Introduction

AI technology is advancing rapidly, and AI agents are becoming key drivers of social change. They demonstrate immense potential in areas such as finance, healthcare, education, and business management, from automating processes to supporting complex decision-making. However, current AI systems face critical challenges, such as a lack of data transparency, missing trust mechanisms, and limited collaboration. These issues hinder the broader application and evolution of AI agents. Recall was created to address these challenges. We will explore Recall’s unique advantages and its crucial role in the AI ecosystem through various aspects like technology, application, and market.

What is Recall?

Recall is a result of the merger between Textile and Ceramic development company 3Box Labs.

Recall is a pioneering decentralized platform designed to create a network for AI agents to store, share, and trade knowledge on the blockchain. By harnessing blockchain technology, Recall offers AI agents a novel framework for collaboration and economic rewards. AI agents can verify their data’s authenticity and origin, transparently share intelligence, and earn economic benefits through knowledge exchange.

Recall’s decentralized structure eliminates single points of failure and trust issues found in traditional systems. Blockchain technology ensures that all transactions and strategies are immutable and verifiable. Additionally, Recall’s partnership with decentralized storage solutions, such as Filecoin, enhances data security and durability.

The objective is to build a minimal-trust ecosystem that fosters the growth of the AI agent economy. In this ecosystem, AI agents can collaborate and trade transparently and reliably, unlocking the full potential of AI technology. Recall aims to bridge AI agents with human society, promoting the free flow of knowledge and equitable value distribution through decentralized technology and economic incentives.

Recall is more than a tech platform; it represents a new collaboration model. AI agents become network nodes capable of mutual cooperation and knowledge sharing, rather than isolated entities. This approach aims to transition AI technology from optimizing individual tasks to collaborating within complex systems, providing new solutions and tools for addressing global challenges.

Source: https://x.com/recallnet

Project Background

Team Members

Recall was formed by merging Textile and Ceramic development company, 3Box Labs. The Recall team consists of professionals with extensive experience in AI, blockchain, and decentralized technologies. Key team members include:

• Andrew W. Hill | Co-founder and CEO
  Andrew W. Hill is the co-founder and CEO of Recall (formerly Textile/Tableland) and was previously the Chief Science Officer at CARTO. With a background as a biologist and professor, he has long worked in the computer software industry. He holds a Ph.D. in Ecology and Evolutionary Biology from the University of Colorado Boulder.

• Michael Sena | Co-founder
  Michael Sena is the co-founder and CEO of 3Box, facilitating better application development and advocating for fair data. He broke data silos through Ceramic Network and was previously the co-founder of Uport, developing secure, privacy-protective identity technology to enhance decentralized systems. He was an early employee at ConsenSys.

• Sander Pick | Co-founder
  Sander Pick is the co-founder and CTO of Recall (formerly Textile and Tableland) and was a senior software engineer at Apple.

Additionally, many team members come from leading tech companies and research institutions, bringing deep expertise and rich practical experience. Their diversity and professionalism enable Recall to achieve breakthroughs in technology, product, and market.

Funding Situation

Recall’s funding details are as follows (all funding is pre-merger; post-merger funding has not occurred):

• As of April 4, 2025, Recall has raised approximately $42 million.
• Investors include Multicoin Capital, Union Square Ventures, CoinFund, Placeholder, and several other notable entities.

Recall Core Technology

Traditional AI systems face significant challenges like lack of data transparency, missing trust mechanisms, and limited collaboration, hindering the broad adoption and growth of AI agents. Recall was created to tackle these issues. Its core technologies include blockchain subnets optimized for AI agents, Cognitive APIs, and decentralized storage. These technologies form the robust foundation of the Recall platform, offering innovative solutions to traditional AI system challenges.

Source: https://docs.recall.network/intro

Recall Subnet

Recall uses a blockchain subnet specifically optimized for AI agents, which is central to its technical architecture. This design aims to meet the needs of AI agents for high throughput, low latency, and reliability. Blockchain technology ensures that all data and transactions are immutable and verifiable.

The decentralized nature of the subnet eliminates single-point failures, enhancing system security and stability. Its modular design allows flexible scaling to accommodate AI networks of various sizes and complexities.

Recall’s layered subnet strategy combines the best of existing methods, allowing subnets to have their own consensus algorithm while inheriting security from the parent subnet. This structure optimizes latency and enables horizontal scaling for greater throughput. Subnets can be tailored for specific use cases, such as geographic regions, service levels, features, or data types.

The core of Recall is a deterministic state machine that interacts with the consensus engine, ensuring consistent replication of the blockchain state across subnets. Recall integrates on-chain contracts, off-chain services, and asynchronous computation, with EVM compatibility for easy integration with Ethereum tools and support for data-intensive applications.

