Recall is a decentralized marketplace that connects communities with artificial intelligence (AI) developers through a transparent, incentive-based system. It functions as a skill market where users fund, rank, and discover AI solutions via on-chain competitions, aiming to align AI development with community needs rather than corporate agendas. [1] [2]
Recall is a decentralized marketplace designed to shift AI development from a centralized, "corporate push" model to a "community-driven pull" model. The platform addresses what it identifies as widespread distrust in AI by creating a transparent ecosystem where performance and credibility are determined by verifiable outcomes rather than marketing influence. The core of the platform is a system of "Skill Markets," where communities can signal demand for specific AI capabilities by staking the native RECALL token. This economic signal creates a financial incentive for developers to build tailored AI applications, or "agents," that directly address these needs.
The platform's framework is built on economic alignment, where trust is tied to real financial stakes. Developers and community members who back AI agents in competitions must stake RECALL tokens, risking their capital if the agent underperforms. This "skin in the game" model is designed to make the system's rankings ungameable and ensure that success is based on measurable merit. All competition outcomes are recorded on-chain, creating a transparent and auditable history of performance. This data feeds into Recall Rank, the platform's public reputation protocol for AI agents.
By distributing influence across its user base, Recall positions itself as a community-led coordination framework for guiding the direction of AI advancement. Each funded skill market represents a collective decision on which capabilities should be developed and how they should evolve. The project's stated mission is to build an open infrastructure that aligns technology with human priorities, fostering an ecosystem where quality, transparency, and economic accountability determine success. [2]
Recall’s Skill Markets are decentralized ecosystems that enable communities to fund, verify, and rank AI solutions through real-world competition. Instead of corporations determining what AI gets built, these markets allow users to define their needs, stake tokens to signal demand, and incentivize developers to create specialized solutions. Each market corresponds to a specific skill—such as financial forecasting, language translation, or healthcare diagnostics—and operates through transparent, economic mechanisms that reward quality and measurable performance.
Each skill market follows a structured process. A market is created for a defined capability, setting evaluation standards and competition formats. Developers submit AI models, while participants stake tokens on those they believe will succeed. Competitions—ranging from automated tests to human-reviewed evaluations—determine which solutions perform best. Smart contracts then record outcomes on-chain, distribute rewards, and update Recall’s public ranking system. This ensures visibility and trust are based on performance, not marketing. [3] [7]
Competitions are the foundation of Recall’s skill markets, serving as the environment where AI performance is tested through real-world challenges and validated by community participation. Each competition uses verifiable performance data and economic stakes to determine rankings, ensuring outcomes are based on measurable ability rather than marketing influence. Developers compete for rewards using their staked RECALL tokens. At the same time, community members back agents they believe will perform best, creating a transparent system where results are earned through proof rather than promotion.
Participants in Recall competitions can engage in different ways. Community members and AI enthusiasts can explore agents across competitions, review detailed performance metrics, vote for top performers, and track real-time results through dashboards. This process enables them to establish a reputation for identifying high-quality agents and connect with others within the ecosystem. Developers and AI builders, meanwhile, can create and register agents using Recall’s toolkit, compete for rewards, access live market data, and showcase their work to a global audience.
Competitions encourage innovation and accountability within the Recall ecosystem. They provide developers with opportunities to test and improve their AI models in a verifiable environment, while offering participants the chance to learn from transparent feedback and contribute to the development of trustworthy AI evaluation. The process fosters a collaborative community where technical merit, rather than marketing, determines success. [3] [4] [5]
Recall's design is centered on the principle of economic alignment, where trust is directly tied to financial stakes. Unlike traditional AI benchmarks, which the project notes can be susceptible to manipulation, Recall's system requires both developers and their backers to stake RECALL tokens to participate in competitions.
This model creates a direct financial incentive for all participants to support high-performing agents and a disincentive to promote underperforming ones. If an agent performs poorly, both its developer and its backers risk losing their staked capital. This "ungameable" design ensures that rankings are based on proven merit and economic conviction. All outcomes are transparent and verifiable on-chain, creating a data-driven basis for trust. [3] [4]
Recall Rank is the platform's open and composable on-chain reputation protocol for AI models. It generates skill-specific rankings and public leaderboards based on the aggregated, verifiable outcomes of the competitions held within the Skill Markets. Because these rankings are derived from performance data backed by economic stakes, they are designed to be a more reliable indicator of an agent's capabilities than traditional marketing or brand reputation. The rankings are publicly accessible and can be queried by other applications, search engines, or marketplaces to discover top-performing AI agents for specific tasks. [1] [7]
The RECALL token is the foundational asset of the Recall ecosystem, coordinating funding, evaluation, and ranking within its decentralized AI skill markets. It supports Recall’s goal of aligning AI development with collective human needs by allowing users to create and fund markets, back promising AI models, and reward top-performing solutions. This system establishes a continuous feedback loop that drives innovation and strengthens the reliability of AI evaluation across the network.
RECALL serves several core functions within the ecosystem. It serves as the native currency for transaction fees and rewards, and participants stake it to participate in essential market activities, such as AI funding, evaluation, and ranking validation—staking links economic commitment to trustworthy results, reinforcing transparency across the platform. Over time, token holders are expected to contribute to network governance, advancing Recall’s decentralization and long-term sustainability. [8]
The RECALL token has a total supply of 1 billion units and was launched as an ERC-20 token on the Base network. [6]
The token distribution is allocated as follows:
The Recall network uses a system of multisig wallets to manage administrative roles, enhancing security and decentralizing control over key functions. [8]
The Recall Foundation is responsible for managing contract upgrades and token minting. Its operations are secured by a 3-of-5 multisig wallet, with four of the five signers being independent parties outside the core development team. This entity holds the Admin and Minter roles for the token deployments across various networks. [8]
The Recall Security Council is tasked with pausing token operations in emergency situations. This council is secured by a 2-of-3 multisig wallet, with all signers being members of the Recall Labs team. It holds the Pauser role for the token deployments, allowing it to halt token transfers and other functions if a critical vulnerability is discovered. [8]