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Add docs in Chinese (Simplified) #1292

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Add docs in Chinese (Simplified)
lnhsingh Nov 5, 2025
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Merge branch 'ko' into cn
lnhsingh Nov 5, 2025
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140 changes: 140 additions & 0 deletions src/cn/index.mdx
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---
title:
sidebarTitle: Home
mode: "custom"
---

This is a placeholder page. It should follow the same format as src/index.mdx.


<div class="mx-auto max-w-8xl px-0 lg:px-5" style={{ paddingBottom: "8rem" }}>
<div class="mdx-content prose prose-gray dark:prose-invert mx-4 pt-10">
<h1 class="flex whitespace-pre-wrap group font-semibold text-2xl sm:text-3xl mt-8">Documentation</h1>

LangChain is the platform for agent engineering. AI teams at Replit, Clay, Rippling, Cloudflare, Workday, and more trust LangChain's products to engineer reliable agents.

Our **open source frameworks** help you build agents:

- [**LangChain**](/oss/python/langchain/overview) helps you quickly get started building agents, with any model provider of your choice.
- [**LangGraph**](/oss/python/langgraph/overview) allows you to control every step of your custom agent with low-level orchestration, memory, and human-in-the-loop support. You can manage long-running tasks with durable execution.

[**LangSmith**](/langsmith/home) is a platform that helps AI teams use live production data for continuous testing and improvement. LangSmith provides:

- **Observability** to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
- **Evaluation** to test and score agent behavior on production data and offline datasets for continuous improvement.
- **Deployment** to ship your agent in one click, using scalable infrastructure built for long-running tasks.

<Callout icon="bullhorn" color="#DFC5FE" iconType="regular">
LangGraph Platform is now [LangSmith Deployment](/langsmith/deployments). For more information, check out the [Changelog](https://changelog.langchain.com/announcements/product-naming-changes-langsmith-deployment-and-langsmith-studio).
</Callout>

<h2 class="flex whitespace-pre-wrap group font-semibold">Get started</h2>

<CardGroup cols={4}>
<Card title="Build your first agent with LangChain" icon="gear" href="/oss/python/langchain/quickstart" cta="Get started" />
<Card title="Sign up for LangSmith" icon="screwdriver-wrench" href="https://smith.langchain.com/" cta="Try LangSmith" />
<Card title="Build an advanced agent with LangGraph" icon="robot" href="/oss/python/langgraph/quickstart" cta="Get started"/>
<Card title="Enroll in LangChain Academy" icon="graduation-cap" href="https://academy.langchain.com/" cta="Get started"/>
</CardGroup>

<h2 class="flex whitespace-pre-wrap group font-semibold">Open source agent frameworks</h2>

<Tabs>
<Tab title="Python" icon="python">
<CardGroup cols={3}>
<Card
title="LangChain (Python)"
href="/oss/python/langchain/overview"
icon="link"
cta="Learn more"
>
Quickly get started building agents, with any model provider of your choice.
</Card>
<Card
title="LangGraph (Python)"
href="/oss/python/langgraph/overview"
icon="circle-nodes"
cta="Learn more"
>
Control every step of your custom agent with low-level orchestration, memory, and human-in-the-loop support.
</Card>
<Card
title="Deep Agents"
href="/oss/python/deepagents/overview"
icon="robot"
cta="Learn more"
>
Build agents that can tackle complex, multi-step tasks.
</Card>
</CardGroup>
</Tab>
<Tab title="TypeScript" icon="js">
<CardGroup cols={2}>
<Card
title="LangChain (TypeScript)"
href="/oss/javascript/langchain/overview"
icon="link"
cta="Learn more"
>
Quickly get started building agents, with any model provider of your choice.
</Card>
<Card
title="LangGraph (TypeScript)"
href="/oss/javascript/langgraph/overview"
icon="circle-nodes"
cta="Learn more"
>
Control every step of your custom agent with low-level orchestration, memory, and human-in-the-loop support.
</Card>
</CardGroup>
</Tab>
</Tabs>

<h2 class="flex whitespace-pre-wrap group font-semibold">LangSmith</h2>

<CardGroup cols={4}>
<Card
title="Observability"
href="/langsmith/observability"
icon="magnifying-glass"
cta="Learn more"
>
See exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
</Card>
<Card
title="Evaluation"
href="/langsmith/evaluation"
icon="chart-simple"
cta="Learn more"
>
Test and score agent behavior on production data or offline datasets to continuously improve performance.
</Card>
<Card
title="Prompt Engineering"
href="/langsmith/prompt-engineering"
icon="terminal"
cta="Learn more"
>
Iterate on prompts with version control, prompt optimization, and collaboration features.
</Card>
<Card
title="Deployment"
href="/langsmith/deployments"
icon="rocket-launch"
cta="Learn more"
>
Ship your agent in one click, using scalable infrastructure built for long-running tasks.
</Card>
</CardGroup>

