dagent 0.0.5

AI Agents as DAGs - Directed Acyclic Graphs

DAGent - Directed Acyclic Graphs (DAGs) as AI Agents

DAGent is a Python library to create AI Agents without overhead

Quickstart

Install the lib

pip install dagent

DAGent Diagram

graph TD
    A[Function Node] --> B[Decision Node]
    B --> C[Function Node]
    B --> E[Function Node]
    D --> F[Function Node]
    E --> G[Decision Node]
    F --> H[Function Node]
    G --> K[Function Node]
    G -- "Pick Function to Run" --> I[Function Node]
    G --> J[Function Node]
    I --> L[Function Node]
    J --> M[Function Node]
    K --> N[Function Node]
    K -- "Run Both " --> S[Function Node]

    %% Additional annotations
    B -- "Use a Function as a tool" --> D[Function Node]

The idea behind dagent is to structure AI agents in to a workflow. This is done through setting each function up as a node in a graph.

The agentic behavior is through the inferring of what function to run through the use of LLMs which is abstracted by a "Decision Node".

DAGent basics

DecisionNode

• This is where the llm picks a function to run from given options
• The .compile() method autogenerates and saves tool descriptions under Tool. Run with param force_load=True if there are errors or if an option of tool changes
• These tool/function descriptions get generated in

FunctionNode

• Runs a python function
• Can be attached to a DecisionNode to be treated as a tool and allow an LLM to choose which function to run

Example Usage

See dagent/examples/simple_agent.py for more info

Using Different Models

DAGent supports using different LLM models for inference and tool description generation. You can specify the model when calling call_llm or call_llm_tool, or when compiling the DecisionNode.

For example, to use the groq/llama3-70b-8192 model:

# Using groq with decision node
decision_node1 = DecisionNode('groq/llama3-70b-8192')

# Using ollama with decision node
decision_node2 = DecisionNode('ollama_chat/mistral', api_base="http://localhost:11434")

# Call llm function
output = decision_node2.run(messages=[{'role': 'user', 'content': 'add the numbers 2 and 3'}])

Other things to know

• prev_output is needed in the function signature if you want to use the value from the prior function's value. Obviously the prior function should have returned something for this to work
• If there are errors with too many params being passed into a function node, add **kwargs to your function
• Args can be overriden at any time using the following (this merges the kwargs in the background with priority to the user):

add_two_nums_node.user_params = {
    a : 10
}

Acknowledgements

Shoutout to:

• @omkizzy
• @kaelan