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adding crusoe to litellm

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Emmanuel Acheampong 2026-03-09 15:20:58 -07:00 committed by Sameer Kankute
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186
docs/my-website/docs/providers/crusoe.md Normal file
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@ -0,0 +1,186 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Crusoe
## Overview
| Property | Details |
|-------|-------|
| Description | Crusoe Cloud provides GPU-accelerated inference for open-source large language models, optimized for performance and cost efficiency. |
| Provider Route on LiteLLM | `crusoe/` |
| Link to Provider Doc | [Crusoe Managed Inference Documentation ↗](https://docs.crusoecloud.com/managed-inference/overview/index.html) |
| Base URL | `https://managed-inference-api-proxy.crusoecloud.com/v1/` |
| Supported Operations | [`/chat/completions`](#sample-usage) |
<br />
<br />
**We support ALL Crusoe models, just set `crusoe/` as a prefix when sending completion requests**
## Available Models
| Model | Description | Context Window |
|-------|-------------|----------------|
| `crusoe/deepseek-ai/DeepSeek-R1-0528` | DeepSeek R1 reasoning model (May 2025) | 163,840 tokens |
| `crusoe/deepseek-ai/DeepSeek-V3-0324` | DeepSeek V3 chat model (March 2025) | 163,840 tokens |
| `crusoe/google/gemma-3-12b-it` | Google Gemma 3 12B instruction-tuned | 131,072 tokens |
| `crusoe/meta-llama/Llama-3.3-70B-Instruct` | Llama 3.3 70B instruction-tuned | 131,072 tokens |
| `crusoe/moonshotai/Kimi-K2-Thinking` | Kimi K2 extended thinking model | 262,144 tokens |
| `crusoe/openai/gpt-oss-120b` | OpenAI 120B open-source model | 131,072 tokens |
| `crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507` | Qwen3 235B MoE instruction-tuned | 262,144 tokens |
## Required Variables
```python showLineNumbers title="Environment Variables"
os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key
```
## Usage - LiteLLM Python SDK
### Non-streaming
```python showLineNumbers title="Crusoe Non-streaming Completion"
import os
import litellm
from litellm import completion
os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key
messages = [{"content": "Hello, how are you?", "role": "user"}]
# Crusoe call
response = completion(
model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
messages=messages
)
print(response)
```
### Streaming
```python showLineNumbers title="Crusoe Streaming Completion"
import os
import litellm
from litellm import completion
os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key
messages = [{"content": "Write a short story about AI", "role": "user"}]
# Crusoe call with streaming
response = completion(
model="crusoe/meta-llama/Llama-3.1-70B-Instruct",
messages=messages,
stream=True
)
for chunk in response:
print(chunk)
```
### Function Calling
```python showLineNumbers title="Crusoe Function Calling"
import os
import litellm
from litellm import completion
os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}]
messages = [{"role": "user", "content": "What's the weather in Boston?"}]
response = completion(
model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
messages=messages,
tools=tools,
tool_choice="auto"
)
print(response)
```
## Usage - LiteLLM Proxy Server
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: llama-3.3-70b
litellm_params:
model: crusoe/meta-llama/Llama-3.3-70B-Instruct
api_key: os.environ/CRUSOE_API_KEY
- model_name: deepseek-r1
litellm_params:
model: crusoe/deepseek-ai/DeepSeek-R1-0528
api_key: os.environ/CRUSOE_API_KEY
- model_name: deepseek-v3
litellm_params:
model: crusoe/deepseek-ai/DeepSeek-V3-0324
api_key: os.environ/CRUSOE_API_KEY
- model_name: qwen3-235b
litellm_params:
model: crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507
api_key: os.environ/CRUSOE_API_KEY
- model_name: kimi-k2
litellm_params:
model: crusoe/moonshotai/Kimi-K2-Thinking
api_key: os.environ/CRUSOE_API_KEY
```
## Custom API Base
```python showLineNumbers title="Custom API Base"
import os
import litellm
from litellm import completion
# Using environment variable
os.environ["CRUSOE_API_BASE"] = "https://custom.crusoecloud.com/v1/"
os.environ["CRUSOE_API_KEY"] = "" # your API key
# Or pass directly
response = completion(
model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
