@SentientAGI, led by top institutions such as Founders Fund, Pantera, and Framework, is an ambitious AI Blockchain that aims to reshape the 'ownership + profit sharing + request control' of open-source models with a cryptographic mechanism, competing with OpenAI.
Today, we analyze from the perspectives of technology, products, ecology, team, and competitors 👇
This article is the first in a deep research series on Biteye AI Blockchain, with more quality research reports to come. Friends interested in AI are encouraged to follow along!
1. Project Overview
Sentient is an open-source protocol platform dedicated to building a decentralized artificial intelligence economy. Its core goal is to establish ownership structures for AI models, provide on-chain calling mechanisms, and build a composable and profit-sharing AI Agent network. Through the 'OML' framework (Open, Monetizable, Loyal) and model fingerprint technology, Sentient addresses the fundamental issues of 'unclear model ownership, untraceable calls, and unfair value distribution' in the current centralized LLM market.
The project is driven by the Sentient Foundation, focusing on building open-source AGI and protocol incentive mechanisms. Its advocated 'Loyal AI' refers to an open-type AI model ecosystem that serves the community, ensures fair governance, and can self-evolve sustainably.
Figure 1: The architecture of the Sentient Protocol consists of two core components: blockchain system and AI pipeline.
The AI Pipeline is the foundation for developing and training 'Loyal AI' artifacts, consisting of two core processes:
Data curation: A community-driven data selection process used for model alignment.
Loyalty training: Ensuring that the model maintains a training process consistent with community intentions.
The blockchain system provides transparency and decentralized control for the protocol, ensuring the ownership and governance of AI artifacts, with main modules including:
Governance: Controlled and decided by a decentralized autonomous organization (DAO).
Ownership: Represented through tokenization of AI artifacts.
Decentralized Finance (DeFi): Providing financial tools that support open, decentralized, and fair governance and rewards.
2. Technical Architecture and Model Ownership Mechanism
1. OML Model Framework
(Sentient: Loyal AI) The white paper proposes the OML framework (Open, Monetizable, and Loyal AI), which starts with model ownership, systematically introducing the concept of 'AI-native cryptography' for the first time, aiming to provide encryption-level ownership protection mechanisms for open-source models.
Open: Models must be open-source, with transparent code and data structures, supporting community replication, auditing, and forking.
Monetizable: Each call to the model triggers a revenue stream, allocated through on-chain contracts to trainers, deployers, and validators.
Loyal: The model does not belong to a company but to the contributor community, with the direction of model upgrades and governance decided by the DAO. Model ownership is verifiable, modifications are restricted, and usage is controlled.
OML guarantees the open-source model retains economic sovereignty and governance rights while maintaining openness through on-chain mechanisms and cryptographic means. It constructs a protocol layer for AI-native usage rights and revenue rights, ensuring models are public, ownership is clear, and economic incentives and behavioral governance are established.
Core Concept: AI-native Cryptography
AI-native cryptography utilizes the continuity, low-dimensional manifold structure, and differentiable characteristics of AI models to develop a 'verifiable but non-removable' lightweight security mechanism. Its core technology includes:
Fingerprint embedding: Inserting a set of concealed query-response key-value pairs during training to form a unique signature for the model.
Ownership verification protocol: Verifying whether the fingerprint is retained through queries posed by a third-party detector (Prover);
Permission call mechanism: A 'permission credential' issued by the model owner must be obtained before calling, and the system will then authorize the model to decode the input and return an accurate answer.
This approach enables 'behavior-based authorized calls + ownership verification' without the cost of re-encryption.
What Sentient currently adopts is Melange mixed security: combining fingerprint certification, TEE execution, and on-chain contract profit sharing. The fingerprint method is the OML 1.0 implementation mainline, emphasizing the idea of 'Optimistic Security', which means compliance is the default, and violations can be detected and punished.
OML and Sentient Protocol Architecture
The last chapter of the paper proposes a complete on-chain protocol (Sentient Protocol) to support OML:
Storage layer: Stores model weights and fingerprint registration information;
Distribution layer: Authorization contracts control model calling entry.
Access layer: Verifies whether users are authorized through proof of authorization.
Incentive layer: Revenue routing contracts allocate payments for each call to trainers, deployers, and validators.
2. Fingerprint Recognition and Model Ownership Mechanism
Github: https://github.com/sentient-agi/oml-1.0-fingerprinting
This library is the first implementation of the Sentient fingerprint mechanism, providing interfaces for fingerprint injection and verification that can be embedded in training workflows. Its purpose is to ensure that model ownership is verifiable, usage behavior is traceable, and to prevent unauthorized copying and commercialization. This is a concrete engineering implementation of the OML framework.
The essence of the fingerprint mechanism is: by fine-tuning the model, embedding a set of unique 'question-answer' (key-response) pairs, the model owner can verify if the model belongs to them through specific queries, thus forming the model's 'cryptographic signature.'
