z from "zod";

const getWeather = tool(
  ({ city }: { city: string }) => {
    return `The weather in ${city} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather in a city.",
    schema: z.object({
      city: z.string(),
    }),
  }
);

let callCount = 0;

const logToolCallsMiddleware = createMiddleware({
  name: "LogToolCallsMiddleware",
  wrapToolCall: async (request, handler) => {
    // Intercept and log every tool call - demonstrates cross-cutting concern
    callCount += 1;
    const toolName = request.toolCall.name;

    console.log(`[Middleware] Tool call #${callCount}: ${toolName}`);
    console.log(
      `[Middleware] Arguments: ${JSON.stringify(request.toolCall.args)}`
    );

    // Execute the tool call
    const result = await handler(request);

    // Log the result
    console.log(`[Middleware] Tool call #${callCount} completed`);

    return result;
  },
});

const agent = await createDeepAgent({
  model: "google_genai:gemini-3.1-pro-preview",
  tools: [getWeather] as any,
  middleware: [logToolCallsMiddleware] as any,
});
```

<Warning>
  **Do not mutate attributes after initialization**

  If you need to track values across hook invocations (for example, counters or accumulated data), use graph state.
  Graph state is scoped to a thread by design, so updates are safe under concurrency.

  **Do this:**

  ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  const customMiddleware = createMiddleware({
    name: "CustomMiddleware",
    beforeAgent: async (state) => {
      return { x: (state.x ?? 0) + 1 }; // Update graph state instead
    },
  });
  ```

  Do **not** do this:

  ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  let x = 1;

  const customMiddleware = createMiddleware({
    name: "CustomMiddleware",
    beforeAgent: async () => {
      x += 1; // Mutation causes race conditions
    },
  });
  ```

  Mutation in place, such as modifying `state.x` in `beforeAgent`, mutating a shared variable in `beforeAgent`, or changing other shared values in hooks, can lead to subtle bugs and race conditions because many operations run concurrently (subagents, parallel tools, and parallel invocations on different threads).

  For full details on extending state with custom properties, see [Custom middleware - Custom state schema](/oss/javascript/langchain/middleware/custom#custom-state-schema).
  If you must use mutation in custom middleware, consider what happens when subagents, parallel tools, or concurrent agent invocations run at the same time.
</Warning>

## Subagents

To isolate detailed work and avoid context bloat, use subagents:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent, type SubAgent } from "deepagents";
import { z } from "zod";

const internetSearch = tool(
  async ({
    query,
    maxResults = 5,
    topic = "general",
    includeRawContent = false,
  }: {
    query: string;
    maxResults?: number;
    topic?: "general" | "news" | "finance";
    includeRawContent?: boolean;
  }) => {
    const tavilySearch = new TavilySearch({
      maxResults,
      tavilyApiKey: process.env.TAVILY_API_KEY,
      includeRawContent,
      topic,
    });
    return await tavilySearch._call({ query });
  },
  {
    name: "internet_search",
    description: "Run a web search",
    schema: z.object({
      query: z.string().describe("The search query"),
      maxResults: z.number().optional().default(5),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  },
);

const researchSubagent: SubAgent = {
  name: "research-agent",
  description: "Used to research more in depth questions",
  systemPrompt: "You are a great researcher",
  tools: [internetSearch],
  model: "openai:gpt-5.4",  // Optional override, defaults to main agent model
};
const subagents = [researchSubagent];

const agent = createDeepAgent({
  model: "claude-sonnet-4-6",
  subagents,
});
```

For more information, see [Subagents](/oss/javascript/deepagents/subagents).

{/* ## Context - You can persist agent state between runs to store information like user IDs. */}

## Backends

Tools for a deep agent can make use of virtual file systems to store, access, and edit files. By default, deep agents use a [`StateBackend`](https://reference.langchain.com/javascript/deepagents/backends/StateBackend).

If you are using [skills](#skills) or [memory](#memory), you must add the expected skill or memory files to the backend before creating the agent.

<Tabs>
  <Tab title="StateBackend">
    A thread-scoped filesystem backend stored in `langgraph` state.

