Decentralized Python Packages

open-aea

A framework for open autonomous economic agent (AEA) development - no package vendor is prioritised over other package vendors

open-autonomy

Open Autonomy Framework

nvflare

NVIDIA Federated Learning Application Runtime Environment

dnsmesh

Federated end-to-end encrypted messaging over DNS. Identity = DNS name. No central directory, no phone numbers, no servers to trust.

content-hash

Python implementation of EIP 1577 content hash

moeralib

Python library to interact with Moera decentralized social network

a2al

Decentralized networking protocol for AI agents — sovereign identity, peer-to-peer capability discovery, and direct encrypted connections. Works as an MCP server.

aea

A framework for autonomous economic agent (AEA) development

prsm-network

PRSM: Protocol for Recursive Scientific Modeling

bigchaindb

BigchainDB: The Blockchain Database

pilotprotocol

Pilot Protocol: The Internet of Agents

quinkgl

research-based, fully open-source, peer-to-peer gossip learning framework

plexlabexchange

A Python interface to the Plex Go CLI.

subgrounds

An intuitive Python library for interfacing with subgraphs and GraphQL

bacalhau-sdk

Compute over Data framework for public, transparent, and optionally verifiable computation using IPFS & Filecoin.

autonomi-client

Autonomi combines the spare capacity of everyday devices to form a new, autonomous, data and communications layer of the Internet

rotki-content-hash

Python implementation of EIP 1577 content hash

bacalhau-apiclient

Community-driven, simple, yet powerful framework for fast, cost-effective distributed Compute over Data.

create-lilypad-module

Create Lilypad modules

blockstack

Name registrations on the Bitcoin blockchain with external storage

clawrtc

DePIN for Vintage Hardware — Proof-of-Antiquity blockchain where old machines outmine new ones. AI-powered hardware fingerprinting, 15+ CPU architectures, Solana bridge (wRTC). $0 VC.

moralstack

MoralStack is a governance and safety layer for LLM applications. It analyzes user requests before generation, evaluates risk and intent, and decides whether the AI should answer normally, answer safely, or refuse. The goal is to make AI systems more auditable, controllable, and reliable in sensitive or regulated contexts.

mycellm

Distributed LLM inference across heterogeneous hardware

chaincraft

Chaincraft: The platform for blockchain research and prototyping decentralized protocols