The possibilities of Eliza are only limited by the user's imagination.
Author: Deep Tide TechFlow
After much anticipation, Eliza has finally released its technical white paper today.
While we often hear about many AI agents built on the open-source framework of Eliza, there has been a lack of a detailed and serious explanation of how Eliza defines itself technically.
This white paper provides a great answer, describing how Eliza enables the **deep integration of AI with Web3, its modular system architecture design, and the technical implementation details of its *open-source framework*.
The white paper was co-authored by Shaw, several members of Eliza Labs, and technical personnel from other related organizations. However, due to the extensive technical details and specialized concepts involved, it may not be very friendly to the average reader.
Deep Tide TechFlow has simplified and distilled it to help everyone quickly understand the content of this white paper in plain language.
1. Why Eliza?
Note that the editor believes the premise of thinking is to define the scope—specifically, in the field of cryptocurrency or Web3, why create Eliza instead of comparing this framework with a broader range of similar AI frameworks.
Following this line of thought, the introduction and background section of the technical white paper actually provides a good answer to this question:
In the intersection of AI and Web3, there has been a clear gap: a lack of a proxy framework that can perfectly integrate Web3 applications.
Specifically, the white paper identifies three main challenges facing the Web3 space:
Complexity of Decentralized Trading With the booming development of public chains like Ethereum, Solana, and BASE, managing assets and executing trades across different chains has become increasingly challenging. Although there are some trading platforms available, their basic functionalities often fall short for mid to advanced users with customization needs.
Value Mining of On-Chain Data The blockchain contains a vast amount of valuable information, from basic indicators like changes in wallet addresses, token prices, and market capitalization, to more advanced metrics like whale account proportions and market maker styles. Effectively transforming this complex data into valuable insights is a pressing issue that needs to be addressed.
Fragmentation of Social Media Information For the Web3 industry, social platforms like Twitter, Discord, and Farcaster are important channels for obtaining information. However, with the increasing number of opinion leaders (KOLs), information has become increasingly fragmented, making it a common challenge for every trader to extract valuable insights from the information flood.
It is precisely based on these real needs that Eliza was born. As the first open-source Web3-friendly AI agent operating system, Eliza adopts a modular design that allows developers and users to customize solutions according to their needs.
Eliza attempts to lower the barrier for ordinary users to utilize advanced AI features, enabling them to build their own AI agents without requiring extensive programming experience.
At the same time, the white paper compares itself with several other common AI frameworks, and the table below clearly shows that Eliza claims to be the most compatible in terms of Web3 support, which is also the key point conveyed throughout the white paper.
2. Eliza's Design Philosophy and Technological Innovations
Three Design Principles: Simple but Not Simplistic
Eliza's success is not accidental. From the outset of its design, the team established three core principles:
Web3 Developer First Considering that Web3 primarily uses JavaScript/TypeScript for development, Eliza chose TypeScript as its development language. This not only allows developers to use familiar tools but also enables them to easily integrate blockchain functionalities into existing web applications. In simple terms, it allows Web3 developers to "plug and play."
Modular Plugin Design Eliza breaks the system down into a core runtime and four key components:
Adapter (Data Adapter)
Character (Agent Personality)
Client (Message Interaction)
Plugin (General Functionality)
This design allows developers to freely add their own plugins, clients, characters, and adapters without worrying about the details of the core runtime. It also enables Eliza to support the widest range of model providers (such as OpenAI, Llama, Qwen, etc.), platform integrations (Twitter, Discord, Telegram, etc.), and chain compatibility (Solana, Ethereum, Ton, etc.).
- Prefer Simplicity Over Complexity:
With limited engineering resources, maintaining a simple internal implementation can save time for developing new features, adapting to new scenarios, and keeping pace with the rapid developments in the AI and Web3 fields.
Technological Innovations: Internal and External Enhancements
In terms of specific implementation, Eliza's innovations can be divided into internal enhancements and external extensions.
- Internal Enhancements To improve the cognitive abilities of AI models, Eliza integrates several cutting-edge technologies:
Chain-of-Thoughts:
Technical Definition: Introduces step-by-step explanations.
Layman's Understanding: Just like writing out the steps to solve a math problem, AI will also write out its thought process step by step instead of giving a direct answer. This not only makes the results more accurate but also allows humans to understand how AI reached its conclusions.
