mindfork 0.18.2

A terminal AI chat with a memory and a self-model. The assistant keeps notes about you, a model of itself that it revisits, and a knowledge base over your files — on a local Gemma or Qwen through llama.cpp, or on OpenAI, Anthropic, Gemini, Grok and OpenRouter in the cloud — and works with real tools behind switches you set. One native Rust binary for Windows, Linux and macOS on Apple Silicon (a preview), built on ratatui.

Not a screen recording: every release draws this turn from its own interface, so it shows the version you download. The stills are generated from code too, against a demo profile, and a gate test fails the build the moment they drift from what the app renders (demo-reel.md, demo-screenshots.md).

Install

Linux, or a Mac: one line installs the portable build, and refuses the archive unless it matches the release's checksums.

curl -fsSL https://github.com/vshylov/mindfork-rs/releases/latest/download/install.sh | sh

macOS on Apple Silicon, through Homebrew (a preview):

brew install vshylov/tap/mindfork

Windows, through Scoop, which also keeps it current:

scoop bucket add mindfork https://github.com/vshylov/scoop-bucket; scoop install mindfork/mindfork

Then mindfork demo opens the app with sample chats and a scripted model in a throwaway folder: no model to download, no key, nothing written outside a temporary directory. mindfork on its own starts with your data, and Getting started connects a model.

Already running Ollama or LM Studio? Start mindfork. With no model connected yet, it looks for both on this computer and lists what they serve: Enter connects one — with that server's embedding model too when none is set up — and /local looks again whenever you type it. LM Studio's server is off until started (lms server start, or its Developer tab); an Ollama with no model needs one first:

ollama pull gemma4:e4b
mindfork

Give the server a window of 16k: for Ollama, the context slider in its app's settings or OLLAMA_CONTEXT_LENGTH=16384 for ollama serve; for LM Studio, the model's Context Length when it loads it, or lms load <model> -c 16384. mindfork reads the window from either and folds the conversation into a summary before it fills — which matters, because both cut an overlong prompt in silence: Ollama drops the oldest messages, and when the last message alone does not fit, the start of the prompt with the instructions; LM Studio drops the whole middle of the conversation. Ollama's default is 4096 tokens on a GPU under 24 GB, and mindfork's first turn already takes about 3500: there the app says once that the window is too small, and how to raise it, and a prompt either server cut is told (install.md §3, which also has the mindfork setup line for a script).

The releases also carry a Windows installer and zip, and Linux deb, rpm and pkg.tar.zst packages. cargo install mindfork builds from crates.io, and cargo build --release from a checkout with a recent stable Rust. On a rented GPU box or in a container, the curl line run again repairs what a restart lost. All of it is in install.md §1 and §3.4.

mindfork is a TUI and needs a real terminal. Started with its output redirected or with no console, it says so and exits; the line commands (backup, import, llama setup, …) work anywhere.

What it does

mindfork is not trying to be yet another LLM client. The premise is that a local Gemma or Qwen becomes more self-aware and more interesting to talk to once it is given room to reflect: it keeps notes about you, maintains a self-model it can revisit, reads and adjusts its own system message and sampling mid-conversation, and can delegate work to a subagent that has its own tools.

• Any engine, one contract. A managed llama-server the app launches itself, any OpenAI-compatible server you already run (vLLM, LM Studio, Ollama, a gateway like LiteLLM), the OpenAI, Gemini, Claude and Grok clouds, or the OpenRouter gateway — one key in front of every vendor's models — each with its own settings, all configured at once, and switching loses nothing.
• Memory that persists. A self-model the assistant maintains about itself and about you (F3), notes it writes and links into a graph, and a knowledge base with semantic retrieval over your own files. Isolated per companion profile, all of it on your disk.
• Real tools, behind switches you set. Web search and page fetching, a Python sandbox (Wasmer/WASIX — no host file access), file access jailed to one directory you name, YouTube video understanding, and tools from any MCP server. Everything off by default; an optional confirmation prompt shows exactly what a call is about to do.
• It can hand work off. Subagents with their own tools and no chat history, a staged dialogue between two personas, and both of those in the background — F7 lists every run across every chat, with what it is doing now or how it ended.
• Your code project, contained. Attach a directory and the assistant can list, read, search and change files inside it — never above it. F4 shows every change as a diff, with one key to put a file back; build/run/test are exact command lines you set, which it cannot extend.
• A real TUI. Markdown with tables and LaTeX, Mermaid drawn as text graphics, streamed "thoughts" in a foldable block, images in and out, search across every conversation, spellcheck, emoji, mouse, themes — over your terminal's own background, painted whole with the contrast held for you (a theme of your own is a file of one colour or of twenty), or no colours at all (NO_COLOR is honoured) — and an interface in English or Russian.
• Private by construction. No telemetry, no update check, no account. Everything lives in one data folder on your disk, which F1 → About names: in your user profile when a package or the installer put the app there, beside the binary for the portable build — take the folder, or the USB stick it is on, and it comes with you. API keys are encrypted and bound to the machine; backups are a zip with optional AES-256.

