agents-for-all 0.1.4

Modular agent framework combining LLMs and Tools

agents-for-all

Modular agent framework combining LLMs and tools.

Designed to help developers quickly build task-solving agents using large language models and pluggable toolchains.

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✨ Features

• 🔌 Pluggable tool architecture
• 🤖 LLM integration (OpenAI, Anthropic, Gemini, Direct, etc.)
• 🧪 Built-in testing with pytest
• 📝 Documentation support via sphinx
• ⚡ Fast dependency management using uv

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📦 Installation and Usage

pip install agents-for-all

from agents_for_all import Agent
from agents_for_all.llms.direct import DirectModel
from agents_for_all.tools.python import Python

llm = DirectModel(
  api_endpoint="http://localhost:1234/v1/chat/completions",
  model="deepseek-r1-distill-llama-8b"
)
agent = Agent(llm=llm, tools=[Python()])
result = agent.do("Generate a Fibonacci sequence of length 10.")
print(result.output) # Final output
print(result.history) # History of steps taken

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🤝 Contributing

1. Fork this repository
2. Create your feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -am 'Add YourFeature')
4. Push to the branch (git push origin feature/YourFeature)
5. Create a new Pull Request

Prerequisites

• Python 3.10+
• uv installed:

  curl -Ls https://astral.sh/uv/install.sh | sh
  

Installation

Create a virtual environment and install dependencies:

uv sync

🧪 Running Tests

uv run pytest

📚 Documentation

To build the Sphinx docs (if configured):

uv run sphinx-build docs docs/_build

🛠 Project Structure (Sample)

agents_for_all/
├── agent.py           # Agent class
├── tools/             # Modular tools the agent can use
├── llms/              # Collection of LLMs the agent can connect to
├── tests/             # Pytest test cases
├── README.md
├── pyproject.toml
└── .gitignore

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📄 License

Apache License 2.0

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🔗 Links

• 📦 GitHub: agents-for-all
• ✉️ Maintainer: Sailesh Dhungana
• 📘 Docs: dhungana.github.io/agents-for-all
• 📦 PyPI: agents-for-all