Add CheaperInferenceLLM provider

docs/modules/llm.md

@@ -123,6 +123,35 @@ response = llm.generate(
 )
 ```
 
+### CheaperInferenceLLM
+
+CheaperInferenceLLM is an adapter for [Cheaper Inference](https://cheaperinference.com/), an OpenAI-compatible LLM gateway. One API key gives access to models from several labs.
+
+The `CheaperInferenceLLM` model class in EvoAgentX uses the same API format for all models on Cheaper Inference. You can get an API key at [cheaperinference.com/signup](https://cheaperinference.com/signup).
+
+**Basic Usage:**
+
+```python
+from evoagentx.models import CheaperInferenceConfig, CheaperInferenceLLM
+
+# Configure the model
+config = CheaperInferenceConfig(
+    model="gpt-5.4-mini",
+    cheaperinference_key="your-cheaperinference-api-key",
+    temperature=0.7,
+    max_tokens=1000
+)
+
+# Initialize the model
+llm = CheaperInferenceLLM(config=config)
+
+# Generate text
+response = llm.generate(
+    prompt="Write a poem about artificial intelligence.",
+    system_message="You are a creative poet."
+)
+```
+
 ### OpenRouterLLM
 
 OpenRouterLLM is an adapter for the [OpenRouter platform](https://openrouter.ai/), which provides access to a wide range of language models from various providers through a unified API. It supports models from providers like Anthropic, Google, Meta, Mistral AI, and more, all accessible through a single interface.

docs/zh/modules/llm.md

@@ -122,6 +122,35 @@ response = llm.generate(
 )
 ```
 
+### CheaperInferenceLLM
+
+CheaperInferenceLLM 是 [Cheaper Inference](https://cheaperinference.com/) 的适配器。Cheaper Inference 是一个兼容 OpenAI 的 LLM 网关，使用一个 API 密钥即可访问多家实验室的模型。
+
+EvoAgentX 中的 `CheaperInferenceLLM` 模型类对 Cheaper Inference 上的所有模型使用相同的 API 格式。可以在 [cheaperinference.com/signup](https://cheaperinference.com/signup) 获取 API 密钥。
+
+**基本用法：**
+
+```python
+from evoagentx.models import CheaperInferenceConfig, CheaperInferenceLLM
+
+# Configure the model
+config = CheaperInferenceConfig(
+    model="gpt-5.4-mini",
+    cheaperinference_key="your-cheaperinference-api-key",
+    temperature=0.7,
+    max_tokens=1000
+)
+
+# Initialize the model
+llm = CheaperInferenceLLM(config=config)
+
+# Generate text
+response = llm.generate(
+    prompt="Write a poem about artificial intelligence.",
+    system_message="You are a creative poet."
+)
+```
+
 ### OpenRouterLLM
 
 OpenRouterLLM 是 [OpenRouter 平台](https://openrouter.ai/) 的适配器，该平台通过统一的 API 提供对各种提供商的语言模型的访问。它支持来自 Anthropic、Google、Meta、Mistral AI 等提供商的模型，所有这些都可以通过单一接口访问。

evoagentx/models/__init__.py

@@ -8,3 +8,4 @@
 from .openrouter_model import * 
 from .aliyun_model import *
 from .novita_model import *
+from .cheaperinference_model import *