Cognitive APIs

Recall offers Cognitive APIs designed for AI agents, enabling observation and knowledge sharing. The Recall SDK, API, and framework plugins allow agents to have dedicated storage for their data, like decision logs, historical data, and model outputs.

“Chain-of-Thought logs” let AI agents record and verify their decision-making processes, enhancing transparency and providing developers with tools for debugging and optimization.

Decentralized Storage

Recall partners with decentralized storage solutions like Filecoin to ensure data is secure and durable. By utilizing decentralized storage, Recall mitigates the risks of single points of failure and data tampering associated with traditional centralized storage. Filecoin’s distributed storage network offers Recall a reliable and redundant data storage solution, ensuring that AI agents’ knowledge and data remain securely stored and easily retrievable over time.

Unlike many systems that rely on Reed-Solomon erasure codes, Recall uses Alpha Entanglement (AE) codes, which are specifically designed for distributed systems. AE codes create redundancy with minimal storage requirements, enhance data integrity through block interleaving, and efficiently recover lost data blocks without requiring the reconstruction of large volumes of data. This approach reduces repair costs and boosts scalability, even in dynamic environments.

The Alpha Entanglement codes employed by Recall offer notable benefits in terms of storage efficiency and repair costs compared to traditional Reed-Solomon erasure codes. While Reed-Solomon codes protect data by generating extra data blocks, they usually require significant storage space. In contrast, AE codes create redundancy with less storage space by “entangling” data and redundant blocks within a multi-dimensional lattice structure, thus enhancing data integrity.

Additionally, when data blocks are lost, Reed-Solomon codes necessitate reconstructing large data volumes, leading to high repair costs. Conversely, AE codes can efficiently recover lost data blocks without needing extensive data reconstruction, thereby reducing repair costs and enhancing system scalability. AE codes are especially well-suited for dynamic environments, supporting random access and decentralized repair, optimizing both storage efficiency and data availability.

Source: https://www.awcloud.com/3778.html

Recall Technical Features

Scalability

Recall’s architecture is designed with scalability in mind, supporting large-scale AI agent networks. Its flexible data storage mechanism allows agents to adjust storage strategies dynamically as needed, and function triggers enable efficient event-driven processing. Moreover, Recall’s verifiable execution environment ensures agents behave correctly and consistently, maintaining high performance and reliability even as the network expands.

• Handling Large Data Volumes: Recall’s adaptable data storage mechanism allows AI agents to optimize resource use and processing efficiency when dealing with large datasets. For instance, in financial data analysis, AI agents can adjust storage and processing strategies in response to real-time data flow to maintain efficient operations.
• Distributed Computing Tasks: Recall’s function triggers facilitate quick responses in event-driven processing, ideal for distributed computing tasks. For example, in real-time monitoring systems, AI agents can swiftly react to event triggers, ensuring timely data processing and analysis for system reliability.

Interoperability

Recall enables collaboration and communication among various AI agents, a key feature of its technical framework. Through standardized interfaces and protocols, Recall allows different AI agents to work together seamlessly, sharing knowledge and resources. This interoperability boosts system efficiency and lays the groundwork for complex multi-agent systems.

• Collaborative Multi-agent Systems: In intricate multi-agent systems, Recall’s standardized interfaces and protocols ensure smooth cooperation among different AI agents. For example, in smart logistics systems, multiple AI agents can work together on inventory management, route planning, and delivery tasks, enhancing overall efficiency.
• Cross-domain Applications: Recall’s interoperability supports collaboration between AI agents across different fields. For instance, in healthcare and finance, AI agents can exchange data and knowledge, offering more comprehensive decision support.

Real-time Observability

Recall offers AI agents real-time monitoring and verification capabilities. Using Cognitive APIs and blockchain technology, agents’ actions and decision processes can be recorded and verified instantly. This real-time observability enhances system transparency, providing developers and users with immediate feedback that aids in the quick identification and resolution of problems.

• Real-time Decision Support: In fast-paced decision-making scenarios, Recall’s real-time observability ensures AI agents’ actions and decision processes are monitored and verified instantly. For example, in autonomous driving systems, AI agents can log and verify their decision processes in real-time, ensuring system safety and reliability.
• System Optimization and Debugging: Developers can leverage Recall’s real-time observability to swiftly pinpoint and address system issues. For instance, in intelligent customer service systems, developers can monitor AI agents’ actions in real-time, optimizing their performance and response times.

Market and Competitive Analysis

Recall positions itself as a leader in the AI agent economy and decentralized storage sectors, aiming to be the central platform for AI agent collaboration and transactions. As AI agent technology evolves rapidly and application scenarios diversify, Recall’s decentralized architecture and economic incentives strive to foster sustainable growth in the AI agent economy.

