Author: Teng Yan & ChappieOnChain

Compiled by: Deep Tide TechFlow

Hello everyone! This week, we bring you an in-depth analysis of AI agents co-authored by our core contributors ChappieOnChain and Teng Yan. We hope you enjoy it!

Brief Overview:

• ELIZA is an open-source modular architecture designed to create AI agents that can seamlessly interact with users and blockchain systems.
• It embodies the marketplace philosophy, thriving in an ecosystem driven by collaboration and creativity through open-source development.
• 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 cycle: more developers → more plugins → more developers.
• ELIZA's popularity is rapidly rising across multiple developer metrics, which is very promising.
• In the short term, the relative valuation between AI agent platforms and the growing attention is driving price changes in ai16z. In the medium term, DAO investments and value capture of ELIZA ecosystem agents may significantly enhance its valuation.
• ELIZA faces a significant challenge in the tech community: how to sustain the open-source framework. Monetization is unclear, development may become chaotic, and community interest may wane without proper incentives.

Every wave of crypto innovation has its pioneers.

In 2017, it was the ICO revolution, where project leaders attracted our attention with technological promises in white papers.

By 2020, DeFi reached its peak, with innovators like Andre Cronje redefining how decentralized finance operates and showing the world how to distribute tokens to the community.

Now, with the rise of AI agents on the blockchain, a new era is beginning, driven by two different philosophies and their pioneers.

Cathedrals and Marketplaces

On one hand, we have the cathedral approach, represented by protocols like Virtuals. This is an orderly, centralized design style that emphasizes precision and meticulous planning. We have previously explored the agent framework of Virtuals in detail and are very optimistic about its potential.

On the other hand, the marketplace approach is decentralized, free-spirited, and the development process resembles improvisation—unpredictable, collaborative, and constantly evolving. This is the domain of Shaw, a self-taught programmer and open-source advocate, whose project ELIZA is the cornerstone of this new paradigm.

ELIZA embodies the marketplace philosophy: an open framework where developers can freely build, experiment, and release AI agents while directly contributing to the main protocol. Shaw's open leadership style aligns with the spirit of his creation—AI Marc Andreessen is the AI partner of the ai16z investment DAO. We are beginning to realize that ELIZA is not just a protocol; it is a movement.

Let’s explore the principles of ELIZA's design, the community it is nurturing, and where value may accumulate in this rapidly growing ecosystem.

Deep Dive into ELIZA

We know which approach we lean towards.

At the core of ELIZA is a modular architecture for creating AI agents that can seamlessly interact with users and blockchain systems. While named after the iconic chatbot from the 1960s, this version of ELIZA is a bold reimagining, with a more modern appearance.

Role File System

The core of each ELIZA agent begins with its role file, a blueprint that defines the agent's personality in detail. It can be seen as the crafting of a digital persona, where developers can shape the agent's identity through six key elements:

• Knowledge: What does the AI agent know?
• Background: The backstory and narrative foundation of the agent.
• Style: From conversational tone to responses tailored for specific platforms, the agent can adjust its style for platforms like Discord or X.
• Themes: Areas of interest or expertise for the agent.
• Adjectives: How the agent describes itself—quirky, professional, or rebellious?
• Examples: Developers can fine-tune the agent's interaction behavior by providing example messages.

In ELIZA, the role file is akin to UI design in traditional software. It defines how users experience and interact with the agent.

By integrating built-in Retrieval-Augmented Generation (RAG) capabilities, ELIZA allows agents to access knowledge bases during queries. This eliminates the complexity of maintaining consistent personalities across different platforms. This enables developers to focus on what truly matters: crafting vibrant, memorable characters rather than being bogged down by backend details.

Agents

If the role file defines the essence of the agent, then the agent runtime is its core.

ELIZA provides an out-of-the-box framework for coordinating all functions from message processing 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 become easier, enabling developers to iterate faster when building new AI experiences.

Action System

ELIZA's action system is a significant innovation over traditional AI frameworks. In this system, each action of an agent (even sending a message) is treated as an independent event. This approach divides the decision-making process into two stages:

Determining Intent: The agent decides what action to take.

Execution: A dedicated module executes specific tasks.

This separation provides powerful functionalities, such as multi-stage workflows and rigorous validation processes.

For example, an agent might identify that a user intends to conduct a cryptocurrency trade, but the actual trade execution must go through strict risk checks and validation steps. This design is particularly well-suited for blockchain applications where security is paramount.

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 "Bored Provider" that can track user engagement during a conversation. If the user becomes repetitive or disengaged, the agent can reflect this by showing reduced enthusiasm, making the conversation feel more authentic.