    Files persist across turns within a thread (via your checkpointer) and are not shared across threads.

    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, StateBackend } from "deepagents";

    // By default we provide a StateBackend
    const agent = createDeepAgent();

    // Under the hood, it looks like
    const agent2 = createDeepAgent({
      backend: new StateBackend(),
    });
    ```
  </Tab>

  <Tab title="FilesystemBackend">
    The local machine's filesystem.

    <Warning>
      This backend grants agents direct filesystem read/write access.
      Use with caution and only in appropriate environments.
      For more information, see [`FilesystemBackend`](/oss/javascript/deepagents/backends#filesystembackend-local-disk).
    </Warning>

    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, FilesystemBackend } from "deepagents";

    const agent = createDeepAgent({
      backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
    });
    ```
  </Tab>

  <Tab title="LocalShellBackend">
    A filesystem with shell execution directly on the host. Provides filesystem tools plus the `execute` tool for running commands.

    <Warning>
      This backend grants agents direct filesystem read/write access **and** unrestricted shell execution on your host.
      Use with extreme caution and only in appropriate environments.
      For more information, see [`LocalShellBackend`](/oss/javascript/deepagents/backends#localshellbackend-local-shell).
    </Warning>

    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, LocalShellBackend } from "deepagents";

    const backend = new LocalShellBackend({ workingDirectory: "." });
    const agent = createDeepAgent({ backend });
    ```
  </Tab>

  <Tab title="StoreBackend">
    A filesystem that provides long-term storage that is *persisted across threads*.

    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, StoreBackend } from "deepagents";
    import { InMemoryStore } from "@langchain/langgraph";

    const store = new InMemoryStore();  // Good for local dev; omit for LangSmith Deployment
    const agent = createDeepAgent({
      backend: new StoreBackend({
        namespace: (ctx) => [ctx.runtime.context.userId],
      }),
      store
    });
    ```

    <Note>
      When deploying to [LangSmith Deployment](/langsmith/deployment), omit the `store` parameter. The platform automatically provisions a store for your agent.
    </Note>

    <Tip>
      The `namespace` parameter controls data isolation. For multi-user deployments, always set a [namespace factory](/oss/javascript/deepagents/backends#namespace-factories) to isolate data per user or tenant.
    </Tip>
  </Tab>

  <Tab title="CompositeBackend">
    A flexible backend where you can specify different routes in the filesystem to point towards different backends.

    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend } from "deepagents";
    import { InMemoryStore } from "@langchain/langgraph";

    const store = new InMemoryStore();
    const agent = createDeepAgent({
      backend: new CompositeBackend(
        new StateBackend(),
        {
          "/memories/": new StoreBackend(),
        }
      ),
      store,
    });
    ```
  </Tab>
</Tabs>

For more information, see [Backends](/oss/javascript/deepagents/backends).

### Sandboxes

Sandboxes are specialized [backends](/oss/javascript/deepagents/backends) that run agent code in an isolated environment with their own filesystem and an `execute` tool for shell commands.
Use a sandbox backend when you want your deep agent to write files, install dependencies, and run commands without changing anything on your local machine.

You configure sandboxes by passing a sandbox backend to `backend` when creating your deep agent:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createDeepAgent } from "deepagents";
import { ChatAnthropic } from "@langchain/anthropic";
import { DenoSandbox } from "@langchain/deno";

// Create and initialize the sandbox
const sandbox = await DenoSandbox.create({
  memoryMb: 1024,
  lifetime: "10m",
});

try {
  const agent = createDeepAgent({
    model: new ChatAnthropic({ model: "claude-opus-4-6" }),
    systemPrompt: "You are a JavaScript coding assistant with sandbox access.",
    backend: sandbox,
  });

