fix according to xuchen's comments [hyperlinks]

docs/sphinx_doc/source/tutorial/103-example.md

@@ -26,7 +26,7 @@ model_config = {
 }
 ```
 
-For open-source models, we support integration with various model interfaces such as HuggingFace, ModelScope, FastChat, and vllm. You can find scripts on deploying these services in the `scripts` directory, and we defer the detailed instructions to [[Using Different Model Sources with Model API]](https://alibaba.github.io/AgentScope/tutorial/203-model.md).
+For open-source models, we support integration with various model interfaces such as HuggingFace, ModelScope, FastChat, and vllm. You can find scripts on deploying these services in the `scripts` directory, and we defer the detailed instructions to [[Using Different Model Sources with Model API]](https://alibaba.github.io/AgentScope/tutorial/203-model.html).
 
 You can register your configuration by calling AgentScope's initilization method as follow. Besides, you can also load more than one config by calling init mutliple times.
 ```python
@@ -55,7 +55,7 @@ dialogAgent = DialogAgent(name="assistant", model="gpt-4")
 userAgent = UserAgent()
 ```
 
-**NOTE**: Please refer to [[Customizing Your Custom Agent with Agent Pool]](https://alibaba.github.io/AgentScope/tutorial/201-agent.md) for all available agents.
+**NOTE**: Please refer to [[Customizing Your Custom Agent with Agent Pool]](https://alibaba.github.io/AgentScope/tutorial/201-agent.html) for all available agents.
 
 ## Step3: Agent Conversation
 
@@ -96,7 +96,7 @@ while x is None or x.content != "exit":
 	x = sequentialpipeline([dialog_agent, user_agent])
 ```
 
-For more details about how to utilize pipelines for complex agent interactions, please refer to [[Agent Interactions: Dive deeper into Pipelines and Message Hub]](https://alibaba.github.io/AgentScope/tutorial/202-pipeline.md).
+For more details about how to utilize pipelines for complex agent interactions, please refer to [[Agent Interactions: Dive deeper into Pipelines and Message Hub]](https://alibaba.github.io/AgentScope/tutorial/202-pipeline.html).
 
 
 

docs/sphinx_doc/source/tutorial/104-usecase.md

@@ -10,7 +10,7 @@ Let the adventure begin to unlock the potential of multi-agent applications with
 
 ## Getting Started
 
-Firstly, ensure that you have installed and configured AgentScope properly. Besides, we will involve the basic concepts of `Model API`,  `Agent`, `Msg`, and `Pipeline,` as described in [Tutorial-Concept](https://alibaba.github.io/AgentScope/tutorial/102-concepts.md). The overview of this tutorial is shown below:
+Firstly, ensure that you have installed and configured AgentScope properly. Besides, we will involve the basic concepts of `Model API`,  `Agent`, `Msg`, and `Pipeline,` as described in [Tutorial-Concept](https://alibaba.github.io/AgentScope/tutorial/102-concepts.html). The overview of this tutorial is shown below:
 
 * [Step 1: Prepare **Model API** and Set Model Configs](#step-1-prepare-model-api-and-set-model-configs)
 * [Step 2: Define the Roles of Each **Agent**](#step-2-define-the-roles-of-each-agent)
@@ -22,7 +22,7 @@ Firstly, ensure that you have installed and configured AgentScope properly. Besi
 
 ### Step 1: Prepare Model API and Set Model Configs
 
-As we discussed in the last tutorial, you need to prepare your model configurations into a JSON file for standard OpenAI chat API, FastChat, and vllm. More details and advanced usages such as configuring local models with POST API are presented in [Tutorial-Model-API](https://alibaba.github.io/AgentScope/tutorial/203-model.md).
+As we discussed in the last tutorial, you need to prepare your model configurations into a JSON file for standard OpenAI chat API, FastChat, and vllm. More details and advanced usages such as configuring local models with POST API are presented in [Tutorial-Model-API](https://alibaba.github.io/AgentScope/tutorial/203-model.html).
 
 ```json
 [

docs/sphinx_doc/source/tutorial/201-agent.md

@@ -8,11 +8,11 @@ The `AgentBase` class is the architectural cornerstone for all agent constructs
 
 Each AgentBase derivative is composed of several key characteristics:
 
-* `memory`: This attribute enables agents to retain and recall past interactions, allowing them to maintain context in ongoing conversations. For more details about `memory`, we defer to [Memory and Message Management](https://alibaba.github.io/AgentScope/tutorial/205-memory.md).
+* `memory`: This attribute enables agents to retain and recall past interactions, allowing them to maintain context in ongoing conversations. For more details about `memory`, we defer to [Memory and Message Management](https://alibaba.github.io/AgentScope/tutorial/205-memory.html).
 
-* `model`: The model is the computational engine of the agent, responsible for making a response given existing memory and input. For more details about `model`, we defer to [Using Different Model Sources with Model API]https://alibaba.github.io/AgentScope/tutorial/203-model.md).
+* `model`: The model is the computational engine of the agent, responsible for making a response given existing memory and input. For more details about `model`, we defer to [Using Different Model Sources with Model API]https://alibaba.github.io/AgentScope/tutorial/203-model.html).
 
-* `sys_prompt` & `engine`: The system prompt acts as predefined instructions that guide the agent in its interactions; and the `engine` is used to dynamically generate a suitable prompt. For more details about them, we defer to [Prompt Engine](https://alibaba.github.io/AgentScope/tutorial/206-prompt.md).
+* `sys_prompt` & `engine`: The system prompt acts as predefined instructions that guide the agent in its interactions; and the `engine` is used to dynamically generate a suitable prompt. For more details about them, we defer to [Prompt Engine](https://alibaba.github.io/AgentScope/tutorial/206-prompt.html).
 
 In addition to these attributes, `AgentBase` endows agents with pivotal methods such as `observe` and `reply`: