add Crusoe AI chat model integration

src/oss/python/integrations/chat/crusoe.mdx

@@ -0,0 +1,196 @@
+---
+title: ChatCrusoe
+description: Crusoe AI Managed Inference chat model integration for LangChain.
+---
+
+# ChatCrusoe
+
+This page will help you get started with Crusoe AI [chat models](/oss/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 | Local | Serializable | JS support | Downloads | Version |
+| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
+| [ChatCrusoe](https://docs.crusoecloud.com/managed-inference/overview) | [langchain-crusoe](https://pypi.org/project/langchain-crusoe/) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-crusoe?style=flat-square&label=%20&color=blue) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-crusoe?style=flat-square&label=%20&color=orange) |
+
+### Model features
+
+| [Tool calling](/oss/langchain/tools) | [Structured output](/oss/langchain/structured-output) | JSON mode | [Image input](/oss/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/langchain/streaming#llm-tokens) | Native async | [Token usage](/oss/langchain/models#token-usage) | [Logprobs](/oss/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
+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](https://docs.langchain.com/langsmith/home) API key:
+
+```python
+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:
+
+```python pip
+pip install -qU langchain-crusoe
+```
+
+```python uv
+uv add langchain-crusoe
+```
+
+## Instantiation
+
+Now we can instantiate our model object and generate chat completions:
+
+```python
+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
+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
+```
+
+```python
+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
+print(ai_msg.content)
+```
+
+```
+J'adore la programmation.
+```
+
+## Chaining
+
+We can [chain](/oss/langchain/overview) our model with a prompt template like so:
+
+```python
+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.",
+    }
+)
+```
+
+```python
+AIMessage(content='Ich liebe Programmieren.', response_metadata={...}, id='run-...')
+```
+
+## Streaming
+
+```python
+for chunk in llm.stream(messages):
+    print(chunk.content, end="", flush=True)
+```
+
+## Tool calling
+
+```python
+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
+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")
+```
+
+```python
+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).