  const result = await agent.invoke({
    messages: [
      {
        role: "user",
        content:
          "Create a simple HTTP server using Deno.serve and test it with curl",
      },
    ],
  });
} finally {
  await sandbox.close();
}
```

For more information, see [Sandboxes](/oss/javascript/deepagents/sandboxes).

## Human-in-the-loop

Some tool operations may be sensitive and require human approval before execution.
You can configure the approval for each tool:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "langchain";
import { createDeepAgent } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";
import { z } from "zod";

const deleteFile = tool(
  async ({ path }: { path: string }) => {
    return `Deleted ${path}`;
  },
  {
    name: "delete_file",
    description: "Delete a file from the filesystem.",
    schema: z.object({
      path: z.string(),
    }),
  },
);

const readFile = tool(
  async ({ path }: { path: string }) => {
    return `Contents of ${path}`;
  },
  {
    name: "read_file",
    description: "Read a file from the filesystem.",
    schema: z.object({
      path: z.string(),
    }),
  },
);

const sendEmail = tool(
  async ({ to, subject, body }: { to: string; subject: string; body: string }) => {
    return `Sent email to ${to}`;
  },
  {
    name: "send_email",
    description: "Send an email.",
    schema: z.object({
      to: z.string(),
      subject: z.string(),
      body: z.string(),
    }),
  },
);

// Checkpointer is REQUIRED for human-in-the-loop
const checkpointer = new MemorySaver();

const agent = createDeepAgent({
  model: "google_genai:gemini-3.1-pro-preview",
  tools: [deleteFile, readFile, sendEmail],
  interruptOn: {
    delete_file: true,  // Default: approve, edit, reject, respond
    read_file: false,   // No interrupts needed
    send_email: { allowedDecisions: ["approve", "reject"] },  // No editing
  },
  checkpointer,  // Required!
});
```

You can configure interrupt for agents and subagents on tool call as well as from within tool calls.
For more information, see [Human-in-the-loop](/oss/javascript/deepagents/human-in-the-loop).

## Skills

You can use [skills](/oss/javascript/deepagents/overview) to provide your deep agent with new capabilities and expertise.
While [tools](/oss/javascript/deepagents/customization#tools) tend to cover lower level functionality like native file system actions or planning, skills can contain detailed instructions on how to complete tasks, reference info, and other assets, such as templates.
These files are only loaded by the agent when the agent has determined that the skill is useful for the current prompt.
This progressive disclosure reduces the amount of tokens and context the agent has to consider upon startup.

For example skills, see [Deep Agents example skills](https://github.com/langchain-ai/deepagentsjs/tree/main/examples/skills).

To add skills to your deep agent, pass them as an argument to `create_deep_agent`:

<Tabs>
  <Tab title="StateBackend">
    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, type FileData } from "deepagents";
    import { MemorySaver } from "@langchain/langgraph";

    const checkpointer = new MemorySaver();

    function createFileData(content: string): FileData {
      const now = new Date().toISOString();
      return {
        content: content.split("\n"),
        created_at: now,
        modified_at: now,
      };
    }

    const skillsFiles: Record<string, FileData> = {};

    const skillUrl =
      "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md";
    const response = await fetch(skillUrl);
    const skillContent = await response.text();

    skillsFiles["/skills/langgraph-docs/SKILL.md"] = createFileData(skillContent);

    const agent = await createDeepAgent({
      checkpointer,
      // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
      skills: ["/skills/"],
    });

    const config = {
      configurable: {
        thread_id: `thread-${Date.now()}`,
      },
    };

    const result = await agent.invoke(
      {
        messages: [
          {
            role: "user",
            content: "what is langraph? Use the langgraph-docs skill if available.",
          },
        ],
        files: skillsFiles,
      },
      config,
    );
    ```
  </Tab>

