infinexstudio 0.1.0

A lightweight, professional Agentic AI framework for multi-agent workflows.

InfinexaStudio 🚀

InfinexaStudio is a lightweight, professional-grade Python framework for orchestrating role-playing AI agents. It simplifies building multi-agent workflows, enabling developers to build agent swarms that collaborate sequentially to solve complex tasks.

Similar to CrewAI, but optimized for minimal overhead, full transparency, and clean, type-safe structures.

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Key Features

• 🛠️ Automatic Schema Generation: Transform standard Python functions into AI-ready tools using a simple @tool decorator. No complex schemas required.
• 🔁 Robust ReAct/Tool Loop: Built-in tool calling cycle that handles function invocation, captures errors, and feeds context back to the LLM automatically.
• 🔗 Sequential Workflows: Coordinate multiple agents in a structured crew where each task feeds its findings into the next as context.
• 🔌 OpenAI & Custom Backends: Full support for OpenAI API models (like gpt-4o-mini) and local/custom endpoints (like Ollama, LiteLLM, or LocalAI).
• 📜 Type Safety & Logging: Fully type-hinted classes with detailed, color-coded execution logs for transparent debugging.

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Architecture at a Glance

graph TD
    A[Studio] --> B[Task 1: Researcher]
    A --> C[Task 2: Writer]
    B --> D[Agent: Analyst]
    C --> E[Agent: Writer]
    D --> F[LLM call]
    F --> G{Requires Tool?}
    G -- Yes --> H[Run @tool function]
    H --> F
    G -- No --> I[Task 1 Output]
    I -->|Passed as Context| C

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Installation

pip install infinexstudio

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Quickstart

Set up your OpenAI API key:

export OPENAI_API_KEY="your-api-key"

Create a python file app.py:

from infinexstudio import Agent, Task, Studio, tool

# 1. Define tools from standard Python functions
@tool
def get_stock_price(ticker: str) -> str:
    """Retrieves current stock price for a company."""
    if ticker.upper() == "AAPL":
        return "$182.50"
    return "$100.00"

# 2. Define Agents with roles, goals, and backstories
analyst = Agent(
    role="Financial Analyst",
    goal="Provide accurate financial analysis of requested stocks.",
    backstory="You are a meticulous Wall Street analyst specializing in technology sector evaluations.",
    tools=[get_stock_price],
    verbose=True
)

writer = Agent(
    role="Financial Reporter",
    goal="Draft concise financial news snippets for retail investors.",
    backstory="You are a veteran financial news writer. You make complex finance numbers simple and fun.",
    verbose=True
)

# 3. Create Tasks
task_1 = Task(
    description="Research the price and performance of Apple stock (AAPL).",
    expected_output="A summary of stock stats.",
    agent=analyst
)

task_2 = Task(
    description="Write a short newsletter paragraph about the findings.",
    expected_output="An engaging news paragraph in markdown.",
    agent=writer
)

# 4. Initialize and Run the Studio
studio = Studio(
    agents=[analyst, writer],
    tasks=[task_1, task_2],
    verbose=True
)

result = studio.kickoff()
print(result)

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🛠️ Developer Guide: How to Package & Publish to PyPI

One of the best ways to impress interviewers is to demonstrate your understanding of professional packaging pipelines. Here is the step-by-step workflow of how to publish InfinexaStudio to PyPI:

1. Requirements

Ensure you have the build tools installed:

pip install --upgrade build twine

2. Configure Build Metadata (pyproject.toml)

We use standard PEP 518/621 configuration. In your root pyproject.toml, you define metadata, entrypoints, and dependencies:

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "infinexstudio"
version = "0.1.0"
description = "A lightweight, professional Agentic AI framework for multi-agent workflows."
dependencies = [
    "openai>=1.0.0",
    "pydantic>=2.0.0"
]

3. Build Source and Wheel Distributions

Generate package distribution archives by running:

python -m build

This creates a dist/ directory with two files:

• A source archive: infinexstudio-0.1.0.tar.gz
• A built wheel: infinexstudio-0.1.0-py3-none-any.whl

4. Test upload to TestPyPI

Before pushing to live PyPI, it's best practice to upload to TestPyPI to ensure the formatting and package are error-free:

python -m twine upload --repository testpypi dist/*

You will need a TestPyPI account and API token for this step.

5. Publish to live PyPI

Once tested, publish the package to PyPI:

python -m twine upload dist/*

This requires a standard PyPI account and API token. Enter __token__ as the username and paste your API key as the password.

6. Install from PyPI

Anyone can now install your package globally:

pip install infinexstudio

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License

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