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feat: add docs translations (#5552)

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Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Tat Dat Duong <david@duong.cz>
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Hunter Lovell
2025-07-30 02:18:30 +00:00
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co-authored by Eugene Yurtsev Tat Dat Duong
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docs/docs/agents/models.md
+141 -6
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@@ -2,18 +2,70 @@
LangGraph provides built-in support for [LLMs (language models)](https://python.langchain.com/docs/concepts/chat_models/) via the LangChain library. This makes it easy to integrate various LLMs into your agents and workflows.
## Initialize a model
:::python
Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
{% include-markdown "../../snippets/chat_model_tabs.md" %}
:::
:::js
Use model provider classes to initialize models:
=== "OpenAI"
```typescript
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
});
```
=== "Anthropic"
```typescript
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
temperature: 0,
maxTokens: 2048,
});
```
=== "Google"
```typescript
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
const model = new ChatGoogleGenerativeAI({
model: "gemini-1.5-pro",
temperature: 0,
});
```
=== "Groq"
```typescript
import { ChatGroq } from "@langchain/groq";
const model = new ChatGroq({
model: "llama-3.1-70b-versatile",
temperature: 0,
});
```
:::
:::python
### Instantiate a model directly
If a model provider is not available via `init_chat_model`, you can instantiate the provider's model class directly. The model must implement the [BaseChatModel interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) and support tool calling:
```python
# Anthropic is already supported by `init_chat_model`,
# but you can also instantiate it directly.
@@ -26,19 +78,20 @@ model = ChatAnthropic(
)
```
:::
!!! important "Tool calling support"
If you are building an agent or workflow that requires the model to call external tools, ensure that the underlying
language model supports [tool calling](../concepts/tools.md). Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
## Use in an agent
:::python
When using `create_react_agent` you can specify the model by its name string, which is a shorthand for initializing the model using `init_chat_model`. This allows you to use the model without needing to import or instantiate it directly.
=== "model name"
```python
from langgraph.prebuilt import create_react_agent
@@ -70,10 +123,33 @@ When using `create_react_agent` you can specify the model by its name string, wh
)
```
:::
:::js
When using `createReactAgent` you can pass the model instance directly:
```typescript
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
const model = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
});
const agent = createReactAgent({
llm: model,
tools: tools,
});
```
:::
## Advanced model configuration
### Disable streaming
:::python
To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
=== "`init_chat_model`"
@@ -101,9 +177,25 @@ To disable streaming of the individual LLM tokens, set `disable_streaming=True`
```
Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`
:::
:::js
To disable streaming of the individual LLM tokens, set `streaming: false` when initializing the model:
```typescript
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
model: "gpt-4o",
streaming: false,
});
```
:::
### Add model fallbacks
:::python
You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
=== "`init_chat_model`"
@@ -136,6 +228,28 @@ You can add a fallback to a different model or a different LLM provider using `m
```
See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
:::
:::js
You can add a fallback to a different model or a different LLM provider using `model.withFallbacks([...])`:
```typescript
import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";
const modelWithFallbacks = new ChatOpenAI({
model: "gpt-4o",
}).withFallbacks([
new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
}),
]);
```
See this [guide](https://js.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
:::
:::python
### Use the built-in rate limiter
@@ -152,28 +266,49 @@ rate_limiter = InMemoryRateLimiter(
)
model = ChatAnthropic(
model_name="claude-3-opus-20240229",
model_name="claude-3-opus-20240229",
rate_limiter=rate_limiter
)
```
See the LangChain docs for more information on how to [handle rate limiting](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/).
:::
## Bring your own model
If your desired LLM isn't officially supported by LangChain, consider these options:
:::python
1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
:::
:::js
1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://js.langchain.com/docs/how_to/custom_chat/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
:::
2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
Refer to the [custom streaming documentation](../how-tos/streaming.md#use-with-any-llm) for guidance. This approach suits custom workflows where prebuilt agent integration is not necessary.
## Additional resources
:::python
- [Multimodal inputs](https://python.langchain.com/docs/how_to/multimodal_inputs/)
- [Structured outputs](https://python.langchain.com/docs/how_to/structured_output/)
- [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
- [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
- [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
- [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)
:::
:::js
- [Multimodal inputs](https://js.langchain.com/docs/how_to/multimodal_inputs/)
- [Structured outputs](https://js.langchain.com/docs/how_to/structured_output/)
- [Model integration directory](https://js.langchain.com/docs/integrations/chat/)
- [Force model to call a specific tool](https://js.langchain.com/docs/how_to/tool_choice/)
- [All chat model how-to guides](https://js.langchain.com/docs/how_to/#chat-models)
- [Chat model integrations](https://js.langchain.com/docs/integrations/chat/)
:::