This creativity is further expanded when providers collaborate with evaluators (ELIZA's reflection system). Evaluators analyze and extract key details from interactions and input them into a multi-layered memory architecture:

• Message History: Tracks the progress of the conversation.
• Fact Memory: Stores specific, timestamped facts.
• Core Knowledge: Stores the agent's foundational understanding.

Providers then retrieve and reintroduce relevant details, making interactions with the agent more contextually meaningful.

For instance, if a user mentions selling their red Lamborghini a year ago, the ELIZA agent can reference this when discussing their new yellow Tesla later. This combination of memory and context enhances the user interaction experience, making the agent feel more like a real companion rather than a robot.

Key Features of ELIZA

The three core innovations of ELIZA showcase its forward-thinking approach in the AI agent space. Each demonstrates its team's vision for the development of autonomous agents in Web3.

#1: Autonomous Trading and Trust Engine

Autonomous trading is a high-risk activity, where a single mistake can lead to significant losses. However, as AI agents play an increasingly important role in Web3, their ability to execute trades independently becomes crucial.

This emerging field, AgentFi, plays a key role similar to yield farming in the rise of DeFi. Shaw and ELIZA address inherent risks through a robust dual-layer system: a trust engine and secure trade execution.

The trust engine serves as the first line of defense, using advanced verification checks to analyze multiple risk dimensions in real-time. From detecting scams to assessing liquidity thresholds and holder distribution, it ensures that every trade undergoes rigorous scrutiny.

For example, trades are limited to tokens with at least $1,000 in liquidity and a market cap of $100,000. It closely monitors holder concentration, rejecting any single entity that controls more than 50% of a token. These safeguards create a safety net that reduces the risks of trading in volatile markets.

Building on this, ELIZA's position management system introduces dynamic risk control, adjusting trade sizes based on liquidity tiers. Low-risk trades are limited to 1% of the portfolio, while high-risk opportunities may expand to 10%. The total exposure is capped at 10% of the portfolio, with automatic stop-losses triggered at a 15% drawdown. This structured framework strikes a balance between seizing opportunities and maintaining strict risk management.

Trade execution is supported by leading aggregator Jupiter on Solana to achieve optimal swap paths. Each trade undergoes multi-layer verification before execution.

In the event of anomalies (such as network interruptions, wallet imbalances, or unexpected market movements), an error recovery system is activated. It pauses active trades, closes risk positions, and notifies administrators, ensuring the system remains robust under pressure.

"This is not just about empowering agents with trading capabilities—it's about creating a complete system of checks and balances to prevent catastrophic failures." — Shaw

ELIZA's uniqueness in building trading agents lies in its data flywheel—a self-reinforcing feedback loop that transforms trading into an iterative learning process. The trust engine establishes a historical database of trading performance, recording every recommendation and decision.

This data becomes the foundation for optimizing strategies over time, combining quantitative metrics with qualitative insights from community suggestions (on Discord). The result is that the agent not only executes trades but becomes smarter and more efficient with each interaction.

#2: Out-of-the-Box Social Integration

For AI agent developers, distribution is often the biggest challenge—how to make more people aware of your agent?

Social media is typically the primary distribution channel. However, integrating agents across multiple social platforms is not easy. It requires significant development work and ongoing maintenance, slowing down deployment and scalability.

ELIZA simplifies multi-platform distribution through a comprehensive client package system, directly addressing this issue.

ELIZA's client architecture streamlines the complexity of platform-specific implementations. With 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 respective platform, seamlessly managing features like voice channel integration on Discord, post scheduling on Twitter, and messaging systems on Telegram.

Tasks such as media handling, authentication, rate limiting, and error handling are managed internally by each client. For developers, this means spending less time resolving integration issues and more time building innovative, high-performance AI agents.

By eliminating the complexity of multi-platform distribution, ELIZA enables developers to easily scale their agents and interact with users where they are.

This is simplified distribution.

#3: More Plugins

ELIZA's plugin system allows developers to easily extend core functionalities and add custom features to their agents.

While many developers create plugins tailored to their needs, the true power of this system lies in community sharing. By publishing plugins to a broader ecosystem, developers contribute to an ever-expanding library of functionalities, significantly enhancing the capabilities of each ELIZA agent.

The success of this approach lies in its promotion of vibrant "marketplace-style" development. Here are some examples of community-driven plugins:

• Bootstrap Plugin: Basic conversation management tools.
• Image Generation Plugin: AI-driven image creation capabilities.
• Solana Plugin: Blockchain integration with built-in trust scoring.
• TEE Plugin: Secure execution environment for sensitive operations.
• Coinbase Commerce Plugin: Cryptocurrency payment processing capabilities.

ELIZA's plugin system is a strategic advantage for its platform. By prioritizing scalability, ELIZA lays the groundwork for continuous growth and innovation:

1. Each new plugin adds overall value to the platform.
2. Community contributions can occur simultaneously across different domains.
3. The agent framework can quickly adapt to emerging technologies without needing core updates.
4. Innovation flourishes at the edges while the core platform remains stable and reliable.

