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docs: consolidate models (#5200)

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* Expand chat model documentation a bit. 
* Adds links to langchain docs to make relevant information easier to find.
* This is a stop-gap until we merge langchain and langgraph docs
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Eugene Yurtsev
2025-06-25 15:55:17 -04:00
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commit 10a0a8633a
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docs/docs/agents/models.md
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---
search:
boost: 2
tags:
- anthropic
- openai
- agent
hide:
- tags
---
# Models
This page describes how to configure the chat model used by an agent.
## Tool calling support
To enable tool-calling agents, the underlying LLM must support [tool calling](https://python.langchain.com/docs/concepts/tool_calling/).
Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
## Specifying a model by name
You can configure an agent with a model name string:
=== "OpenAI"
```python
import os
from langgraph.prebuilt import create_react_agent
os.environ["OPENAI_API_KEY"] = "sk-..."
agent = create_react_agent(
# highlight-next-line
model="openai:gpt-4.1",
# other parameters
)
```
=== "Anthropic"
```python
import os
from langgraph.prebuilt import create_react_agent
os.environ["ANTHROPIC_API_KEY"] = "sk-..."
agent = create_react_agent(
# highlight-next-line
model="anthropic:claude-3-7-sonnet-latest",
# other parameters
)
```
=== "Azure"
```python
import os
from langgraph.prebuilt import create_react_agent
os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
agent = create_react_agent(
# highlight-next-line
model="azure_openai:gpt-4.1",
# other parameters
)
```
=== "Google Gemini"
```python
import os
from langgraph.prebuilt import create_react_agent
os.environ["GOOGLE_API_KEY"] = "..."
agent = create_react_agent(
# highlight-next-line
model="google_genai:gemini-2.0-flash",
# other parameters
)
```
=== "AWS Bedrock"
```python
from langgraph.prebuilt import create_react_agent
# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
agent = create_react_agent(
# highlight-next-line
model="bedrock_converse:anthropic.claude-3-5-sonnet-20240620-v1:0",
# other parameters
)
```
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.
## Using `init_chat_model`
## Initialize a model
The [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) utility simplifies model initialization with configurable parameters:
Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
=== "OpenAI"
{!snippets/chat_model_tabs.md!}
```
pip install -U "langchain[openai]"
```
```python
import os
from langchain.chat_models import init_chat_model
os.environ["OPENAI_API_KEY"] = "sk-..."
model = init_chat_model(
"openai:gpt-4.1",
temperature=0,
# other parameters
)
```
=== "Anthropic"
```
pip install -U "langchain[anthropic]"
```
```python
import os
from langchain.chat_models import init_chat_model
os.environ["ANTHROPIC_API_KEY"] = "sk-..."
model = init_chat_model(
"anthropic:claude-3-5-sonnet-latest",
temperature=0,
# other parameters
)
```
=== "Azure"
```
pip install -U "langchain[openai]"
```
```python
import os
from langchain.chat_models import init_chat_model
os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
model = init_chat_model(
"azure_openai:gpt-4.1",
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
temperature=0,
# other parameters
)
```
=== "Google Gemini"
```
pip install -U "langchain[google-genai]"
```
```python
import os
from langchain.chat_models import init_chat_model
os.environ["GOOGLE_API_KEY"] = "..."
model = init_chat_model(
"google_genai:gemini-2.0-flash",
temperature=0,
# other parameters
)
```
=== "AWS Bedrock"
```
pip install -U "langchain[aws]"
```
```python
from langchain.chat_models import init_chat_model
# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
model = init_chat_model(
"anthropic.claude-3-5-sonnet-20240620-v1:0",
model_provider="bedrock_converse",
temperature=0,
# other parameters
)
```
Refer to the [API reference](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for advanced options.
## Using provider-specific LLMs
### 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.
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
model = ChatAnthropic(
model="claude-3-7-sonnet-latest",
temperature=0,
max_tokens=2048
)
agent = create_react_agent(
# highlight-next-line
model=model,
# other parameters
model="claude-3-7-sonnet-latest",
temperature=0,
max_tokens=2048
)
```
!!! note "Illustrative example"
!!! important "Tool calling support"
The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.
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/).
## Disable streaming
## Use in an agent
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
create_react_agent(
# highlight-next-line
model="anthropic:claude-3-7-sonnet-latest",
# other parameters
)
```
=== "model instance"
```python
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
model = ChatAnthropic(
model="claude-3-7-sonnet-latest",
temperature=0,
max_tokens=2048
)
# Alternatively
# model = init_chat_model("anthropic:claude-3-7-sonnet-latest")
agent = create_react_agent(
# highlight-next-line
model=model,
# other parameters
)
```
## Advanced model configuration
### Disable streaming
To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
@@ -257,7 +102,7 @@ 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`
## Adding model fallbacks
### Add model fallbacks
You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
@@ -292,7 +137,43 @@ 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.
### Use the built-in rate limiter
Langchain includes a built-in in-memory rate limiter. This rate limiter is thread safe and can be shared by multiple threads in the same process.
```python
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_anthropic import ChatAnthropic
rate_limiter = InMemoryRateLimiter(
requests_per_second=0.1, # <-- Super slow! We can only make a request once every 10 seconds!!
check_every_n_seconds=0.1, # Wake up every 100 ms to check whether allowed to make a request,
max_bucket_size=10, # Controls the maximum burst size.
)
model = ChatAnthropic(
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:
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.
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
- [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/)
- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)
- [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/)
docs/snippets/chat_model_tabs.md
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=== "OpenAI"
```
```shell
pip install -U "langchain[openai]"
```
```python
@@ -12,9 +12,11 @@
llm = init_chat_model("openai:gpt-4.1")
```
👉 Read the [OpenAI integration docs](https://python.langchain.com/docs/integrations/chat/openai/)
=== "Anthropic"
```
```shell
pip install -U "langchain[anthropic]"
```
```python
@@ -26,9 +28,11 @@
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
```
👉 Read the [Anthropic integration docs](https://python.langchain.com/docs/integrations/chat/anthropic/)
=== "Azure"
```
```shell
pip install -U "langchain[openai]"
```
```python
@@ -44,10 +48,12 @@
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
```
👉 Read the [Azure integration docs](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/)
=== "Google Gemini"
```
```shell
pip install -U "langchain[google-genai]"
```
```python
@@ -59,9 +65,11 @@
llm = init_chat_model("google_genai:gemini-2.0-flash")
```
👉 Read the [Google GenAI integration docs](https://python.langchain.com/docs/integrations/chat/google_generative_ai/)
=== "AWS Bedrock"
```
```shell
pip install -U "langchain[aws]"
```
```python
@@ -75,3 +83,5 @@
model_provider="bedrock_converse",
)
```
👉 Read the [AWS Bedrock integration docs](https://python.langchain.com/docs/integrations/chat/bedrock/)