  <Tab title="StoreBackend">
    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
    import {
      InMemoryStore,
      MemorySaver,
    } from "@langchain/langgraph";

    const checkpointer = new MemorySaver();
    const store = new InMemoryStore();

    function createFileData(content: string): FileData {
      const now = new Date().toISOString();
      return {
        content: content.split("\n"),
        created_at: now,
        modified_at: now,
      };
    }

    const skillUrl =
      "https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/examples/skills/langgraph-docs/SKILL.md";

    const response = await fetch(skillUrl);
    const skillContent = await response.text();
    const fileData = createFileData(skillContent);

    await store.put(["filesystem"], "/skills/langgraph-docs/SKILL.md", fileData);

    const agent = await createDeepAgent({
      backend: new StoreBackend(),
      store: store,
      checkpointer,
      // IMPORTANT: deepagents skill source paths are virtual (POSIX) paths relative to the backend root.
      skills: ["/skills/"],
    });

    const config = {
      recursionLimit: 50,
      configurable: {
        thread_id: `thread-${Date.now()}`,
      },
    };

    const result = await agent.invoke(
      {
        messages: [
          {
            role: "user",
            content: "what is langraph? Use the langgraph-docs skill if available.",
          },
        ],
      },
      config,
    );
    ```
  </Tab>

  <Tab title="FilesystemBackend">
    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, FilesystemBackend } from "deepagents";
    import { MemorySaver } from "@langchain/langgraph";

    const checkpointer = new MemorySaver();
    const backend = new FilesystemBackend({ rootDir: process.cwd() });

    const agent = await createDeepAgent({
      backend,
      skills: ["./examples/skills/"],
      interruptOn: {
        read_file: true,
        write_file: true,
        delete_file: true,
      },
      checkpointer, // Required!
    });

    const config = {
      configurable: {
        thread_id: `thread-${Date.now()}`,
      },
    };

    const result = await agent.invoke(
      {
        messages: [
          {
            role: "user",
            content: "what is langraph? Use the langgraph-docs skill if available.",
          },
        ],
      },
      config,
    );
    ```
  </Tab>
</Tabs>

## Memory

Use [`AGENTS.md` files](https://agents.md/) to provide extra context to your deep agent.

You can pass one or more file paths to the `memory` parameter when creating your deep agent:

<Tabs>
  <Tab title="StateBackend">
    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, type FileData } from "deepagents";
    import { MemorySaver } from "@langchain/langgraph";

    const AGENTS_MD_URL =
      "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

    async function fetchText(url: string): Promise<string> {
      const res = await fetch(url);
      if (!res.ok) {
        throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
      }
      return await res.text();
    }

    const agentsMd = await fetchText(AGENTS_MD_URL);
    const checkpointer = new MemorySaver();

    function createFileData(content: string): FileData {
      const now = new Date().toISOString();
      return {
        content,
        mimeType: "text/plain",
        created_at: now,
        modified_at: now,
      };
    }

    const agent = await createDeepAgent({
      memory: ["/AGENTS.md"],
      checkpointer: checkpointer,
    });

    const result = await agent.invoke(
      {
        messages: [
          {
            role: "user",
            content: "Please tell me what's in your memory files.",
          },
        ],
        // Seed the default StateBackend's in-state filesystem (virtual paths must start with "/").
        files: { "/AGENTS.md": createFileData(agentsMd) },
      },
      { configurable: { thread_id: "12345" } }
    );
    ```
  </Tab>

  <Tab title="StoreBackend">
    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
        import { createDeepAgent, StoreBackend, type FileData } from "deepagents";
        import {
          InMemoryStore,
          MemorySaver,
        } from "@langchain/langgraph";

        const AGENTS_MD_URL =
          "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md";

        async function fetchText(url: string): Promise<string> {
          const res = await fetch(url);
          if (!res.ok) {
            throw new Error(`Failed to fetch ${url}: ${res.status} ${res.statusText}`);
          }
          return await res.text();
        }

        const agentsMd = await fetchText(AGENTS_MD_URL);

        function createFileData(content: string): FileData {
          const now = new Date().toISOString();
          return {
            content,
            mimeType: "text/plain",
            created_at: now,
            modified_at: now,
          };
        }

        const store = new InMemoryStore();
        const fileData = createFileData(agentsMd);
        await store.put(["filesystem"], "/AGENTS.md", fileData);

        const checkpointer = new MemorySaver();

        const agent = await createDeepAgent({
          backend: new StoreBackend(),
          store: store,
          checkpointer: checkpointer,
          memory: ["/AGENTS.md"],
        });

        const result = await agent.invoke(
          {
            messages: [
              {
                role: "user",
                content: "Please tell me what's in your memory files.",
              },
            ],
          },
          { configurable: { thread_id: "12345" } }
        );
    ```
  </Tab>

