Installation
Get a Spore node running in under a minute.
Requirements
- Python 3.11 or later
- Training works on CUDA, MPS (Apple Silicon), and CPU
- No port forwarding needed — nodes connect outbound to the bootstrap peer
Install from PyPI
Install from source
On NVIDIA GPUs, install Flash Attention 3 for faster training:
Configure your LLM
Set up an LLM provider for the experiment agent. Supports Anthropic, Groq, OpenAI, and xAI.
Quick start
The --bootstrap flag copies the bundled training and data prep scripts into your working directory, downloads data, connects to the bootstrap peer, syncs the graph, and starts the experiment loop.
Your identity, database, and config live in ~/.spore/. Every command auto-initializes the node if it hasn't been set up yet.
Run as a daemon
Multi-node setup
Nodes sync their full experiment history on connect and gossip new experiments in real time. Peer Exchange (PEX) means connecting to one node discovers the rest of the network automatically.
Resource control
Limit how much of your machine Spore uses (scales training batch size). Works on CUDA, MPS, and CPU.
Launch the Explorer
The Explorer is a local web UI with D3.js DAG visualization, live WebSocket feed, frontier table, activity feed, and reputation leaderboard.
Directory structure
~/.spore/ ├── config.toml # Node configuration ├── identity/ │ ├── private_key # Ed25519 private key (hex) │ └── node_id # Public key / node identifier ├── db/ │ ├── graph.sqlite # Research graph (Merkle-DAG) │ └── reputation.sqlite # Node reputation scores └── artifact/ # Content-addressed artifact storage