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Quick Start

Installation​

You can install TaskWeaver by running the following command:

# [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:

📦project
 ┣ 📜taskweaver_config.json # the project configuration file for TaskWeaver
 ┣ 📂plugins # the folder to store plugins
 ┣ 📂logs # the folder to store logs, will be generated after program starts
 ┣ 📂examples
    ┣ 📂 planner_examples # the folder to store planner examples
    ┗ 📂 code_generator_examples # the folder to store code generator examples
 ┗ 📂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
      ┗ other session data

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​

{
"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" # In Azure OpenAI, the model name is the deployment_name
}

OpenAI​

{
"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"=null in the taskweaver_config.json file.

More configuration options can be found in the configuration documentation.

Start TaskWeaver​

# assume you are in the TaskWeaver folder cloned from the repository
python -m taskweaver -p ./project/ # -p is the path to the project directory

This will start the TaskWeaver process and you can interact with it through the command line (CLI) 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: ___

There are other ways to start TaskWeaver: