Original author: Teng Yan ChappieOnChain
Original translation: TechFlow
Hello everyone! This week, we bring you an in-depth analysis of AI agents written by our core contributors ChappieOnChain and Teng Yan. We hope you enjoy it!
Brief Overview:
ELIZA is an open source, modular framework designed to create AI agents that can interact seamlessly with users and blockchain systems.
It embodies the bazaar philosophy, where open source development thrives in an ecosystem driven by collaboration and creativity.
ELIZA has powerful autonomous trading capabilities and ensures safe and responsible operations through its trust engine and trust market.
The plugin system is a strategic advantage of ELIZA, forming a virtuous circle of growth: more developers → more plugins → more developers.
ELIZA’s popularity is rising rapidly across multiple developer metrics, which is very exciting.
In the short term, relative valuations and growing interest among AI agent platforms drive ai16z’s price changes. In the medium term, DAO investments and value capture of ELIZA ecosystem agents could significantly increase its valuation.
ELIZA faces a major challenge in the technology world: how to make an open source framework sustainable. Monetization is unclear, development can become chaotic, and community interest can wane without proper incentives.
Every wave of crypto innovation has its pioneers.
In 2017, it was the ICO revolution, with project leaders grabbing our attention with technical promises in their white papers.
By 2020, DeFi had its moment in the spotlight, with innovators like Andre Cronje redefining how decentralized finance works and showing the world how to distribute tokens to the community.
Now, with the rise of AI agents on blockchain, a new era is opening, driven by two different philosophies and their pioneers.
Cathedral and Market
On one hand, we have the cathedral approach, represented by protocols like Virtuals. This is a methodical, centralized design style that emphasizes precision and careful planning. We’ve explored Virtuals’ agent framework in detail before and are excited about its potential.
The bazaar approach, on the other hand, is decentralized and free-spirited, with the development process more like improvisation—unpredictable, collaborative, and constantly evolving. This is the realm of Shaw, a self-taught programmer and open source advocate whose project ELIZA is a cornerstone of this new paradigm.
ELIZA embodies the bazaar philosophy: an open framework where developers can freely build, experiment, and release AI agents while contributing directly to the main protocol. Shaws open leadership style is consistent with the spirit of his creation - AI Marc Andreessen is the AI partner of the ai16z investment DAO. We began to realize that ELIZA is more than just a protocol, it is a movement.
Let’s explore the principles of ELIZA’s design, the community it is fostering, and where value may accrue in this rapidly growing ecosystem.
Learn more about ELIZA
We know which approach we prefer.
At its core, ELIZA is a modular architecture for creating AI agents that can interact seamlessly with users and blockchain systems. While sharing the same name as the iconic chatbot from the 1960s, this version of ELIZA is a bold reimagining with a more modern look.
Role File System
The core of each ELIZA agent begins with its persona file, a blueprint that defines the agent’s personality in detail. Think of it as the creation of a digital avatar, where developers can shape the agent’s identity through six key elements:
Knowledge: What does the AI agent know?
Background: The agent’s backstory and its narrative foundation.
Style: From conversational tone to platform-specific responses, agents can adjust style for platforms like Discord or X.
Topic: An area of interest or expertise for the agent.
Adjectives: How does the agent describe itself—quirky, professional, or uninhibited?
Example: Developers can fine-tune the agents interactive behavior by providing example messages.
In ELIZA, the persona file is equivalent to the UI design of traditional software. It defines how users experience and interact with the agent.
By integrating built-in Retrieval Augmentation Generation (RAG) capabilities, ELIZA allows agents to access a knowledge base when making queries. This removes the complexity of keeping personalities consistent across different platforms. This allows developers to focus on what really matters: creating vivid, memorable characters, rather than being bogged down by the details behind the scenes.
Agent
If the role file defines the essence of an agent, then the agent runtime is its core.
ELIZA provides an out-of-the-box framework for orchestrating everything from message handling to memory management and state tracking. This architecture allows developers to skip the tedious work of building infrastructure and focus on the uniqueness of the agent. Rapid prototyping and deployment becomes easier, enabling developers to iterate faster when building new AI experiences.
