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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/janhq/jan/llms.txt

Use this file to discover all available pages before exploring further.

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Introduction

Jan’s CLI enables you to run local AI models from the command line and wire them to AI coding agents like Claude Code or OpenCode — no cloud required. The CLI shares all core logic with the Jan desktop app, so models downloaded in the desktop app are automatically available in the CLI. 
jan serve qwen3.5-35b-a3b              # Expose a model at localhost:6767/v1
jan launch claude --model qwen3.5-35b  # Start model + launch Claude Code
jan models list                        # Show all installed models

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Key Features

  • OpenAI-Compatible API: Serves models via an OpenAI-compatible endpoint
  • Auto-Detection: Automatically detects LlamaCPP or MLX engines
  • Agent Integration: Pre-wires environment variables for Claude Code, Codex, and OpenClaw
  • HuggingFace Downloads: Auto-download models from HuggingFace repos
  • Background Mode: Run models in detached mode with --detach
  • Context Auto-Fit: Maximize context window based on available VRAM

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Installation

The Jan CLI is included with the Jan desktop application. Build it from source: 
cargo build --features cli --bin jan
The binary will be located at target/release/jan-cli (or target/debug/jan-cli for debug builds).

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Quick Start

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Serve a Model

Start a local model server: 
jan serve qwen3.5-35b-a3b
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Output
✓ qwen3.5-35b-a3b ready · http://127.0.0.1:6767

  Endpoint  http://127.0.0.1:6767/v1

  Press Ctrl+C to stop.

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Launch an AI Agent

Start a model and automatically launch Claude Code with pre-configured environment variables: 
jan launch claude --model qwen3.5-35b-a3b
The CLI will:
  1. Load the specified model
  2. Set environment variables (OPENAI_BASE_URL, ANTHROPIC_BASE_URL, etc.)
  3. Launch Claude Code with the local model pre-wired

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List Available Models

View all models installed in your Jan data folder: 
jan models list
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Output
JSON
[
  {
    "id": "qwen3.5-35b-a3b",
    "engine": "llamacpp",
    "name": "Qwen 3.5 35B",
    "model_path": "/path/to/model.gguf",
    "size_bytes": 21474836480,
    "embedding": false,
    "capabilities": ["text"],
    "mmproj_path": null
  }
]

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Architecture

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Data Folder

Jan stores models, threads, and configuration in a platform-specific data folder:
  • macOS: ~/Library/Application Support/Jan
  • Linux: ~/.config/Jan
  • Windows: %APPDATA%\Jan
View your data folder location: 
jan app data-folder

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Engine Auto-Detection

Jan automatically detects the appropriate inference engine:
  • LlamaCPP: For GGUF models (most common)
  • MLX: For MLX models on macOS/Apple Silicon
The engine is determined from the model’s model.yml file in the data folder.

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Binary Discovery

Jan auto-discovers inference binaries from the Jan app installation:
  • llama-server: Found in Jan’s app bundle or data folder
  • mlx-server: Found in Jan.app on macOS
You can override with --bin <path> if needed.

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Environment Variables

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HuggingFace Authentication

Set a HuggingFace token to download private/gated models: 
export HF_TOKEN="your_token_here"
# or
export HUGGING_FACE_HUB_TOKEN="your_token_here"

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Logging

Enable verbose logging: 
jan serve qwen3.5-35b --verbose
Or set the log level via environment variable: 
RUST_LOG=info jan serve qwen3.5-35b

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Next Steps

Commands Reference

Complete reference for all CLI commands

Serve Command

Detailed guide for serving models

Launch Command

Wire AI agents to local models

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