Claudette

Your AI never leaves your laptop. Claudette is a personal AI assistant and coding agent that runs entirely on local hardware — REPL, fullscreen TUI, one-shot CLI, and a Telegram bot, all driving one local model through Ollama or LM Studio. No cloud brain. No API key. No subscription. No telemetry. One Rust binary.

Pull a model, unplug the network, and she still works. The whole core — chat, notes, todos, file editing, code search, repo work, even the autonomous code-change pipeline — runs with zero internet. See Air-gapped by design.

One turn driving four tool groups (note_list, weather, tv_get_quote, calendar_list_events) — the model enables groups on demand and dispatches the calls. TUI tabs: [1]Chat [2]Tools [3]Notes [4]Todos [5]HW.

Install in 30 seconds

Linux / macOS:

curl -fsSL https://raw.githubusercontent.com/mrdushidush/claudette/main/install.sh | sh

Windows (PowerShell):

iwr -useb https://raw.githubusercontent.com/mrdushidush/claudette/main/install.ps1 | iex

Then pull a brain and talk:

ollama pull qwen3.5:4b           # 3.4 GB brain — one-time download
claudette "what time is it?"

Prefer not to pipe the network into a shell? Grab a prebuilt archive from Releases and drop claudette (or claudette.exe) onto your PATH. Every artifact ships a SHA256 sidecar so you can verify the download wasn't corrupted in transit.

Rust user? cargo install claudette.
No GPU? The 4B brain runs on plain CPU — slower, but it works. See CPU-only mode.
First time? docs/show-me.md is plain-English examples — calendar, notes, weather, screenshots, voice from your phone.

🔒 Air-gapped by design

Most "local AI" tools quietly phone home — telemetry, model downloads, a cloud fallback when the local model struggles. Claudette has no such path. There is no cloud-brain code in the binary to begin with.

• One required dependency: a model server (Ollama or LM Studio) on localhost. That's it.
• Every outbound call is opt-in and feature-gated — voice TTS, Telegram, web search, GitHub, Google Calendar/Gmail. Each one only exists if you turn it on with a token. The full inventory of every place a byte could leave your machine is in PRIVACY.md.
• Truly offline: CLAUDETTE_SKIP_OLLAMA_PROBE=1 skips even the localhost startup check. Pull your model, disconnect the network, and the entire core works — chat, notes, todos, files, code search, repo editing, and the Forge autonomous code-change pipeline.
• Nothing is written outside ~/.claudette/ without an explicit permission prompt.

If you work on a regulated, classified, or simply trust-no-cloud machine, this is the headline feature — not an afterthought. Your code and your conversations never touch someone else's servers.

Why Claudette

The open-source agent space is crowded with cloud-API coding tools. Claudette aims at a different slot: a general-purpose assistant and a capable coding agent that run entirely on your own hardware, with no cloud brain in the loop.

• Private by construction. Not "private mode" — there is no other mode. See above.
• Fits real hardware. The default qwen3.5:4b brain uses ~3.4 GB VRAM and runs on an 8 GB GPU or plain CPU. Step up to qwen3.6-35b-a3b on a 16 GB GPU for near-frontier quality. No hidden 32 GB-VRAM requirement.
• Personal, not just code. Calendar, Gmail, scheduler/briefings, notes, todos, markets, weather, web search — code generation is one capability, not the whole product.
• Messaging-first. A first-class Telegram bot with voice in (Whisper) and voice out (edge-tts) — drive the whole agent from your phone at a bus stop.
• Honest about itself. Side-by-side vs. OpenHands, Aider, opencode, Cline, Continue: docs/comparison.md. Claudette doesn't win every column — it's the only one built for this slot.

What she can do

Four interfaces, one local brain

80+ tools, ~200-token base schema

Almost every tool lives in a group the model opts into via enable_tools(group), so the prompt stays tiny until the surface is actually needed. 22 groups: notes, todos, files, code, meta, git, ide, search, advanced, facts, registry, github, markets, telegram, calendar, schedule, gmail, recall, quality (run_tests / diagnostics / apply_patch), semantic (semantic_grep), vision (screenshot_capture / image_describe), and clipboard. Point Claudette at a repo (CLAUDETTE_WORKSPACE) and the lean coding core (Files + Search + Advanced + Quality) is pre-enabled so she can read, edit, search, and run tests immediately.

