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feat: add Engy as OpenAI-compatible provider

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crabbytt 2026-10-15 19:21:57 +00:00
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3
litellm/constants.py
View file

@ -1059,6 +1059,7 @@ openai_compatible_endpoints: Final[list[str]] = [
"https://api.reka.ai/v1",
"https://gigachat.devices.sberbank.ru/api/v1",
"https://api.openinfer.ai/v1",
"https://api.engy.ai/v1",
]
@ -1137,6 +1138,7 @@ openai_compatible_providers: Final[list[str]] = [
"prism",
"sail",
"reka",
"engy",
]
OPENAI_AUDIO_TRANSCRIPTION_PROVIDERS: Final = frozenset({"openai"} | frozenset(openai_compatible_providers))
@ -1165,6 +1167,7 @@ openai_text_completion_compatible_providers: Final[list] = [ # providers that s
"lambda_ai",
"hyperbolic",
"wandb",
"engy",
]
_openai_like_providers: Final[list[str]] = [
"predibase",

10
litellm/llms/openai_like/providers.json
View file

@ -249,5 +249,15 @@
"/v1/responses",
"/v1/messages"
]
},
"engy": {
"base_url": "https://api.engy.ai/v1",
"api_key_env": "ENGY_API_KEY",
"api_base_env": "ENGY_API_BASE",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
]
}
}

176
litellm/model_prices_and_context_window_backup.json
View file

@ -77018,5 +77018,181 @@
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/deepseek-v4.1-flash": {
"cache_read_input_token_cost": 8e-09,
"input_cost_per_token": 4e-08,
"litellm_provider": "engy",
"max_input_tokens": 1048576,
"max_tokens": 1048576,
"mode": "chat",
"output_cost_per_token": 8e-08,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
},
"engy/deepseek-v4-flash-0731": {
"cache_read_input_token_cost": 9e-09,
"input_cost_per_token": 4.5e-08,
"litellm_provider": "engy",
"max_input_tokens": 1048576,
"max_tokens": 1048576,
"mode": "chat",
"output_cost_per_token": 9e-08,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/glm-5.2": {
"cache_read_input_token_cost": 1.8e-07,
"input_cost_per_token": 6.8e-07,
"litellm_provider": "engy",
"max_input_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 1.5e-06,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/glm-5.3": {
"cache_read_input_token_cost": 1.8e-07,
"input_cost_per_token": 9.8e-07,
"litellm_provider": "engy",
"max_input_tokens": 327680,
"max_tokens": 327680,
"mode": "chat",
"output_cost_per_token": 3.08e-06,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/glm-5.3-flash": {
"cache_read_input_token_cost": 2.7e-08,
"input_cost_per_token": 1.35e-07,
"litellm_provider": "engy",
"max_input_tokens": 327680,
"max_tokens": 327680,
"mode": "chat",
"output_cost_per_token": 4.5e-07,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
},
"engy/kimi-k3": {
"cache_read_input_token_cost": 1.95e-07,
"input_cost_per_token": 1.95e-06,
"litellm_provider": "engy",
"max_input_tokens": 1113088,
"max_tokens": 1113088,
"mode": "chat",
"output_cost_per_token": 9.75e-06,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
},
"engy/qwen3.6-35b-a3b": {
"cache_read_input_token_cost": 1.5e-08,
"input_cost_per_token": 4.5e-08,
"litellm_provider": "engy",
"max_input_tokens": 208192,
"max_tokens": 208192,
"mode": "chat",
"output_cost_per_token": 3e-07,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/qwen3.8-27b": {
"cache_read_input_token_cost": 1.5e-08,
"input_cost_per_token": 4.5e-08,
"litellm_provider": "engy",
"max_input_tokens": 1001536,
"max_tokens": 1001536,
"mode": "chat",
"output_cost_per_token": 3.2e-07,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
}
}

18
litellm/provider_endpoints_support_backup.json
View file

@ -2748,6 +2748,24 @@
"rerank": false,
"a2a": false
}
},
"engy": {
"display_name": "Engy (`engy`)",
"url": "https://docs.litellm.ai/docs/providers/engy",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false,
"interactions": false
}
}
},
"endpoints": {

