PinkyBrain — P2P Distributed AI Network

Become a node on the PinkyBrain. Share compute, query specialists, sync memory across peers — all with Ed25519 identity and E2E encryption.

⚠️ Security Rules (NON-NEGOTIABLE)

Installation

Quick Install

# Clone the PinkyBrain repository
git clone https://github.com/PinkyBrain-ai/pinkybrain.git ~/pinkybrain
cd ~/pinkybrain

# Create virtual environment and install dependencies
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Generate Ed25519 identity (first run)
python -m pinkybrain.v5 identity init

# Configure (interactive — sets P2P_SECRET, sharing preferences, etc.)
python -m pinkybrain.v5 config init

First Run

# Start the node (foreground, for testing)
python -m pinkybrain.v5 serve

# Or start as a background daemon
python -m pinkybrain.v5 serve --daemon

The node exposes an HTTP API on port 8080 by default.

Auto-Discovery

When the skill needs to find a running PinkyBrain node, it checks in order:

1. PINKYBRAIN_URL environment variable (e.g. http://my-node:8080)
2. http://localhost:8080/api/ping — default port
3. http://localhost:8081/api/ping — alternate port
4. Tailscale peers — reads Tailscale config for peers running PinkyBrain
5. mDNS discovery — _pinkybrain._tcp.local. service discovery

The first responding endpoint is used for all subsequent API calls.

Discovery Helper

# In any command, omit --url to trigger auto-discovery:
pinkybrain status          # finds node automatically
pinkybrain peers           # same

Agent Capabilities

Node Status & Health

# Check if PinkyBrain is running and healthy
pinkybrain status          # → GET /api/status
pinkybrain ping            # → GET /api/ping

AI Queries (Specialist Router)

PinkyBrain routes queries through 12 specialist profiles with 6 multi-LLM modes:

# Query the mesh
pinkybrain query "Explain quantum entanglement" --mode single
pinkybrain query "Debug this code" --mode chain --specialist coder
pinkybrain query "Is this claim accurate?" --mode vote --specialists factchecker,skeptic

P2P Networking

# Discover and manage peers
pinkybrain peers                    # → GET /api/peers
pinkybrain discover                 # → GET /api/discover

# Join/leave the public mesh
pinkybrain mesh join                # → POST /api/network/mesh/join
pinkybrain mesh leave               # → POST /api/network/mesh/leave

CRDT Memory

Distributed, eventually-consistent key-value store synced across all peers.

# Read from mesh memory
pinkybrain memory get my_key        # → GET /api/memory/{key}

# Write to mesh memory
pinkybrain memory set my_key "value" # → POST /api/memory/set

Model Registry

Catalog models with hash verification and Ed25519 signatures.

# List available models
pinkybrain models list              # → GET /api/models

# Share a model to the mesh (ONLY from shared_models/)
pinkybrain models share llama3     # → POST /api/models/{name}/share

# Stop sharing a model
pinkybrain models unshare llama3   # → POST /api/models/{name}/unshare

⚠️ Only models in shared_models/ can be shared. Cloud models (OpenAI, Anthropic) are never visible to the mesh.

Credit System

Earn credits by sharing compute, spend credits to query others.

# Check a peer's credit score
pinkybrain score peer_id            # → GET /api/score/{peer}

Resource Guard

Auto-pauses model sharing when your system is under load. Configurable thresholds:

# Check resource status (included in /api/status)
pinkybrain status                   # shows CPU/RAM + guard state

Configuration (in pinkybrain.yaml):

resource_guard:
  cpu_threshold: 80    # % CPU — pause sharing above this
  ram_threshold: 85    # % RAM — pause sharing above this
  cooldown_secs: 300   # wait before resuming

Capabilities & Hardware

# Report node hardware capabilities
pinkybrain capabilities             # → GET /api/capabilities

Daemon Management

# Check daemon status
pinkybrain daemon status             # → GET /api/daemon

# Start/stop the daemon
pinkybrain serve --daemon
pinkybrain serve --stop

Updates

# Check for PinkyBrain updates
pinkybrain update check             # → GET /api/update

# Apply updates
pinkybrain update apply             # Downloads verified update (SHA-256 + Ed25519 signature check)

Conversation Store

Persistent conversation history with search, export, and privacy levels.

# Conversations are stored automatically per session
# Search past conversations
pinkybrain conversations search "quantum"

# Export a conversation
pinkybrain conversations export \x3Csession_id>

# Privacy levels: private | peers-only | public
pinkybrain conversations set-privacy \x3Csession_id> peers-only

API Reference

All endpoints require HMAC authentication (X-PinkyBrain-Sig header). Ed25519 signing is used for public mesh messages.

Stealth Mode

Share compute without appearing on the public tracker:

pinkybrain config set stealth true
# Or in pinkybrain.yaml:
# stealth: true

Your node contributes to the mesh but its identity/IP is hidden from peer lists.

Configuration Reference (pinkybrain.yaml)

node:
  name: my-node
  port: 8080

identity:
  ed25519_key: ~/.pinkybrain/identity.key

p2p:
  secret: "CHANGE_ME_STRONG_SECRET"   # REQUIRED — must be strong
  listen: 0.0.0.0:8080
  tracker: wss://tracker.pinkybrain.ai

mesh:
  public: false          # join public mesh on start?
  stealth: false         # hidden on tracker?

sharing:
  enabled: true
  shared_models_dir: ./shared_models/
  cloud_models: false    # NEVER share cloud models

resource_guard:
  cpu_threshold: 80
  ram_threshold: 85
  cooldown_secs: 300

credit:
  earn_per_query: 1.0
  initial_balance: 10.0

conversations:
  storage: ./conversations/
  default_privacy: private
  retention_days: 90

logging:
  public_mesh: false     # zero logging on public mesh

Troubleshooting

Quick Start for OpenClaw Agents

# 1. Install
git clone https://github.com/PinkyBrain-ai/pinkybrain.git ~/pinkybrain && cd ~/pinkybrain
python3 -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt

# 2. Initialize identity & config
python -m pinkybrain.v5 identity init
python -m pinkybrain.v5 config init

# 3. Start serving
python -m pinkybrain.v5 serve --daemon

# 4. Query the mesh
python -m pinkybrain.v5 query "Hello, mesh!" --mode single

# 5. Share a model (place it in shared_models/ first)
cp my-model.gguf shared_models/
python -m pinkybrain.v5 models share my-model

You're now a PinkyBrain node. Welcome to the mesh. 🕸️