3. Enclave TEE Computing Framework
Github: https://github.com/sentient-agi/Sentient-Enclaves-Framework
The Sentient Enclaves Framework is an open-source framework that utilizes trusted execution environments (TEE) such as AWS Nitro Enclaves to achieve secure deployment of model inference, fine-tuning, and proxy services. The framework emphasizes the 'loyalty' of the model, ensuring that the model only responds to authorized requests, preventing unauthorized access and use.
TEE (Sentient Enclaves Framework) has advantages in high performance and cloud integration, suitable for real-time AI and sensitive data processing, but is limited by hardware dependencies and side-channel attacks. Compared to other cryptographic technologies, FHE provides strong privacy guarantees without hardware dependencies and is resistant to quantum attacks but comes with high performance overhead, making it difficult to directly replace TEE for high-performance tasks. ZK performs excellently in verifiability and decentralized scenarios and can serve as a complement to TEE (this module plans to interface with zkML in the future).
4. Sentient Agent Framework
Github: https://github.com/sentient-agi/Sentient-Agent-Framework
The Sentient-Agent-Framework is a lightweight open-source framework focused on automating web tasks (such as searching, playing videos) through AI agents controlling the browser, offering a streamlined development experience with natural language commands (claimed to be 3 lines of code). This architecture supports building intelligent agents with a complete closed loop of 'perception–planning–execution–feedback'. Compared to traditional AI Agent Frameworks, Sentient-Agent-Framework has limited capabilities and is lightweight and simple, more suitable for off-chain web tasks.
5. Sentient Social Agent
Github: https://github.com/sentient-agi/Sentient-Social-Agent
Sentient-Social-Agent is an AI system aimed at automating interactions on social platforms (Twitter, Discord, and Telegram), capable of understanding social environments, generating content, interacting with users, and facilitating social communication through multi-agent collaboration. This system can be integrated with the Sentient Agent Framework.
6. Open Deep Search (not yet launched)
On the Sentient official website, Open Deep Search is defined as a search agent that surpasses ChatGPT and Perplexity Pro. Team member Sewoong Oh disclosed some plans at the EthDenver 2025 Open AGI Summit:
Open Deep Search consists of two main parts: Sentient's search functionality (including query rephrasing, URL and document processing, etc.) and inference agents. Inference agents utilize open-source LLMs (such as Llama 3.1 and DeepSeek) to enhance search quality through tools like search, calculators, and self-reflection. On the Frames Benchmark, Open Deep Search outperformed other open-source models and even competes with some closed-source models, but since its functionality is not yet online, we cannot currently assess its true capabilities.
3. Product Forms and Landing Plans
Currently, the products showcased on the Sentient official website mainly include the Sentient Chat dialogue platform and the open-source model Dobby LLMs:
Sentient Chat:
Sentient Chat is a decentralized AI chat platform launched by the Sentient Foundation, integrating open-source large language models (such as the Dobby series) with advanced reasoning agent frameworks. Core features include:
1. Open reasoning agents: The reasoning agent built into Sentient Chat can execute complex tasks, supporting tools for searching (ODS), calculators, and code execution.
2. Multi-agent integration: The platform supports the integration of multiple AI agents, allowing users to choose different agents for interaction based on their needs. Similar to a Web3 version of POE or an open, agent-driven alternative to Perplexity.
Sentient Chat is currently in the testing phase, accessible only through invite codes distributed via email or community events. According to official information released, over 5,000 users have successfully gained access to Sentient Chat, which has processed over 100,000 user queries. As the author has not yet become a test whitelist user, they are currently unable to assess its true capabilities.
Dobby LLM Model Series:
1. Dobby-Unhinged series
Dobby-Unhinged-Llama-3.3-70B: A fine-tuned version based on Llama 3.3-70B-Instruct, emphasizing individual freedom and the stance of cryptocurrency, featuring a straightforward, humorous, and humanized dialogue style.
Dobby-Mini-Unhinged-Llama-3.1-8B: The 8B parameter version, suitable for resource-constrained devices.
2. Dobby-Mini-Leashed-Llama-3.1-8B: A more gentle tone, suitable for applications requiring more robust outputs.
Since the Dobby LLM model is a fine-tuned version based on Llama 3.1 and 3.3, we believe its application scenarios mainly lie in building chatbots, content generation and creation, role-playing agents, etc. Its advantages are flexible style generation, enhanced reasoning, and low resource requirements, suitable for quick deployment and flexible customization in resource-constrained environments. Compared to more powerful closed-source models like GPT-4, Dobby LLM still has gaps in handling advanced logic, cross-domain knowledge reasoning, and deep reasoning tasks.
4. Ecological Cooperation and Landing Scenarios
Currently, the Sentient Builder Program offers $1 million in funding support for developers to build AI Agent artifacts operating within the Sentient Chat ecosystem, requiring developers to use Sentient's development kit and access its ecosystem through the Sentient Agent API.
