llamactl

!Build and Release !Go Tests !WebUI Tests !User Docs

Unified management and routing for llama.cpp, MLX and vLLM models with web dashboard.

📚 Full Documentation →

!Dashboard Screenshot

Features

🚀 Easy Model Management

• Built-in Model Downloader: Download GGUF and Safetensors models directly from HuggingFace for llama.cpp, vLLM, and MLX backends
• Dynamic Multi-Model Instances: llama.cpp router mode - serve multiple models from a single instance with on-demand loading
• Smart Resource Management: Automatic idle timeout, LRU eviction, instance groups with per-group limits, and configurable instance limits
• Web Dashboard: Modern React UI for managing instances, monitoring health, and viewing logs

• API Compatible: OpenAI chat completions and resources endpoints, Anthropic messages endpoint (depending on backend) - route requests to different models by instance name
• Multi-Backend Support: Native support for llama.cpp, MLX (Apple Silicon optimized), and vLLM, plus any OpenAI-compatible server via configurable custom backends
• Docker Ready: Run backends in containers with full GPU support

• Remote Instances: Deploy instances on remote hosts
• Central Management: Manage everything from a single dashboard with automatic routing

Quick Start

1. Install a backend (llama.cpp, MLX, or vLLM) - see Prerequisites below
2. Download llamactl for your platform
3. Run llamactl and open http://localhost:8080
4. Create an instance and start inferencing!

Prerequisites

Backend Dependencies

For llama.cpp backend: You need llama-server from llama.cpp installed:

# Homebrew (macOS)
brew install llama.cpp
Or build from source - see llama.cpp docs
Or use Docker - no local installation required

For MLX backend (macOS only): You need MLX-LM installed:

# Install via pip (requires Python 3.8+)
pip install mlx-lm
Or in a virtual environment (recommended)
python -m venv mlx-env
source mlx-env/bin/activate
pip install mlx-lm

For vLLM backend: You need vLLM installed:

# Install via pip (requires Python 3.8+, GPU required)
pip install vllm
Or in a virtual environment (recommended)
python -m venv vllm-env
source vllm-env/bin/activate
pip install vllm
Or use Docker - no local installation required

Docker Support

llamactl can run backends in Docker containers, eliminating the need for local backend installation:

backends:
  llama-cpp:
    docker:
      enabled: true
  vllm:
    docker:
      enabled: true

Installation

Option 1: Download Binary (Recommended)

# Linux/macOS - Get latest version and download
LATEST_VERSION=$(curl -s https://api.github.com/repos/lordmathis/llamactl/releases/latest | grep '"tag_name":' | sed -E 's/."([^"]+)"./\1/')
curl -L https://github.com/lordmathis/llamactl/releases/download/${LATEST_VERSION}/llamactl-${LATEST_VERSION}-$(uname -s | tr '[:upper:]' '[:lower:]')-$(uname -m).tar.gz | tar -xz
sudo mv llamactl /usr/local/bin/
Or download manually from the releases page:
https://github.com/lordmathis/llamactl/releases/latest
Windows - Download from releases page

Option 2: Docker (No local backend installation required)

# Clone repository and build Docker images
git clone https://github.com/lordmathis/llamactl.git
cd llamactl
mkdir -p data/llamacpp data/vllm models
Build and start llamactl with llama.cpp CUDA backend
docker-compose -f docker/docker-compose.yml up llamactl-llamacpp -d
Build and start llamactl with vLLM CUDA backend
docker-compose -f docker/docker-compose.yml up llamactl-vllm -d
Build from source using multi-stage build
docker build -f docker/Dockerfile.source -t llamactl:source .

Note: Dockerfiles are configured for CUDA. Adapt base images for other platforms (CPU, ROCm, etc.).

Option 3: Build from Source

git clone https://github.com/lordmathis/llamactl.git
cd llamactl
cd webui && npm ci && npm run build && cd ..
go build -o llamactl ./cmd/server

Usage

1. Open http://localhost:8080
2. Click "Create Instance"
3. Choose backend type (llama.cpp, MLX, or vLLM)
4. Configure your model and options (ports and API keys are auto-assigned)
5. Start the instance and use it with any OpenAI-compatible client

Configuration

llamactl works out of the box with sensible defaults. Configuration is loaded with the following precedence:

Defaults < .env file < Configuration file < LLAMACTL_* environment variables

Config files support ${VAR} and ${VAR:-default} placeholders, and a .env file is automatically loaded.

server:
  host: "0.0.0.0"                # Server host to bind to
  port: 8080                     # Server port to bind to
  allowed_origins: ["*"]         # Allowed CORS origins (default: all)
  allowed_headers: ["*"]         # Allowed CORS headers (default: all)
  enable_swagger: false          # Enable Swagger UI for API docs
backends:
  llama-cpp:
    command: "llama-server"
    args: []
    environment: {}               # Environment variables for the backend process
    docker:
      enabled: false
      image: "ghcr.io/ggml-org/llama.cpp:server"
      args: ["run", "--rm", "--network", "host", "--gpus", "all", "-v", "~/.local/share/llamactl/llama.cpp:/root/.cache/llama.cpp"]
      environment: {}             # Environment variables for the container
vllm:
    command: "vllm"
    args: ["serve"]
    environment: {}               # Environment variables for the backend process
    docker:
      enabled: false
      image: "vllm/vllm-openai:latest"
      args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g", "-v", "~/.local/share/llamactl/huggingface:/root/.cache/huggingface"]
      environment: {}             # Environment variables for the container
mlx:
    command: "mlx_lm.server"
    args: []
    environment: {}               # Environment variables for the backend process
data_dir: ~/.local/share/llamactl  # Main data directory (database, instances, logs), default varies by OS
instances:
  port_range: [8000, 9000]                        # Port range for instances
  logs_dir: ~/.local/share/llamactl/logs          # Logs directory (platform dependent)
  auto_create_dirs: true                          # Auto-create data/config/logs dirs if missing
  max_instances: -1                               # Max instances (-1 = unlimited)
  max_running_instances: -1                       # Max running instances (-1 = unlimited)
  enable_lru_eviction: true                       # Enable LRU eviction for idle instances
  default_idle_timeout: 30                        # Default idle timeout in minutes (0 = no timeout)
  default_auto_restart: true                      # Auto-restart new instances by default
  default_max_restarts: 3                         # Max restarts for new instances
  default_restart_delay: 5                        # Restart delay (seconds) for new instances
  default_on_demand_start: true                   # Default on-demand start setting
  on_demand_start_timeout: 120                    # Default on-demand start timeout in seconds
  group_limits: {}                                # Per-group instance limits (e.g., large: 1, small: 3)
  timeout_check_interval: 5                       # Idle instance timeout check in minutes
  log_rotation_enabled: true                      # Enable log rotation (default: true)
  log_rotation_max_size: 100                      # Max log file size in MB before rotation (default: 100)
  log_rotation_compress: false                    # Compress rotated log files (default: false)
database:
  path: ~/.local/share/llamactl/llamactl.db  # Database file path (platform dependent)
  max_open_connections: 25                   # Maximum open database connections
  max_idle_connections: 5                    # Maximum idle database connections
  connection_max_lifetime: 5m                # Connection max lifetime
auth:
  require_inference_auth: true   # Require auth for inference endpoints, API keys are created in web UI
  require_management_auth: true  # Require auth for management endpoints
  management_keys: []            # Keys for management endpoints

For detailed configuration options including environment variables, file locations, and advanced settings, see the Configuration Guide.

License

MIT License - see LICENSE file.