adorable-cli 0.2.17

A powerful CLI super agent

Adorable CLI - Deep Agent built on Agno

Quick Install • Features • Usage • Configuration

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Adorable is a deep agent for complex, long-horizon tasks, powered by the Agno framework. It operates through continuous interleaved reasoning and action—thinking before every step, executing with precision, and analyzing results—to handle research, coding, and system automation reliably.

Built on Agno's agent architecture with persistent memory, tool orchestration, and OpenAI-compatible APIs.

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🧩 Features

• Deep Agent: Built on Agno framework for planning, web search, coding, and file operations.
• Interleaved Thinking: Continuous Think → Act → Analyze loop—reasons before every step, never guesses, verifies all assumptions.
• Persistent Memory: Uses SQLite (~/.adorable/memory.db) and session summaries to maintain context across long sessions.
• Multi-Modal Toolset:
  • Planning: Reasoning engine & Todo list management.
  • File Operations: File reading, writing, and directory navigation.
  • Web Search: Deep web search (DuckDuckGo) & web content fetching (Fetch MCP).
  • Coding: Python scripting & Shell commands.
  • Vision: Vision capabilities for image analysis.
• Interactive UI: Rich terminal interface with history, autocompletion, and shortcuts.

⚡ Quick Install

We recommend using uv to install and manage Adorable CLI.

Install

uv tool install --python 3.13 adorable-cli

Upgrade

uv tool upgrade adorable-cli --no-cache

If you run into missing dependencies after upgrading, force a reinstall:

uv tool upgrade adorable-cli --reinstall --no-cache

On first run you will be guided to set API_KEY, BASE_URL, MODEL_ID into ~/.adorable/config.json (and a legacy ~/.adorable/config is also maintained for compatibility). You can run ador config anytime to update.

🖥 Platform Support

• OS: macOS, Linux x86_64
• Arch: x86_64; Linux arm64 currently not supported
• Python: >= 3.10 (recommended 3.11)
• Linux glibc: >= 2.28 (e.g., Debian 12, Ubuntu 22.04+, CentOS Stream 9)

🚀 Usage

# Start interactive session
adorable
# Or use alias
ador

# Configure settings
ador config

# Show help
ador --help

CLI Commands

• ador / adorable: Start interactive chat
• ador config: Configure API keys and models
• ador version: Print CLI version
• ador kb check: Smoke-check knowledge backend (e.g., pgvector)

Interactive Shortcuts

• Enter: Submit message
• Alt+Enter / Ctrl+J: Insert newline
• @: File path completion
• /: Command completion (e.g., /help, /clear)
• Ctrl+D / exit: Quit session
• Ctrl+Q: Quick exit

Global Options

• --model <ID>: Primary model ID (e.g., gpt-4o)
• --base-url <URL>: OpenAI-compatible base URL
• --api-key <KEY>: API key
• --debug: Enable debug logging
• --plain: Disable color output

Example:

ador --api-key sk-xxxx --model gpt-4o chat

🔧 Configuration

• Config File: ~/.adorable/config.json (legacy: ~/.adorable/config)
• Environment Variables:
  • OPENAI_API_KEY / API_KEY
  • OPENAI_BASE_URL / BASE_URL
  • DEEPAGENTS_MODEL_ID / MODEL_ID

Advanced Configuration

• Database Path: set ADORABLE_DB_PATH (or db.path in config.json) to share sessions between CLI and ador serve.
• Knowledge Backend: set knowledge.backend to lancedb (default) or pgvector.
  • For pgvector, set knowledge.pgvector.dsn and optional knowledge.pgvector.table.
• Extensions:
  • ~/.adorable/tools, ~/.adorable/skills, ~/.adorable/commands
  • Optional: ~/.claude/skills

Example (~/.adorable/config.json):

{
  "openai": {
    "api_key": "sk-xxxx",
    "base_url": "https://api.openai.com/v1"
  },
  "models": {
    "default": "gpt-4o",
    "fast": "gpt-4o-mini",
    "vlm": "gpt-4o"
  },
  "confirm_mode": "ask",
  "server": {
    "host": "0.0.0.0",
    "port": 7777
  }
}

🧠 Capabilities

• Planning: ReasoningTools for strategy; TodoTools for task tracking.
• Research: DuckDuckGoTools for search; Fetch MCP for web content extraction; FileTools for local context.
• Execution: PythonTools for logic/data; ShellTools for system ops.
• Perception: ImageUnderstandingTool for visual inputs.

See src/adorable_cli/agent/prompts.py for the full system prompt and guidelines.

🧪 Example Prompts

• "Research the current state of quantum computing and write a summary markdown file."
• "Clone the 'requests' repo, analyze the directory structure, and create a diagram."
• "Plan and execute a data migration script for these CSV files."