Interpreting Eliza Technical White Paper: A Web3-friendly AI Agent Operating System

Originalautor: TechFlow

After much anticipation, Eliza finally released their technical white paper today.

Although we often hear that many AI agents are made based on the Eliza open source framework, there has always been a lack of a detailed and serious technical explanation of how Eliza defines itself.

This white paper is a good answer, describing how Eliza deeply integrates AI and Web3, its modular system architecture design, and its technical implementation details as an open source framework.

The white paper was written by Shaw, several Eliza Labs members and technical personnel from other related organizations, but because the white paper involves a lot of technical details and professional concepts, it may not be very friendly to ordinary readers.

TechFlow has simplified and refined it in order to help everyone quickly understand the contents of this white paper in plain language.

1. Why do you want to be Eliza?

Note that the editor believes that the premise of thinking is to define the scope – that is, in the field of encryption or Web3, why should we do Eliza, rather than comparing the framework with a larger range of similar AI frameworks.

Following this line of thought, the introduction and background section of the technical white paper actually gave a good answer to this question:

At the intersection of AI and Web3, there has always been a clear gap: the lack of an agent framework that can perfectly integrate Web3 applications.

Specifically, the white paper believes that the Web3 field faces three major challenges:

• The complexity of decentralized transactions With the booming development of public chains such as Ethereum, Solana, and BASE, it has become increasingly challenging to manage assets and execute transactions on different chains. Although there are some trading platforms on the market, the basic functions of these platforms are often not enough for intermediate and advanced users with customized needs.

• The value of on-chain data mining The blockchain contains a huge amount of valuable information, from basic indicators such as changes in currency holding addresses, token prices, market capitalization, to more advanced indicators such as the proportion of whale accounts and market maker styles. How to effectively transform these complex data into valuable insights is an urgent problem to be solved.

• Fragmentation of social media information For the Web3 industry, social platforms such as Twitter, Discord, and Farcaster are important channels for obtaining information. However, with the increase in the number of opinion leaders (KOLs), information has become more fragmented. How to obtain valuable insights in the flood of information has become a common challenge for every trader.

Eliza was born based on these practical needs. As the first open source Web3-friendly AI agent operating system, Eliza adopts a modular design, allowing developers and users to customize solutions according to their needs.

Eliza attempts to lower the threshold for ordinary users to use advanced AI functions, allowing them to build their own AI agents without deep programming experience.

At the same time, the white paper also compares itself with several other common AI frameworks. The table below shows intuitively that in terms of Web3 support, Eliza claims that it is the most adaptable, and this is also the key point that the entire white paper wants to convey.

2. Eliza’s design concept and technological innovation

Three design principles: simple but not simple

Eliza’s success is not accidental. At the beginning of the design, the team established three core principles:

• Web3 developers prioritize JavaScript/TypeScript development. Eliza chose TypeScript as the development language. This not only allows developers to use familiar tools, but also allows them to easily integrate blockchain functions into existing web applications. In short, it allows Web3 developers to use it right out of the box.

• Modular plug-in design Eliza decomposes the system into the core runtime and four key components:

• Adapter

• Character

• Client (message interaction)

• Plugin (common functionality)

This design allows developers to freely add their own plugins, clients, roles, and adapters without having to worry about the details of the core runtime. This also enables Eliza to support the widest range of model providers (such as OpenAI, Llama, Qwen, etc.), platform integration (Twitter, Discord, Telegram, etc.) and chain compatibility (Solana, Ethereum, Ton, etc.).

• Better to keep it simple than to complicate it:

With limited engineering resources, keeping the internal implementation simple can save time for developing new features, adapting to new scenarios, and keeping up with the rapid pace of development in the AI and Web3 fields.

Technological innovation: both internal and external

In terms of specific implementation, Elizas innovation is divided into two dimensions: internal enhancement and external expansion.

1. Internal Enhancement In order to improve the thinking ability of AI models, Eliza integrates a number of cutting-edge technologies:

• Chain-of-Thoughts:

• Technical definition: introducing a step-by-step explanation

• Popular understanding: Just like writing down the process of solving a math problem, AI will also write down the thinking process step by step instead of giving the answer directly. This will not only make the result more accurate, but also allow humans to understand how AI came to the conclusion.

