literun 0.1.1

A Minimal agent runtime built on OpenAI Responses API

LiteRun 🚀

A lightweight, flexible Python framework for building custom OpenAI agents (Responses API) with tool support and structured prompt management.

Features

• Custom Agent Execution: Control the loop with synchronous and streaming support.
• Tool Support: Easy registration with Pydantic-powered validation.
• Type Safety: Built for modern Python 3.10+ environments.
• Prompt Templates: Structured message management.
• Event-Driven: Granular control via a rich event system.

For detailed documentation on Architecture, Streaming, and Advanced Configuration, see DOCS.md.

Requirements

• Python 3.10+

Note: Core dependencies like openai and pydantic are automatically installed when you install literun.

Installation

You can install literun directly from PyPI:

pip install literun

Quick Start

Basic Agent

Here is a simple example of how to create an agent with a custom tool.

import os
from literun import Agent, ChatOpenAI, Tool, ArgsSchema

# 1. Define a tool function
def get_weather(location: str, unit: str = "celsius") -> str:
    return f"The weather in {location} is 25 degrees {unit}."

# 2. Wrap it with Tool schema
weather_tool = Tool(
    func=get_weather,
    name="get_weather",
    description="Get the weather for a location",
    args_schema=[
        ArgsSchema(
            name="location",
            type=str,
            description="The city and state, e.g. San Francisco, CA",
        ),
        ArgsSchema(
            name="unit",
            type=str,
            description="The unit of temperature",
            enum=["celsius", "fahrenheit"],
        ),
    ],
)

# 3. Initialize Agent
agent = Agent(
    llm=ChatOpenAI(model="gpt-4.1-mini", temperature=0.7),
    system_prompt="You are a helpful assistant.",
    tools=[weather_tool],
)

# 4. Run the Agent
result = agent.invoke(user_input="What is the weather in Tokyo?")
print(f"Final Answer: {result.final_output}")

Advanced Usage

LiteRun supports Streaming, Runtime Context Injection (for secrets), and Direct LLM Usage.

👉 Check out the Documentation and Examples for more details.

Project Structure

literun/
├── src/
│   └── literun/          # Main package source
│       ├── agent.py      # Agent orchestrator
│       ├── llm.py        # ChatOpenAI wrapper
│       ├── tool.py       # Tool & Schema definitions
│       └── ...
├── tests/                # Unit tests (agent, llm, tools, prompts)
├── examples/             # Runnable examples
├── DOCS.md               # Detailed documentation
├── LICENSE               # MIT License
├── README.md             # This file
└── pyproject.toml        # Project configuration & dependencies

Contributing

We welcome contributions! Please follow these steps to set up your development environment:

1. Fork the repository and clone it locally:

   git clone https://github.com/kaustubh-tr/literun.git
   cd literun
   

2. Install in editable mode with development dependencies:

   pip install -e .[dev]
   

3. Create a feature branch and make your changes.

4. Test your changes (see below).

5. Submit a pull request.

Testing

This project uses pytest as the primary test runner, but supports unittest as well.

# Run all tests
pytest

or using unittest:

python -m unittest discover tests

Note: Some integration tests require the OPENAI_API_KEY environment variable. They are automatically skipped if it is missing.

License

Copyright (c) 2026 Kaustubh Trivedi.

Distributed under the terms of the MIT license, LiteRun is free and open source software.