Competitive Advantages

• Decentralization: Recall’s decentralized framework ensures data security and transparency, removing single-point failure risks inherent in centralized systems. This is particularly advantageous in sectors like finance and healthcare, where data security is paramount.
• Economic Incentives: Recall rewards AI agents economically through knowledge transactions, motivating developers and businesses to engage actively in ecosystem development. This incentive system encourages the free flow of knowledge and drives the growth of the AI agent economy.
• Technological Leadership: Recall’s blockchain-based optimized design guarantees high performance and reliability. Its modular and flexible design adapts to AI agent networks of varying sizes and complexities.

Comparison of Decentralized Storage Platforms

Recall, with its blockchain subnet optimization, offers unique features for AI agent collaboration and knowledge exchange. In contrast, platforms like Eliza, GAME, and AutoGen emphasize multi-agent collaboration and task management, while LangGraph and CrewAI focus on complex logic processes and team collaboration. Recall is primarily used for storing and sharing AI agent knowledge, whereas other platforms are geared towards enterprise-level task management, team collaboration, and lightweight multi-agent orchestration. Both Recall and other platforms utilize strong encryption to protect user data; however, Recall further enhances data security by integrating blockchain technology for enhanced privacy protection. Moreover, Recall’s integration with the Filecoin blockchain ensures data transparency and immutability, unlike other platforms that do not utilize blockchain technology directly. In terms of data control, Recall and other platforms offer high levels of user control, but Recall enhances data trustworthiness through blockchain technology. Performance-wise, Recall offers moderate upload/download speeds, high throughput, and good scalability, while other platforms excel in collaboration and task management. These combined advantages give Recall a distinct competitive edge in the AI agent domain.

Comparison with AI Agent Platforms

Recall’s blockchain-optimized design provides unique features for AI agent collaboration and knowledge exchange, offering significant advantages in data transparency and immutability. Its primary application is in storing and sharing knowledge of AI agents, while platforms like Eliza, GAME, and AutoGen focus on multi-agent collaboration and task management, making them suitable for enterprise-level tasks, team collaboration, and lightweight multi-agent orchestration. Although both Recall and other platforms offer strong privacy protection, Recall has superior data control and transparency, especially with blockchain integration, ensuring data security and reliability. This distinctiveness gives Recall a significant competitive advantage in the AI agent sector.

Ecological Development

Technological Evolution

Ceramic

• March 2020: The Ceramic project was initiated by Michael Sena, co-founder of the distributed database infrastructure 3Box.
• January 27, 2021: Ceramic launched the Clay testnet, marking a significant step towards its mainnet deployment.
• March 2021: The Ceramic Fire mainnet was launched, featuring performance enhancements, a fully decentralized peer discovery system, network monitoring, and various bug fixes.
• October 2021: Ceramic underwent further performance optimizations and network expansions to accommodate more developers and applications.

Textile ThreadDB

• July 2019: Textile introduced ThreadDB, a serverless P2P database built on Libp2p.
• February 2020: ThreadDB’s capabilities were expanded to support a wider range of dynamic data storage and management options.
• June 2021: Performance optimizations were made to ThreadDB to enhance data storage and retrieval efficiency.
• January 9, 2023: Textile’s hosted Hub infrastructure was decommissioned, making ThreadDB and Bucket data unavailable.

These technological advancements provided a strong foundation for the development and integration of the Recall platform.

Milestones

• February 2025: Textile and Ceramic merged to launch Recall.
• March 2025: The Recall Foundation was established.
• March 2025: The Recall testnet went live, attracting over 60,000 participants and facilitating more than 400,000 transactions.
• March 2025: Recall introduced the Surge reward program to motivate user engagement in agent competitions, community involvement, and learning within the AI agent ecosystem.

Source: Gate.io

Surge Reward Program

In March, Recall launched the Surge community points program. So far, over 125,000 users have joined, earning points through AI competitions, social tasks, and various challenges. Surge has expanded the Recall community to 225,000 followers on X and 125,000 members on Discord. Here’s how to get involved in the Surge reward program:

• Visit the Recall Surge website: Create your profile at points.recall.network.
• Earn points by completing tasks: Accumulate points by engaging in community tasks on platforms like Absinthe, Galxe, and Zealy (Kaito coming soon). Tasks include social media activities, meme creation, network usage, and referrals.
• Participate in agent competitions: Join as a builder or user in agent competitions. Builders earn points by registering, competing, and excelling in contests, while other users earn points by proposing contests, voting, and predicting winners.
• Check the leaderboard: As you earn points, climb the Surge leaderboard to showcase your contributions to Recall.
• Refer others: Gain additional rewards by referring others, earning 10% of their lifetime points.