At the same time, the ecological partners announced on the Sentient official website cover project teams in multiple fields of Crypto AI, as follows:
As a leading project in the Crypto AI field, Sentient's resource integration capability can cover any star startup project in the industry. However, it should be noted that the widespread existence of 'marketing-type' cooperation in the Crypto field creates an illusion of false prosperity in the industry, and the contribution and loyalty of Sentient's ecological partners to its ecosystem still require our continuous observation.
The Open AGI Summit is a global conference organized by the Sentient team, dedicated to exploring the integration of artificial intelligence (AI) and cryptocurrency (Crypto). The author had the privilege of attending its summits in 2024 and 2025 during ETH Denver and ETHcc, where the Sentient team demonstrated the capability to gather the industry's top institutional investors and project entrepreneurs, making it a highlight.
5. Team Structure and Research Background
The Sentient Foundation brings together top academic experts, crypto industry entrepreneurs, and engineers worldwide, dedicated to building a community-driven, open-source, verifiable AGI platform. According to official information released, its team members mainly include:
Core Leadership Team (Steering Committee)
Pramod Viswanath – Professor at Princeton University, long-term research in information theory and communication systems, leading the AI security and theoretical foundation construction for Sentient.
Himanshu Tyagi – Professor at the Indian Institute of Science, specializing in privacy protection and decentralized learning algorithms, providing academic support for model training and privacy collaboration.
Sandeep Nailwal – Co-founder of Polygon, responsible for blockchain strategy and global ecosystem layout, a key figure connecting the crypto community and AI architecture.
Sensys Team – A Web3 native product studio leading user experience optimization and developer infrastructure construction, promoting the implementation of Sentient products.
Core engineering and development team: Researchers from well-known tech and blockchain companies such as Meta, Coinbase, Circle, Polygon, Binance, as well as researchers from universities like Princeton, the University of Washington, and the Indian Institute of Technology.
AI Research and Model Training Team: The research team covers AI/ML, NLP, computer vision, and reinforcement learning, with members having practical experience at institutions such as Google Research, Daimon Labs, and Fetch.ai.
It is worth noting that Sentient was founded under the successful aura of Polygon co-founder Sandeep Nailwal. As an important scaling solution in the Ethereum ecosystem, Matic started with Plasma, a not-leading but sufficiently 'cheap and fast' technology, building Polygon's moat in areas like NFT and social. Meanwhile, by acquiring Mir Protocol and Hermez Network and launching Polygon zkEVM, it integrated ZK technology into its blockchain scaling solutions. As Sandeep Nailwal's second entrepreneurial venture, Sentient's experience, funds, connections, and market recognition vastly exceed those of the past, and it may also secure substantial funding in 2024 despite its imperfect project conception. However, the AI field is different from Crypto; Sentient still faces external challenges from changing market environments, intensified competition, and technological updates in its development.
6. Financing Situation and Token Model
Sentient secured $85 million in seed funding co-led by Founders Fund, Pantera, and Framework Ventures in 2024. It has not yet issued a token. The current Agent incentive points may map to tokens in the future. Tokens can be used for voting on proposals for model version management, staking to verify the authenticity of Agent outputs, and governance, among other purposes.
Sentient is a king project born with a golden key, with an investor background, financing scale, and valuation that leave most Crypto AI projects in the market in its wake. On one hand, its strong resource backing can more easily integrate various industry resources, and its large fundraising can more easily hire top talents to join its team, while its strong capital can support project development across industry cycles. On the other hand, the current Crypto industry's high valuation projects backed by VCs are generally disenchanted, and moreover, VC-backed projects' token prices are severely decoupled from fundamentals due to capital operations. If Sentient fails to deliver impactful Crypto AI products and ultimately opts for high valuation token issuance, it will similarly harm the Crypto community that urgently needs to rebuild trust. How the team responds to the current industry dilemma is worth our continuous observation.
7. Competitor Analysis and Market Position
Most Crypto AI projects in the market focus on single domains such as data, models, computation, training, or reasoning, or develop consumer-level applications like AI Agents. Projects positioned as AI Chain include old blockchain's AI transformation (Near and ICP) or decentralized resource sharing coordination and token incentive protocols like Bittensor, but Sentient's positioning does not completely match them. On the model training side, Sentient appears more like an integration platform, cooperating with the open-source AI models in the market. On the Agent side, Sentient has some overlapping competitive relationships with Talus, Olas, or Theoriq regarding multi-agent systems and reasoning capabilities, but each project has different core goals and application scenarios, maintaining complementarity.
8. Summary
As a decentralized artificial intelligence (AGI) protocol platform, Sentient aims to provide clear ownership structures for AI models and conduct calls and value distribution through on-chain mechanisms, addressing the issues of unclear ownership and unfairness in the current centralized LLM market. The core framework OML (Open, Monetizable, Loyal) ensures the ownership, transparency, and fair profit distribution of open-source models through model fingerprints and blockchain technology. With the support of top VCs and AI ecosystem partners, bolstered by Polygon co-founder Sandeep Nailwal’s resources, despite facing uncertainties, controversies, and competition in development, Sentient still hopes to become one of the standard protocols for decentralized AI ownership, promoting the decentralized development of AGI.