# GenericAgent — Self-Evolving Agent with Skill Trees

> A lightweight self-evolving agent that grows its skill tree from a compact seed, achieving full system control with significantly reduced token consumption.

## Install

Save as a script file and run:

# GenericAgent — Self-Evolving Agent with Skill Trees

## Quick Use
```bash
pip install generic-agent
generic-agent init --workspace ./my-project
generic-agent run "refactor the authentication module"
```

## Introduction
GenericAgent is a self-evolving AI agent that starts from a minimal seed of around 3,300 lines and progressively builds a skill tree through task execution. It learns reusable skills from completed tasks and applies them to future work, reducing token consumption by up to 6x compared to stateless approaches.

## What GenericAgent Does
- Executes complex multi-step tasks across code, files, and system operations
- Builds and maintains a persistent skill tree from completed task patterns
- Reuses learned skills to handle similar tasks with fewer tokens
- Supports browser automation, desktop control, and shell execution
- Works with multiple LLM providers including Claude, Gemini, and GPT

## Architecture Overview
GenericAgent uses a skill-tree architecture where each completed task can be distilled into a reusable skill node. The agent maintains a memory system that maps task descriptions to skill sequences. When a new task arrives, it searches the skill tree for applicable patterns, composes them into an execution plan, and only falls back to raw LLM reasoning for truly novel subtasks.

## Self-Hosting & Configuration
- Install via pip with minimal dependencies
- Configure your preferred LLM provider via environment variables
- Set the workspace directory for file operations
- Customize the initial skill seed to match your domain
- Export and import skill trees between agent instances

## Key Features
- Self-evolving skill tree that improves with each completed task
- 6x token reduction through skill reuse and pattern caching
- Multi-modal control: browser, desktop, terminal, and file system
- Lightweight 3.3K-line seed that grows organically
- Cross-provider support with Claude, Gemini, and OpenAI

## Comparison with Similar Tools
- **AutoGPT** — general-purpose agent; GenericAgent focuses on skill-tree evolution
- **CrewAI** — multi-agent orchestration; GenericAgent is a single self-improving agent
- **OpenInterpreter** — code execution focus; GenericAgent adds persistent skill learning
- **Evolver** — GEP-based evolution; GenericAgent uses task-driven skill trees

## FAQ
**Q: What is a skill tree?**
A: A persistent graph of learned task patterns. Each node represents a reusable skill the agent discovered while completing previous tasks.

**Q: How does it reduce token usage?**
A: By recognizing when a new task matches a known skill pattern, the agent skips the reasoning steps and executes the cached skill sequence directly.

**Q: Can I transfer skills between projects?**
A: Yes. Skill trees can be exported as JSON and imported into other GenericAgent instances.

**Q: Which LLM works best?**
A: Claude and Gemini models with large context windows work best for complex skill chains.

## Sources
- https://github.com/lsdefine/GenericAgent


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Source: https://tokrepo.com/en/workflows/asset-c668623d
Author: Script Depot