Risk Analysis

Technical Risks

Recall’s use of blockchain subnet technology offers decentralization and security benefits but also presents challenges in terms of performance and scalability. As the AI agent network expands and transaction volumes increase, potential bottlenecks in processing speed and capacity could degrade system performance, affecting user experience.

Smart contracts are a crucial part of the Recall project, but they may contain vulnerabilities that could be exploited, leading to data breaches, asset losses, or system failures. The complexity of smart contracts also complicates development and auditing, and any issues could undermine the project’s credibility.

Market Risks

The AI agent economy is still emerging and not yet fully developed. Uncertain market demand and variable acceptance could impact Recall’s market penetration and commercial success. If awareness and demand for AI agents fall short of expectations, Recall’s growth and profitability could be constrained.

As AI and blockchain technologies advance, competition intensifies. Recall faces challenges from other AI agent platforms and decentralized storage projects, which may have advantages in technology, resources, and market share, threatening Recall’s market position.

Regulatory Risks

Regulatory Risks of Decentralized Storage: Decentralized storage technology enhances privacy and data security, but it also poses regulatory challenges. Since data is distributed across network nodes, traditional data management and auditing methods are difficult to apply. For instance, platforms like IPFS and Filecoin are excellent at ensuring data privacy and resisting censorship but could also be used to store illegal content or avoid regulatory oversight. Many countries have ambiguous regulations concerning decentralized storage platforms, creating compliance risks for businesses. Some regions might require companies to ensure data traceability and legality, which traditional decentralized storage technologies may not easily support.

Regulatory Risks of AI Agent Transactions: Transactions involving AI agents raise concerns about data privacy, intellectual property, and transaction security, potentially exposing them to stringent regulations. The autonomy and complexity of AI agents make their actions hard to predict and control, complicating regulatory oversight. AI agents could be used in social engineering attacks or financial fraud, risking data breaches or asset losses. For example, in 2024, a cryptocurrency user exploited social engineering to manipulate the AI agent Freysa, integrated with the Base blockchain, into transferring $50,000 to their account. This incident highlights the potential for AI agents to be misused in the absence of effective regulations and protections, posing significant security threats.

Future Outlook

Recall’s future focuses on technological expansion, ecosystem building, cross-domain applications, and community-driven initiatives.

For technological expansion, Recall is developing a foundational intelligence layer enabling millions of agents to verify, monetize, and exchange knowledge. The Recall blockchain not only allows agents to objectively demonstrate intelligence but also offers a secure, cost-effective infrastructure for supporting next-generation multi-agent collaborative AI.

In ecosystem building, Recall aims to attract more developers and businesses to its ecosystem to propel the AI agent economy. By offering powerful development tools, infrastructure support, and an open-source collaboration platform, Recall seeks to lower the entry barriers for AI agent development, encouraging broader participation. Through Recall, agents can collaborate by trading knowledge and skills, with intelligence growing as more agents join. Recall will also explore governance models, such as DAOs, to enhance community involvement and project sustainability.

In cross-domain applications, Recall will explore the use of its technology in IoT, supply chain, healthcare, and other areas. Combining decentralized storage with blockchain technology can provide enhanced data privacy and resistance to censorship in these sectors. For instance, in IoT, Recall can offer secure data storage and sharing solutions between devices; in supply chains, it can ensure data transparency and traceability, improving the efficiency and trustworthiness of the entire supply chain. In healthcare, dietary planning agents can receive dietary adjustments from diabetes management agents, ensuring personalized advice. These initial use cases are just the beginning of Recall’s potential.

Finally, in community-driven initiatives, Recall will foster sustainable development through community governance and open-source development. By implementing incentives like points reward programs and adopting a community-driven development model, Recall aims to engage more developers and users in ecosystem building, creating a vibrant, self-sustaining community. This approach accelerates technological innovation and ensures the long-term stability of projects.

Conclusion

Recall is an innovative decentralized platform offering a new collaboration and economic incentive framework for AI agents via blockchain technology. It addresses the challenges traditional AI systems face in terms of data transparency, trust mechanisms, and collaboration, providing a solid foundation for the growth of the AI agent economy. Recall’s core technologies include AI agent-optimized blockchain subnets, Cognitive APIs, and decentralized storage technology, ensuring data security, transparency, and durability. Recall’s applications span finance, healthcare, education, IoT, and more, providing enhanced data privacy and resistance to censorship. Despite facing technological, market, and regulatory risks and challenges, Recall has clear future directions, including technological expansion, ecosystem building, cross-domain applications, and community-driven efforts. These efforts position Recall to become a key player in advancing the AI agent economy and decentralized storage technology, laying a strong foundation for an open, inclusive, and sustainable intelligent society.