Rei (REI)

Data Delay

Overview

Rei (REI), also known as Unit 00 - Rei, is a cryptocurrency token on Base, an Ethereum layer-2 network. It belongs to an artificial intelligence project developed by the 0xReisearch collective and Rei Labs. The project works on AI systems that can retain information, respond to new evidence, and support applications built by other developers. REI is the ecosystem token described in the project’s token plan. (coingecko.com)

The name Rei has several uses within the project. Unit 00 refers to an early AI agent that demonstrated its research ideas. Core is the project’s system for building and retaining useful knowledge. Factory provides tools for creating agents, while later research has focused on Adapt-1, a system designed to learn during use. These parts help explain the token’s intended role: connecting access to AI tools with a shared digital asset. (0xreisearch.gitbook.io)

History & Team

From an AI agent to a research platform

An early focus of 0xReisearch was the meeting point between AI and blockchains. Its original framework explored how AI could analyze information while blockchain software recorded results in a consistent, verifiable form. Unit 00 - Rei served as an example agent. Project descriptions presented it as a way to demonstrate memory, reasoning, and interaction with blockchain data. (0xreisearch.gitbook.io)

The project later broadened toward tools that developers could use beyond that first agent. Its documentation describes Core, a platform for creating agents through Factory, an API for software integration, and Catalog, a collection of specialized models. Rei Labs has since published research on Adapt-1. An August 2026 release described Adapt-1 as a system that builds persistent knowledge from experience while it operates. A September 2026 research update explored how it could learn sequences of actions from feedback. (0xreisearch.gitbook.io)

Public project materials identify 0xReisearch as the group behind the work. In a Google Cloud case study, a founder is identified by the name Reisearch. The same case study describes a team working across several countries and using Google Cloud infrastructure for its AI services. The project’s published materials center on the collective and its products rather than a detailed list of individual founders or investors. (0xreisearch.gitbook.io)

Technology & How It Works

AI and the Base blockchain

REI exists as a token on Base. A token balance and a token transfer are recorded on that network. The project’s AI systems have a different job: they process questions, information, and feedback. Keeping these roles separate helps explain the design. A blockchain can record a transfer according to set rules, while an AI system can work with language and changing evidence. The project’s earlier framework described ways for these two kinds of software to exchange structured information. (coingecko.com)

One early design included an Oracle Bridge to pass information between AI processing and blockchain execution. It also described ERCData as an approach to organizing AI-generated information on-chain. These ideas show how 0xReisearch first approached the gap between flexible AI outputs and the fixed rules used by blockchain applications. (0xreisearch.gitbook.io)

Persistent knowledge in Core

Core is built around what Rei Labs calls an instance: a body of knowledge that can change as it receives records, observations, corrections, and outcomes. For example, an instance working on a research task could retain a useful pattern from one question and consider it during a later question. Rei Labs says users can reach Core through an API, a chat interface, or an embedded workflow. Its design also lets users inspect the evidence and competing ideas behind an answer. (reilabs.org)

Factory applies this idea to agents that users create. Its documentation describes a “unit” with memory that develops through interaction. The associated SDK and API give developers a way to connect a unit to their own software. Adapt-1 extends the research toward learning from results during operation, rather than relying only on a fixed set of instructions prepared in advance. (0xreisearch.gitbook.io)

Tokenomics & Utility

Supply and original allocation

The project’s token plan sets a total supply of 1 billion REI. It divides that supply into five allocations:

• 54%, or 540 million REI, for a public donation event
• 30%, or 300 million REI, for Base liquidity
• 8%, or 80 million REI, for additional liquidity
• 3%, or 30 million REI, for research grants and partnerships
• 5%, or 50 million REI, for the team

The plan lists the team allocation with a six-month waiting period followed by a six-month gradual release. It describes the research and partnership allocation as controlled by a multisignature wallet, which requires more than one approval to move funds. These figures describe the project’s published allocation plan, rather than a changing measure of trading activity. (0xreisearch.gitbook.io)

What the token is meant to do

The project describes REI as an ecosystem access token for its SDK and API. Its token documentation also says REI is intended to support the deployment of user-created agents. That creates a link between the asset and the project’s developer tools: a developer using those tools may need the token for access under the project’s model. The documentation does not set out every future access rule or price for using each service. (0xreisearch.gitbook.io)

Because REI is held on Base, a normal on-chain token transfer uses ETH on Base to pay the network fee. The token’s planned use for Rei services and ETH’s role in paying Base transaction fees are two separate functions. (help.coinbase.com)

