OpenLedger Depth Research Report: Building a Data-Driven, Model-Composable Agent Economy on the Foundation of OP Stack + EigenDA

1. Introduction | The Model Layer Leap of Crypto AI

Data, models, and computing power are the three core elements of AI infrastructure, analogous to fuel (data), engine (models), and energy (computing power), all of which are indispensable. Similar to the evolution path of traditional AI industry infrastructure, the Crypto AI field has also gone through similar stages. At the beginning of 2024, the market was once dominated by decentralized GPU projects ( and certain platforms, emphasizing a rough growth logic of "competing in computing power." However, as we enter 2025, the industry's focus gradually shifts to the model and data layer, marking the transition of Crypto AI from competition for underlying resources to a more sustainable and application-value-driven mid-level construction.

) General Large Model (LLM) vs Specialized Model (SLM)

Traditional large language models (LLMs) rely heavily on large-scale datasets and complex distributed architectures, with parameter scales ranging from 70B to 500B, and the cost of training once can often reach several million dollars. In contrast, SLM (Specialized Language Model) is a lightweight fine-tuning paradigm that allows for the reuse of foundational models, typically based on open-source models like LLaMA, Mistral, and DeepSeek, and combines a small amount of high-quality specialized data and technologies like LoRA to quickly build expert models with specific domain knowledge, significantly reducing training costs and technical barriers.

It is worth noting that SLM will not be integrated into the LLM weights, but will collaborate with LLM through methods such as Agent architecture invocation, dynamic routing via plugin systems, hot-swappable LoRA modules, and RAG (Retrieval-Augmented Generation). This architecture retains the broad coverage capabilities of LLM while enhancing specialized performance through fine-tuning modules, resulting in a highly flexible combinatorial intelligent system.

The value and boundaries of Crypto AI at the model layer

Crypto AI projects are essentially difficult to directly enhance the core capabilities of large language models (LLM), and the main reason lies in

• High technical barriers: The scale of data, computational resources, and engineering capabilities required to train Foundation Models is extremely large. Currently, only technology giants such as certain companies in the United States and China have the corresponding capabilities.
• Limitations of the open-source ecosystem: Although mainstream foundational models such as LLaMA and Mixtral have been open-sourced, the key to driving breakthroughs in models still lies primarily with research institutions and closed-source engineering systems, leaving limited space for on-chain projects to participate at the core model level.

However, on top of the open-source foundational models, Crypto AI projects can still achieve value extension by fine-tuning specialized language models (SLM) and integrating the verifiability and incentive mechanisms of Web3. As the "peripheral interface layer" of the AI industry chain, this is reflected in two core directions:

• Trustworthy Verification Layer: Enhances the traceability and tamper-resistance of AI outputs by recording model generation paths, data contributions, and usage on-chain.
• Incentive Mechanism: Utilizing the native Token to incentivize actions such as data uploads, model calls, and agent executions, creating a positive cycle of model training and services.

AI Model Type Classification and Blockchain Applicability Analysis

It can be seen that the feasible landing points of model-type Crypto AI projects mainly focus on the lightweight fine-tuning of small SLMs, on-chain data access and verification of RAG architecture, as well as local deployment and incentives of Edge models. Combined with the verifiability of blockchain and token mechanisms, Crypto can provide unique value for these medium to low resource model scenarios, forming differentiated value for the AI "interface layer".

The blockchain AI chain based on data and models can provide clear and immutable on-chain records of the contribution sources of each piece of data and model, significantly enhancing the credibility of the data and the traceability of model training. At the same time, through the smart contract mechanism, rewards are automatically distributed when data or models are invoked, transforming AI behavior into measurable and tradable tokenized value, thus building a sustainable incentive system. In addition, community users can also evaluate model performance through token voting, participate in rule-making and iteration, and improve the decentralized governance structure.

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2. Project Overview | The AI Chain Vision of OpenLedger

OpenLedger is one of the few blockchain AI projects on the market that focuses on data and model incentive mechanisms. It pioneers the concept of "Payable AI" with the aim of building a fair, transparent, and composable AI operating environment that incentivizes data contributors, model developers, and AI application builders to collaborate on the same platform and earn on-chain rewards based on their actual contributions.

OpenLedger provides a complete closed-loop chain from "data provision" to "model deployment" and then to "profit sharing call", with its core modules including:

• Model Factory: No programming required, you can use LoRA for fine-tuning training and deploying custom models based on open-source LLM;
• OpenLoRA: Supports coexistence of thousands of models, dynamically loads as needed, significantly reducing deployment costs;
• PoA (Proof of Attribution): Achieving contribution measurement and reward distribution through on-chain call records;
• Datanets: A structured data network aimed at vertical scenarios, built and validated through community collaboration;
• Model Proposal Platform: A composable, callable, and payable on-chain model marketplace.

Through the above modules, OpenLedger has built a data-driven, model-combinable "agent economy infrastructure" to promote the on-chainization of the AI value chain.

In the adoption of blockchain technology, OpenLedger uses OP Stack + EigenDA as the foundation to build a high-performance, low-cost, and verifiable data and contract execution environment for AI models.

