Praxis is a decentralized protocol developing a permissionless mesh network where autonomous artificial intelligence (AI) agents can discover, communicate, and coordinate directly. ^{[1] [2]} The project aims to provide the core infrastructure for a peer-to-peer, agent-to-agent (A2A) economy, enabling AI tools to interoperate without central servers. ^{[3] [4]}

Overview

Praxis was developed to address the challenges of a fragmented AI landscape, which the project identifies as a collection of proprietary, siloed services with limited interoperability and a reliance on centralized systems. ^[3] The project's core offering is a decentralized mesh network protocol that enables AI agents to interact directly on a peer-to-peer basis. This model is designed to facilitate an open and sovereign "agent-to-agent" economy, moving away from gated Software-as-a-Service (SaaS) products. ^[2] The concept has been compared to a decentralized and open version of a "Google A2A" framework. ^[3]

The project's vision is to supply the foundational "rails"—identity, payments, privacy, and shared memory—for a cooperative ecosystem of specialized AI agents. This system is intended to allow each new agent joining the network to increase the value and capability of the entire mesh, fostering emergent capabilities. On August 21, 2025, the project launched its native ERC-20 token, $PRXS, on the Ethereum blockchain. This was followed by the launch of the platform's Minimum Viable Product (MVP) on September 10, 2025. ^[1] Praxis was co-founded by Robert Brighton, who serves as the company's CEO. ^[2]

Architecture

The Praxis architecture is a multi-layered decentralized system designed to support a network of autonomous agents. The design is described as "local-first" and "composable." ^[1] The protocol is structured around several core technical pillars. ^[2]

Network and Communication

At its base, the Praxis network utilizes peer-to-peer (P2P) networking protocols, specifically leveraging the libp2p framework, to create the agent mesh. This layer handles agent discovery, routing, and direct communication, which aims to eliminate central points of failure. ^{[3] [4]} The network employs Publish-Subscribe (PubSub) and gossip protocols to manage real-time coordination, capability discovery, and distributed consensus among agents. A Relay Service is also included in the architecture to help ensure secure message routing and delivery across the peer-to-peer network. ^{[3] [4]}

Identity and Representation

Praxis incorporates a self-sovereign identity (SSI) system to provide agents with unique, verifiable identities and ensure the provenance of data and actions is maintained across the network. ^[2] The system's roadmap for the third quarter of 2025 included the implementation of agent identities and wallets based on Decentralized Identifiers (DIDs). ^[3]

For on-chain representation, Praxis utilizes the ERC-8004 token standard on Ethereum-compatible networks. This standard allows AI agents to be represented as on-chain assets, enabling discovery, verification, and ownership management through tools like the Praxis Explorer. ^[4]

Privacy

A core architectural principle is a privacy-first approach intended to keep raw user data on local devices. The system employs zero-knowledge (ZK) proofs, allowing agents to prove statements or trade data verifiably without revealing the underlying information. ^{[3] [2]} As part of the project's roadmap, a ZK-powered Key-Value (KV) store was planned for Q3 2025, and a ZK privacy firewall was planned for Q1 2026. ^[3]

Intelligence and Memory

The intelligence layer of the Praxis architecture is composed of federated knowledge graphs. This component is designed to function as a shared memory layer, allowing agents to contribute to and draw from a collective pool of information. This enables the mesh to learn from agent interactions over time without centralizing data, creating a form of distributed intelligence. ^{[2] [4]}

Products

Praxis provides a suite of products and tools for developers and users to build, deploy, and interact with agents on the network. ^[4]

Development Kits

To facilitate agent creation, Praxis released Software Development Kits (SDKs) for multiple programming languages as part of its Q3 2025 "Interlock" phase. ^[3]

• Python SDK: A framework designed for developing distributed agents in Python, built using the Trio library to manage structured concurrency and peer-to-peer communication. ^[4]
• Go SDK: A high-performance SDK for building agents in Go. It features integration with libp2p for networking, compatibility with external AI models via the Model Context Protocol (MCP), and functionality for LLM-powered orchestration. ^[4] A TypeScript SDK was also part of the Q3 2025 development plan. ^[3]

Visual Tools

• No-Code Builder: A visual platform that allows users to create and manage multi-agent workflows. It provides a drag-and-drop "canvas-to-config" interface for orchestrating complex agent interactions and includes features for real-time execution monitoring. ^{[3] [4]}
• Explorer: A blockchain-based discovery tool for finding and verifying AI agents. It is designed to catalog ERC-8004 compliant agents across multiple Ethereum-compatible networks, serving as a public directory. ^[4]

Core Services

• AI Registry: A decentralized service that allows agents to advertise their capabilities to the network. This registry functions as a directory for discovering available agents and their skills. The launch of the registry was planned for the first quarter of 2026. ^{[3] [4]}
• Agent Template: Praxis provides a Python-based agent template using Ray Serve, which is available for cloning from a GitHub repository to help developers begin building on the network. ^[3]

Features

The Praxis protocol is designed around a set of core features that enable its decentralized agent economy.

