FAQ

This FAQ page addresses common questions about setting up and using TaskFlowAI. If you have additional questions, please feel free to open an issue on our GitHub repository.

Setting Up Agents and Tasks

How do I set up an agent?

There are two main ways to set up agents in TaskFlowAI:

One way to set up agents is by creating a class of agents. This approach allows you to group related agents together and provides a structured way to organize your agents.

# Option 1: Class of agents
class Agents:
    web_researcher = Agent(
        role="web researcher",
        goal="find relevant information on the web",
        attributes="detail-oriented, analytical",
        llm=OpenrouterModels.haiku
    )
    summarizer = Agent(
        role="summarizer",
        goal="condense information into concise summaries",
        attributes="concise, clear communicator",
        llm=OpenrouterModels.haiku
    )
    programmer = Agent(
        role="programmer",
        goal="write and debug code",
        attributes="logical, problem-solver",
        llm=OpenrouterModels.haiku
    )

Another way to set up agents is by creating them directly in your script. This approach is more straightforward and can be useful for simpler setups or when you need to create agents on the fly.

# Option 2: Direct setup in script
researcher = Agent(
    role="researcher",
    goal="conduct thorough research on given topics",
    attributes="analytical, detail-oriented",
    llm=OpenrouterModels.haiku
)

How do I set up a task?

To set up a task, you can wrap the Task.create method in a function that imports what it needs to perform the task. This makes the task modular and easy to use in sequences:

from taskflowai import Task, OpenrouterModels
 
def research_task(topic):
    result = Task.create(
        agent="web_researcher",
        context=f"The user wants information about {topic}",
        instruction=f"Explain {topic} and provide a comprehensive summary",
        llm=OpenrouterModels.haiku
    )
    return result
 
# Usage
topic = "artificial intelligence"
research_result = research_task(topic)

Language Models and Customization

How do I use a model that's not supported in the LLM function list?

You can use the custom_model method available for each provider to use models that are not explicitly listed in the LLM function list:

from taskflowai import OpenrouterModels, AnthropicModels, OllamaModels, OpenaiModels
 
# OpenRouter custom model
llm = OpenrouterModels.custom_model(model_name="meta-llama/llama-3-70b-instruct")
 
# Anthropic custom model
llm = AnthropicModels.custom_model(model_name="claude-3-opus-20240229")
 
# Ollama custom model
llm = OllamaModels.custom_model(model_name="llama3.1:405b")
 
# OpenAI custom model
llm = OpenAIModels.custom_model(model_name="gpt-4o-mini")

Privacy and Local Usage

How do I use TaskFlowAI locally and privately?

To use TaskFlowAI locally and privately:

Download and install Ollama
Pull the model you want to use
Host it from the terminal
Select the model function from llm.py or set a newer model card with the custom_model function

All requests will stay internal to your device in this configuration, ensuring privacy and local usage.

Future Development

Are you adding more tools? Are you open to requests?

Yes, we are open to adding new features and toolkits as needed. If you have a request for a new tool or feature, please open an issue on our GitHub repository or contribute directly to the project.

Additional Questions

How do I handle errors when using tools?

Implement proper error handling when using tools, especially those that interact with external APIs. Tools that print and return errors rather than fail are preferable, as they can be provided back to the agent in a retry loop, allowing multiple attempts to succeed.

How should I manage API keys for various tools?

Use environment variables to manage API keys required by various tools. This enhances security and makes it easier to deploy your application across different environments.

Can I create custom tools?

Yes, you can create custom tools following the same pattern as the built-in ones. This allows you to extend TaskFlowAI's functionality to meet your specific needs.

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