autobot v0.5.2

Ultra-efficient personal AI assistant powered by Crystal

2MB binary · ~5MB RAM · <20ms startup · Zero runtime dependencies

Why Autobot?

Inspired by OpenClaw — rebuilt in Crystal with security and efficiency first.

2.0MB binary, ~5MB RAM, boots in under 20ms, zero runtime dependencies. Run dozens of bots on a single machine — each with its own personality, workspace, and config.

✨ Features

• 🤖 Multi-Provider LLM — Anthropic, OpenAI, DeepSeek, Groq, Gemini, OpenRouter, AWS Bedrock, DuckAI, vLLM
• 💬 Chat Channels — Telegram, Slack, WhatsApp, Zulip with allowlists and custom slash commands
• 👁️ Vision — Send photos via Telegram and get AI-powered image analysis
• 🎤 Voice — Voice messages auto-transcribed via Whisper (Groq/OpenAI)
• 🔒 Kernel Sandbox — Docker/bubblewrap OS-level isolation, not regex path checks
• 🧠 Memory — JSONL sessions with consolidation and persistent long-term memory
• ⏰ Cron — Cron expressions, intervals, one-time triggers, per-owner isolation
• 🔧 Extensible — Plugins, bash auto-discovery, markdown skills, subagents
• 📊 Observable — Token tracking, credential sanitization, audit trails
• 🏃 Multi-Bot — Isolated directories per bot, run dozens on one machine

🛡️ Production-Grade Security

Autobot uses kernel-enforced sandboxing via Docker or bubblewrap — not application-level validation. When the LLM executes commands:

• ✅ Only workspace directory is accessible (enforced by Linux mount namespaces)
• ✅ Everything else is invisible to the LLM — your /home, /etc, system files simply don't exist in the sandbox
• ✅ No symlink exploits, TOCTOU, or path traversal — kernel guarantees workspace isolation
• ✅ Process isolation — LLM can't see or interact with host processes
• ✅ Auto-detected — Uses Docker (macOS/production) or bubblewrap (Linux/dev)

Example: When LLM tries ls ../, it fails at the OS level because parent directories aren't mounted. No regex patterns, no validation bypasses — just kernel namespaces.

→ Security architecture

🚀 Quick Start

1. Install

# macOS (Homebrew)
brew tap crystal-autobot/tap
brew install autobot

# Linux/macOS - Download binary
curl -L "https://github.com/crystal-autobot/autobot/releases/latest/download/autobot-$(uname -s | tr '[:upper:]' '[:lower:]')-$(uname -m)" -o autobot
chmod +x autobot
sudo mv autobot /usr/local/bin/

# Or build from source
git clone https://github.com/crystal-autobot/autobot.git
cd autobot
make release
sudo install -m 0755 bin/autobot /usr/local/bin/autobot

# Or use Docker (multi-arch: amd64, arm64)
docker pull ghcr.io/crystal-autobot/autobot:latest

2. Create a new bot

autobot new optimus
cd optimus

This creates an optimus/ directory with everything you need:

optimus/
├── .env              # API keys (add yours here)
├── .gitignore        # Excludes secrets, sessions, logs
├── config.yml        # Configuration (references .env vars)
├── sessions/         # Conversation history
├── logs/             # Application logs
└── workspace/        # Sandboxed LLM workspace
    ├── AGENTS.md     # Agent instructions
    ├── SOUL.md       # Personality definition
    ├── USER.md       # User preferences
    ├── memory/       # Long-term memory
    └── skills/       # Custom skills

3. Configure

Edit .env and add your API keys:

ANTHROPIC_API_KEY=sk-ant-...

The generated config.yml references these via ${ENV_VAR} — no secrets in config files.

4. Run

# Validate configuration
autobot doctor

# Interactive mode
autobot agent

# Single command
autobot agent -m "Summarize this project"

# Gateway (all channels)
autobot gateway

Autobot automatically detects and logs the sandbox method on startup — Docker on macOS/production, bubblewrap on Linux.

→ Full quick start guide

📚 Documentation

• Getting started
• Providers
• Channels
• Security
• Deployment
• Full docs

💡 Examples

channels:
  telegram:
    enabled: true
    token: "BOT_TOKEN"
    allow_from: ["your_username"]
    custom_commands:
      macros:
        summarize: "Summarize our conversation in 3 bullet points"
        translate:
          prompt: "Translate the following to English"
          description: "Translate text to English"
      scripts:
        deploy:
          path: "/home/user/scripts/deploy.sh"
          description: "Deploy to production"
        status: "/home/user/scripts/system_status.sh"

# Daily morning greeting
autobot cron add --name "morning" \
  --message "Good morning! Here's today's summary" \
  --cron "0 9 * * *"

# Hourly reminder
autobot cron add --name "reminder" \
  --message "Stand up and stretch!" \
  --every 3600

# One-time meeting notification
autobot cron add --name "meeting" \
  --message "Team sync in 5 minutes!" \
  --at "2025-03-01T10:00:00"

providers:
  anthropic:
    api_key: "${ANTHROPIC_API_KEY}"
  openai:
    api_key: "${OPENAI_API_KEY}"
  deepseek:
    api_key: "${DEEPSEEK_API_KEY}"
  vllm:
    api_base: "http://localhost:8000"
    api_key: "token"

agents:
  defaults:
    model: "anthropic/claude-sonnet-4-5"
    max_tokens: 8192
    temperature: 0.7

mcp:
  servers:
    github:
      command: "npx"
      args: ["-y", "@modelcontextprotocol/server-github"]
      env:
        GITHUB_TOKEN: "${GITHUB_TOKEN}"
    garmin:
      command: "uvx"
      args: ["--python", "3.12", "--from", "git+https://github.com/Taxuspt/garmin_mcp", "garmin-mcp"]
      env:
        GARMIN_EMAIL: "${GARMIN_EMAIL}"

autobot agent -m "list my recent garmin activities"
autobot agent -m "show open issues in crystal-autobot/autobot"

🔧 Development

Prerequisites

• Crystal >= 1.10.0

Commands

make build          # Debug binary
make release        # Optimized binary (~2MB)
make test           # Run test suite
make lint           # Run ameba linter
make format         # Format code

make docker         # Build Docker image
make release-all    # Cross-compile for all platforms
make help           # Show all targets

→ Development guide