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Why Chaty

• 🔒 Truly private — every model, document, and conversation stays on your device. No sign-up, no server, nothing phoned home.
• ⚡ Native and fast — a Rust + llama.cpp core with Vulkan / Metal GPU offload that auto-tunes to your hardware and falls back gracefully to CPU.
• 🧰 More than a chat box — a coding agent, a knowledge base (RAG), Deep Research, hands-free voice, and a self-healing Design Canvas — all offline.
• 🧠 Runs almost anything — Llama 3, Gemma 3 / 4, Qwen 3 / 3.5 / 3.6, any GGUF from Hugging Face — and MLX models natively on Apple Silicon — plus Chaty's own fine-tuned model.
• 💻 Friendly to modest hardware — a first-launch “Set up for me” picks a model sized to your RAM and downloads it in one click.

A local coding agent

Flip the Chat · Code switch and Chaty becomes an agent for your codebase. Point it at a folder, describe the task, and it explores, edits, and verifies the project by itself — every step shown live, every change behind an approval + diff.

• 🌐 The whole web as a tool — key-less search of GitHub (repos, issues, and code), Reddit, YouTube, Bilibili, and any domain; fetching adapts to the content (articles → Markdown, PDFs → text, videos → transcripts).
• 🧭 Drives a real browser — opens pages, reads dynamic content as text, clicks and fills whole forms with real mouse events, logs in and paginates — and looks with the vision model when it matters.
• 🧠 Tools that do the thinking — understand_repo orients in one call, search_code ranks files by relevance, read_file lifts a single symbol plus its call sites, validate_change runs just the tests the change touches. Small models spend their steps on decisions, not grunt work.
• ✏️ Precise edits, real shell — exact-string patches behind a diff preview with a syntax gate, plus commands and long background jobs (dev servers, builds) sandboxed to the workspace.
• ⏪ You stay in control — per-action approval, a command allowlist, prompt-injection defense on everything it reads, and one-click checkpoint rewind that restores files and rolls back the conversation.
• 🔌 MCP, sized for small models — connect any Model Context Protocol server (stdio or streamable HTTP), or one-click a curated, version-pinned store entry that's live-certified against Chaty's own client. Tool docs are synthesized lean so a 16K context fits as many servers as you like; every result is injection-defended and untrusted servers need per-call approval.
• 📚 Skills & project memory — drop a SKILL.md of procedural steps in ~/.chaty/skills/ (or per-project) and the agent loads it only when relevant; remember saves non-obvious findings to .chaty/memory/ so the next session starts knowing them. Plain markdown, human-editable, never leaves the machine.

More Code-mode details

• Reads PDF / Word / Excel / PowerPoint (scanned PDFs get OCR'd); search_files finds by name or content; file outlines navigate big files; failed patches get “did-you-mean” hints.
• Browser automation is verified end-to-end against real sites, and can run in your real Chrome — watch it work, logins and all.
• Built for local models: an Off / Normal / Deep reasoning switch, a prompt-processing progress ring, a context-usage ring with automatic compaction, whole-file reads sized to your context window, ranked search_code + knowledge-base search_docs, and loop-breaking for repetitive small models.
• Persistent sessions, project memory (AGENTS.md), custom /skills, and slash commands.
• Tune it under Settings → Code: step limit, command timeout, step temperature, an auto-approve-edits toggle, a headless-browser toggle, and a command allowlist.
• File access never leaves the folder you pick; out-of-workspace access asks per folder; a sudo command asks first with a secure password prompt; downloads land in the workspace and are covered by checkpoints too.

Benchmarks

One local model for every row — Qwen3.5-35B-A3B (MoE, ~3 B active per token), mxfp8 on MLX, reasoning off, entirely on one machine:

SWE-bench Verified — 45-task macOS-validated subset	Resolved
Chaty agent (v1.9) — the full tool loop, 16K context	15/45 (33 %)
qwen-code 0.20 — the model family's own CLI (needs 32K)	12/45 (27 %)
pi 0.81 — minimal 4-tool agent CLI	10/45 (22 %)
opencode 1.18	7/45 (16 %)
bare bash agent — single-tool ablation	6/45 (13 %)

Same model, same tasks, same grading, one machine — five agent designs. Chaty leads the field, including the model family's own first-party CLI (qwen-code) while using half its context window, and resolves 2.5× the bare-bash ablation. That's the design thesis measured: with frontier models a thin scaffold is enough — on small local models, the intelligence has to live in the tools (repo-aware search, symbol reads, precise edits, recovery guards, post-edit diagnostics). Methodology, per-agent configs, and honest-comparison notes (subset, macOS harness — not comparable to leaderboard numbers): docs/BENCHMARKS.md.

Design Canvas

• Preview | code, side by side — every page opens as a split studio: live preview left, the actual source right, syntax-highlighted and palette-following. Three drag-resizable columns, fullscreen, page reload, and a Console tab for the page's logs and errors.
• Point at what you mean — Inspect links the panes both ways: hover an element and the code jumps to its line; click a code line and the element flashes. Click to select (⌘/Ctrl multi-select) and your next instruction edits exactly those elements — or open the source yourself with the Edit button.
• Watch the edit happen — iterations stream in Cursor-style: the code pane scans the document line by line and lands on a Changes diff (+N/−N, same language as Code mode).

• Self-healing, persistent — runtime errors offer a one-click Fix (always asks first); a compat layer keeps browser-clean pages clean here too (history API, cookies, clipboard); and each reply keeps its canvas session across close/reopen, with version history, a confirmed reset, and export to a standalone .html.

