diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -14,9 +14,11 @@
 
 ## Why CNLLM?
 
-CNLLM 提供了一个**统一的 OpenAI 兼容接口层**与一套**标准化的参数规则和响应格式规范**。通过 CNLLM，开发者可以无障碍地在 OpenAI 生态内的 langchain、LlamaIndex、AutoGen、Haystack、DeepEval 等主流大模型应用框架中使用中文大模型；尤其在需要多模型协作的开发和应用场景中，使用 CNLLM 可**显著减少适配解析、功能实现及维护工程量，并有效降低 AI agent 开发中的 Token 消耗**。
+CNLLM 为中文大模型提供了一个**统一的 OpenAI 兼容接口层**与一套**标准化的参数规则和响应格式规范**。
 
-- **统一接口** - 一套接口和参数调用不同中文大模型，返回 OpenAI API 标准格式的响应
+通过 CNLLM，开发者可以无障碍地在 OpenAI 生态内的 langchain、LlamaIndex、AutoGen、Haystack、DeepEval 等主流大模型应用框架中使用中文大模型；尤其在需要多模型协作的开发和应用场景中，使用 CNLLM 可**显著减少适配解析、功能实现及维护工程量，并有效降低 AI agent 开发中的 Token 消耗**。
+
+- **统一接口** - 一套接口和参数调用不同中文大模型，返回 OpenAI API 标准响应
 - **参数验证** - 对所有参数进行验证和明确反馈，尤其是厂商原生参数，并支持参数处理行为控制 (`drop_params`)
 - **流式响应** - 通过 `repr()` 进行流式生命周期监测，以及通过 `.still/.think/.tools` 属性访问增量值自动累积
 - **批量能力** - 支持批量任务中单个请求的独立配置、实时批量进度统计 (`.status`)，以及可配置的失败策略 (`stop_on_error`) 和内存管理 (`keep`).
diff --git a/README_en.md b/README_en.md
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--- a/README_en.md
+++ b/README_en.md
@@ -14,13 +14,7 @@
 
 ## Why CNLLM?
 
-Chinese LLMs have reached the top tier in capabilities, yet in real production environments they face a lack of infrastructure. An unavoidable **dilemma** is:
-
-When using OpenAI SDK/LiteLLM with vendor-provided compatible interfaces, **unsupported native parameters are silently ignored**, leading to **uncontrollable results and missing features**; using vendor proprietary SDKs requires **extra field parsing and structure transformation**. When workflows involve multiple models from different vendors, different code adaptations are needed for each model, resulting in **increased engineering workload and maintenance costs**.
-
-CNLLM provides a **unified OpenAI-compatible interface layer** and a set of **standardized parameter rules and response format specifications**. CNLLM achieves **bidirectional mapping** of requests and responses through standardized YAML configuration files tailored for each vendor, mapping CNLLM standard parameters to vendor-accepted parameter names, passing through other native parameters, and finally automatically encapsulating heterogeneous model responses into OpenAI standard responses.
-
-This implementation path uniformly defines CNLLM standard parameters, aligns with OpenAI standard response structures, preserves the complete capabilities of Chinese LLMs, and ensures scalability for integrating more vendors. Compared to OpenAI SDK and vendor proprietary SDKs, CNLLM also implements **systematic enhancements** for key field parsing, frontend streaming rendering, and engineering batch processing scenarios.
+CNLLM provides Chinese LLMs with a **unified OpenAI-compatible interface layer** and a set of **standardized parameter rules and response format specifications**. 
 
 Through CNLLM, developers can seamlessly use Chinese LLMs in the OpenAI ecosystem — LangChain, LlamaIndex, AutoGen, Haystack, DeepEval and other mainstream large language model application frameworks. Especially in development and application scenarios requiring multi-model collaboration, using CNLLM can **significantly reduce adaptation, parsing, feature implementation, and maintenance workload, and effectively lower token consumption in AI agent development**.