flexiagent 0.1.0a4

FlexiAgent is an open-source framework for creating agents based on Directed Acyclic Graphs (DAGs), featuring a user-friendly interface, built-in practical agents, and high configurability for efficient task management and rapid deployment.

FlexiAgent

FlexiAgent is an open-source project on GitHub that provides a simple and easy-to-use interface for creating agents based on Directed Acyclic Graphs (DAGs). The agent supports structured output and includes built-in practical agents such as a text2sql agent, allowing for quick deployment in applications.

Features

• Efficient and Simple Agent Interface Design: Supports structured output, making it easier to utilize in practical applications.

• DAG-Based Task Implementation: The agent's tasks are built on DAGs, supporting distributed concurrent scheduling.

• Configuration-Driven Agent Creation: Offers high flexibility and extensibility through comprehensive configuration support.

• Built-In Practical Agents: Includes useful agents like text2sql, llm_chat, and api_call for fast deployment and reduced development time.

Installation

To begin using FlexiAgent, clone the repository from GitHub:

git clone https://github.com/dzhsurf/flexiagent.git
cd flexiagent

Ensure you have Python installed and set up a virtual environment(conda recommand):

# python3.11 recommendation, support python3.8+
conda create -n proj python=3.11 

Install the necessary dependencies:

pip install -e .
# or poetry
# poetry install

Usage

Before using FlexiAgent, you'll need to set up your OPENAI_API_KEY. You can set this environment variable in your system or include it in your code:

export OPENAI_API_KEY='your-api-key-here' 

FlexiAgent can be easily integrated into your existing projects. Below is a basic setup to get you started:

from flexiagent.llm.config import LLMConfig
from flexiagent.task.task_node import (
    FxTaskAction,
    FxTaskActionLLM,
    FxTaskAgent,
    FxTaskConfig,
    FxTaskEntity,
)

llm_config = LLMConfig(engine="OpenAI", params={"openai_model": "gpt-4o-mini"})

class Step1Output(FxTaskEntity):
    num1: float 
    num2: float 
    op: str 

class Step2Output(FxTaskEntity):
    result: float

def compute_nums(input: Dict[str, Any]) -> Step2Output:
    nums: Step1Output = input["step_1"]
    result = 0.0
    if nums.op == "+":
        result = nums.num1 + nums.num2
    elif nums.op == "-":
        result = nums.num1 - nums.num2
    elif nums.op == "*":
        result = nums.num1 * nums.num2
    elif nums.op == "/":
        result = nums.num1 / nums.num2
    else:
        result = 0
    return Step2Output(
        result=result,
    )

agent = FxTaskAgent(
    task_graph=[
        # step 1: llm extract data
        FxTaskConfig(
            task_key="step_1",
            input_schema={"input": str},
            output_schema=Step1Output,
            action=FxTaskAction(
                type="llm",
                act=FxTaskActionLLM(
                    llm_config=llm_config,
                    instruction="""
Extract the numbers and operators from mathematical expressions based on the user's questions. 
Only support +, -, *, / operations with two numbers.

Question: {input}
""",
                ),
            ),
        ),
        # step 2: compute
        FxTaskConfig(
            task_key="output",
            input_schema={"step_1": Step1Output},
            output_schema=Step2Output,
            action=FxTaskAction(
                type="function",
                act=compute_nums,
            ),
        ),
    ],
)

output = agent.invoke("Compute: 3 + 5 =")
# output is Step2Output, result is 8

Quickstart

To get started quickly with FlexiAgent, please refer to the Quickstart Guide for example usage.

Using Local Deployment Model (Llama.cpp)

FlexiAgent utilizes llama-cpp-python, allowing the LLM to support not only OpenAI but also Llama.cpp. The above code example can load a local model using Llama.cpp by changing the configuration.

Parameter Explanation

repo_id_or_model_path: If specified as a repo_id, the model will be downloaded to the cache directory (~/.cache/huggingface/...) using huggingface-cli. If it is a local model path (e.g., xxx_model.gguf), it will be loaded directly.

repo_filename: This is only effective when repo_id_or_model_path is a repo_id. It selects the specified model based on the filename rules, such as Q4_K_M, Q8_0, etc., and should be set according to the file names in the huggingface repo.

n_ctx: Context window size; defaults to 512 if not set.

llm_config = LLMConfig(
    engine="LlamaCpp",
    params={
        "repo_id_or_model_path": "QuantFactory/Llama-3.2-3B-Instruct-GGUF",
        "repo_filename": "*Q4_K_M.gguf",
        "n_ctx": 4096,
    },
)

Contributing

Contributions are welcome! Please fork the repository and use a branch for your feature or bug fix. Submitting a pull request is the best way to see your feature merged.

License

This project is licensed under the MIT License - see the LICENSE file for details.