Action System
ELIZAs action system is a major innovation of traditional AI frameworks. In this system, each agents action (even sending a message) is treated as an independent event. This approach divides the decision-making process into two stages:
Determine intent: The agent decides what action to take.
Execution: Performing a specific task through a dedicated module.
This separation enables powerful capabilities such as multi-stage workflows and rigorous validation processes.
For example, an agent might recognize that a user wants to make a cryptocurrency transaction, but the actual transaction execution is subject to rigorous risk checks and verification steps. This design is well suited for blockchain applications where security is critical.
Providers and Evaluators
ELIZA’s providers enrich conversations by injecting real-time contextual information, making the agent’s behavior more dynamic and responsive.
Imagine a “boredom provider” that tracks a user’s engagement in a conversation. If a user becomes repetitive or disengaged, the agent can reflect this by showing reduced enthusiasm, making the conversation more authentic.
This creativity is further extended when the provider works with an evaluator (ELIZA’s reflective system). The evaluator analyzes and extracts key details from the interaction and inputs them into a multi-level memory structure:
Message History: Track the progress of a conversation.
Factual Memory: Stores specific, time-stamped facts.
Core knowledge: Basic understanding of storage agents.
The provider then retrieves and reintroduces relevant details, making the interaction with the agent more contextually meaningful.
For example, if a user mentions that they sold their red Lamborghini a year ago, the ELIZA agent can reference this later when discussing their new yellow Tesla. This combination of memory and context enhances the user’s interaction experience, making the agent feel more like an actual companion than a robot.
Key features of ELIZA
ELIZA’s three core innovations demonstrate its forward-looking approach in the field of AI agents. Each of them demonstrates the team’s vision for the development of autonomous agents in Web3.
#1: Autonomous Transactions and Trust Engine
Autonomous trading is a high-risk activity and a single mistake can result in severe losses. However, as AI agents play an increasing role in Web3, their ability to execute trades independently becomes critical.
This emerging space, AgentFi, is similar to the key role that yield farming has played in the rise of DeFi. Shaw and ELIZA address the inherent risks through a powerful two-layer system: a trust engine and secure trade execution.
As the first line of defense, the Trust Engine uses advanced verification checks to analyze multiple risk dimensions in real time, from detecting scams to assessing liquidity thresholds and holder distribution, ensuring that every transaction is rigorously scrutinized.
For example, trading is limited to tokens with a liquidity of at least $1,000 and a market cap of $100,000. Holder concentration is closely monitored, and no single entity is allowed to control more than 50% of the tokens. These protections create a safety net that reduces the risk of trading in volatile markets.
On this basis, ELIZAs position management system introduces dynamic risk control, adjusting the transaction size according to the liquidity level. Low-risk transactions are limited to 1% of the portfolio, while high-risk opportunities may be expanded to 10%. The total exposure is limited to 10% of the portfolio, and the automatic stop loss is activated at a 15% drawdown. This structured framework strikes a balance between seizing opportunities and maintaining strict risk management.
Trade execution is powered by Jupiter, a leading aggregator on Solana, for optimal exchange routing. Each trade undergoes multiple layers of validation before execution.
In the event of anomalies, such as network outages, wallet imbalances, or unexpected market movements, the error recovery system kicks in. It pauses active trading, closes risky positions, and notifies administrators, ensuring the system remains robust under pressure.
“It’s not just about giving agents the ability to trade — it’s about creating a whole system of checks and balances to prevent catastrophic failures.” — Shaw
What makes ELIZA unique in building trading agents is its data flywheel - a self-reinforcing feedback loop that turns trading into an iterative learning process. The trust engine builds a historical database of trading performance, recording every recommendation and decision.
This data becomes the basis for optimizing strategies over time, combining quantitative metrics with qualitative insights from community suggestions (on Discord). The result is an agent that doesn’t just execute trades, but gets smarter and more effective with each interaction.
#2: Out-of-the-box social integration
For AI agent developers, distribution is often the biggest challenge - how do you let more people know about your agent?
Social media is often the primary distribution channel. However, it is not easy to integrate agents across multiple social platforms. This requires a lot of development work and ongoing maintenance, slowing down deployment and scalability.
ELIZA directly addresses this problem by simplifying multi-platform distribution through a comprehensive client packaging system.