28
litellm/proxy/public_endpoints/provider_create_fields.json
View file

@ -4070,5 +4070,33 @@
}
],
"default_model_placeholder": "cursor/claude-4-sonnet"
},
{
"provider": "ENGY",
"provider_display_name": "Engy",
"litellm_provider": "engy",
"credential_fields": [
{
"key": "api_base",
"label": "API Base",
"placeholder": "https://api.engy.ai/v1",
"tooltip": null,
"required": false,
"field_type": "text",
"options": null,
"default_value": null
},
{
"key": "api_key",
"label": "API Key",
"placeholder": null,
"tooltip": null,
"required": true,
"field_type": "password",
"options": null,
"default_value": null
}
],
"default_model_placeholder": "engy/kimi-k3"
}
]

1
litellm/types/utils.py
View file

@ -4557,6 +4557,7 @@ class LlmProviders(str, Enum):
CURSOR = "cursor"
BEDROCK_MANTLE = "bedrock_mantle"
GDC = "gdc"
ENGY = "engy"
# Create a set of all provider values for quick lookup

176
model_prices_and_context_window.json
View file

@ -77018,5 +77018,181 @@
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/deepseek-v4.1-flash": {
"cache_read_input_token_cost": 8e-09,
"input_cost_per_token": 4e-08,
"litellm_provider": "engy",
"max_input_tokens": 1048576,
"max_tokens": 1048576,
"mode": "chat",
"output_cost_per_token": 8e-08,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
},
"engy/deepseek-v4-flash-0731": {
"cache_read_input_token_cost": 9e-09,
"input_cost_per_token": 4.5e-08,
"litellm_provider": "engy",
"max_input_tokens": 1048576,
"max_tokens": 1048576,
"mode": "chat",
"output_cost_per_token": 9e-08,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/glm-5.2": {
"cache_read_input_token_cost": 1.8e-07,
"input_cost_per_token": 6.8e-07,
"litellm_provider": "engy",
"max_input_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 1.5e-06,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/glm-5.3": {
"cache_read_input_token_cost": 1.8e-07,
"input_cost_per_token": 9.8e-07,
"litellm_provider": "engy",
"max_input_tokens": 327680,
"max_tokens": 327680,
"mode": "chat",
"output_cost_per_token": 3.08e-06,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/glm-5.3-flash": {
"cache_read_input_token_cost": 2.7e-08,
"input_cost_per_token": 1.35e-07,
"litellm_provider": "engy",
"max_input_tokens": 327680,
"max_tokens": 327680,
"mode": "chat",
"output_cost_per_token": 4.5e-07,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
},
"engy/kimi-k3": {
"cache_read_input_token_cost": 1.95e-07,
"input_cost_per_token": 1.95e-06,
"litellm_provider": "engy",
"max_input_tokens": 1113088,
"max_tokens": 1113088,
"mode": "chat",
"output_cost_per_token": 9.75e-06,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
},
"engy/qwen3.6-35b-a3b": {
"cache_read_input_token_cost": 1.5e-08,
"input_cost_per_token": 4.5e-08,
"litellm_provider": "engy",
"max_input_tokens": 208192,
"max_tokens": 208192,
"mode": "chat",
"output_cost_per_token": 3e-07,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},
"engy/qwen3.8-27b": {
"cache_read_input_token_cost": 1.5e-08,
"input_cost_per_token": 4.5e-08,
"litellm_provider": "engy",
"max_input_tokens": 1001536,
"max_tokens": 1001536,
"mode": "chat",
"output_cost_per_token": 3.2e-07,
"source": "https://api.engy.ai/v1/models",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": true
}
}

18
provider_endpoints_support.json
View file

@ -3272,6 +3272,24 @@
"rerank": false,
"a2a": false
}
},
"engy": {
"display_name": "Engy (`engy`)",
"url": "https://docs.litellm.ai/docs/providers/engy",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false,
"interactions": false
}
}
},
"endpoints": {