messages=[{"content": "Hello!", "role": "user"}],
api_base="https://custom.crusoecloud.com/v1/",
api_key="your-api-key"
)
```
## Supported OpenAI Parameters
- `temperature`
- `max_tokens`
- `max_completion_tokens`
- `top_p`
- `frequency_penalty`
- `presence_penalty`
- `stop`
- `n`
- `stream`
- `tools`
- `tool_choice`
- `response_format`
- `seed`
- `user`
- `logit_bias`
- `logprobs`
- `top_logprobs`

4
litellm/__init__.py
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@ -600,6 +600,7 @@ publicai_models: Set = set()
v0_models: Set = set()
morph_models: Set = set()
lambda_ai_models: Set = set()
crusoe_models: Set = set()
hyperbolic_models: Set = set()
black_forest_labs_models: Set = set()
recraft_models: Set = set()
@ -847,6 +848,8 @@ def add_known_models(model_cost_map: Optional[Dict] = None):
morph_models.add(key)
elif value.get("litellm_provider") == "lambda_ai":
lambda_ai_models.add(key)
elif value.get("litellm_provider") == "crusoe":
crusoe_models.add(key)
elif value.get("litellm_provider") == "hyperbolic":
hyperbolic_models.add(key)
elif value.get("litellm_provider") == "black_forest_labs":
@ -1082,6 +1085,7 @@ models_by_provider: dict = {
"v0": v0_models,
"morph": morph_models,
"lambda_ai": lambda_ai_models,
"crusoe": crusoe_models,
"hyperbolic": hyperbolic_models,
"black_forest_labs": black_forest_labs_models,
"recraft": recraft_models,

1
litellm/_lazy_imports_registry.py
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@ -1140,6 +1140,7 @@ _LLM_CONFIGS_IMPORT_MAP = {
"MorphChatConfig": (".llms.morph.chat.transformation", "MorphChatConfig"),
"RAGFlowConfig": (".llms.ragflow.chat.transformation", "RAGFlowConfig"),
"LambdaAIChatConfig": (".llms.lambda_ai.chat.transformation", "LambdaAIChatConfig"),
"CrusoeChatConfig": (".llms.crusoe.chat.transformation", "CrusoeChatConfig"),
"HyperbolicChatConfig": (
".llms.hyperbolic.chat.transformation",
"HyperbolicChatConfig",

4
litellm/constants.py
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@ -610,6 +610,7 @@ LITELLM_CHAT_PROVIDERS = [
"oci",
"morph",
"lambda_ai",
"crusoe",
"vercel_ai_gateway",
"wandb",
"ovhcloud",
@ -768,6 +769,7 @@ openai_compatible_endpoints: List = [
"https://api.v0.dev/v1",
"https://api.morphllm.com/v1",
"https://api.lambda.ai/v1",
"https://managed-inference-api-proxy.crusoecloud.com/v1/",
"https://api.hyperbolic.xyz/v1",
"https://ai-gateway.helicone.ai/",
"https://ai-gateway.vercel.sh/v1",
@ -823,6 +825,7 @@ openai_compatible_providers: List = [
"helicone",
"morph",
"lambda_ai",
"crusoe",
"hyperbolic",
"vercel_ai_gateway",
"aiml",
@ -851,6 +854,7 @@ openai_text_completion_compatible_providers: List = (
"chutes",
"v0",
"lambda_ai",
"crusoe",
"hyperbolic",
"wandb",
]

10
litellm/litellm_core_utils/get_llm_provider_logic.py
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@ -353,6 +353,9 @@ def get_llm_provider( # noqa: PLR0915
elif endpoint == "https://api.lambda.ai/v1":
custom_llm_provider = "lambda_ai"
dynamic_api_key = get_secret_str("LAMBDA_API_KEY")
elif endpoint == "https://managed-inference-api-proxy.crusoecloud.com/v1/":
custom_llm_provider = "crusoe"
dynamic_api_key = get_secret_str("CRUSOE_API_KEY")
elif endpoint == "https://api.hyperbolic.xyz/v1":
custom_llm_provider = "hyperbolic"
dynamic_api_key = get_secret_str("HYPERBOLIC_API_KEY")
@ -919,6 +922,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915
) = litellm.LambdaAIChatConfig()._get_openai_compatible_provider_info(
api_base, api_key
)
elif custom_llm_provider == "crusoe":
(
api_base,
dynamic_api_key,
) = litellm.CrusoeChatConfig()._get_openai_compatible_provider_info(
api_base, api_key
)
elif custom_llm_provider == "hyperbolic":
(
api_base,

0
litellm/llms/crusoe/__init__.py Normal file
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0
litellm/llms/crusoe/chat/__init__.py Normal file
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litellm/llms/crusoe/chat/transformation.py Normal file
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@ -0,0 +1,42 @@
"""
Translate from OpenAI's `/v1/chat/completions` to Crusoe's `/v1/chat/completions`
"""
from typing import Optional, Tuple
from litellm.secret_managers.main import get_secret_str
from ...openai_like.chat.transformation import OpenAILikeChatConfig
class CrusoeChatConfig(OpenAILikeChatConfig):
"""
Crusoe is OpenAI-compatible with standard endpoints.
