Overview
GenericAgent is a minimal autonomous agent framework whose core is about 3,000 lines of code, built around nine atomic tools and an agent loop of roughly 100 lines. Those tools give a model system-level control of a local computer: a real browser, the terminal, the filesystem, keyboard and mouse input, screen vision, and Android devices over ADB. The design philosophy the README states is 'don't preload skills, evolve them.'
Every time the agent solves a new task it crystallises the execution path into a reusable Skill, so capability accumulates into a personal skill tree grown from the seed code rather than shipped with it. Browser work goes through TMWebdriver, which injects into a real browser and preserves logged-in sessions instead of driving a fresh headless instance. The project reports running under a 30K context window — a fraction of what agents in the 200K–1M range consume — which the authors tie to less noise and lower cost per task.
Dependencies are deliberately tiered: the agent core needs only `requests` plus four lightweight packages for TMWebdriver's local server, with Streamlit and the TUI libraries behind an optional `[ui]` extra — no Playwright, no LangChain, no browser binaries to download. It works with Claude, Gemini, Kimi, MiniMax and other major models and runs cross-platform. The code is MIT-licensed; a technical report accompanies it on arXiv, and the maintainers note that the GitHub repository and gaagent.ai are the only official channels.
What it does
- Nine atomic tools covering browser, terminal, filesystem, keyboard/mouse, screen vision and mobile (ADB)
- Self-evolution: each solved task is crystallised into a reusable Skill, building a personal skill tree
- TMWebdriver injects into a real browser and keeps existing login sessions
- Runs under a ~30K context window rather than the 200K–1M other agent frameworks consume
- ~3K lines of core code with a ~100-line agent loop — the core needs only requests plus four small packages
- Model-agnostic: Claude, Gemini, Kimi, MiniMax and other major providers
- Ships a terminal UI and a Streamlit web UI, or runs headless if you skip the [ui] extra
Getting started
Use Python 3.11 or 3.12 — the README warns that 3.14 is incompatible with pywebview and several other dependencies. A detailed guide lives in docs/installation.md.
Clone and install
The [ui] extra pulls in the bundled front ends; skip it to drive the agent headless.
git clone https://github.com/lsdefine/GenericAgent.git && cd GenericAgent
uv venv && uv pip install -e ".[ui]"Add your model API key
cp mykey_template_en.py mykey.py
$EDITOR mykey.pyLaunch a front end
The terminal UI is the recommended entry point; launch.pyw starts the Streamlit web UI instead.
python frontends/tui_v3.py # terminal UI
python launch.pyw # Streamlit web UILet an agent install it for you
The project publishes an installation guide written for LLM agents to fetch and follow.
curl -fsSL https://raw.githubusercontent.com/lsdefine/GenericAgent/refs/heads/main/docs/installation.mdCommands and code are distilled from the project's own documentation — always check the official repo for the latest.
When to use it
- Automate desktop and browser work that needs an existing logged-in session rather than a clean headless browser
- Build up a private, reusable skill library from tasks you actually run instead of curating one up front
- Run an agent under a tight token budget where a 200K-context harness is too expensive
- Drive an Android device over ADB from the same agent loop that drives the desktop
How GenericAgent compares
GenericAgent alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.
| Tool | Stars | What it does |
|---|---|---|
| DeepSeek Harness | ★ 219k | DeepSeek AI's open-source agent harness (dsh), built on Cordis, where models, tools, skills, sessions, sandboxes, storage and the UI are all plugins composed through profiles. |
| AutoGPT | ★ 187k | One of the earliest autonomous agent projects, now a platform for building and running agents from reusable blocks and workflows. |
| DeerFlow | ★ 82.2k | ByteDance's open-source super agent harness built on LangGraph: skills, sub-agents, sandboxes, a filesystem and long-term memory for long-horizon research, coding and content tasks. |
| nanobot | ★ 48k | Lightweight self-hosted personal AI agent framework in Python, with a WebUI, terminal and chat-app channels, tools, long-term memory, MCP and scheduled automations. |
| Agno | ★ 42.1k | A fast Python framework (formerly Phidata) for building agents with memory, tools, and multimodal inputs, plus a runtime for deploying them in production. |
| LangGraph | ★ 41.4k | A library from the LangChain team for building stateful, graph-based agent workflows with explicit control over steps, memory, and human-in-the-loop checkpoints. |
| AgentGPT | ★ 36.3k | AgentGPT lets you name a custom AI, give it a goal, and watch it plan tasks, run them, and learn from the results, all from a web browser. |
| GenericAgent | ★ 14.2k | A ~3K-line agent framework with nine atomic tools that grows its own skill tree instead of shipping preloaded skills |