Tree-of-Thoughts:
Technical Definition: Allows branching exploration of multiple solutions.
Layman's Understanding: Just like considering multiple possible moves in chess, AI will explore multiple solutions simultaneously and then choose the optimal one. It's like selecting the best branch on a tree of thoughts.
Graph-of-Thoughts:
Technical Definition: Connects reasoning paths.
Layman's Understanding: Viewing problems as a web where various ideas are interconnected. Just like when we solve complex problems, we relate various relevant ideas to form a mind map.
Layer-of-Thoughts:
Technical Definition: Hierarchical reasoning AI.
Layman's Understanding: Like a filter, breaking the thought process into different layers. Just as we consider the big picture first and then refine it to specific details, progressing layer by layer.
- External Extensions To enhance the ability to solve real-world problems, Eliza integrates various external capabilities:
RAG (Retrieval-Augmented Generation):
Technical Definition: Enhances generative capabilities through retrieval.
Layman's Understanding: Just like a student can refer to textbooks while doing homework, AI can also refer to its "database" when answering questions to ensure more accurate answers.
Vector Database:
Technical Definition: Stores and retrieves structured data.
Layman's Understanding: Equivalent to AI's "library," allowing it to quickly find similar content. For example, if you say, "I want to find a poem about the moon," it can quickly locate all related poems.
Web Search:
Technical Definition: Real-time access to internet information.
Layman's Understanding: Allows AI to search the internet for the latest information, not limited to a fixed knowledge base.
Text-to-Image/Video/3D Model:
Technical Definition: Converts textual descriptions into multimedia content.
Layman's Understanding: Just like a painter can create artwork based on a textual description, AI can generate images, videos, and even 3D models based on your descriptions.
Comparison with Other Frameworks in the Web3 Space
In the current Web3 AI agent frameworks, Eliza demonstrates clear advantages. Based on feedback from over 50 AI researchers and senior blockchain developers, Eliza outperforms other frameworks in the following key metrics:
Support for model providers
Chain compatibility
Completeness of functionalities
Integration with social media
3. Eliza OS: A Carefully Crafted Web3 AI Ecosystem
Having understood Eliza's design philosophy, let's take a look at how this framework actually operates. You can think of Eliza as a meticulously designed Lego block system, where each part fits perfectly while maintaining a high degree of flexibility.
Core Components: Five Key Roles
In the world of Eliza, five core components work together to form a complete intelligent system.
- Agents: The Protagonists of the System
They act like independent "digital assistants," responsible for handling various autonomous interactions. Each agent has its own "memory" and "personality," enabling coherent conversations and interactions with users through different channels such as Discord and Twitter.
- Character Files: The "Persona" of the Agent
To give these agents personality, support from Character Files is necessary. This is akin to the agent's "resume," defining not only its identity and personality traits but also specifying which models it can use (such as OpenAI, Anthropic) and what operations it can perform (like blockchain transactions, NFT minting). With carefully designed character configurations, each agent can showcase unique expertise and behavioral styles.
- Providers: The Agent's "Perception System"
When interacting with the outside world, agents require Providers as their "perception system." Just as humans need senses to perceive the world, providers supply agents with real-time information such as market data, wallet details, and sentiment analysis, helping them better understand the current environment and context.
- Actions: The Agent's "Skill Set"
When specific actions need to be taken, Actions become the agent's "skill set." From simple buy and sell orders to complex NFT generation, each operation undergoes strict security verification to ensure flawless handling of financial-related tasks. These skills enable agents to truly function in the Web3 world.
- Evaluators: The Agent's "Decision-Making System"
Finally, Evaluators serve as the agent's "decision-making system," responsible for assessing dialogue content, extracting important information, and helping the agent establish long-term memory. It not only tracks the progress of goal completion but also ensures the coherence of the entire dialogue process.
Intelligent Interaction: More Than Just Simple Conversations
In terms of interaction, Eliza employs a multi-layered understanding system, much like an experienced translator who must grasp not only the literal meaning but also the background and intent of the speaker. This system can accurately understand the user's true needs, maintain a consistent experience across different communication platforms, and flexibly adjust responses based on context.
Plugin System: Infinite Expansion Possibilities
Eliza's plugin system is essentially a toolbox that brings powerful extensibility to the entire framework, reflected in three directions: multimedia generation, Web3 integration, and infrastructure:
In multimedia generation, it can create images, videos, and 3D models, support automatic generation of NFT series, and provide image description and analysis capabilities.