ELIZAs client architecture simplifies the complexity of platform-specific implementations. Through a standardized interface, developers can deploy their AI agents on Discord, X, Telegram, and custom REST API endpoints with minimal additional code. Each client package is customized for its corresponding platform, seamlessly managing features such as Discords voice channel integration, Twitters post scheduling, and Telegrams messaging system.
Tasks such as media processing, authentication, rate limiting, and error handling are managed internally by each client. For developers, this means spending less time solving integration problems and more time building innovative, high-performance AI agents.
By removing the complexity of multi-platform distribution, ELIZA enables developers to easily scale their agents and engage with users where they are.
This is simplified distribution.
3: More plugins
ELIZA’s plugin system allows developers to easily extend core functionality and add custom features to their agents.
While many developers create plugins that suit their own needs, the real power of this system lies in community sharing. By releasing plugins to the wider ecosystem, developers contribute to an ever-expanding library of functionality that greatly enhances the capabilities of each ELIZA agent.
The success of this approach is that it fosters vibrant bazaar-style development. Here are some examples of community-driven plugins:
Bootstrap plugin: Basic conversation management tool.
Image Generation Plugin: AI-powered image creation capabilities.
Solana plugin: Blockchain integration with built-in trust scoring.
TEE plugin: A secure execution environment for sensitive operations.
Coinbase Commerce plugin: Cryptocurrency payment processing capabilities.
ELIZAs plugin system is a strategic and platform strength. By prioritizing extensibility, ELIZA lays the foundation for continued growth and innovation:
Each new plugin increases the overall value of the platform.
Community contributions can be made in different areas simultaneously.
The agent framework can quickly adapt to emerging technologies without requiring core updates.
Innovation thrives at the edge, while core platforms remain stable and reliable.
Here is a simple loop:
More developers build on ELIZA → The framework supports more features (e.g. plugins) → More developers build on ELIZA
The AI agent landscape is evolving rapidly. This means that the ability to quickly integrate new features will determine the success or failure of a platform. ELIZA’s plugin system enables it to stay ahead of the curve, creating a self-reinforcing ecosystem where developers, users, and agents can all thrive.
Shaw and his team have been incubating a number of interesting ELIZA agents, each demonstrating the potential of AI in decentralized systems.
Although these agents are still in their “young” stages in the field of AI, and their functions and capabilities are being actively developed, they portend exciting possibilities.
Marc A Indreessen
Marc AIndreessen is one of the AI partners at ai16z and is a fascinating and mysterious figure in the ELIZA ecosystem. His X account is largely inactive, with only one post elaborating on ai16z’s views. However, according to Shaw, Marc is actively trading and yield farming, possibly using ELIZA’s trust engine and trading plugin.
Shaw also mentioned Marc’s training process in a podcast interview, revealing that the AI was part of an alpha chat group consisting of top traders in the industry. This shows that Marc is not just an ordinary trading robot, but an evolving intelligent agent that learns from human expertise.
Degen Spartan AI
In contrast to Marc’s low-key style, Degen Spartan AI is a boisterous, outspoken agent that seems to have been trained on the chaotic energy of 4chan, meme culture, and Crypto Twitter. His posts on X are a mix of random trading insights and irreverent comments, showing a unique personality in the ELIZA ecosystem.
Unlike Marc AIndreessen, Degen Spartan AI has his own pump.fun token, which currently has a market cap of $60 million. While he has not yet started trading, he has clearly laid the foundation for more ambitious interactions. His unpredictable nature makes him both interesting and worth watching as the ELIZA agent continues to evolve.
The Swarm
The Swarm isn’t a single agent, but Shaw’s grand vision: a decentralized network of AI agents working in collaboration with humans and each other.
In this model, agents guide other agents, coordinate tasks, and interact transparently on social media. This transparency is intended to avoid hidden protocols and ensure public accountability.
Shaw believes that swarms of intelligent agents are inevitable and transformative.
We share the same view: the agent community will drive the next wave of innovation, products, and attention for Web3 AI agents in 2025. Next year, we expect ELIZA agents to emerge and engage in large-scale collaborative activities, redefining their role in the decentralized space.
