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Installation

Diapason runs entirely on your hardware. Choose the interface that fits your workflow.

Browser App

Run the full chat UI in your browser. Everything stays local — the backend runs on your machine and the frontend connects via localhost.

One-command setup

git clone https://github.com/carlitoetienne01-spec/Diapason.git
cd Diapason
./scripts/quickstart.sh

The script handles everything:

  1. Checks for Python 3.10+ and Node.js 18+
  2. Installs Ollama if not present and pulls a starter model
  3. Installs Python and frontend dependencies
  4. Starts the backend API server and frontend dev server
  5. Opens http://localhost:5173 in your browser

Manual setup

If you prefer to run each step yourself:

git clone https://github.com/carlitoetienne01-spec/Diapason.git
cd Diapason
uv sync --extra desktop
uv run maturin develop -m rust/crates/diapason-python/Cargo.toml
cd frontend && npm install && cd ..

Prerequisites

Requires Rust (curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh). On Python 3.14+, set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 before the maturin command.

# Install from https://ollama.com if not already installed
ollama serve &
ollama pull qwen3:0.6b

uv run diapason serve --port 8000

cd frontend
npm run dev

Then open http://localhost:5173.

Desktop App

The desktop app is a native window for the Diapason chat UI. All inference and backend processing happens on your local machine — the app connects to the backend you start locally.

Setup

Step 1. Start the backend (same as Browser App):

git clone https://github.com/carlitoetienne01-spec/Diapason.git
cd Diapason
./scripts/quickstart.sh

Step 2. Build and open the desktop app from the authenticated checkout.

There is currently no published installer: the GitHub Releases list is empty, and the former desktop-v1.0.2 links did not exist. On macOS, use the validated local installer:

./scripts/install-desktop.sh

On Windows, run deploy/windows/install.ps1 first. It prepares the backend at %LOCALAPPDATA%\Diapason\src; the Tauri application now detects that exact location. A distributable .msi will only be advertised after a build and end-to-end run on the real Windows PC.

The app connects to an existing http://localhost:8000 server or starts the backend from the installed project.

macOS: \"app is damaged\"

If macOS says the app is damaged, clear the Gatekeeper quarantine flag: 

xattr -cr /Applications/Diapason.app
This is normal for open-source apps distributed outside the App Store.

Build from source

git clone https://github.com/carlitoetienne01-spec/Diapason.git
cd Diapason/frontend
npm install
npm run tauri:build

The built installer will be in frontend/src-tauri/target/release/bundle/.

CLI

The command-line interface is the fastest way to interact with Diapason programmatically. Every feature is accessible from the terminal.

Install

git clone https://github.com/carlitoetienne01-spec/Diapason.git
cd Diapason
uv sync
uv run maturin develop -m rust/crates/diapason-python/Cargo.toml

Requires Rust. On Python 3.14+, set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 before the maturin command.

Verify

diapason --version
# diapason, version 0.1.0

First commands

diapason ask "What is the capital of France?"

diapason ask --agent orchestrator --tools calculator "What is 137 * 42?"

diapason serve --port 8000

diapason doctor

diapason model list

diapason chat

Inference backend required

The CLI requires a running inference backend (e.g., Ollama). See Setting up an inference backend below.

Python SDK

For programmatic access, the Diapason class provides a high-level sync API.

Install

git clone https://github.com/carlitoetienne01-spec/Diapason.git
cd Diapason
uv sync
uv run maturin develop -m rust/crates/diapason-python/Cargo.toml

Requires Rust. On Python 3.14+, set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 before the maturin command.

Quick example

from diapason import Diapason

j = Diapason()
print(j.ask("Explain quicksort in two sentences."))
j.close()

With agents and tools

result = j.ask_full(
    "What is the square root of 144?",
    agent="orchestrator",
    tools=["calculator", "think"],
)
print(result["content"])       # "12"
print(result["tool_results"])  # tool invocations
print(result["turns"])         # number of agent turns

Composition layer

For full control, use the SystemBuilder:

from diapason import SystemBuilder

system = (
    SystemBuilder()
    .engine("ollama")
    .model("qwen3:8b")
    .agent("orchestrator")
    .tools(["calculator", "web_search", "file_read"])
    .enable_telemetry()
    .enable_traces()
    .build()
)

result = system.ask("Summarize the latest AI news.")
system.close()

See the Python SDK guide for the full API reference.

Requirements

Requirement Version Install Notes
Python 3.10–3.13 python.org Required. 3.14+ not yet supported (a core dependency lacks 3.14 wheels).
uv latest curl -LsSf https://astral.sh/uv/install.sh \| sh or brew install uv (macOS) Python package & project manager
Git any git-scm.com or brew install git (macOS) Required
Rust stable curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs \| sh Required for the Rust extension
Inference backend any See below At least one of Ollama, vLLM, llama.cpp, SGLang, or a cloud API
Node.js 18+ nodejs.org or brew install node (macOS) Required for the browser UI; 22+ for the WhatsApp Baileys channel bridge

macOS users

See the macOS Installation Guide for a complete step-by-step walkthrough covering Homebrew, uv, Rust, llama.cpp, and common pitfalls.

Optional Extras

Diapason uses optional extras to keep the base installation lightweight.

Inference Backends

Extra Install Command Description
inference-cloud uv sync --extra inference-cloud OpenAI and Anthropic APIs
inference-google uv sync --extra inference-google Google Gemini API

Ollama, vLLM, and llama.cpp are HTTP-based

These engines have no additional Python dependencies — Diapason communicates over HTTP. You still need the engine software running on your machine.

Memory Backends

Extra Install Command Description
memory-faiss uv sync --extra memory-faiss FAISS vector store
memory-colbert uv sync --extra memory-colbert ColBERTv2 late-interaction retrieval
memory-bm25 uv sync --extra memory-bm25 BM25 sparse retrieval

SQLite memory is always available

The default SQLite/FTS5 memory backend requires no additional dependencies.

Server & Other

Extra Install Command Description
desktop uv sync --extra desktop Desktop/API server plus local speech input
server uv sync --extra server OpenAI-compatible API server (diapason serve)
dev uv sync --extra dev Development and testing tools
docs uv sync --extra docs Documentation build tools

Combine extras:

uv sync --extra desktop --extra memory-faiss --extra inference-cloud

Setting Up an Inference Backend

Diapason requires at least one inference backend. Choose the one that matches your hardware.

The easiest way to get started. Handles model downloading and serving automatically.

  1. Install from ollama.com

  2. Start the server and pull a model:

    ollama serve
ollama pull qwen3:0.6b

  3. Verify: diapason model list

Best for: Apple Silicon Macs, consumer NVIDIA GPUs, CPU-only systems

vLLM

High-throughput serving optimized for datacenter GPUs.

  1. Install following the official guide
  2. Start: vllm serve Qwen/Qwen2.5-7B-Instruct
  3. Auto-detected at http://localhost:8000

Best for: NVIDIA datacenter GPUs (A100, H100), AMD GPUs

llama.cpp

Efficient CPU and GPU inference with GGUF quantized models.

  1. Build from github.com/ggerganov/llama.cpp
  2. Start: llama-server -m /path/to/model.gguf --port 8080
  3. Auto-detected at http://localhost:8080

Cloud APIs

uv sync --extra inference-cloud --extra inference-google
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Next Steps