In Web3 integration, it supports multi-chain operations such as Ethereum and Solana, offers a complete suite of trading functionalities, and integrates various DeFi operations.
In infrastructure, it provides basic capabilities such as browser services, document processing, and speech-to-text.
Through this modular design, Eliza not only maintains system stability but also offers developers nearly limitless expansion possibilities. This also allows Eliza to adapt to the continuously emerging new demands and scenarios in the Web3 world.
4. How Powerful is Eliza? The Truth from the Data
When a new technological framework emerges, what people often care about most is its actual performance. Eliza provides an honest answer in this regard.
In the GAIA benchmark test (a platform specifically designed to evaluate the ability of AI agents to solve real-world problems), Eliza demonstrated impressive strength. This test does not assess simple Q&A capabilities but requires AI agents to possess skills such as logical reasoning, multimodal processing, web browsing, and tool usage.
Although Eliza's score (19.42%) in the test still lags behind the current top solutions, considering it is a framework focused on the Web3 domain, this result is already quite remarkable. Particularly in handling basic tasks (Level 1), Eliza achieved a completion rate of 32.21%, showcasing its solid foundational capabilities.
Web3 Domain: A Pioneering Standard Setter
More importantly, Eliza actually plays the role of a "standard setter" in the Web3 domain. As AI systems oriented towards Web3 are still in their early stages, Eliza has taken the lead in proposing a complete evaluation standard system, guiding the development direction for the entire industry.
This evaluation system is divided into three levels, which the white paper refers to as the Web3 AI version of the "Turing Test":
Basic Capabilities: Including basic operations such as wallet creation, token trading, and smart contract interaction.
Advanced Functions: Integrating the latest AI technologies, such as text-to-video/3D, RAG support, etc.
High-Level Features: Capable of autonomously planning and reasoning based on user instructions, achieving true intelligent decision-making.
Currently, Eliza has successfully implemented all functionalities at the basic level and is progressing towards the advanced level. The team expresses confidence that within the next few years, they will be able to achieve a fully autonomous AI agent system.
5. Practical Applications: The Market Votes with Real Money
The original white paper also included a section on code demonstrations to illustrate the actual applications that can be built using this framework; considering the difficulty of understanding and technical details, this will be omitted, focusing instead on a more macro view of practical applications.
According to the white paper, as of January 2025, several significant Web3 projects have built their AI agent systems based on Eliza, with a total market capitalization of over $20 billion.
This figure may itself be the best endorsement of the market for Eliza's technological strength.
More importantly, the Eliza team is confident about the future. They believe that as these "intelligent agents" continue to evolve, we will witness a new era where multiple AI units work collaboratively. As Dario Amodei, CEO of Anthropic, stated regarding the vision of a "genius data center," Eliza is paving the way for this future.
6. Existing Limitations and Future Prospects: A Sincere Self-Analysis
No technological framework can be perfect, and the Eliza team candidly points out the current limitations of the framework in the white paper.
Three Major Challenges to Address
Lack of Workflow Systems: Just as a skilled assistant needs a standardized workflow, when developers want to accomplish routine tasks (such as regularly aggregating data from multiple sources), the existing Eliza framework cannot yet provide ready-made solutions. For such needs, it may still be necessary to rely on workflow systems with graphical interfaces like 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 handle grow exponentially. Particularly when processing a large number of input-output tasks, balancing computational overhead and operational efficiency remains a technical challenge to be solved.
Expansion Needs for Multi-Language Support: Currently, Eliza is primarily based on TypeScript, but to attract more developers from various fields, it needs to expand support for other programming languages such as Python and Rust.
Outlook: Pioneering a New Era of Decentralized AI
Despite these limitations, the significance of Eliza has far exceeded that of a mere technological framework. It represents a groundbreaking attempt at the deep integration of AI technology and Web3 applications.
By designing each functional module as a standard TypeScript program, Eliza ensures complete user control over the system. At the same time, it provides seamless integration capabilities with blockchain data and smart contracts. This design guarantees both security and strong extensibility.
As stated at the end of the white paper, the possibilities of Eliza are only limited by the user's imagination. With the continuous evolution of AI and Web3 technologies, Eliza will also continue to develop, leading the direction of decentralized AI.
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