diff --git a/website/docs/overview.md b/website/docs/overview.md
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@@ -1,3 +1,36 @@
 # Overview
 
-TaskWeaver is a code-first agent framework
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+A **code-first** agent framework for seamlessly planning and executing data analytics tasks. 
+This innovative framework interprets user requests through coded snippets and efficiently 
+coordinates a variety of plugins in the form of functions to execute 
+data analytics tasks
+
+**Highlighted Features**
+
+- [x] **Rich data structure** - TaskWeaver allows you to work with rich data 
+    structures in Python, such as DataFrames, instead of having to work with 
+    text strings.
+- [x] **Customized algorithms** - TaskWeaver allows you to encapsulate your 
+    own algorithms into plugins (in the form of Python functions), 
+    and orchestrate them to achieve complex tasks.
+- [x] **Incorporating domain-specific knowledge** - TaskWeaver is designed to 
+    be easily incorporating domain-specific knowledge, such as the knowledge 
+    of execution flow, to improve the reliability of the AI copilot.
+- [x] **Stateful conversation** - TaskWeaver is designed to support stateful 
+    conversation. It can remember the context of the conversation and 
+    leverage it to improve the user experience.
+- [x] **Code verification** - TaskWeaver is designed to verify the generated code 
+    before execution. It can detect potential issues in the generated code 
+    and provide suggestions to fix them.
+- [x] **Easy to use** - TaskWeaver is designed to be easy to use. 
+    We provide a set of sample plugins and a tutorial to help you get started.
+    Users can easily create their own plugins based on the sample plugins.
+    TaskWeaver offers an open-box experience, allowing users to run a service immediately after installation.
+- [x] **Easy to debug** - TaskWeaver is designed to be easy to debug. 
+    We have detailed logs to help you understand what is going on during calling the LLM, 
+    the code generation, and execution process.
+- [x] **Security consideration** - TaskWeaver supports a basic session management to keep
+    different users' data separate. The code execution is separated into different processes in order not to interfere with each other.
+- [x] **Easy extension** - TaskWeaver is designed to be easily extended to accomplish 
+    more complex tasks. You can create multiple AI copilots to
+    act in different roles, and orchestrate them to achieve complex tasks.
diff --git a/website/docs/quickstart.md b/website/docs/quickstart.md
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-# This is the quick start
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+# Quick Start
+
+## Installation
+You can install TaskWeaver by running the following command:
+```bash
+# [optional] create a conda environment to isolate the dependencies
+# conda create -n taskweaver python=3.10
+# conda activate taskweaver
+
+# clone the repository
+git clone https://github.com/microsoft/TaskWeaver.git
+cd TaskWeaver
+# install the requirements
+pip install -r requirements.txt
+```
+
+
+## Project Directory
+TaskWeaver runs as a process, you need to create a project directory to store plugins and configuration files. 
+We provided a sample project directory in the `project` folder. You can copy the `project` folder to your workspace.
+A project directory typically contains the following files and folders:
+
+```bash
+📦project
+ ┣ 📜taskweaver_config.json # the configuration file for TaskWeaver
+ ┣ 📂plugins # the folder to store plugins
+ ┣ 📂planner_examples # the folder to store planner examples
+ ┣ 📂codeinterpreter_examples # the folder to store code interpreter examples
+ ┣ 📂sample_data # the folder to store sample data used for evaluations
+ ┣ 📂logs # the folder to store logs, will be generated after program starts
+ ┗ 📂workspace # the directory stores session data， will be generated after program starts
+    ┗ 📂 session_id 
+      ┣ 📂ces # the folder used by the code execution service
+      ┗ 📂cwd # the current working directory to run the generated code
+```
+
+## OpenAI Configuration
+Before running TaskWeaver, you need to provide your OpenAI API key and other necessary information. 
+You can do this by editing the `taskweaver_config.json` file. 
+If you are using Azure OpenAI, you need to set the following parameters in the `taskweaver_config.json` file:
+### Azure OpenAI
+```json
+{
+"llm.api_base": "https://xxx.openai.azure.com/",
+"llm.api_key": "your_api_key",
+"llm.api_type": "azure",
+"llm.api_version": "the api version",
+"llm.model": "the model name, e.g., gpt-4"
+}
+```
+
+### OpenAI
+```json
+{
+"llm.api_key": "the api key",
+"llm.model": "the model name, e.g., gpt-4"
+}
+```
+>💡 Only the latest OpenAI API supports the `json_object` response format. 
+> If you are using an older version of OpenAI API, you need to set the `llm.response_format` to `null`.
+
+More configuration options can be found in the [configuration documentation](docs/configuration.md).
+
+## Start TaskWeaver
+```bash
+# assume you are in the taskweaver folder
+# -p is the path to the project directory
+python -m taskweaver -p ./project/
+```
+This will start the TaskWeaver process and you can interact with it through the command line interface. 
+If everything goes well, you will see the following prompt:
+
+```
+=========================================================
+ _____         _     _       __
+|_   _|_ _ ___| | _ | |     / /__  ____ __   _____  _____
+  | |/ _` / __| |/ /| | /| / / _ \/ __ `/ | / / _ \/ ___/
+  | | (_| \__ \   < | |/ |/ /  __/ /_/ /| |/ /  __/ /
+  |_|\__,_|___/_|\_\|__/|__/\___/\__,_/ |___/\___/_/
+=========================================================
+TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request?
+Human: ___
+```