mindfork 0.10.2

A terminal AI chat written in Rust: local models via llama.cpp or OpenAI, Anthropic, Gemini and Grok in the cloud, with persistent memory, notes, RAG and tools. One native binary for Windows and Linux, built on ratatui.

No model yet? mindfork demo opens the app with sample chats and a scripted model in a throwaway folder — no download, no key, nothing written outside a temporary directory.

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. The full story is in spec.md; the original idea, in docs/history/request.md.

Every screenshot here is generated from code against a demo profile — never captured from a real session — and a gate test fails the build the moment they drift from what the app renders (docs/history/demo-screenshots.md).

What it does

• 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 OpenRouter), or the OpenAI, Gemini, Claude and Grok clouds — 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, and an interface in English or Russian.
• Private by construction. No telemetry, no update check, no account. Everything lives in a data/ folder next to the binary — 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

1. Install

Grab a build from the releases: a Windows installer or zip archive, Linux deb / rpm / pkg.tar.zst packages or a tar.gz. Or build from source with a recent stable Rust (edition 2024):

cargo build --release          # binary lands in target/release/

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.

2. 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 — 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.

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

The model itself is a GGUF file, which llama.cpp does not ship — Hugging Face hosts them; search the model's name with GGUF.

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.

3. 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). Five providers live behind that one contract: a local OpenAI-compatible server, the OpenAI Responses API, native Gemini generateContent, the Anthropic Messages API, and xAI (which needed no client of its own).

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. The suite stands at 3337 unit tests plus 196 #[ignore] smoke tests that are run against real stacks — a local llama-server and the live cloud APIs — before provider-touching changes ship. See the changelog for what is new and the roadmap for what may come next.