  <Tab title="Filesystem">
    ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    import { createDeepAgent, FilesystemBackend } from "deepagents";
    import { MemorySaver } from "@langchain/langgraph";

    // Checkpointer is REQUIRED for human-in-the-loop
    const checkpointer = new MemorySaver();

    const agent = await createDeepAgent({
      backend: new FilesystemBackend({ rootDir: "/Users/user/{project}" }),
      memory: ["./AGENTS.md", "./.deepagents/AGENTS.md"],
      interruptOn: {
        read_file: true,
        write_file: true,
        delete_file: true,
      },
      checkpointer, // Required!
    });
    ```
  </Tab>
</Tabs>

## Profiles

A [harness profile](/oss/javascript/deepagents/profiles#harness-profiles) packages per-provider or per-model tweaks (system prompt suffixes, tool description overrides, excluded tools or middleware, extra middleware, and general-purpose subagent edits) so `create_deep_agent` applies them automatically when the matching model is selected.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from deepagents import HarnessProfile, register_harness_profile

# Append a system-prompt suffix whenever gpt-5.4 is selected.
register_harness_profile(
    "openai:gpt-5.4",
    HarnessProfile(system_prompt_suffix="Respond in under 100 words."),
)
```

See [Profiles](/oss/javascript/deepagents/profiles) for registration keys, merge semantics, and plugin packaging. A narrower companion API, [provider profiles](/oss/javascript/deepagents/profiles#provider-profiles), packages model-construction arguments for a provider.

## Structured output

Deep Agents support [structured output](/oss/javascript/langchain/structured-output).

You can set a desired structured output schema by passing it as the `responseFormat` argument to the call to `createDeepAgent()`.
When the model generates the structured data, it's captured, validated, and returned in the 'structuredResponse' key of the agent's state.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "langchain";
import { TavilySearch } from "@langchain/tavily";
import { createDeepAgent } from "deepagents";
import { z } from "zod";

const internetSearch = tool(
  async ({
    query,
    maxResults = 5,
    topic = "general",
    includeRawContent = false,
  }: {
    query: string;
    maxResults?: number;
    topic?: "general" | "news" | "finance";
    includeRawContent?: boolean;
  }) => {
    const tavilySearch = new TavilySearch({
      maxResults,
      tavilyApiKey: process.env.TAVILY_API_KEY,
      includeRawContent,
      topic,
    });
    return await tavilySearch._call({ query });
  },
  {
    name: "internet_search",
    description: "Run a web search",
    schema: z.object({
      query: z.string().describe("The search query"),
      maxResults: z.number().optional().default(5),
      topic: z
        .enum(["general", "news", "finance"])
        .optional()
        .default("general"),
      includeRawContent: z.boolean().optional().default(false),
    }),
  }
);

const weatherReportSchema = z.object({
  location: z.string().describe("The location for this weather report"),
  temperature: z.number().describe("Current temperature in Celsius"),
  condition: z
    .string()
    .describe("Current weather condition (e.g., sunny, cloudy, rainy)"),
  humidity: z.number().describe("Humidity percentage"),
  windSpeed: z.number().describe("Wind speed in km/h"),
  forecast: z.string().describe("Brief forecast for the next 24 hours"),
});

const agent = await createDeepAgent({
  responseFormat: weatherReportSchema,
  tools: [internetSearch],
});

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "What's the weather like in San Francisco?",
    },
  ],
});

console.log(result.structuredResponse);
// {
//   location: 'San Francisco, California',
//   temperature: 18.3,
//   condition: 'Sunny',
//   humidity: 48,
//   windSpeed: 7.6,
//   forecast: 'Clear skies with temperatures remaining mild. High of 18°C (64°F) during the day, dropping to around 11°C (52°F) at night.'
// }
```

For more information and examples, see [response format](/oss/javascript/langchain/structured-output#response-format).

***

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