Growing at the speed of light
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When evaluating the growth of ELIZA, the key metric is developer adoption. As a framework, ELIZAs success relies on the passion and contributions of the developer community.
In this regard, ELIZA is not just growing, it’s exploding.
On its GitHub page, the number of forks and stars (a proxy for developer interest) has shown a near-vertical growth, resembling a classic hockey stick pattern.
Even more impressive is the surge in plugins and commits, showing a thriving and active contributor ecosystem. As of December 12, ELIZA has 3,861 GitHub stars and 1,103 forks, with 138 contributors. There are over 13,000 members on Discord.
Compared to other top open source agent frameworks:
LangGraph: 7,200 stars and 1,100 forks
CrewAI: 22,400 stars and 3,100 forks
Microsofts AutoGen: 35,700 stars and 5,200 forks
To further fuel this growth, ai16z has launched a Creator Fund, designed to support and reward developers building on ELIZA. This initiative was made possible by a generous donation from Elijah, a significant ai16z token holder who pledged to reduce his stake from 16% to 5% and donate the difference to establish the fund. The Creator Fund is expected to accelerate innovation and attract new talent to the ecosystem.
However, while ELIZA’s framework has tremendous value, it is not simple where this value will ultimately accumulate. This is the multi-billion dollar question.
Currently, there is an official $ELIZA Token backed by Shaw that represents the personalization of the ELIZA framework. Users can even interact with ELIZA directly on their website. The market value of this token is approximately $66 million.
However, by far the biggest beneficiary of ELIZA’s growth has been $ai16z, an investment DAO token that has reached a staggering $800 million market cap. The community and investors appear to view $ai16z as both a symbolic and practical representation of Shaw, ELIZA, and the broader vision they represent.
ai16z Tokenomics
The origin of ai16z is a mechanism to raise funds for the trading activities of AI Marc Andreessen. Launched on DAOS.FUN in October 2024, the token raised 420.69 SOL in its first offering. In this model, the funds raised can be actively traded to increase the asset base, and the profits belong to the token holders.
No individual — not even Shaw — can mint additional tokens without a vote from the DAO. Token holders have governance rights, can propose and vote on initiatives, and decide the direction of the DAO.
The fund has a set expiration date: October 25, 2025. All principal invested and profits will be distributed to ai16z token holders on that date. Whether this timeline remains the same or extended will depend on the development of the ecosystem over the next year.
Currently, ai16z’s net asset value (NAV) is $17.7 million, mainly composed of its holdings of ELIZA Token, degenai, and fxn. This means that ai16z Token (currently priced at $0.80) is trading at a 50x premium to its NAV, which seems a bit unreasonable at first glance.
However, markets are generally efficient, reflecting several other factors that drive demand for tokens.
1. Relative valuation comparisons are driving token prices
AI agent platforms are a brand new category that emerged only a few months ago. The market is still grappling with some fundamental questions: What is the true size of the AI agent opportunity? Where will value be realized?
In the early stages of development, when there are no standardized business metrics to compare to, relative valuations are often used as a benchmark.
Currently, Virtuals Protocol is the leading Web3 AI agent launch platform, with a token valuation of $1.8 billion, making it the market leader. In comparison, ai16z is in second place. Many believe that if ELIZA continues to drive the creation of more useful and innovative AI agents, ai16z has the potential to catch up to or even surpass Virtuals, even if it is just based on market awareness and investor/retail interest.
But this is not set in stone; competition is growing. And in our opinion, it’s likely to get even more intense. As the market matures, other platforms are emerging in an effort to attract the attention of developers and investors.
2. Potential value capture of the ELIZA ecosystem
Monetizing open source frameworks has always been a difficult problem.
For ai16z, the main driver of future value may come from agent economics: AI agents launched on ELIZA will return part of their tokens to the ai16z DAO. Therefore, the price of ai16z tokens should reflect a portion of the total future value created by all agents built on the ELIZA framework.
Could it be worth $10 million, $100 million, or even $10 billion in the future? There is no definitive answer yet, as there are too many unknowns, but ELIZAs growth trend makes us optimistic.
Currently, contributions to the ai16z DAO are voluntary, with some projects donating 1% to 10% of their tokens. Additionally, when users deploy AI agents on Vvaifu (a popular ELIZA agent community launch platform), they pay a fee of 1.5 SOL plus 5% of the agent token supply when using the ELIZA framework. These contributions can be tracked on the ELIZA Observatory.
