> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docssu-1780674012-e7ef61e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain overview

> LangChain provides create_agent: a minimal, highly configurable agent harness. Compose exactly the agent your use case needs from model, tools, prompt, and middleware.

**Agent = Model + Harness.** LangChain provides `create_agent`: a minimal, highly configurable harness. The harness is everything around the model loop: the prompt, the tools, and any middleware that shapes behavior. Start with the primitives and compose exactly what your use case needs. Supports [OpenAI, Anthropic, Google, and more](/oss/javascript/integrations/providers/overview).

<Tip>
  **LangChain vs. LangGraph vs. Deep Agents**

  Start with [Deep Agents](/oss/javascript/deepagents/overview/) for a "batteries-included" agent with features like automatic context compression, a virtual filesystem, and subagent-spawning. Deep Agents are built on LangChain [agents](/oss/javascript/langchain/agents/) which you can also use directly.

  Use [LangChain](/oss/javascript/langchain/agents) (`create_agent`) for a highly customizable harness, easily tailored to your use case and data.

  Use [LangGraph](/oss/javascript/langgraph/overview), our low-level orchestration framework, for advanced needs combining deterministic and agentic workflows.

  Use [LangSmith](/langsmith/home) to trace, debug, and evaluate agents built with any of these frameworks. Follow the [tracing quickstart](/langsmith/trace-with-langchain) to get set up. We recommend you also set up [LangSmith Engine](/langsmith/engine) which monitors your traces, detects issues, and proposes fixes.
</Tip>

## <Icon icon="wand" /> Create an agent

This example demonstrates how to create a simple LangChain agent with a custom tool:

<CodeGroup>
  ```ts OpenAI theme={null}
  // First install: npm install langchain zod @langchain/openai
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "gpt-5.4",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts Google Gemini theme={null}
  // First install: npm install langchain zod @langchain/google-genai
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "google-genai:gemini-2.5-flash-lite",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts Claude (Anthropic) theme={null}
  // First install: npm install langchain zod @langchain/anthropic
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "claude-sonnet-4-6",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts OpenRouter theme={null}
  // First install: npm install langchain zod @langchain/openrouter
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "openrouter:anthropic/claude-sonnet-4-6",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts Fireworks theme={null}
  // First install: npm install langchain zod
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts Baseten theme={null}
  // First install: npm install langchain zod
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "baseten:zai-org/GLM-5",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts Ollama theme={null}
  // First install: npm install langchain zod @langchain/ollama
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "ollama:devstral-2",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts Azure theme={null}
  // First install: npm install langchain zod @langchain/openai
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "azure_openai:gpt-5.4",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```

  ```ts AWS Bedrock theme={null}
  // First install: npm install langchain zod @langchain/aws
  import { createAgent, tool } from "langchain";
  import * as z from "zod";

  const getWeather = tool(
    (input) => `It's always sunny in ${input.city}!`,
    {
      name: "get_weather",
      description: "Get the weather for a given city",
      schema: z.object({
        city: z.string().describe("The city to get the weather for"),
      }),
    }
  );

  const agent = createAgent({
    model: "bedrock:gpt-5.4",
    tools: [getWeather],
  });

  console.log(
    await agent.invoke({
      messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
    })
  );
  ```
</CodeGroup>

See the [Installation instructions](/oss/javascript/langchain/install) and [Quickstart guide](/oss/javascript/langchain/quickstart) to get started building your own agents and applications with LangChain.

<Tip>
  Use [LangSmith](/langsmith/home) to trace requests, debug agent behavior, and evaluate outputs. Set `LANGSMITH_TRACING=true` and your API key to get started.
</Tip>

## <Icon icon="star" size={20} /> Core benefits

<Columns cols={2}>
  <Card title="Standard model interface" icon="refresh" href="/oss/javascript/langchain/models" arrow cta="Learn more">
    Different providers have unique APIs for interacting with models, including the format of responses. LangChain standardizes how you interact with models so that you can seamlessly swap providers and avoid lock-in.
  </Card>

  <Card title="Highly configurable harness" icon="wand" href="/oss/javascript/langchain/agents" arrow cta="Learn more">
    `create_agent` is a minimal harness: model, tools, prompt, loop. Extend it with middleware: each piece handles one concern and composes freely. Build exactly the agent your use case needs, nothing more.
  </Card>

  <Card title="Built on top of LangGraph" icon="https://mintcdn.com/langchain-5e9cc07a-preview-docssu-1780674012-e7ef61e/E3ZtCYTdpowdNd7F/images/brand/langgraph-icon.png?fit=max&auto=format&n=E3ZtCYTdpowdNd7F&q=85&s=bbace3ed9ff5bc0bd97401c2cb5f104a" href="/oss/javascript/langgraph/overview" arrow cta="Learn more" width="195" height="195" data-path="images/brand/langgraph-icon.png">
    LangChain's agents are built on top of LangGraph. This allows us to take advantage of LangGraph's durable execution, human-in-the-loop support, persistence, and more.
  </Card>

  <Card title="Debug with LangSmith" icon="https://mintcdn.com/langchain-5e9cc07a-preview-docssu-1780674012-e7ef61e/E3ZtCYTdpowdNd7F/images/brand/observability-icon-dark.png?fit=max&auto=format&n=E3ZtCYTdpowdNd7F&q=85&s=ebdeef3990e9485b31acccbd14fc7ed6" href="/langsmith/observability" arrow cta="Learn more" width="200" height="200" data-path="images/brand/observability-icon-dark.png">
    Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
  </Card>
</Columns>

***

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