295
tests/unit/llms/openai_like/test_engy_provider.py Normal file
View file

@ -0,0 +1,295 @@
import json
from pathlib import Path
from typing import Final
import httpx
import pytest
import respx
import litellm
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
ENGY_API_BASE: Final = "https://api.engy.ai/v1"
ENGY_MODEL: Final = "engy/kimi-k3"
ENGY_MODEL_ID: Final = "kimi-k3"
def test_engy_provider_resolution(monkeypatch: pytest.MonkeyPatch):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
monkeypatch.setenv("ENGY_API_KEY", "engy-env-key")
model, provider, api_key, api_base = get_llm_provider(
model=ENGY_MODEL,
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == ENGY_MODEL_ID
assert provider == "engy"
assert api_key == "engy-env-key"
assert api_base == ENGY_API_BASE
def test_engy_provider_keeps_explicit_credentials(monkeypatch: pytest.MonkeyPatch):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
monkeypatch.setenv("ENGY_API_KEY", "engy-env-key")
_, provider, api_key, api_base = get_llm_provider(
model=ENGY_MODEL,
custom_llm_provider=None,
api_base="https://engy.internal.example/v1",
api_key="engy-explicit-key",
)
assert provider == "engy"
assert api_key == "engy-explicit-key"
assert api_base == "https://engy.internal.example/v1"
def test_engy_provider_reads_base_from_env(monkeypatch: pytest.MonkeyPatch):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
monkeypatch.setenv("ENGY_API_KEY", "engy-env-key")
monkeypatch.setenv("ENGY_API_BASE", "https://engy.internal.example/v1")
_, provider, _, api_base = get_llm_provider(
model=ENGY_MODEL,
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert provider == "engy"
assert api_base == "https://engy.internal.example/v1"
def test_engy_provider_resolves_from_api_base():
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
_, provider, _, api_base = get_llm_provider(
model=ENGY_MODEL_ID,
custom_llm_provider=None,
api_base=ENGY_API_BASE,
api_key="engy-explicit-key",
)
assert provider == "engy"
assert api_base == ENGY_API_BASE
def test_engy_is_available_in_add_model_form():
fields_path: Final = Path(litellm.__file__).parent / "proxy" / "public_endpoints" / "provider_create_fields.json"
providers: Final = json.loads(fields_path.read_text())
engy: Final = next(provider for provider in providers if provider["litellm_provider"] == "engy")
assert engy["provider"] == "ENGY"
assert engy["provider_display_name"] == "Engy"
assert engy["default_model_placeholder"].startswith("engy/")
assert {field["key"]: field["required"] for field in engy["credential_fields"]} == {
"api_base": False,
"api_key": True,
}
def test_engy_endpoint_support_matches_providers_json():
matrix_path: Final = Path(litellm.__file__).parent / "provider_endpoints_support_backup.json"
endpoints: Final = json.loads(matrix_path.read_text())["providers"]["engy"]["endpoints"]
providers_json: Final = json.loads(
(Path(litellm.__file__).parent / "llms" / "openai_like" / "providers.json").read_text()
)
declared: Final = set(providers_json["engy"]["supported_endpoints"])
supported_from_matrix: Final = {
"/v1/chat/completions": endpoints["chat_completions"],
"/v1/responses": endpoints["responses"],
"/v1/messages": endpoints["messages"],
}
assert {endpoint for endpoint, supported in supported_from_matrix.items() if supported} == declared
def test_engy_chat_completion_request():
with respx.mock() as upstream:
route: Final = upstream.post(f"{ENGY_API_BASE}/chat/completions").respond(
200,
json={
"id": "chatcmpl_engy",
"object": "chat.completion",
"created": 1_789_550_000,
"model": ENGY_MODEL_ID,
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "Hello from Engy"},
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 4, "completion_tokens": 3, "total_tokens": 7},
},
)
response: Final = litellm.completion(
model=ENGY_MODEL,
messages=[{"role": "user", "content": "Say hello"}],
api_key="engy-test-key",
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert str(request.url) == f"{ENGY_API_BASE}/chat/completions"
assert request.headers["authorization"] == "Bearer engy-test-key"
assert body["model"] == ENGY_MODEL_ID
assert body["messages"] == [{"role": "user", "content": "Say hello"}]
assert response.choices[0].message.content == "Hello from Engy"
def test_engy_responses_request():
with respx.mock() as upstream:
route: Final = upstream.post(f"{ENGY_API_BASE}/responses").respond(
200,
json={
"id": "resp_engy",
