GenericAgent vs LiveAgent
GenericAgent is much bigger: 14.1k stars against 2.0k. Over the days we have tracked them GenericAgent moved +11.8% and LiveAgent +12926.7%, so LiveAgent is growing faster right now.
They split the axes: GenericAgent leads on context depth and noise control, LiveAgent on customization.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Self-evolving agent that crystallizes each solved task into a persistent, reusable personal skill tree from a minimal (~3K-line) seed.
- Stars
- 14.1k
- Tracked growth
- +11.8%
- Maturity
- ●●●●●
- Last commit
- 2d ago
- Language
- Python
- License
- MIT
- Cost to run
- Your LLM API key; provider charges apply
A local‑first desktop AI agent that actually performs file-system operations, runs bash/managed processes, and extends via an MCP/Skills ecosystem with an optional remote Gateway.
- Stars
- 2.0k
- Tracked growth
- +12926.7%
- Maturity
- ●●●●●
- Last commit
- 3h ago
- Language
- TypeScript
- License
- MIT
- Cost to run
- Use your API key or third‑party relays for external models; desktop app is local-first
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | GenericAgent | LiveAgent |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Whole-repo analysis | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Human-in-the-loop | ●●●●● None mentioned |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config & prompts | ●●●●● Extensible skills & prompts |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud API key | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-own-key | ●●●●● Bring-your-own-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● One-line installer | ●●●●● Desktop installers |
Which one to pick
Pick GenericAgent if…
Self-evolving — pick this when you want an agent that autonomously builds reusable skills and gains system-level desktop/browser control over time.
- Context depth: Whole-repo analysis (5/5 against 3/5)
- Noise control: Human-in-the-loop (3/5 against 1/5)
Pick LiveAgent if…
Privacy-first — run a fully functional agent on your desktop with local key storage and extensible Skills while optionally routing models via your own endpoints.
- Customization: Extensible skills & prompts (5/5 against 3/5)
What people want from each one
lsdefine/GenericAgent
Stack-Cairn/LiveAgent
Questions people ask
Is GenericAgent better than LiveAgent?
They split the axes: GenericAgent leads on context depth and noise control, LiveAgent on customization. GenericAgent is worth picking when self-evolving — pick this when you want an agent that autonomously builds reusable skills and gains system-level desktop/browser control over time.
Which of GenericAgent and LiveAgent keeps my code private?
GenericAgent: Cloud API key (3/5). LiveAgent: Self-hostable (4/5).
What does each one cost to run?
GenericAgent: Your LLM API key; provider charges apply. LiveAgent: Use your API key or third‑party relays for external models; desktop app is local-first.
Full profiles: lsdefine/GenericAgent and Stack-Cairn/LiveAgent. Everything else in Computer use.