• Tree-of-Thoughts:

• Technical definition: Allow branching to explore multiple solutions

• Popular understanding: Just like considering multiple possible moves when playing chess, AI will explore multiple solutions at the same time and then choose the best one. This is like choosing the best branch on the tree of thinking.

• Graph-of-Thoughts

• Technical Definition: Connecting Reasoning Paths

• Popular understanding: Think of the problem as a network, with each idea connected to each other. Just like when we solve complex problems, we connect various related ideas to form a mind map.

• Layer-of-Thoughts

• Technical definition: Hierarchical reasoning AI

• Popular understanding: Like a filter, it divides the thinking process into different levels. Just like when we solve a problem, we first consider the general direction, then refine it to the specific details, layer by layer.

2. External expansion In order to enhance the ability to solve practical problems, Eliza integrates a variety of external capabilities:

• RAG (Retrieval Enhanced Generation):

• Technical definition: Enhancing generation through retrieval

• Popular understanding: Just like students can consult textbooks when doing homework, AI can also consult its database when answering questions to ensure more accurate answers

• Vector database:

• Technical definition: Storing and retrieving structured data

• Popular understanding: It is equivalent to the library of AI, which can quickly find similar content. For example, if you say I want to find a poem about the moon, it can quickly find all related poems.

• Web Search:

• Technical definition: Real-time access to Internet information

• Popular understanding: Let AI search for the latest information online like humans, without being limited to a fixed range of knowledge

• Text to image/video/3D model:

• Technical definition: Convert text descriptions into multimedia content

• Popular understanding: Just like a painter can draw a painting based on a text description, AI can generate pictures, videos, and even 3D models based on your description.

Comparison with other Web3 frameworks

Eliza has shown clear advantages among current Web3 AI agent frameworks. According to feedback from more than 50 AI researchers and senior blockchain developers, Eliza outperforms other frameworks in the following key indicators:

• Model provider support

• Chain compatibility

• Functional completeness

• Social Media Integration

3. Eliza OS: A carefully crafted Web3 AI ecosystem

Now that we understand Elizas design concept, lets take a look at how this framework actually works. You can think of Eliza as a carefully designed Lego building system where each part fits perfectly while remaining extremely flexible.

Core components: five key roles

In Elizas world, five core components work together to form a complete intelligent system.

• Agents: The protagonists of the system

They are like independent digital assistants that handle various autonomous interactions. Each agent has its own memory and personality and can have coherent conversations and interactions with users through different channels such as Discord and Twitter.

• Character Files: The agents personality

To make these agents more personalized, Character Files are needed. This is equivalent to the agents resume, which not only defines its identity and personality traits, but also specifies which models it can use (such as OpenAI, Anthropic), and which operations it can perform (such as blockchain transactions, NFT casting). Through carefully designed character configurations, each agent can show unique professional expertise and behavior.

• Providers: The perception system of the agent

When interacting with the outside world, agents need Providers as their perception system. Just like humans need senses to perceive the world, providers provide agents with real-time information such as market data, wallet details, sentiment analysis, etc., to help them better understand the current environment and context.

• Actions: The Agents Skills Library

When specific actions are needed, Actions become the agents skill library. From simple buy and sell orders to complex NFT generation, each operation undergoes strict security verification to ensure that there is no room for error when handling financial-related tasks. These skills allow agents to truly play a role in the Web3 world.

• Evaluators: The decision-making system of an agent

Finally, Evaluators act as the decision-making system of the agent, responsible for evaluating the conversation content, extracting important information, and helping the agent build long-term memory. It not only tracks the progress of goal completion, but also ensures the coherence of the entire conversation process.

Intelligent interaction: more than just a simple conversation

In terms of interaction, Eliza adopts a multi-level understanding system, just like an experienced translator, who not only understands the literal meaning, but also the context and intention of the speech. This system can accurately understand the real needs of users, maintain a consistent experience on different communication platforms, and flexibly adjust the response method according to the context.

Plugin system: unlimited expansion possibilities

Elizas plugin system is essentially a toolbox that brings powerful extensibility to the entire framework. This extensibility is reflected in three areas: multimedia generation, Web3 integration, and infrastructure:

• In terms of multimedia generation, it can generate pictures, videos, 3D models, support automatic generation of NFT series, and also provide picture description and analysis capabilities.

