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cli-assistant

A fast, lightweight CLI assistant for Linux system administration — powered by any OpenAI-compatible API.

Inspired by command-line-assistant by RHEL Lightspeed. This is a Rust rewrite that improves portability, performance, and flexibility.

Features

• Ask questions in natural language from your terminal
• OpenAI-compatible — works with OpenAI, Azure OpenAI, local LLMs, or any /v1/chat/completions endpoint
• Chat sessions & history — persistent SQLite-backed conversation history
• Interactive mode — continuous conversation with context
• Markdown rendering — colored terminal output with code blocks, tables, and headers
• Configurable language — force replies in your preferred language
• D-Bus daemon architecture — client/daemon separation for system-level integration

Installation

From Release (Recommended)

Download the latest tarball from Releases, then run the install script:

# Download x86_64 (replace VERSION with actual version, e.g. v0.6.1)
curl -LO https://github.com/wenyinos/cli-assistant/releases/download/VERSION/cli-assistant-x86_64-linux-gnu.tar.gz

# Or download aarch64
curl -LO https://github.com/wenyinos/cli-assistant/releases/download/VERSION/cli-assistant-aarch64-linux-gnu.tar.gz

# Extract
tar xzf cli-assistant-*-linux-gnu.tar.gz
cd cli-assistant

# Install (requires root)
sudo ./install.sh

The install script will:

• Copy binaries (c, clad) to /usr/local/bin
• Install D-Bus policy to /etc/dbus-1/system.d/
• Register clad as a systemd service
• Write default config to /etc/cli-assistant/config.toml

# Edit config — set your API key and endpoint
sudo vim /etc/cli-assistant/config.toml

# Restart the daemon to apply changes
sudo systemctl restart clad

# Test
c "How do I check disk space?"

From Source

# Build
cargo build --release

# Configure
sudo mkdir -p /etc/cli-assistant
sudo cp config/config.toml /etc/cli-assistant/config.toml
# Edit /etc/cli-assistant/config.toml — set your API key and endpoint

# Install D-Bus policy
sudo cp config/com.cli-assistant.conf /etc/dbus-1/system.d/

# Run
sudo ./target/release/clad &          # start daemon
./target/release/c "How do I check disk space?"  # ask a question

For detailed build, test, and run instructions, see docs/BUILD.md.

Uninstall

sudo ./scripts/uninstall.sh

Service Management

sudo systemctl status clad     # Check service status
sudo systemctl restart clad    # Restart (required after config changes)
sudo systemctl stop clad       # Stop the daemon
sudo systemctl start clad      # Start the daemon
sudo systemctl enable clad     # Enable on boot (done by install.sh)
sudo systemctl disable clad    # Disable on boot
journalctl -u clad -f          # View live logs

Configuration

Config file: /etc/cli-assistant/config.toml

[backend]
endpoint  = "https://api.openai.com"   # any OpenAI-compatible endpoint
model     = "gpt-4"
api_key   = "sk-..."
prompt    = "You are a helpful assistant for Linux system administration."
language  = "zh-CN"                     # reply language (empty = auto)
max_tokens   = 4096
temperature  = 0.7
timeout      = 60

[database]
path = "~/.local/share/cli-assistant/cla.db"

[history]
enabled = true

[logging]
level = "INFO"

The API key can also be set via the CL_API_KEY environment variable (takes precedence over config).

Usage

c "question"                    # ask a question (default: chat)
c chat "question"               # same as above
c chat --interactive            # interactive conversation mode
c chat -a /path/to/file "explain this"  # attach a file
c history --all                 # view all history
c history --filter "keyword"    # search history
c history --clear               # clear history for a chat
c feedback                      # show feedback info
c shell --enable-interactive    # enable Ctrl+G shortcut

Improvements Over the Original

Area	Python Original	This Project (Rust)
Language	Python 3.9+	Rust (edition 2021)
Runtime	CPython + pip dependencies	Single static binary, no runtime deps
API Backend	RHEL Lightspeed only	Any OpenAI-compatible endpoint
LLM Config	Hardcoded backend	Configurable model, key, prompt, temperature, max_tokens, language
Database	SQLAlchemy (SQLite/MySQL/PostgreSQL)	sqlx + SQLite only (simpler, lighter)
IPC	dasbus (Python D-Bus)	zbus 4.x (native async Rust D-Bus)
HTTP	requests + urllib3	reqwest + rustls (async, no OpenSSL)
CLI	argparse with decorator pattern	clap 4 derive (type-safe, auto-completions)
Rendering	python-markdown → ANSI	Custom markdown→ANSI renderer
Dependencies	~10 Python packages	Pure Rust crates, vendored via Cargo
Startup	~200ms (Python import)	~5ms (native binary)
Docker/CI	Included but complex	Not needed — just cargo build

Architecture

c (client)  ──D-Bus──▶  clad (daemon)  ──HTTP──▶  LLM API
                           │
                           └── SQLite (history)

Crate	Purpose
cla-common	Config, errors, session, file utils, system info
cla-dbus	D-Bus interface definitions & data structures
cla-client	CLI parser, renderer, D-Bus client
cla-daemon	D-Bus server, HTTP client, SQLite storage, history

License

MIT