Docs: https://docs.crusoecloud.com/managed-inference/overview/index.html
"""
@property
def custom_llm_provider(self) -> Optional[str]:
return "crusoe"
def _get_openai_compatible_provider_info(
self, api_base: Optional[str], api_key: Optional[str]
) -> Tuple[Optional[str], Optional[str]]:
api_base = (
api_base
or get_secret_str("CRUSOE_API_BASE")
or "https://managed-inference-api-proxy.crusoecloud.com/v1/"
) # type: ignore
dynamic_api_key = api_key or get_secret_str("CRUSOE_API_KEY")
return api_base, dynamic_api_key
def get_supported_openai_params(self, model: str) -> list:
return [
"messages",
"model",
"temperature",
"top_p",
"frequency_penalty",
"presence_penalty",
]

89
model_prices_and_context_window.json
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@ -22109,6 +22109,95 @@
"tool_use_system_prompt_tokens": 346,
"supports_native_structured_output": true
},
"crusoe/deepseek-ai/DeepSeek-R1-0528": {
"input_cost_per_token": 3e-06,
"litellm_provider": "crusoe",
"max_input_tokens": 163840,
"max_output_tokens": 163840,
"max_tokens": 163840,
"mode": "chat",
"output_cost_per_token": 7e-06,
"supports_function_calling": false,
"supports_system_messages": true,
"supports_tool_choice": false
},
"crusoe/deepseek-ai/DeepSeek-V3-0324": {
"input_cost_per_token": 1.5e-06,
"litellm_provider": "crusoe",
"max_input_tokens": 163840,
"max_output_tokens": 163840,
"max_tokens": 163840,
"mode": "chat",
"output_cost_per_token": 1.5e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_system_messages": true,
"supports_tool_choice": true
},
"crusoe/google/gemma-3-12b-it": {
"input_cost_per_token": 1e-07,
"litellm_provider": "crusoe",
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 1e-07,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_system_messages": true,
"supports_tool_choice": true
},
"crusoe/meta-llama/Llama-3.3-70B-Instruct": {
"input_cost_per_token": 2e-07,
"litellm_provider": "crusoe",
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 2e-07,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_system_messages": true,
"supports_tool_choice": true
},
"crusoe/moonshotai/Kimi-K2-Thinking": {
"input_cost_per_token": 2.5e-06,
"litellm_provider": "crusoe",
"max_input_tokens": 262144,
"max_output_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 2.5e-06,
"supports_function_calling": false,
"supports_system_messages": true,
"supports_tool_choice": false
},
"crusoe/openai/gpt-oss-120b": {
"input_cost_per_token": 8e-07,
"litellm_provider": "crusoe",
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 8e-07,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_system_messages": true,
"supports_tool_choice": true
},
"crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507": {
"input_cost_per_token": 3e-06,
"litellm_provider": "crusoe",
"max_input_tokens": 262144,
"max_output_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 3e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_system_messages": true,
"supports_tool_choice": true
},
"lambda_ai/deepseek-llama3.3-70b": {
"input_cost_per_token": 2e-07,
"litellm_provider": "lambda_ai",

164
tests/llm_translation/test_crusoe.py Normal file
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@ -0,0 +1,164 @@
"""
Tests for Crusoe provider integration
"""
import os
from unittest import mock
import pytest
import litellm
from litellm import completion
from litellm.llms.crusoe.chat.transformation import CrusoeChatConfig
CRUSOE_API_BASE = "https://managed-inference-api-proxy.crusoecloud.com/v1/"
def test_crusoe_config_initialization():
"""Test CrusoeChatConfig initializes correctly"""
config = CrusoeChatConfig()
assert config.custom_llm_provider == "crusoe"
def test_crusoe_get_openai_compatible_provider_info():
"""Test Crusoe provider info retrieval"""
config = CrusoeChatConfig()
# Test with default values (no env vars set)
with mock.patch.dict(os.environ, {}, clear=True):
api_base, api_key = config._get_openai_compatible_provider_info(None, None)