The longer version, with the reasoning: the manual and the articles at mindfork.io.

Getting started

Connect a model

Three routes, and they can all stay configured side by side.

A cloud provider. Open settings (Ctrl+P, or /settings), pick the mode — openai / gemini / claude / grok / openrouter — paste your API key right there (stored encrypted and machine-bound, never shown back), and choose the model: Enter on that field asks the provider what it serves and offers the list, filtered as you type.

The OpenRouter gateway is the openrouter mode: one key serves the assistant, impersonation, embeddings, speech and video, each with a model of its own, and its model list shows each model's context window and price per million tokens (install.md §3.2).

An external server. Run any OpenAI-compatible server and point the app at it (mode external, the URL includes /v1):

# --jinja is required for the chat template and tool calling
llama-server -m google_gemma-4-E4B-it-Q4_1.gguf \
  --host 0.0.0.0 --port 8000 -ngl 99 -c 8192 --jinja

A gateway or an authenticated server takes its key in the field below the URL, and its model name in the one above — that name is what a multi-model endpoint routes on, while a single-model server ignores it.

A managed server. The app launches and supervises llama-server itself, and can fetch one for your machine:

mindfork llama backends                 # what llama.cpp publishes for your machine
mindfork llama setup --backend vulkan --set-binary   # …and point the settings at it

Or everything in one line — llama.cpp, the Python sandbox, the model and its context written into the settings, and a test start to see that it loads:

mindfork setup --llama vulkan --sandbox --model ~/models/chat.gguf --ctx 16384 --verify

The model itself is a GGUF file, which llama.cpp does not ship — Hugging Face hosts them; search the model's name with GGUF and fetch it with hf download (install.md §3.4). Whatever llama.cpp takes that the settings have no field for goes in the section's Extra arguments — --n-cpu-moe 20, say, to fit a large mixture-of-experts model on a smaller card (install.md §3).

Embeddings (memory, RAG, attachment search) use a separate server, configured in the same section's Embeddings tab; without one those features decline politely and everything else keeps working.

docs/install.md has the long version: every setting, the data paths, the Python sandbox, MCP servers, speech, backups and the environment variables.

Learn the app

docs/manual.md — the screens, what it remembers, files and your project, the tools and their switches, and the full list of keys and commands. Inside the app, F1 is the same reference, always current and listed by screen.

How it's built

The app is an HTTP client to an inference engine behind the EngineBackend trait — it deliberately embeds no ML stack (ADR 0004). Six providers live behind that one contract: a local OpenAI-compatible server, the OpenAI Responses API, native Gemini generateContent, the Anthropic Messages API, xAI (which needed no client of its own), and the OpenRouter gateway (the same client again, writing the request in the gateway's dialect).

Decisions that shape the code: the agentic loop is client-side — tools return a result plus effects, and the orchestrator, sole owner of Chat, applies them, so there are no locks; the UI↔orchestrator flow is unidirectional (AppCommand up, AppEvent down); storage is JSON + SQLite with sqlite-vec and soft delete everywhere; the markdown renderer and the input box are the project's own, for control over tables, LaTeX, theming and spellcheck underlines.

The structure follows Feature-Sliced Design, dependencies pointing strictly downward: app → screens → widgets → features → entities → shared. The full code map with its invariants is docs/architecture.md.

Development

cargo test                                 # unit tests — no server, no key needed
cargo clippy --all-targets -- -D warnings
cargo fmt --check

Anything that needs a real model is an #[ignore] smoke test, silently skipped unless the matching environment variable points at a live server. No local GPU? cd docker && docker compose up --build brings up a CPU stack, and python tools/e2e_hf.py run rents a pair of ephemeral Hugging Face endpoints and deletes them afterwards.

CONTRIBUTING.md is where to start; the full task workflow (design doc → branch → live run → docs → PR) is AGENTS.md, the traps worth knowing are docs/lessons.md, and security reports go through SECURITY.md.

Documentation

docs/README.md is the index. The short version:

License, disclaimer and privacy

The software is under the MIT License (LICENSE) — the standard text, unmodified.

It ships no model. Every word on screen is written by a model you chose and obtained yourself, and the app applies no content filtering of its own. What that means for warranty and liability, and for the tools a model can invoke on your machine, is in DISCLAIMER.md. What stays on your machine, what leaves it and only on which setting of yours, and what reaches the author of this software — which is nothing — is in PRIVACY.md. Both are also on the F1 → "Legal" tab, and the Windows installer shows the privacy policy during setup.

All three have a Russian translation (docs/legal/), unofficial and for convenience: the English originals are the texts with legal force.

Project status

Actively developed, in small reviewed tracks; the original ten-milestone plan (docs/history/plan.md) is long finished. Every change lands with its unit tests, and the smoke tests that need a real stack — a local llama-server and the live cloud APIs — are run before a change that touches a provider ships. See the changelog for what is new and the roadmap for what may come next.