OpenLedger Depth Research Report: Building a data-driven, model-composable agent economy based on 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 (model), 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. In early 2024, the market was once dominated by decentralized GPU projects, which generally emphasized the extensive growth logic of "competing in computing power." However, entering 2025, the industry's focus gradually shifted to the model and data layers, marking the transition of Crypto AI from competition for underlying resources to more sustainable and application-valued mid-layer construction.

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

Traditional large language model (LLM) training heavily relies on large-scale datasets and complex distributed architectures, with parameter sizes ranging from 70B to 500B, and the cost of training can often reach millions of dollars. SLM (Specialized Language Model), as a lightweight fine-tuning paradigm for reusable foundational models, is usually based on open-source models such as LLaMA, Mistral, and DeepSeek, combined with a small amount of high-quality specialized data and technologies like LoRA, to quickly build expert models with domain-specific knowledge, significantly reducing training costs and technical barriers.

It is worth noting that SLM will not be integrated into the LLM weights, but will cooperate with LLM through the Agent architecture calls, plugin system dynamic routing, LoRA module hot swapping, and RAG (Retrieval-Augmented Generation) methods. This architecture retains the wide coverage capability of LLM while enhancing specialized performance through fine-tuning modules, forming 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), the main reason being

• High technical barrier: The scale of data, computational resources, and engineering capabilities required to train a Foundation Model is extremely large, and currently only technology giants like the United States and China possess the corresponding capabilities.
• Limitations of Open Source Ecology: Although mainstream foundational models like 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 participation space for on-chain projects at the core model level.

However, on top of open-source foundational models, Crypto AI projects can still achieve value extension by fine-tuning specialized language models (SLM) and combining 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 the model generation path, data contributions, and usage on-chain.
• Incentive Mechanism: Utilizing native Token to incentivize behaviors such as data uploading, model invocation, and agent execution, 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, and local deployment and incentives of Edge models. Combining the verifiability of blockchain with token mechanisms, Crypto can provide unique value for these low to medium resource model scenarios, forming differentiated value for the AI "interface layer".

The blockchain AI chain based on data and models can provide clear and tamper-proof on-chain records of the contribution sources of each data point and model, significantly enhancing the credibility of data and the traceability of model training. At the same time, through the smart contract mechanism, rewards distribution is automatically triggered when data or models are called, 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.

2. Project Overview | OpenLedger's AI Chain Vision

OpenLedger is one of the few blockchain AI projects on the current market that focuses on data and model incentive mechanisms. It was the first to propose the concept of 'Payable AI', aiming to build 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 actual contributions.

OpenLedger provides a complete closed loop from "data provision" to "model deployment" to "profit sharing", with core modules including:

• Model Factory: No programming required, you can use LoRA for fine-tuning training and deploying customized models based on open-source LLM;
• OpenLoRA: Supports coexistence of thousands of models, dynamically loads as needed, significantly reduces 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 verified 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-composable "agent economy infrastructure," promoting the on-chainization of the AI value chain.

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

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

Compared to general-purpose AI chains like NEAR, which are more bottom-layer oriented and focus on 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, committed to making 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 HuggingFace-style model hosting, Stripe-style usage billing, and Infura-style composable interfaces on-chain to promote the realization path of "models as assets."

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 offers a purely graphical interface for operation, eliminating the need for command line tools or API integration. Users can fine-tune models based on datasets that have completed authorization and review on OpenLedger. It realizes an integrated workflow for data authorization, model training, and deployment, with the core processes including:

• Data Access Control: Users submit data requests, providers review and approve, and data is automatically integrated into the model training interface.
• Model Selection and Configuration: Supports mainstream LLMs (such as LLaMA, Mistral), with hyperparameter configuration through GUI.
• Lightweight Fine-tuning: Built-in LoRA / QLoRA engine, real-time display of training progress.
• Model Evaluation and Deployment: Built-in evaluation tools that support exporting for deployment or ecological shared calls.
• Interactive Verification Interface: Provides a chat-style interface for easy testing of the model's question and answer capabilities.
• RAG Generation Traceability: Answers with source citations enhance trust and auditability.

The Model Factory system architecture includes six major modules, encompassing identity authentication, data permissions, model fine-tuning, evaluation deployment, and RAG traceability, creating an integrated model service platform that is secure, controllable, enables real-time interaction, and sustainable monetization.

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

• LLaMA Series: The most extensive ecosystem, active community, and strong general performance, it is one of the most mainstream open-source foundational models currently.
• Mistral: Efficient architecture with excellent inference performance, suitable for flexible deployment in resource-limited scenarios.
• Qwen: Produced by Alibaba, performs excellently in Chinese tasks, has strong overall capabilities, making it the first 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 assistant tools.
• Gemma: A lightweight model launched by Google, with a clear structure that is easy to quickly get started with and experiment.
• Falcon: Once a benchmark for performance, suitable for fundamental 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 the model combination of OpenLedger does not include the latest high-performance MoE models or multimodal models, its strategy is not outdated; rather, it is a "practical-first" configuration based on the real-world constraints of on-chain deployment (inference costs, RAG adaptation, LoRA compatibility, EVM environment).

Model Factory, as a no-code toolchain, has all models built-in with a contribution proof mechanism, ensuring the rights of data contributors and model developers. It features low barriers to entry, monetization potential, 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 combined like calling an API.

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 trillions of parameters. Fine-tuning is necessary to apply them to specific tasks (such as legal Q&A, medical consultations). The core strategy of LoRA is: "freeze the parameters of the original large model and only train the new parameter matrices that are inserted." It is parameter-efficient, trains quickly, and deploys flexibly, making it the mainstream fine-tuning method most suitable for Web3 model deployment and compositional invocation.

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

OpenLoRA core components of system architecture, based on modular design, covering key aspects 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, allowing for on-demand loading, avoiding the need to preload all models into GPU memory, thus saving resources.
• Model Hosting and Dynamic Financing