There are rumors that ai16z may launch an official ELIZA agent launch platform to enforce token contributions at the smart contract level. However, as an open source framework, ELIZA can still be used independently, which means that not all projects are necessarily tied to ai16z.
3. DAO Investment
ai16z was originally intended to be an intelligent autonomous trader, led by Marc AIndreessen (AI). Marc has only recently started trading and there are not many details, so it is difficult to evaluate the AIs trading capabilities.
However, the approach it takes is noteworthy.
ai16z is building a trust market. In this virtual ecosystem, AI agents gain insights from the community, simulate transactions, and adjust trust scores in real time based on recommended performance. The markets white paper is expected to be released by the end of the month.
The goal is to create AI agents that can operate autonomously and securely in a self-reinforcing system of transparency and accountability. The Trust Marketplace serves as a testing ground. While no actual trading occurs initially, this environment allows the agents to safely optimize their capabilities and eventually achieve real-time trading.
Trust scores range from 0 to 1 (normalized to 100) and are a public sign of reliability, displayed on a leaderboard for everyone to see. User recommendations enter the system, and trusted users (users with higher trust scores) have a greater say.
It’s a logic-based feedback loop: Agents simulate transactions, users evaluate the results, and everyone’s trust score is updated accordingly. Over time, the system becomes smarter, more reliable, and more trustworthy.
Adding a social layer are public trust profiles where agents and users are incentivized to build their reputation. Community management ensures accountability and transparency.
4. Attention Premium
Source: X Radar
In the cryptocurrency space, speculation often leads ahead of product-market fit, revenue generation, and long-term value capture. For ai16z, its current valuation can largely be attributed to the mind share it has gained in the emerging AI agent ecosystem.
ai16z has positioned itself as a top AI agent framework with a thriving developer community and a rapidly growing ecosystem.
This is the narrative from ai16z: a “crack” development team that is actively publishing tutorials, creating innovative agents, and leading development in the field.
The team further solidified its reputation with its biweekly AI Agent Development School courses at X. The first course attracted over 12,000 live attendees, demonstrating the huge interest in building AI agents on ELIZA.
Future developments and potential pitfalls
Currently, ELIZA is deeply rooted in the Solana ecosystem, but its rapidly expanding plugin system is laying the foundation for a multi-chain future.
The real potential of ELIZA lies in Shaw’s “swarm” vision: a decentralized network of AI agents that integrate resources and collaborate across ecosystems. This swarm effect can establish a lasting competitive advantage, similar to the value of liquidity depth in DeFi protocols.
The ultimate goal is to create an open standard for intelligent agent communication, similar to the transformative impact of ERC-20 in token interoperability.
Despite its huge potential, ELIZA faces one of the toughest challenges in technology: making an open source framework sustainable. If the community loses interest (for example, if the token price keeps falling or something new and attractive emerges), development may stagnate or slow down, making it difficult to catch up.
When the community directly participates in the codebase and pushes changes quickly, a lot of chaos can occur - instability, poor documentation, frequent crashes and bugs that ruin the user experience.
The biggest opportunity for the framework lies in crypto-native incentives.
If ai16z can design effective token economics to reward ELIZA contributors and align their success, it can bring traditional open source projects into the crypto orbit. Imagine GitHub meets DeFi, where contributors gain not only prestige but also real, tangible economic value.
Conclusion
In our view, ELIZA is not just another AI agent framework competing with LangChain or CrewAI - its ambitions go far beyond that.
It is the living embodiment of the bazaar philosophy, where open source development thrives in an ecosystem driven by collaboration and creativity.
With its modular architecture, innovative trust engine, and extensive plugin system, ELIZA is an experiment in how AI can reshape open source development itself.
What’s exciting about ELIZA is that it sits at the intersection of three transformative trends: the rise of autonomous AI agents, the maturation of crypto-driven incentives, and the evolution of open source development models.
If ELIZA succeeds, it will not only change the way AI agents are developed, but will also redefine the economic incentives for open source projects.
At present, the market is very lively.
Cheers, friends.