🛠️ Forge-mode: an autonomous code-change pipeline

claudette --forge "<prompt>" runs a Planner → Coder → Verifier loop against a git repo, with a configurable fix-loop (default 2 rounds) and an optional deterministic security-review stage, before it opens a PR. Inside an existing repo it auto-bootstraps an ephemeral mission at the repo root — no clone needed. Roles are independently routable, so you can pin a stronger model to the Verifier and a cheap one to the Coder. Full walkthrough: docs/forge.md.

🌿 Brownfield missions: clone, edit, ship a PR in one chain

mission_start("owner/repo") clones into ~/.claudette/missions/<slug>/ and transparently re-routes git_status / glob_search / grep_search / write_file / bash into the mission tree. mission_submit auto-branches, commits, pushes, and opens the PR. Resumable across sessions with mission_state(action="attach").

🧠 Tiered-brain auto-fallback

Three presets — Fast / Auto / Smart. Auto runs qwen3.5:4b and escalates to qwen3.5:9b only on real stuck signals (empty response after retry, max-iterations with no text, ≥3 consecutive tool errors), reverting per-turn rather than sticking. On a 16 GB GPU, pin qwen3.6-35b-a3b instead — see Claudette Certified.

🎙️ Voice in, voice out, vision in

Whisper transcription for Telegram voice notes, edge-tts replies (English or Hebrew), and image attachments in TUI/REPL via Alt+V (clipboard), drag-drop, or @/path/to/img.png — when the loaded brain is multimodal.

⚙️ Codet sidecar for code generation

generate_code routes through a dedicated coder model (default qwen3-coder:30b), runs a real syntax check across 4 languages (Python py_compile, rustc --emit=metadata, JavaScript + TypeScript via tsc --noEmit), then an Aider-style SEARCH/REPLACE fix loop on failure, then optional pytest / cargo-test / jest. Hot-swaps into VRAM on demand on memory-constrained boxes.

🔎 Cross-session semantic recall

/recall <query> searches every past conversation turn across sessions via a local embedding index, dropping the relevant fragments straight into the current context.

🤝 Sub-agents

spawn_agent delegates to a Researcher (web + file + code search), GitOps (rebase/squash/push), or Code Reviewer (read-only). Only the final answer comes back — sub-agent chatter never pollutes the main context.

🛡️ Per-tool permission gating

ReadOnly and WorkspaceWrite tools auto-allow; DangerFullAccess (bash, edit_file, git add/commit/push, cross-org PRs) prompts [y/N] every time. Telegram default-denies DangerFullAccess (no TTY to confirm at).

🏅 Claudette Certified — the local-model benchmark

A local agent is only as good as the model behind it, and "which model should I run?" is the question every new user asks. So we answer it with data, not vibes.

Every candidate brain runs the same objective 50-task battery — 11 languages/surfaces (Rust, Python, JS, TS, Go, shell, HTML, CSS, SQL, a large real repo, git) × 12 task types (bugfix, add-feature, multi-file, refactor, create-file, explain, locate, enumerate, run-tests, debug-error, git-workflow, answer-from-codebase) — through Claudette's real tool loop, then an automated verifier checks the result (build/test passes, the file is correct, or ground-truth tokens appear in the transcript). No model grades itself. All runs below: 24k context, --parallel 1, RTX 5060 Ti 16 GB (2026-05-30). The harness is reusable and lives at runs/eval-2026-05-29/battery/ — bring your own model.

Not ranked — qwen3.6-27b (dense), incomplete run. Its sweep was cut short when the model unloaded partway through (the dense 27b runs hot, ~72 °C, and got evicted): the 12 hardest tasks — all 8 large-repo + 4 git-workflow — never actually executed (they log a 0-second HTTP 400 "No models loaded", i.e. an infrastructure halt, not a real attempt). So there is no comparable /50 to rank it by — a score over only the ~38 tasks that ran isn't apples-to-apples with the full-50 numbers above, so we leave it out rather than flatter it. It held up fine on what it did run, but it's the slow dense "precision" tier regardless (~67 s/task — accurate, not interactive). Raw per-task rows: SCORES-qwen36-27b.tsv.

The lessons that shaped the recommendations:

1. Fitting in VRAM beats parameter count. q3_k_xl (fits 16 GB) beats q4_k_xl (spills to RAM → ~20% slower → loses tasks to timeouts) despite lower precision. On 16 GB, pick q3_k_xl.
2. Small models punch up. A 4B model hits 90% in 8 minutes and runs on an 8 GB GPU — the value/accessibility star.
3. Chat-template compatibility is the #1 local-model failure mode. gemma-4-26b and qwen3-coder-30b stock GGUFs return HTTP 400 on tool calls in LM Studio's template engine; glm-4.7-flash narrated prose instead of emitting tool calls. Always pull a lmstudio-community / unsloth repack and validate one real tool call before trusting a model.
4. Thermals follow architecture, not size. MoE brains keep the GPU ~55 °C at any size; the dense qwen3.6-27b runs hot (72 °C) — it's the slow precision tier, not for sustained interactive use.