Ecosystem & Use Cases

Rei’s ecosystem brings together an example agent, tools for creating other agents, software interfaces, and AI research. Unit 00 showed how an agent could interact with users and blockchain information. Factory describes a more personal use: a user creates a unit, interacts with it over time, and lets it retain useful context. Developers can use the ReiCore SDK or REST API to connect that agent to an application. (0xreisearch.gitbook.io)

Catalog describes specialized AI models that can be connected to Core or reached through an API. This gives developers another way to add a focused capability to an application. Rei Labs’ newer Adapt-1 work examines tasks in which a system receives feedback, updates what it has learned, and uses that change in a later decision. The project has published research examples involving changing task rules and sequences of actions. (0xreisearch.gitbook.io)

These tools point to use cases such as research assistants, software agents with persistent context, and applications that learn from repeated interactions. REI’s documented connection to those uses is access to the project’s tools and its stated plan for agent deployment. How broadly the token is used within finished applications depends on the access systems the project puts into practice. (0xreisearch.gitbook.io)

Advantages & Challenges

Rei has a clear technical theme: building AI systems that retain useful knowledge and adapt when new evidence arrives. The project publishes explanations of Core’s design and research reports on Adapt-1. It also provides developer-facing material for Factory, an SDK, and an API. For a reader assessing the ecosystem, these materials make the project’s intended uses easier to understand than a token description alone would. (reilabs.org)

The main challenge is turning research and developer tools into widely used products. An AI agent needs useful information, a way to handle corrections, and an interface suited to its task. Applications that combine AI with blockchain records also have to decide which information belongs in each system. Rei’s earlier framework addressed that division directly, while its later work has put more attention on how an AI system forms and updates knowledge. (0xreisearch.gitbook.io)

The token has a separate challenge: its practical role grows only when people use services that call for REI. Its published plan describes access and future agent deployment, so the connection between token utility and product adoption is an important part of the project’s development. (0xreisearch.gitbook.io)

Where to Buy & Wallets

REI is available through Aerodrome and Uniswap on Base. CoinEx has also announced a REI/USDT market for Unit 00 - Rei. A Base-compatible wallet, such as MetaMask, can hold REI and connect to supported decentralized exchanges. Coinbase’s wallet tools also support Base transactions. (coingecko.com)

The Base token contract is 0x6b2504a03ca4d43d0d73776f6ad46dab2f2a4cfd. A wallet can use this address to identify the asset when adding it manually. On-chain transfers and swaps require ETH on Base for network fees; a decentralized exchange swap may also require another Base asset to exchange for REI. (dex.lcx.com)

Regulatory & Compliance

Rules for REI-related services depend on where they are offered and how they operate. In the United States, the Securities and Exchange Commission’s March 2026 interpretation explains that a crypto asset’s features and the way it is offered can affect how securities law applies. In the European Union, the Markets in Crypto-Assets framework sets requirements for firms providing crypto-asset services, including authorization rules for covered providers. Those are rules for activities and providers; they do not, by themselves, give REI a project-specific legal classification. (sec.gov)

REI’s Shariah or halal status remains undetermined in the project’s published token materials. Its stated use for access to AI tools is relevant to a Shariah assessment, but token utility alone does not settle the question. Malaysia’s Securities Commission Shariah Advisory Council says an assessment of a digital token considers both how issuance proceeds are used and the rights and benefits attached to the token. Its resolution applies to digital assets within that regulator’s jurisdiction, rather than serving as a certification of REI. (0xreisearch.gitbook.io)

Future Outlook

Rei Labs’ recent work points toward AI systems that can form useful knowledge during use, retain it, and show what shaped an answer or decision. Adapt-1 Preview and later experiments give the project a research path beyond its original Unit 00 agent. Factory and the developer interfaces provide a path for turning that work into applications others can build with. (main-web.reilabs.org)

For the REI token, the key development is how access to those applications is put into practice. The token plan identifies SDK and API access and future agent deployment as its roles. As the tools develop, the details of that connection will determine how closely the token is tied to everyday use of the ecosystem. (0xreisearch.gitbook.io)

Summary

Rei combines a Base-based token with a project focused on AI agents, persistent knowledge, and learning from feedback. Unit 00 introduced the idea, while Core, Factory, and Adapt-1 show how the work has expanded. REI’s published purpose is to support access to the project’s developer tools and, under its plan, the deployment of agents. Its place in the crypto ecosystem rests on how that AI research becomes useful software connected to the token. (0xreisearch.gitbook.io)