assert api_base == CRUSOE_API_BASE
assert api_key is None
# Test with environment variables
with mock.patch.dict(
os.environ,
{
"CRUSOE_API_KEY": "test-key",
"CRUSOE_API_BASE": "https://custom.crusoecloud.com/v1/",
},
):
api_base, api_key = config._get_openai_compatible_provider_info(None, None)
assert api_base == "https://custom.crusoecloud.com/v1/"
assert api_key == "test-key"
# Test with explicit parameters (should override env vars)
with mock.patch.dict(
os.environ,
{
"CRUSOE_API_KEY": "env-key",
"CRUSOE_API_BASE": "https://env.crusoecloud.com/v1/",
},
):
api_base, api_key = config._get_openai_compatible_provider_info(
"https://param.crusoecloud.com/v1/", "param-key"
)
assert api_base == "https://param.crusoecloud.com/v1/"
assert api_key == "param-key"
def test_get_llm_provider_crusoe():
"""Test that get_llm_provider correctly identifies Crusoe"""
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
# Test with crusoe/model-name format
model, provider, api_key, api_base = get_llm_provider(
"crusoe/meta-llama/Llama-3.3-70B-Instruct"
)
assert model == "meta-llama/Llama-3.3-70B-Instruct"
assert provider == "crusoe"
# Test with api_base containing Crusoe endpoint
model, provider, api_key, api_base = get_llm_provider(
"meta-llama/Llama-3.3-70B-Instruct",
api_base=CRUSOE_API_BASE,
)
assert model == "meta-llama/Llama-3.3-70B-Instruct"
assert provider == "crusoe"
assert api_base == CRUSOE_API_BASE
def test_crusoe_in_provider_lists():
"""Test that Crusoe is registered in all necessary provider lists"""
assert "crusoe" in litellm.openai_compatible_providers
assert "crusoe" in litellm.provider_list
assert CRUSOE_API_BASE in litellm.openai_compatible_endpoints
def test_crusoe_models_configuration():
"""Test that Crusoe models are configured correctly"""
from litellm import get_model_info
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm.crusoe_models = set()
litellm.add_known_models()
crusoe_models = [
"crusoe/meta-llama/Llama-3.3-70B-Instruct",
"crusoe/deepseek-ai/DeepSeek-R1-0528",
"crusoe/deepseek-ai/DeepSeek-V3-0324",
"crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507",
"crusoe/moonshotai/Kimi-K2-Thinking",
"crusoe/openai/gpt-oss-120b",
"crusoe/google/gemma-3-12b-it",
]
for model in crusoe_models:
model_info = get_model_info(model)
assert model_info is not None, f"Model info not found for {model}"
assert model_info.get("litellm_provider") == "crusoe", (
f"{model} should have crusoe as provider"
)
assert model_info.get("mode") == "chat", f"{model} should be in chat mode"
def test_crusoe_model_list_populated():
"""Test that crusoe_models list is populated correctly"""
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm.crusoe_models = set()
litellm.add_known_models()
assert len(litellm.crusoe_models) > 0, "crusoe_models list should not be empty"
for model in litellm.crusoe_models:
assert model.startswith("crusoe/"), (
f"Model {model} should start with 'crusoe/'"
)
expected_models = [
"crusoe/meta-llama/Llama-3.3-70B-Instruct",
"crusoe/deepseek-ai/DeepSeek-R1-0528",
"crusoe/deepseek-ai/DeepSeek-V3-0324",
"crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507",
"crusoe/moonshotai/Kimi-K2-Thinking",
"crusoe/openai/gpt-oss-120b",
"crusoe/google/gemma-3-12b-it",
]
for model in expected_models:
assert model in litellm.crusoe_models, (
f"{model} should be in crusoe_models list"
)
@pytest.mark.asyncio
async def test_crusoe_completion_call():
"""Test completion call with Crusoe provider (requires CRUSOE_API_KEY)"""
if not os.getenv("CRUSOE_API_KEY"):
pytest.skip("CRUSOE_API_KEY not set")
try:
response = await litellm.acompletion(
model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
messages=[{"role": "user", "content": "Hello, this is a test"}],
max_tokens=10,
)
assert response.choices[0].message.content
assert response.model
assert response.usage
except Exception as e:
if "crusoe" not in str(e) and "provider" not in str(e).lower():
raise