<Callout icon="lock" color="#DFC5FE" iconType="regular">
LangSmith meets the highest standards of data security and privacy with HIPAA, SOC 2 Type 2, and GDPR compliance. For more information, see the [Trust Center](https://trust.langchain.com/).
</Callout>
</div>
</div>

{/* Hack to hide the callout cards*/}
<div className="relative">
<div className="absolute top-0 left-0 w-screen h-48 bg-background-light dark:bg-background-dark z-10">
</div>
</div>
142 changes: 142 additions & 0 deletions src/cn/oss/langchain/overview.mdx
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---
title: LangChain Overview (Chinese)
sidebarTitle: Overview
---

This is a placeholder page. It should follow the same format as src/oss/langchain/overview.mdx.


:::python
<Callout icon="bullhorn" color="#DFC5FE" iconType="regular">
**LangChain v1.0 is now available!**

For a complete list of changes and instructions on how to upgrade your code, see the [release notes](/oss/releases/langchain-v1) and [migration guide](/oss/migrate/langchain-v1).

If you encounter any issues or have feedback, please [open an issue](https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml) so we can improve. To view v0.x documentation, [go to the archived content](https://github.com/langchain-ai/langchain/tree/v0.3/docs/docs).
</Callout>
:::

:::js
<Callout icon="bullhorn" color="#DFC5FE" iconType="regular">
**LangChain v1.0 is now available!**

For a complete list of changes and instructions on how to upgrade your code, see the [release notes](/oss/releases/langchain-v1) and [migration guide](/oss/migrate/langchain-v1).

If you encounter any issues or have feedback, please [open an issue](https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml) so we can improve. To view v0.x documentation, [go to the archived content](https://github.com/langchain-ai/langchainjs/tree/v0.3/docs/core_docs/docs).
</Callout>
:::

LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and [more](/oss/integrations/providers/overview). LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.

We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use [LangGraph](/oss/langgraph/overview), our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.

LangChain [agents](/oss/langchain/agents) are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. You do not need to know LangGraph for basic LangChain agent usage.

## <Icon icon="download" size={20} /> Install

:::python
<CodeGroup>
```bash pip
pip install -U langchain
```

```bash uv
uv add langchain
```
</CodeGroup>
:::
:::js
<CodeGroup>
```bash npm
npm install langchain @langchain/core
```

```bash pnpm
pnpm add langchain @langchain/core
```

```bash yarn
yarn add langchain @langchain/core
```

```bash bun
bun add langchain @langchain/core
```
</CodeGroup>
:::

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

:::python
```python
# pip install -qU "langchain[anthropic]" to call the model

from langchain.agents import create_agent

def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"

agent = create_agent(
model="claude-sonnet-4-5-20250929",
tools=[get_weather],
system_prompt="You are a helpful assistant",
)

# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
```
:::

:::js
```ts
import * as z from "zod";
// npm install @langchain/anthropic to call the model
import { createAgent, tool } from "langchain";

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

const agent = createAgent({
model: "claude-sonnet-4-5-20250929",
tools: [getWeather],
});

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


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

<Columns cols={2}>
<Card title="Standard model interface" icon="arrows-rotate" href="/oss/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="Easy to use, highly flexible agent" icon="wand-magic-sparkles" href="/oss/langchain/agents" arrow cta="Learn more">
LangChain's agent abstraction is designed to be easy to get started with, letting you build a simple agent in under 10 lines of code. But it also provides enough flexibility to allow you to do all the context engineering your heart desires.
</Card>

<Card title="Built on top of LangGraph" icon="circle-nodes" href="/oss/langgraph/overview" arrow cta="Learn more">
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="eye" href="/langsmith/home" arrow cta="Learn more">
Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
</Card>
</Columns>
43 changes: 43 additions & 0 deletions src/docs.json
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}
]
},
{
"language": "cn",
"products": [
{
"product": "Home",
"icon": "house",
"pages": [
"cn/index"
]
},
{
"product": "LangChain + LangGraph",
"icon": "link",
"description": "Open source frameworks",
"dropdowns": [
{
"dropdown": "Python",
"icon": "python",
"tabs": [
{
"tab": "LangChain",
"pages": [
"cn/oss/python/langchain/overview"
]
}
]
},
{
"dropdown": "JavaScript",
"icon": "square-js",
"tabs": [
{
"tab": "LangChain",
"pages": [
"cn/oss/javascript/langchain/overview"
]
}
]
}
]
}
]
},
{
"language": "ko",
"products": [
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