Chat that renders everything

• A streaming, foldable <think> panel that follows the model's reasoning as it generates.
• KaTeX math, tables, Mermaid diagrams, per-block code copy, and in-app rendering of single-file HTML — including playable web games.
• A ⌘K command palette, pinnable / renameable conversations, drag-and-drop attachments, export (Markdown / JSON), and full-text search.
• Four palettes (two dark, two light) with system-theme following, native UI zoom, reduced-motion support, and an English / 简体中文 / Português (BR) UI.

Chaty can see

Load a vision model (its weights and mmproj encoder live together in one folder, paired automatically) and image understanding turns on everywhere:

• Chat — attach a picture and ask about it; follow-ups stay fast (already-seen images aren't re-encoded).
• Code — the agent reads screenshots and can look at any image with view_image; the composer takes images and documents just like chat.
• Knowledge base — imported images get a written description beside their OCR text, so search finds what's in them; images embedded inside PDFs, Word, Excel and PowerPoint files are extracted and described too.
• Canvas — the model sees the live rendered page when you ask for an edit.

Text-only models keep the OCR path, so nothing regresses — and updating from an older version, a one-time prompt tidies your existing loose .gguf files into the one-folder-per-model layout with a single click.

Models: the store, native MLX — and Chaty's own

• A built-in model store: search Hugging Face by name or author, filter GGUF / MLX, sort by trending or downloads — then pick a quantization from a dropdown and hit download. Models, not file lists.
• Parameter / architecture / vision badges, the repo's README rendered in-app, and a "fits fully in memory" hint sized to your machine. Vision models fetch their encoder automatically; pasting a repo link still works.
• MLX runs natively on Apple Silicon: mlx-community folder models load through Apple's MLX stack in an isolated sidecar — same chat, vision, reasoning controls, Code agent and knowledge-base support as GGUF, and ejecting a model always returns its memory.
• Chaty's own fine-tune — a Qwen3.5-4B distilled from a much larger teacher for leaner on-device single-file web design, with a baked-in Chaty identity and grounded citations. A one-click pick in “Set up for me”, fully open on Hugging Face.

A private knowledge base

Index PDF, Word, Excel, Markdown, ~90 text/code formats, and images into an on-device store — one file or a whole folder. Images are read by OCR and, with a vision model, described in words so you can search what's in the picture. Hybrid retrieval: bge-m3 vectors + BM25 keywords, fused with RRF, de-duplicated with MMR, expanded with neighbors. Strict grounding — answers come only from your files, with per-file citations and hover-preview of the source passage. Chaty says when something isn't covered instead of guessing. One-click report — a cited, NotebookLM-style overview of the whole base, exportable to PDF or Markdown.

Deep Research & the web

• Give a topic and Chaty plans queries, runs multiple rounds of web search interleaved with reasoning, and writes a structured, cited report — exportable to PDF or Markdown.
• Honest by design: the reference list contains only sources it actually cited.
• A free, key-less, multi-provider search chain (Brave → Bing → DuckDuckGo → Wikipedia) so one blocked provider never breaks search.

Hands-free voice

Live mode — continuous, hands-free conversation with an animated orb. Voice in/out with silence auto-send and read-aloud — 11 voices with speed control. Deep-dive podcast — turn your knowledge base into a NotebookLM-style two-host audio show, with WAV export. All voice runs on the CPU, so it never competes with the LLM for VRAM.

Everything stays on your machine

Conversations, models, and indexes live in one local data folder — copy it to back up, clear it in a click. GPU acceleration: cross-vendor Vulkan (Windows) and Metal (Apple Silicon, offload-all on unified memory), VRAM-aware auto-tuning with OOM back-off and CPU fallback. Any .gguf — or MLX folder — tokenizer and chat template come from the model itself; first-class handling for Llama 3, Gemma 3 / 4, and Qwen 3 / 3.5 / 3.6. Adjustable context that auto-fits the model's trained length to your memory and summarizes older turns near the limit; safe model switching and full sampling controls with saveable presets.

Offline-first. The network is used only for optional web search and one-time model downloads.

Install

Grab the latest build from the Releases page:

Platform	File	Notes
Windows x64	Chaty_*_x64-setup.exe	Per-user installer — no admin required
macOS (Apple Silicon)	Chaty_*_aarch64.dmg	See the first-launch note below

macOS first launch. Chaty is ad-hoc signed but not notarized (there's no paid Apple Developer account behind it), so Gatekeeper warns on first open. The app is safe — everything runs locally. Clear the download quarantine once:

xattr -dr com.apple.quarantine /Applications/Chaty.app

then open Chaty normally. (Or: open it, dismiss the warning, and choose System Settings → Privacy & Security → Open Anyway.) On macOS the writable models folder lives in app data — use Open models folder in the model menu.

Build

Full details in BUILD.md.

# Windows
npm install
.\dev.ps1                            # dev
npm run tauri build -- --no-bundle   # release exe → compile the Inno installer

# macOS (Apple Silicon)
npm install
npm run tauri dev      # dev (Metal)
npm run tauri build    # → .app + .dmg

Releases are produced by CI: bump with scripts/bump-version.sh x.y.z, then push a vx.y.z tag — GitHub Actions builds both installers onto a single release.

Architecture

Layer	Stack
Shell	Tauri 2 — system tray, global shortcut, single-instance
Frontend	React 19 · Vite · react-markdown · KaTeX
Inference	Rust · llama-cpp-2 (llama.cpp) — Vulkan (Windows) / Metal (macOS) · MLX via an mlx-swift-lm sidecar (Apple Silicon)
Voice	sherpa-rs (ONNX Runtime, CPU) — Whisper-base.en + Kokoro-82M
Knowledge base	bge-m3 embeddings + BM25 · hybrid RRF / MMR retrieval · SQLite vector store
Storage	SQLite — conversations, messages, full-text search

License

MIT — see LICENSE. Built with llama.cpp, Tauri, and sherpa-onnx.