• In terms of Web3 integration, it supports multi-chain operations such as Ethereum and Solana, provides a complete suite of transaction functions, and integrates various DeFi operations.

• In terms of infrastructure, it provides basic capabilities such as browser services, document processing, and speech-to-text.

Through this modular design, Eliza not only maintains the stability of the system, but also provides developers with nearly unlimited expansion possibilities. This also enables Eliza to adapt to the new demands and new scenarios that continue to emerge in the Web3 world.

4. How strong is Eliza? See the truth from the data

When a new technical framework emerges, people are most concerned about its actual performance. Eliza gave a candid answer in this regard.

Eliza demonstrated impressive performance in the GAIA benchmark test, a test platform that evaluates the ability of AI agents to solve real-world problems. This test does not examine simple question-answering skills, but requires AI agents to have multiple skills such as logical reasoning, multimodal processing, web browsing, and tool use.

Although Elizas score (19.42%) is still a little behind the current top solutions, considering that it is a framework focused on the Web3 field, this result is already quite impressive. Especially in the processing of basic tasks (Level 1), Eliza achieved a completion rate of 32.21%, showing its solid basic capabilities.

Web3: A pioneering standard setter

What is more noteworthy is that Eliza actually plays the role of standard setter in the Web3 field. Since Web3-oriented AI systems are still in their early stages, Eliza took the lead in proposing a complete evaluation standard system, pointing out the development direction for the entire industry.

This evaluation system is divided into three levels, and the white paper calls it the Web3 AI version of the “Turing Test”:

• Basic capabilities: including wallet creation, token trading, smart contract interaction and other basic operations

• Advanced features: Integrate the latest AI technologies, such as text-to-video/3D, RAG support, etc.

• Advanced features: Ability to autonomously plan and reason based on user instructions to achieve truly intelligent decision-making

Currently, Eliza has successfully realized all the functions of the basic level and is moving towards the advanced level. The team said that they firmly believe that in the next few years, they will be able to realize a fully autonomous AI agent system.

5. Practical application: The market votes with real money

The original white paper also has a section about code presentation, which is used to illustrate the actual applications that can be made with this framework. Considering the difficulty of understanding and technical details, it is omitted here and only the more macro-level actual application situation is presented.

According to the white paper, as of January 2025, several important Web3 projects have built their AI agent systems based on Eliza, and the total market value of these partners exceeds US$20 billion.

This number itself may be the best endorsement of Elizas technical strength by the market.

More importantly, the Eliza team is confident about the future. They believe that as these intelligent agents continue to evolve, we will see a new era of multiple AI units working together. As Anthropic CEO Dario Amodei calls it, the genius data center vision, Eliza is paving the way for this future.

6. Existing limitations and future prospects: honest self-analysis

No technical framework can be perfect, and the Eliza team also frankly pointed out the limitations of the current framework in the white paper.

Three major challenges to be solved

• Lack of workflow system: Just like a skilled assistant needs a standardized workflow, when developers want to implement some routine tasks (such as regularly aggregating data from multiple sources), the existing Eliza framework cannot provide a ready-made solution. For such needs, it may still be necessary to use a workflow system with a graphical interface such as Dify or Coze.

• Performance issues in multi-agent systems. As the number of agents increases, the context and memory content that the system needs to process grows exponentially. Especially when dealing with a large number of input and output tasks, how to balance computational overhead and operational efficiency remains a technical challenge to be solved.

• Expansion requirements for multi-language support. Currently, Eliza is mainly based on TypeScript, but to attract developers from more fields, it is also necessary to expand support for other programming languages such as Python and Rust.

Outlook: Ushering in a new era of decentralized AI

Despite these limitations, Elizas significance goes far beyond a technical framework. It represents a groundbreaking attempt to deeply integrate AI technology and Web3 applications.

By designing each functional module as a standard TypeScript program, Eliza ensures that users have full control over the system. At the same time, it also provides seamless integration with blockchain data and smart contracts. This design ensures security while maintaining strong scalability.

As stated at the end of the white paper, Eliza’s possibilities are limited only by the imagination of its users. As AI and Web3 technologies continue to evolve, Eliza will continue to develop and continue to lead the development of decentralized AI.

This article is sourced from the internet: Interpreting Eliza Technical White Paper: A Web3-friendly AI Agent Operating System

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