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Three AI agents, one desktop, zero cloud
A self-hosted ReAct agent with 12 tools and real memory
Most "AI agent" apps today fall into one of two camps: 1. **Thin chat wrappers** — they forward your message to an API and stream tokens back. No real tools, no file system access, no memory beyond the context window. 2. **CLI launchers** — they spawn `claude` or `codex` as a subprocess and forward the output. If you don't have those CLIs installed (or don't want to pay for their subscriptions), you get nothing. **KinetAios is neither.** At its core is **Direct Engine** (we call it "Kaios")
Most "AI agent" apps today fall into one of two camps:
1. **Thin chat wrappers** — they forward your message to an API and stream tokens back. No real tools, no file system access, no memory beyond the context window.
2. **CLI launchers** — they spawn `claude` or `codex` as a subprocess and forward the output. If you don't have those CLIs installed (or don't want to pay for their subscriptions), you get nothing.
**KinetAios is neither.**
At its core is **Direct Engine** (we call it "Kaios") — a from-scratch ReAct agent loop built in TypeScript that:
- **Calls any OpenAI-compatible or Anthropic-compatible API** — GLM, DeepSeek, Qwen, Ollama, OpenAI, Mistral, any provider. One API key, no vendor lock-in.
- **Has 12 built-in tools that run on your machine** — not JSON stubs, real Node.js implementations:
- `shell` — execute any command (with user confirmation + sandbox modes)
- `read_file` / `write_file` / `edit_file` — full file system I/O with auto-snapshot before every write
- `grep` / `glob` — search files like a developer
- `git_diff` — inspect uncommitted changes, per-file or per-ref
- `web_search` — multi-engine fallback (Bing + DuckDuckGo)
- `web_fetch` — fetch and denoise any URL (Jina Reader + native fetch)
- `recall_memory` — search long-term memory (FTS5 full-text + embedding)
- `dispatch_agent` — spawn a sub-agent for parallel exploration
- **Manages its own context window** — reactive trim (auto-shrink on context overflow) + history compaction (LLM-summarize old turns instead of dropping them) + orphan tool-call cleanup. Long conversations don't silently break.
- **Auto-extracts long-term memory** — after every turn, a background pass pulls durable facts into SQLite. Next session — even with a different model — gets them injected automatically.
- **Snapshots every file write** — each `write_file` / `edit_file` snapshots the original content to `.kinet-snapshots/`. One-click rollback. The agent can't destroy your code.
**Claude Code and Codex?** They're available as *optional* engines — you can switch to them per-conversation if you happen to have them installed. But they're not required. They're not the point. The point is: **KinetAios gives you a real agent that works with any model, out of the box, with zero dependencies.**
About Three AI agents, one desktop, zero cloud on Product Hunt
“A self-hosted ReAct agent with 12 tools and real memory”
Three AI agents, one desktop, zero cloud was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #159 on the daily leaderboard. Most "AI agent" apps today fall into one of two camps: 1. **Thin chat wrappers** — they forward your message to an API and stream tokens back. No real tools, no file system access, no memory beyond the context window. 2. **CLI launchers** — they spawn `claude` or `codex` as a subprocess and forward the output. If you don't have those CLIs installed (or don't want to pay for their subscriptions), you get nothing. **KinetAios is neither.** At its core is **Direct Engine** (we call it "Kaios")
On the analytics side, Three AI agents, one desktop, zero cloud competes within Developer Tools, Artificial Intelligence, GitHub and Vibe coding — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Three AI agents, one desktop, zero cloud performed against the three products that launched closest to it on the same day.
Who hunted Three AI agents, one desktop, zero cloud?
Three AI agents, one desktop, zero cloud was hunted by PhinnShen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Three AI agents, one desktop, zero cloud including community comment highlights and product details, visit the product overview.
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