Full per-task data and reasoning notes: runs/eval-2026-05-29/battery/MODEL-COMPARISON.md. The next batch of candidates queued for certification (GLM-4.7-Flash, Qwen3-Coder-30B, Granite-4.1-8B, Mistral/Ministral, and more) is in CANDIDATES.md — this is one of the best ways to contribute (see Roadmap).

qwen3.6-35b-a3b is distributed via LM Studio (Unsloth GGUF) rather than packaged on Ollama. Flip the backend with CLAUDETTE_OPENAI_COMPAT=1 and pin the quant explicitly (CLAUDETTE_MODEL=qwen3.6-35b-a3b@q3_k_xl) — LM Studio picks the smallest match otherwise. Recipe in docs/power-user.md.

Hardware

The numbers describe the comfortable setup. You don't need a GPU — Ollama runs on plain CPU (slower, but viable for a 1B/3B/4B brain). See docs/hardware.md#no-gpu-cpu-only-mode.

Full model-footprint table, CPU-only recipes, and the 30b-coder-on-8GB-VRAM env recipe: docs/hardware.md.

Quick start (full setup)

# 1a. Default path — Ollama with the 3.5 family (works on 8 GB VRAM, or CPU).
ollama pull qwen3.5:4b           # brain (default Auto preset)
ollama pull qwen3.5:9b           # fallback brain (optional)
ollama pull qwen3-coder:30b      # Codet coder — only if you'll use generate_code

# 1b. Recommended path — LM Studio with qwen3.6 (best on 16 GB+ VRAM).
# Pull `qwen3.6-35b-a3b` inside LM Studio, then in ~/.claudette/.env:
#   CLAUDETTE_OPENAI_COMPAT=1
#   OLLAMA_HOST=http://localhost:1234
#   CLAUDETTE_MODEL=qwen3.6-35b-a3b@q3_k_xl
#   CLAUDETTE_CODER_MODEL=qwen3.6-35b-a3b@q3_k_xl
# Full LM Studio recipe: docs/power-user.md

# 2. Install Claudette — pick one.
curl -fsSL https://raw.githubusercontent.com/mrdushidush/claudette/main/install.sh | sh   # Linux/macOS
iwr -useb https://raw.githubusercontent.com/mrdushidush/claudette/main/install.ps1 | iex  # Windows
cargo install claudette                                                                    # Rust users

# 3. (Optional) Tokens for opt-in tools that reach the network.
export BRAVE_API_KEY=...         # web_search
export GITHUB_TOKEN=ghp_...      # github group
export TELEGRAM_BOT_TOKEN=...    # --telegram mode

# 4. Run.
claudette                        # REPL
claudette --tui                  # TUI
claudette "what time is it?"     # one-shot
claudette --forge "fix the failing test in src/parser.rs"   # autonomous pipeline
claudette --resume               # resume last session
claudette --telegram             # Telegram bot
claudette --doctor               # diagnose model server, models, tokens, permissions

First launch auto-creates ~/.claudette/ and probes http://localhost:11434. Going fully offline? CLAUDETTE_SKIP_OLLAMA_PROBE=1.

Out of the box (no tokens, no network): notes, todos, files, time, code search, repo editing, forge. Brave / GitHub / Google Calendar / Gmail tools light up when you set the relevant token — full table in docs/configuration.md.

🚀 Roadmap

Claudette has a clear north star: be the most private, most universally-runnable local code assistant in the world. Here's where she's headed — and these are exactly the places a new contributor can leave a mark.

The vision

• 🔒 Hardened air-gap mode. A first-class, audited offline profile: a --air-gapped flag that hard-disables every network-capable tool, a reproducible offline install bundle (binary + model + Whisper weights), and a documented "regulated-machine" deployment story.
• 🧠 A model-agnostic, curated brain menu. Not one blessed model — a recommendation for every hardware tier, from a 4 GB laptop GPU to a 24 GB workstation, so anyone can run her well on what they already own.
• 🏅 The Claudette Certified program, expanded. Keep running the objective 50-task battery on every promising new local model and publish a living, badged recommendation table. The candidate queue is already scouted — GLM-4.7-Flash, Qwen3-Coder-30B, Granite-4.1-8B, the Mistral/Ministral family, and more.

