Agent Selma 👩🏻

A “Toy” Implementation of OpenClaw with Python

🌟 Overview

Agent Selma is a simplified, educational reimplementation of the OpenClaw project.

The primary goal is to deconstruct and understand the underlying architecture of autonomous agents by rebuilding them from scratch using Python. By stripping away complexity, Selma serves as a clean baseline for learning how agentic components interact.

[!WARNING] This is a toy project for learning purposes only. Selma is not hardened for production use. There are no security audits, no SLA guarantees, and no guarantees of stability or correctness. Do not use this in any production or commercial environment. Use at your own risk.

🛠 Tech Stack

🚀 Installation

Prerequisites: uv must be installed. If you don’t have it yet:

pip install uv

git clone https://github.com/YOUR_USERNAME/agent-selma.git
cd agent-selma
uv sync

Ollama (for local models) — download and install from ollama.com, then pull a model:

ollama pull llama3.2        # recommended default (~2 GB)
ollama pull qwen2.5:14b     # more capable, requires ~9 GB RAM

Ollama runs as a background service automatically after installation. Verify it is running:

ollama list

For the browser tool (Playwright), install Chromium once:

uv run playwright install chromium

⚙️ Configuration

Run the setup script once to create the .selma/ directory, generate a default selma.json, and deploy skills and templates into the workspace:

uv run setup.py

The script is safe to re-run — it never overwrites existing files.

The generated .selma/selma.json contains all available options with their defaults. The most important setting is the model:

"model": {
    "model": "ollama/llama3.1",