> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docsdy-1782656769-5d7a089.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/observability) 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-5.5",
    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-5.2",
    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-5.5",
    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-5.5",
    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/observability) 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">
    Use one interface for chat models, embeddings, and more across providers. Switch models with minimal code changes and keep your application portable as requirements evolve.
  </Card>

  <Card title="Highly configurable harness" icon="wand" href="/oss/javascript/langchain/agents" arrow cta="Learn more">
    Start with `create_agent` as a minimal harness and add capabilities incrementally through middleware. Compose only what your use case needs, from guardrails and retries to routing and custom tool policies.
  </Card>

  <Card title="Built on top of LangGraph" icon="https://mintcdn.com/langchain-5e9cc07a-preview-docsdy-1782656769-5d7a089/VWc3YGvLH-xQOqEM/images/brand/langgraph-icon.png?fit=max&auto=format&n=VWc3YGvLH-xQOqEM&q=85&s=688c81af57a4499018673df8c60acc11" 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-docsdy-1782656769-5d7a089/VWc3YGvLH-xQOqEM/images/brand/observability-icon-dark.png?fit=max&auto=format&n=VWc3YGvLH-xQOqEM&q=85&s=d1f166ac9d25618d4de969b88a11f67a" href="/langsmith/observability" arrow cta="Learn more" width="200" height="200" data-path="images/brand/observability-icon-dark.png">
    Inspect traces, tool calls, state transitions, and latency in one place. Find failure modes, evaluate quality, and improve agent behavior with execution data.
  </Card>
</Columns>

***

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