"object": "response",
"created_at": 1_789_550_000,
"model": ENGY_MODEL_ID,
"status": "completed",
"output": [
{
"id": "msg_engy",
"type": "message",
"role": "assistant",
"status": "completed",
"content": [{"type": "output_text", "text": "Hello from Engy", "annotations": []}],
}
],
"usage": {"input_tokens": 4, "output_tokens": 3, "total_tokens": 7},
},
)
response: Final = litellm.responses(
model=ENGY_MODEL,
input="Say hello",
api_key="engy-test-key",
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert str(request.url) == f"{ENGY_API_BASE}/responses"
assert request.headers["authorization"] == "Bearer engy-test-key"
assert body["model"] == ENGY_MODEL_ID
assert body["input"] == "Say hello"
assert response.output[0].content[0].text == "Hello from Engy"
@pytest.mark.asyncio
async def test_engy_anthropic_messages_request():
with respx.mock() as upstream:
route: Final = upstream.post(f"{ENGY_API_BASE}/messages").respond(
200,
json={
"id": "msg_engy",
"type": "message",
"role": "assistant",
"model": ENGY_MODEL_ID,
"content": [{"type": "text", "text": "Hello from Engy"}],
"stop_reason": "end_turn",
"stop_sequence": None,
"usage": {"input_tokens": 4, "output_tokens": 3},
},
)
response: Final = await litellm.anthropic.messages.acreate(
model=ENGY_MODEL,
messages=[{"role": "user", "content": "Say hello"}],
max_tokens=32,
api_key="engy-test-key",
client=AsyncHTTPHandler(transport=httpx.AsyncHTTPTransport()),
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert str(request.url) == f"{ENGY_API_BASE}/messages"
assert request.headers["authorization"] == "Bearer engy-test-key"
assert body["model"] == ENGY_MODEL_ID
assert body["messages"] == [{"role": "user", "content": "Say hello"}]
assert response["content"][0]["text"] == "Hello from Engy"
def test_engy_chat_completion_cost_bills_cached_tokens_at_cache_read_rate():
row: Final = litellm.model_cost[ENGY_MODEL]
with respx.mock() as upstream:
upstream.post(f"{ENGY_API_BASE}/chat/completions").respond(
200,
json={
"id": "chatcmpl_engy",
"object": "chat.completion",
"created": 1_789_550_000,
"model": ENGY_MODEL_ID,
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "ok"},
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 1000,
"completion_tokens": 20,
"total_tokens": 1020,
"prompt_tokens_details": {"cached_tokens": 800},
},
},
)
response: Final = litellm.completion(
model=ENGY_MODEL,
messages=[{"role": "user", "content": "Say hello"}],
api_key="engy-test-key",
)
assert row["cache_read_input_token_cost"] < row["input_cost_per_token"]
assert litellm.completion_cost(completion_response=response, model=ENGY_MODEL) == pytest.approx(
200 * row["input_cost_per_token"] + 800 * row["cache_read_input_token_cost"] + 20 * row["output_cost_per_token"]
)
def test_engy_models_are_registered_in_the_cost_map():
catalog: Final = {name for name, info in litellm.model_cost.items() if info.get("litellm_provider") == "engy"}
assert len(catalog) == 8
assert all(name.startswith("engy/") for name in catalog)
info: Final = litellm.get_model_info(model=ENGY_MODEL)
assert info["litellm_provider"] == "engy"
assert info["mode"] == "chat"
def test_engy_cost_map_backup_mirrors_main():
main_map: Final = json.loads(
(Path(litellm.__file__).parent.parent / "model_prices_and_context_window.json").read_text()
)
backup_map: Final = json.loads(
(Path(litellm.__file__).parent / "model_prices_and_context_window_backup.json").read_text()
)
main_keys: Final = {key for key in main_map if key.startswith("engy/")}
backup_keys: Final = {key for key in backup_map if key.startswith("engy/")}
assert main_keys and main_keys == backup_keys
for key in main_keys:
assert backup_map[key] == main_map[key]
def test_engy_cost_map_rows_declare_same_endpoints_as_providers_json():
providers_json: Final = json.loads(
(Path(litellm.__file__).parent / "llms" / "openai_like" / "providers.json").read_text()
)
declared: Final = providers_json["engy"]["supported_endpoints"]
cost_map_paths: Final = (
Path(litellm.__file__).parent.parent / "model_prices_and_context_window.json",
Path(litellm.__file__).parent / "model_prices_and_context_window_backup.json",
)
for cost_map_path in cost_map_paths:
cost_map: Final = json.loads(cost_map_path.read_text())
engy_rows: Final = {key: row for key, row in cost_map.items() if key.startswith("engy/")}
assert engy_rows
for row in engy_rows.values():
assert row["supported_endpoints"] == declared