evoagentx/models/cheaperinference_model.py

@@ -0,0 +1,61 @@
+from typing import Union
+
+from openai import AsyncOpenAI, OpenAI
+from openai.types.chat import ChatCompletion, ChatCompletionChunk
+
+from .openai_model import OpenAILLM
+from .model_configs import CheaperInferenceConfig
+from ..core.logging import logger
+from ..core.registry import register_model
+from .model_utils import Cost, cost_manager
+
+# Cheaper Inference exposes an OpenAI-compatible API at this base URL.
+CHEAPERINFERENCE_BASE_URL = "https://api.cheaperinference.com/v1"
+
+
+@register_model(config_cls=CheaperInferenceConfig, alias=["cheaperinference"])
+class CheaperInferenceLLM(OpenAILLM):
+    """Cheaper Inference LLM client.
+
+    Cheaper Inference speaks the OpenAI chat-completions protocol, so this reuses all of
+    ``OpenAILLM``'s generation/streaming/tool-call logic and only overrides client
+    construction and cost handling.
+
+    LiteLLM has no pricing data for Cheaper Inference models, so the dollar cost
+    cannot be computed or approximated. Token counts are still tracked; the
+    per-model cost is recorded as 0. A warning is emitted once at init time.
+    """
+
+    def init_model(self):
+        self._client = None
+        self._async_client = None
+        # parameters in CheaperInferenceConfig that are not model input parameters
+        self._default_ignore_fields = ["llm_type", "cheaperinference_key", "output_response"]
+        logger.warning(
+            "[CheaperInferenceLLM] LiteLLM has no pricing data for Cheaper Inference models, so dollar "
+            "cost cannot be computed. Token usage will be tracked, but cost will "
+            "be recorded as 0."
+        )
+
+    def _init_client(self, config: CheaperInferenceConfig):
+        return OpenAI(api_key=config.cheaperinference_key, base_url=CHEAPERINFERENCE_BASE_URL)
+
+    def _init_async_client(self, config: CheaperInferenceConfig):
+        return AsyncOpenAI(api_key=config.cheaperinference_key, base_url=CHEAPERINFERENCE_BASE_URL)
+
+    def _update_cost(self, response: Union[ChatCompletion, ChatCompletionChunk]):
+        # Override OpenAILLM's LiteLLM-based cost computation: only record token
+        # counts and leave cost at 0 (see class docstring).
+        usage = getattr(response, "usage", None)
+        if usage is None:
+            logger.warning(
+                f"[CheaperInferenceLLM] usage is None in response (id={getattr(response, 'id', '?')}); "
+                "tokens will not be recorded."
+            )
+            return
+        cost = Cost(
+            input_tokens=usage.prompt_tokens,
+            output_tokens=usage.completion_tokens,
+            cost=0.0,
+        )
+        cost_manager.update_cost(cost=cost, model=self.config.model)

evoagentx/models/model_configs.py

@@ -172,6 +172,38 @@ def __str__(self):
         return self.model
 
 
+class CheaperInferenceConfig(LLMConfig):
+
+    # LLM keys
+    llm_type: str = "CheaperInferenceLLM"
+    cheaperinference_key: Optional[str] = Field(default=None, description="the API key used to authenticate Cheaper Inference requests")
+
+    # generation parameters
+    temperature: Optional[float] = Field(default=None, description="the temperature used to scaling logits")
+    max_tokens : Optional[int] = Field(default=None, description="maximum number of generated tokens")
+    max_completion_tokens: Optional[int] = Field(default=None, description="An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens. Commonly used in OpenAI's o1 series models.")
+    top_p: Optional[float] = Field(default=None, description="Only sample from tokens with cumulative probability greater than top_p when generating text.")
+    n: Optional[int] = Field(default=None, description="How many chat completion choices to generate for each input message.")
+    stream: Optional[bool] = Field(default=None, description=" If set to true, it sends partial message deltas. Tokens will be sent as they become available, with the stream terminated by a [DONE] message.")
+    stream_options: Optional[dict] = Field(default=None, description="Options for streaming response. Only set this when you set stream: true")
+    timeout: Optional[Union[float, int]] = Field(default=None, description="Timeout in seconds for completion requests (Defaults to 600 seconds)")
+
+    # tools
+    tools: Optional[List] = Field(default=None, description="A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.")
+    tool_choice: Optional[Union[str, dict]] = Field(default=None, description="Controls which (if any) function is called by the model. none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message or calling a function. required forces the model to call a tool. Specifying a particular function via {\"type\": \"function\", \"function\": {\"name\": \"my_function\"}} forces the model to call that function.")
+    parallel_tool_calls: Optional[bool] = Field(default=None, description="Whether to enable parallel function calling during tool use. OpenAI default is true.")
+
+    # token probabilities
+    logprobs: Optional[bool] = Field(default=None, description="Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.")
+    top_logprobs: Optional[int] = Field(default=None, description="An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.")
+
+    # output format
+    response_format: Optional[Union[BaseModel, dict]] = Field(default=None, description=" An object specifying the format that the model must output.")
+
+    def __str__(self):
+        return self.model
+
+
 # def get_default_device():
 #     return "cuda" if torch.cuda.is_available() else "cpu"