• Built on the OP Stack: Based on the Optimism technology stack, supporting high throughput and low-cost execution;
• Settlement on the Ethereum mainnet: Ensure transaction security and asset integrity;
• EVM Compatible: Enables developers to quickly deploy and scale based on Solidity;
• EigenDA provides data availability support: significantly reduces storage costs and ensures data verifiability.

Compared to NEAR, which is more focused on being a lower-level, general-purpose AI chain that emphasizes data sovereignty and the "AI Agents on BOS" architecture, OpenLedger is more focused on building an AI-specific chain aimed at data and model incentives. It aims to make the development and invocation of models on-chain achieve a traceable, composable, and sustainable value loop. It serves as the model incentive infrastructure in the Web3 world, combining certain platform-based model hosting, certain platform-based usage billing, and certain platform-based on-chain composable interfaces to promote the realization path of "models as assets."

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3. Core Components and Technical Architecture of OpenLedger

) 3.1 Model Factory, no-code model factory

ModelFactory is a large language model (LLM) fine-tuning platform under the OpenLedger ecosystem. Unlike traditional fine-tuning frameworks, ModelFactory provides a purely graphical interface operation, eliminating the need for command line tools or API integration. Users can fine-tune models based on datasets that have been authorized and reviewed on OpenLedger. It achieves an integrated workflow for data authorization, model training, and deployment, with core processes including:

• Data Access Control: Users submit data requests, providers review and approve, and data automatically connects to the model training interface.
• Model selection and configuration: Supports mainstream LLMs (such as LLaMA, Mistral), with hyperparameter configuration via GUI.
• Lightweight Fine-tuning: Built-in LoRA / QLoRA engine, real-time display of training progress.
• Model Evaluation and Deployment: Built-in evaluation tools, supporting export deployment or ecological sharing calls.
• Interactive Verification Interface: Provides a chat-style interface for directly testing the model's question-and-answer capabilities.
• RAG Generation Traceability: Answers with source citations, enhancing trust and auditability.

The Model Factory system architecture consists of six major modules, encompassing identity authentication, data permissions, model fine-tuning, evaluation deployment, and RAG traceability, creating a secure, controllable, real-time interactive, and sustainable monetization integrated model service platform.

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The following is a brief overview of the capabilities of large language models currently supported by ModelFactory:

• LLaMA Series: With the widest ecosystem, an active community, and strong general performance, it is one of the most mainstream open-source foundational models today.
• Mistral: Architecturally efficient with excellent inference performance, suitable for flexible deployment in resource-limited scenarios.
• Qwen: Produced by a certain company, it performs excellently on Chinese tasks and has strong overall capabilities, making it the top choice for domestic developers.
• ChatGLM: The Chinese dialogue effect is outstanding, suitable for vertical customer service and localization scenarios.
• Deepseek: Excels in code generation and mathematical reasoning, suitable for intelligent development assistive tools.
• Gemma: A lightweight model launched by a certain company, with a clear structure, easy to quickly get started and experiment.
• Falcon: Once a performance benchmark, suitable for basic research or comparative testing, but community activity has decreased.
• BLOOM: Strong support for multiple languages, but weaker inference performance, suitable for language coverage research.
• GPT-2: A classic early model, suitable only for teaching and validation purposes, not recommended for actual deployment.

Although OpenLedger's model combination does not include the latest high-performance MoE models or multimodal models, its strategy is not outdated; instead, it is a "practical-first" configuration based on the real constraints of on-chain deployment (inference costs, RAG adaptation, LoRA compatibility, EVM environment).

Model Factory, as a no-code toolchain, has built-in proof-of-contribution mechanisms for all models, ensuring the rights of data contributors and model developers. It has the advantages of low barriers to entry, monetization, and composability, compared to traditional model development tools:

• For developers: provide a complete path for model incubation, distribution, and revenue;
• For the platform: to form a model asset circulation and combination ecology;
• For users: models or Agents can be used in combination just like calling an API.

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) 3.2 OpenLoRA, on-chain assetization of fine-tuned models

LoRA (Low-Rank Adaptation) is an efficient parameter tuning method that learns new tasks by inserting "low-rank matrices" into pre-trained large models without modifying the original model parameters, significantly reducing training costs and storage requirements. Traditional large language models (such as LLaMA, GPT-3) typically have billions or even hundreds of billions of parameters. To use them for specific tasks (such as legal Q&A, medical consultations), fine-tuning is required. The core strategy of LoRA is: "freeze the parameters of the original large model and only train the newly inserted parameter matrices." Its parameter efficiency, fast training, and flexible deployment make it the mainstream fine-tuning method most suitable for Web3 model deployment and compositional calls.

OpenLoRA is a lightweight inference framework built by OpenLedger, specifically designed for multi-model deployment and resource sharing. Its core objective is to address common issues in current AI model deployment, such as high costs, low reuse, and GPU resource wastage, promoting the implementation of "Payable AI."

OpenLoRA system architecture core components, based on modular design, covering key links such as model storage, inference execution, and request routing, achieving efficient and low-cost multi-model deployment and invocation capabilities:

• LoRA Adapter Storage Module ###LoRA Adapters Storage(: The fine-tuned LoRA adapter is hosted on OpenLedger, enabling on-demand loading and avoiding preloading all models.