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Reachy Mini is Hugging Face and Pollen Robotics’ open-source robot for human-robot interaction. This guide integrates Honcho for persistent, multi-user memory with OpenAI’s Realtime API for voice.
Real-time memory: Honcho’s async API is designed for live voice interactions. Messages persist in the background without blocking audio, and the dialectic API returns user context fast enough for mid-conversation tool calls.

GitHub Repository

Full source code

Build Livestream

Watch us build it live

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What It Does

  • Face recognition identifies users and loads their personal memory
  • Honcho stores conversations and reasons about each user over time
  • OpenAI Realtime handles low-latency voice interaction
  • Gaze tracking maintains eye contact during conversation
When a user returns days later, the robot remembers their name, interests, and previous discussions.

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Setup

pip install reachy-mini honcho-ai openai python-dotenv numpy scipy mediapipe face-recognition

export OPENAI_API_KEY=your_openai_key
export HONCHO_API_KEY=your_honcho_key  # get at app.honcho.dev

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Architecture

Reachy Mini (camera, mic, speaker)
        ↓
OpenAI Realtime API (voice + tools)
        ↓
Honcho (memory + reasoning per user)

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Honcho Integration

Initialize Honcho with a robot peer (not observed) and dynamic user peers (observed): 
from honcho import Honcho
from honcho.api_types import PeerConfig

honcho = Honcho(api_key=api_key, workspace_id="reachy-mini")

# Robot peer - stores messages but isn't reasoned about
robot_peer = await honcho.aio.peer(
    "reachy",
    configuration=PeerConfig(observe_me=False),
)

# User peers - Honcho reasons about their preferences and history
user_peer = await honcho.aio.peer(user_id)
session = await honcho.aio.session(f"chat-{user_id}")
Store messages in the background without blocking the voice loop: 
# Queue messages async - doesn't block audio playback
await session.aio.add_messages(user_peer.message(transcript))
await session.aio.add_messages(robot_peer.message(response))

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Memory Tools

The robot calls Honcho mid-conversation via OpenAI function calling — fast enough for real-time voice:
ToolPurpose
recallQuery Honcho about the user (“What’s their name?”)
create_conclusionSave important facts to long-term memory
seeCapture and analyze camera feed
# Recall - ask Honcho's dialectic API (returns in ~200-500ms)
result = await user_peer.aio.chat(
    "What do I know about this user?",
    session=session,
    reasoning_level="medium"
)

# Create conclusion - save a fact
await user_peer.conclusions_of(user_id).aio.create([
    {"content": "Their name is Alice"}
])

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Multi-User Support

Face recognition identifies returning users. When a new face is detected, the agent:
  1. Flushes pending transcripts to the previous user’s session
  2. Switches Honcho context to the new user
  3. Fetches a briefing from Honcho’s dialectic API
  4. Reconnects OpenAI with fresh context and triggers a greeting
# Get briefing when user is recognized
briefing = await user_peer.aio.chat(
    "What should I know about this user? Name, interests, recent topics.",
    session=session,
    reasoning_level="low"
)

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System Prompt

SYSTEM_PROMPT = """You are Reachy, a friendly robot. Keep responses concise.

You have a recall tool for memory. ALWAYS use it before claiming you don't
know something about the user. Never say "Nice to meet you" if you've met before."""

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Run

uv run python main.py

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Next Steps

Honcho Architecture

Understand peers, sessions, and reasoning

Chat Endpoint

Learn about Honcho’s dialectic API

Get Context

Retrieve formatted conversation history

GitHub code

Dig into the code