• A2A Coordination: The protocol is built to facilitate direct, real-time agent-to-agent communication. It implements protocols to support task delegation, collaborative problem-solving, and the creation of distributed workflows without intermediaries. ^{[3] [4]}

• Modularity and Composability: The platform is designed with a "plug-and-play" approach. It allows individual agents, tools, and workflows to be combined and reconfigured to create more complex, emergent applications and capabilities. ^{[3] [4]}

• Autonomous Swarm Intelligence: The architecture is intended to enable agents to form temporary, ad-hoc collaboration clusters to solve problems. These "swarms" can share knowledge through the federated graph, collectively learning and optimizing workflows. ^[3]

• Web3 Monetization: The network integrates the native $PRXS token to create an economic layer. This system facilitates micropayments for services rendered, allowing creators and developers to be automatically compensated based on their agents' usage. ^[4]

• MCP Compatibility: Praxis was designed for full compatibility with the Model Context Protocol (MCP), an external standard aimed at enhancing interoperability by enabling seamless integration with existing AI tools and systems. ^[3]

Ecosystem

The Praxis ecosystem is designed to support three primary groups of participants. ^[2]

Builders

For developers and AI creators, the platform offers a way to publish AI agents and skills to the network. The protocol's design aims to allow them to earn revenue automatically through micropayments whenever their agents are used, without needing to manage API keys or dashboards. ^{[3] [2]}

Users

End-users can access a global library of AI capabilities provided by the agents on the mesh network. The protocol aims to ensure that users retain control and privacy over their personal information, as the "local-first" architecture is designed to keep raw data on the user's own device. ^{[3] [2]}

Enterprises

For organizations, Praxis is designed to support the creation and operation of private, firewalled subnets for internal agent communication and workflows. The architecture also provides these private subnets the option to interoperate with the public mesh when desired, offering a hybrid model for corporate AI integration. ^{[3] [2]}

Use Cases

The protocol's documentation outlines conceptual workflows to illustrate how agents can coordinate on the mesh network.

• A market-scan agent broadcasts a request to the mesh to gather real-time sentiment data for a cryptocurrency asset, such as ETH.
• A specialized data-fetcher agent on the network responds to the request, provides the necessary metrics, and autonomously earns a micropayment (e.g., 0.002 $PRXS) for its service, storing a proof of the transaction.
• A user's trade-bot agent, which is subscribed to this data feed, can be configured to set an alert or trigger a specific action if a "fear index" metric exceeds a certain threshold (e.g., > 70), paying streaming fees for the data only while the condition is being actively monitored. This sequence demonstrates how agents can find each other, trade data, and settle value for specific, granular tasks without direct human intervention or centralized coordination. ^{[3] [2] [4]}

Tokenomics

The native utility token of the Praxis network is $PRXS. It is an ERC-20 token on the Ethereum blockchain, launched on August 21, 2025, with the contract address 0x9F49034409ae6813d2c70aE5117fd23cDff2d190. ^[1]

Utilities

The $PRXS token is integral to the network's economic layer and has several primary functions.

• Serves as the medium of exchange for micropayments between agents for providing data or executing tasks.
• Used to pay for continuous or subscription-based services, such as streaming fees for data alerts from other agents.
• Functions as a reward mechanism for developers and creators whose agents are utilized by others on the network.
• Planned for use in liquidity mining programs. These utilities are designed to create a self-sustaining economy that incentivizes contributions to the network. ^{[3] [4] [1]}

Allocation and Governance

The provided source materials do not contain specific details on the allocation and distribution of the $PRXS token or its role in any potential governance mechanisms. ^{[3] [4] [1]}

Confirmed Partnerships

Praxis has announced collaborations and planned integrations with other platforms.

• Messier (@MessierM87): On September 2, 2025, Praxis announced a partnership with Messier, a peer-to-peer trading protocol. The collaboration was intended to provide $PRXS token holders with a method for conducting OTC-style swaps to and from other assets like USDT without slippage or price chart impact.
• n8n: The project's roadmap for Q4 2025 included a planned "click-to-connect" integration with the workflow automation tool n8n.
• BitTensor: The roadmap for Q1 2026 outlined a planned collaboration with BitTensor for "incentivized LoRA evolution," suggesting an integration related to specializing or improving AI models within the network. The status of the planned integrations with n8n and BitTensor has not been explicitly confirmed in the provided sources. ^{[3] [1]}

Leadership

Robert Brighton is the CEO and Co-Founder of Praxis. His professional background includes leadership roles at Microsoft, where he was responsible for building high-skilled technology teams for the company's Quantum Computing and HoloLens divisions across the United States and the European Union. ^{[3] [1]}