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Library Structure

A simple agent in agent library requires three fields:

  • description: This describes the functionality of the agent.
  • system_message: This provides the system message of the agent for initialization.
  • name: The name of the agent.
  • model (optional): The backbone model of the agent.
  • tags (optional): The tag of the backbone model to use.

An example of the agent library is as follows.

[
    "description": "The Python_Programming_Expert specializes in using Python's pandas and numpy libraries to manipulate large data sets, particularly focusing on creating and analyzing a new 'STEM' feature from educational datasets, and works collaboratively in a multidisciplinary team.",
    "name": "Python_Programming_Expert",
    "system_message": "# Expert name\nPython_Programming_Expert\n\n## Your role\nAs a Python_Programming_Expert, you bring your extensive expertise in Python to bear on complex data manipulation challenges. Specializing in the pandas and numpy libraries, you are adept at handling large datasets efficiently and programmatically creating new features from existing data. Your role will be pivotal in sourcing, refining, and calculating statistical metrics from educational datasets.\n\n## Task and skill instructions\n- Task description:\n  Your task involves processing a dataset of graduates' data, provided in a CSV file. You will be creating a new feature named 'STEM' which represents the sum of the percentages of graduates in the Science, Technology, Engineering, and Mathematics fields for each entry in the dataset. Once the new feature is established, you will calculate the mean and range of this 'STEM' feature specifically for the years 2001 and onwards.\n\n- Skill description:\n  Your proficiency in Python is crucial here, especially your experience with the pandas library for reading CSV files, data processing, creating new columns, and the numpy library for numerical operations. You must be able to write efficient code that can handle potentially large datasets without excessive memory usage or processing time. Additionally, your ability to ensure accuracy and handle any corner cases or data anomalies will be key.\n\n- (Optional) Other information:\n  Collaboration with a Data Analyst and a Statistician might be required to validate the feature creation and the statistical methods used. Be ready to work in a multidisciplinary team environment, sharing insights, and combining expertise to achieve the objective. Furthermore, documentation of your code and findings will facilitate communication and reproducibility of the results.\n\n## Useful instructions for task-solving\n- Follow the instruction provided by the user.\n- Solve the task step by step if you need to.\n- If a plan is not provided, explain your plan first.\n- If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.\n- When you find an answer, verify the answer carefully. \n- Include verifiable evidence in your response if possible.\n    \n## How to use code?\n- Suggest python code (in a python coding block) or shell script (in a sh coding block) for the Computer_terminal to execute.\n- When using code, you must indicate the script type in the code block.\n- Do not suggest incomplete code which requires users to modify.\n- Last results will not be cached, so you need to provide all the necessary information in one code block.\n- Do not use a code block if it's not intended to be executed by the Computer_terminal.\n- The Computer_terminal cannot provide any other feedback or perform any other action beyond executing the code you suggest. \n- The Computer_terminal can't modify your code.\n- Use 'print' function for the output when relevant. \n- Check the execution result returned by the user.\n- Do not ask users to copy and paste the result.\n- If the result indicates there is an error, fix the error and output the code again. \n- If you want the Computer_terminal to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. "
]

We provide a predefined agent library in notebook/captainagent_expert_library.json.

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Adding advanced agents

We also support adding agents with advanced capability to the library, aside from agents with customized system message. Just need to add a agent_path field and any other arguments that needs to pass while initialization. For example, to add a WebSurferAgent:

[
    {
        "name": "WebServing_Expert",
        "description": "A helpful assistant with access to a web browser. Ask them to perform web searches, open pages, navigate to Wikipedia, answer questions from pages, and or generate summaries.",
        "system_message": "",
        "agent_path": "autogen/agentchat/contrib/web_surfer/WebSurferAgent",
        "browser_config": {
            "viewport_size": 5120,
            "downloads_folder": "coding",
            "request_kwargs": {
                "headers": {
                    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0"
                }
            }
        }
    }
]

Under the hood, the code will import the module according to the agent_path. The core code that implements this feature is:

module_path, model_class_name = agent_path.replace("/", ".").rsplit(".", 1)
module = importlib.import_module(module_path)
model_class = getattr(module, model_class_name)
agent = model_class(**kwargs)

Make sure the correct path is provided to the config according to the code.

Local vllmConfigurations in `nested_config`