> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# ChatCrusoe integration
> Integrate with the ChatCrusoe chat model using LangChain Python.
This page will help you get started with Crusoe AI [chat models](/oss/python/langchain/models). For detailed documentation of all ChatCrusoe features and configurations, head to the [Crusoe managed inference docs](https://docs.crusoecloud.com/managed-inference/overview).
[Crusoe AI](https://www.crusoe.ai/) provides high-performance managed inference for [leading open-source models](https://docs.crusoecloud.com/managed-inference/overview) via the Crusoe Intelligence Foundry, powered by proprietary MemoryAlloy™ technology for ultra-low latency and high throughput.
## Overview
### Integration details
| Class | Package | Serializable | JS support | Downloads | Version |
| :-------------------------------------------------------------------- | :------------------------------------------------------------- | :----------: | :--------: | :-----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------: |
| [ChatCrusoe](https://docs.crusoecloud.com/managed-inference/overview) | [langchain-crusoe](https://pypi.org/project/langchain-crusoe/) | beta | ❌ |  |  |
### Model features
| [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming#llm-tokens) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) |
| :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-----------------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: |
| ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |
## Setup
To access Crusoe models you'll need to create a Crusoe Cloud account, get an Inference API key, and install the `langchain-crusoe` integration package.
### Credentials
Head to the [Crusoe Cloud Console](https://console.crusoecloud.com/) to sign up. Then navigate to the **Security** tab and select **Inference API Key** to generate your key. Once you've done this, set the CRUSOE\_API\_KEY environment variable:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import getpass
import os
if "CRUSOE_API_KEY" not in os.environ:
os.environ["CRUSOE_API_KEY"] = getpass.getpass("Enter your Crusoe API key: ")
```
To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```
### Installation
The LangChain Crusoe integration is included in the `langchain-crusoe` package:
```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-crusoe
```
```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
uv add langchain-crusoe
```
## Instantiation
Now we can instantiate our model object and generate chat completions:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_crusoe import ChatCrusoe
llm = ChatCrusoe(
model="meta-llama/Llama-3.3-70B-Instruct",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# api_key="...", # if not set via CRUSOE_API_KEY env var
# other params...
)
```
### Available models
Crusoe serves leading open-source models via the Intelligence Foundry. See the [full model list](https://docs.crusoecloud.com/managed-inference/overview) for the latest availability.
| Model | Provider | Context Length |
| :---------------------------------- | :---------- | :------------: |
| `meta-llama/Llama-3.3-70B-Instruct` | Meta | 128k |
| `openai/gpt-oss-120b` | OpenAI | 128k |
| `deepseek-ai/DeepSeek-V3-0324` | DeepSeek | 160k |
| `deepseek-ai/DeepSeek-R1-0528` | DeepSeek | 160k |
| `deepseek-ai/DeepSeek-V3.1` | DeepSeek | 160k |
| `Qwen/Qwen3-235B-A22B` | Qwen | 131k |
| `google/gemma-3-12b-it` | Google | 128k |
| `moonshotai/Kimi-K2-Thinking` | Moonshot AI | 131k |
## Invocation
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
messages = [
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3.3-70B-Instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-...', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})
```
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
print(ai_msg.content)
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
J'adore la programmation.
```
## Chaining
We can [chain](/oss/python/langchain/overview) our model with a prompt template like so:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful assistant that translates {input_language} to {output_language}.",
),
("human", "{input}"),
]
)
chain = prompt | llm
chain.invoke(
{
"input_language": "English",
"output_language": "German",
"input": "I love programming.",
}
)
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
AIMessage(content='Ich liebe Programmieren.', response_metadata={...}, id='run-...')
```
## Streaming
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
stream = llm.stream_events(messages, version="v3")
for token in stream.text:
print(token, end="", flush=True)
```
## Tool calling
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
"""Get the current weather in a given location."""
location: str = Field(description="City and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather])
ai_msg = llm_with_tools.invoke("What's the weather like in San Francisco?")
print(ai_msg.tool_calls)
```
## Structured output
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from pydantic import BaseModel, Field
from typing import Optional
class Joke(BaseModel):
"""Joke to tell user."""
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=7)
```
## API reference
For detailed documentation of all ChatCrusoe features and configurations, head to the [Crusoe managed inference docs](https://docs.crusoecloud.com/managed-inference/overview).
***
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