anyrobo 0.2.3

A local-first voice AI assistant framework. Create your own JARVIS.

anyrobo

Build a voice AI assistant in 10 lines — local-first STT, LLM, TTS, and tool-calling included.

anyrobo is a batteries-included framework for building voice AI assistants that run entirely on your own hardware. It ties together speech-to-text (Whisper, Vosk), an LLM brain (Ollama by default via anyllm), and text-to-speech (pyttsx3, ElevenLabs) behind a single class. It supports multi-step tool-calling, built-in personalities (Jarvis, GLaDOS, assistant), a plugin system for custom skills, an event bus, a RAG knowledge base, and MCP client support for calling external tool servers.

Built by Viet-Anh Nguyen at NRL.ai.

Why anyrobo?

• One-liner API — anyrobo.Robo().listen() is a complete voice assistant
• Plugin architecture — Add custom skills, tools, personalities, and backends
• Local-first — Whisper + Ollama + pyttsx3 run 100% offline
• Minimal core deps — Base install is light; STT/TTS/LLM backends are extras
• Production-ready — Event system, memory persistence, MCP client, RAG

Installation

pip install anyrobo

For backends:

pip install anyrobo[whisper]      # openai-whisper for local STT
pip install anyrobo[vosk]         # Vosk for offline STT
pip install anyrobo[tts]          # pyttsx3 for offline TTS
pip install anyrobo[elevenlabs]   # ElevenLabs cloud TTS
pip install anyrobo[llm]          # anyllm for LLM routing
pip install anyrobo[rag]          # knowledge base with embeddings
pip install anyrobo[mcp]          # MCP client for external tool servers
pip install anyrobo[all]          # everything

Python 3.8+ supported (tested on 3.8, 3.9, 3.10, 3.11, 3.12, 3.13)

Quick Start

import anyrobo

# 1. Simplest possible voice assistant (Whisper + Ollama + pyttsx3)
bot = anyrobo.Robo(personality="jarvis")
bot.listen()    # starts mic, transcribes, replies with voice, loops

# 2. Add a tool the assistant can call (schema auto-extracted from type hints)
def set_timer(minutes: int, label: str = "timer") -> str:
    """Set a countdown timer for the given number of minutes."""
    return f"Timer '{label}' set for {minutes} minutes."

bot.add_tool(set_timer)
bot.listen()   # now: "Hey Jarvis, set a 5 minute pasta timer" -> tool call

# 3. Text-mode (no mic/speaker, useful for testing)
reply = bot.ask("What's the weather like in Tokyo?")

Models & Methods

Backends (all local-first)

Component	Backend	Model	Install
STT	Whisper	openai-whisper tiny/base/small/medium/large	anyrobo[whisper]
STT	Vosk	Vosk offline models	anyrobo[vosk]
LLM	Ollama (default)	Any Ollama model (llama3.1:8b, qwen2.5, ...)	anyrobo[llm]
LLM	OpenAI / Anthropic	via anyllm	anyrobo[llm]
TTS	pyttsx3	OS-native voices (SAPI / NSSpeechSynthesizer / espeak)	anyrobo[tts]
TTS	ElevenLabs	Cloud API	anyrobo[elevenlabs]

Tool / function calling

Pass plain Python functions; anyrobo (via anyllm) auto-extracts parameter schemas from type hints and docstrings, then runs a multi-step agentic loop:

1. LLM receives the user query + tool list
2. LLM decides whether to call a tool (structured output)
3. anyrobo dispatches the tool and feeds the result back
4. Loop until the LLM emits a final natural-language response

Built-in personalities

Name	Style
jarvis	Polite British butler, concise and proactive
glados	Dry, sarcastic, vaguely threatening (Portal-inspired)
assistant	Neutral, helpful default
custom	Pass your own system_prompt

Conversation memory

SlidingWindowMemory keeps the last N turns in context, with optional disk persistence (JSON). Robo.save_memory(path) / load_memory(path) for persistence across sessions.

Event system

Subscribe to any lifecycle event:

bot.on("user_message", lambda text: print("heard:", text))
bot.on("tool_call", lambda name, args: log_tool(name, args))
bot.on("response", lambda text: print("bot:", text))

RAG Knowledge Base

anyrobo.KnowledgeBase() ingests text/PDF/markdown, chunks via anynlp, embeds via anyllm.embed, and performs similarity search to augment the LLM prompt.

MCP client

Robo.add_mcp_server(command, args) connects to any Model Context Protocol server (filesystem, GitHub, web search, a model exposed via anydeploy.mcp, ...) and exposes its tools to the assistant automatically.

Plugin system

Subclass anyrobo.Skill to package reusable behavior:

class WeatherSkill(anyrobo.Skill):
    name = "weather"
    def tools(self):
        return [self.get_weather]
    def get_weather(self, city: str) -> dict: ...

bot.add_skill(WeatherSkill())

API Reference

Function / class	Purpose
anyrobo.Robo(personality, stt, tts, llm)	Main assistant class
Robo.listen(hotword=None)	Voice loop: STT -> LLM -> TTS
Robo.ask(text)	Text-mode interaction
Robo.add_tool(fn)	Register a Python function as a tool
Robo.add_skill(skill)	Register a plugin skill
Robo.add_mcp_server(cmd, args)	Connect to an MCP server
Robo.on(event, handler)	Subscribe to lifecycle events
anyrobo.KnowledgeBase()	RAG knowledge base
anyrobo.Skill	Base class for plugins

CLI Usage

anyrobo listen --personality jarvis --model llama3.1:8b
anyrobo ask "What's on my calendar today?"
anyrobo list-personalities
anyrobo list-voices

Examples

Voice assistant with custom tools

import anyrobo

def turn_on_lights(room: str) -> str:
    """Turn on the smart lights in a specific room."""
    return f"Lights in {room} are now on."

def play_music(genre: str, volume: int = 50) -> str:
    """Play music of a given genre at the specified volume (0-100)."""
    return f"Playing {genre} music at volume {volume}."

bot = anyrobo.Robo(personality="jarvis", model="llama3.1:8b")
bot.add_tool(turn_on_lights)
bot.add_tool(play_music)
bot.listen()

RAG-powered Q&A over your docs

import anyrobo

kb = anyrobo.KnowledgeBase()
kb.ingest("docs/")              # chunks + embeds every markdown/pdf
bot = anyrobo.Robo(knowledge_base=kb, personality="assistant")
print(bot.ask("What's our refund policy?"))

Connect to external MCP servers

import anyrobo

bot = anyrobo.Robo(personality="jarvis")
bot.add_mcp_server("npx", ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"])
bot.listen()   # now the LLM can read/write files via MCP

License

MIT (c) Viet-Anh Nguyen