Where you come in 🙌

New contributors welcome — these are real, scoped, high-impact ways to help:

• 🏅 Certify a model. Have a GPU and a model we haven't benched? Run the reusable battery at runs/eval-2026-05-29/battery/ and open a PR with the scores. This is the single most valuable contribution right now and needs no Rust.
• 📦 Rescue a template. gemma-4-26b, qwen3-coder-30b, and glm-4.7-flash are strong models gated only by broken stock chat templates. Find/build a working lmstudio-community/unsloth GGUF, validate one tool call, and document the fix.
• ⚙️ Sharpen the coder. Route trivial create-file requests straight to write_file so they don't race the generate_code timeout; widen the syntax-check language set; improve the SEARCH/REPLACE fix loop.
• 🛡️ Grow the security-review stage. The Forge security scanner is line-based on diff additions — extend its rule coverage (multi-line sinks, SSRF, path traversal, prototype pollution) and shrink false positives.
• 🎙️ Extend voice & vision. More TTS languages, better multimodal image handling, richer Telegram voice flows.
• 📚 Tell the story. Tutorials, a homelab/Pi deploy guide, screencasts — docs/show-me.md is where the new-user journey lives.

Known open items live on the issue tracker, including the current Dependabot advisories. Start at CONTRIBUTING.md, grab a thread above, and say hi in an issue.

Docs

• docs/show-me.md — start here: plain-English example prompts (notes, calendar, vision, voice, code)
• docs/quickstart.md — 30-second start, common flows
• docs/configuration.md — every env var, token file fallbacks, recall settings
• docs/power-user.md — LM Studio recipe, brain pinning, forge knobs, context tuning
• docs/hardware.md — VRAM/RAM/disk by preset, CPU-only mode, 30b-on-8GB env recipe
• docs/usage.md — CLI flags, slash commands, Telegram-only commands
• docs/architecture.md — module layout, tool-group contract, Codet sidecar contract
• docs/forge.md — forge-mode pipeline, Submitter contract, models.toml schema, auto-bootstrap
• docs/comparison.md — honest side-by-side vs. opencode / Aider / OpenHands / Cline / Continue
• docs/google_setup.md — Calendar + Gmail OAuth walkthrough
• docs/deploy.md — Pi / VPS / home-server deploy via docker-compose
• editor/vscode/ — VS Code extension (REPL / TUI / forge / "ask about selection")
• PRIVACY.md — every place data can leave your machine, and the conditions for each

Storage layout

~/.claudette/
├── notes/            # Markdown notes (ISO-timestamped, optional tags)
├── files/            # Sandboxed scratch dir for write_file/generate_code
├── sessions/         # Auto-saved + named sessions
├── secrets/          # Token files (github.token, telegram.token, brave.token, …)
├── missions/         # Brownfield mission clones
├── models/           # Whisper model (download separately)
├── recall.sqlite     # Cross-session semantic-recall index
├── todos.json        # Task list
├── models.toml       # Optional model-config overlay
├── fallback.jsonl    # Auto-fallback event log
├── .env              # Persistent env-var overrides
└── CLAUDETTE.MD      # Optional user memory (800-char cap)

Nothing outside ~/.claudette/ is written without an explicit permission prompt.

Build from source

git clone https://github.com/mrdushidush/claudette
cd claudette
cargo build --release -p claudette
./target/release/claudette --help

1,000+ tests passing. Before committing: cargo fmt --all && cargo clippy --all-targets --no-deps -- -D warnings && cargo test --lib.

Contributing

See CONTRIBUTING.md, and the Roadmap above for high-impact starting points. Quick version:

• File bugs at https://github.com/mrdushidush/claudette/issues.
• Conventional Commits: feat:, fix:, docs:, refactor:, test:, chore:, style:, ci:.
• By contributing, you agree your work is dual-licensed under MIT OR Apache-2.0.

Security issues: please use the private advisory flow in SECURITY.md — don't open a public issue.

Be kind — CODE_OF_CONDUCT.md has the short version.

License

Dual-licensed under either of:

• MIT — see LICENSE-MIT
• Apache License 2.0 — see LICENSE-APACHE

at your option. Use, modify, redistribute commercially or personally. The Apache option adds an explicit patent grant; neither grants a trademark — don't imply endorsement.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual-licensed as above, without any additional terms or conditions.

Copyright © 2026 mrdushidush.