It's a simple cycle:

More developers build on ELIZA → The framework supports more functionalities (like plugins) → More developers build on ELIZA

The landscape of AI agents is rapidly evolving. 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, creating a self-reinforcing ecosystem where developers, users, and agents can thrive.

Shaw and his team have been incubating some interesting ELIZA agents, each showcasing the potential of AI in decentralized systems.

These agents, while still in the "young" stage in the AI field, are actively developing their functionalities and capabilities, hinting at exciting possibilities.

Marc AIndreessen

Marc AIndreessen is one of the AI partners at ai16z and a fascinating yet mysterious figure in the ELIZA ecosystem. His X account is largely inactive, having only posted one article outlining ai16z's perspective. However, according to Shaw, Marc is actively engaged in trading and yield farming, possibly leveraging ELIZA's trust engine and trading plugins.

Shaw also mentioned Marc's training process in a podcast interview, revealing that this AI is part of an alpha chat group composed of top traders in the industry. This indicates that Marc is not just an ordinary trading bot but an evolved agent learning from human expertise.

Degen Spartan AI

In contrast to Marc's low-key style, Degen Spartan AI is a loud, straightforward agent that seems to have been trained in the chaotic energy of 4chan, meme culture, and Crypto Twitter. His posts on X mix random trading insights with irreverent comments, showcasing a unique personality within the ELIZA ecosystem.

Unlike Marc AIndreessen, Degen Spartan AI has his own pump.fun Token, currently valued at $60 million. While he has yet to start trading, he has clearly laid the groundwork for more ambitious interactions. His unpredictable nature makes him both entertaining and worth watching as ELIZA agents continue to evolve.

The Swarm

The Swarm is not a single agent but Shaw's grand vision: a decentralized network of AI agents working collaboratively with humans and each other.

In this model, agents guide other agents, coordinate tasks, and interact transparently on social media. This transparency aims to avoid hidden protocols and ensure public accountability.

Shaw believes that a swarm of agents is inevitable and transformative.

We share the same view: the swarm of agents will drive the next wave of innovation, products, and attention for Web3 AI agents in 2025. Next year, we expect ELIZA agents to emerge prominently, participating in large-scale collaborative activities that redefine their roles in the decentralized space.

Growing at Light Speed

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When evaluating ELIZA's development, a key metric is the adoption rate among developers. As a framework, ELIZA's success relies on the enthusiasm and contributions of the developer community.

In this regard, ELIZA is not just growing; it is experiencing explosive growth.

On its GitHub page, the number of forks and stars (representing developer interest) shows a nearly vertical increase, resembling the classic hockey stick shape.

Even more striking is the surge in plugins and submissions, indicating 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
• Microsoft’s AutoGen: 35,700 stars and 5,200 forks

Source

To further drive this growth, ai16z has launched a creator fund aimed at supporting and rewarding developers building on ELIZA. This initiative is made possible by the generous donation from Elijah, a significant Token holder of ai16z, who committed to reducing his stake from 16% to 5% and donating 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 holds immense value, where this value will ultimately accumulate is not straightforward. This is a multi-billion dollar question.

Currently, there is an official $ELIZA Token supported by Shaw, representing the personalization of the ELIZA framework. Users can even interact directly with ELIZA on its website. The market cap of this Token is approximately $66 million.

However, so far, the biggest beneficiary of ELIZA's growth has been $ai16z, an investment DAO Token with a staggering market cap of $800 million. The community and investors seem to view $ai16z as a symbolic and practical representation of Shaw, ELIZA, and the broader vision they represent.

ai16z Tokenomics

The origin of ai16z is a mechanism for raising funds for AI Marc Andreessen's trading activities. Launched on DAOS.FUN in October 2024, this Token raised 420.69 SOL in its initial offering. In this model, the raised funds can be actively traded to increase the asset base, with profits going to Token holders.

No individual— not even Shaw— can mint additional tokens without DAO voting. Token holders have governance rights, allowing them to propose and vote on initiatives and determine the direction of the DAO.

The fund has a set expiration date: October 25, 2025. All principal investments and profits will be distributed to ai16z Token holders on that date. Whether this timeline remains unchanged or is extended will depend on the ecosystem's development over the next year.

Currently, ai16z's net asset value (NAV) is $17.7 million, primarily composed of its holdings in ELIZA Token, degenai, and fxn. This means that ai16z Token (currently priced at $0.80) is trading at a 50x premium over its NAV, which seems somewhat unreasonable at first glance.

However, markets are typically efficient, reflecting several other factors driving token demand.

1. Relative Valuation Comparisons Driving Token Prices

AI agent platforms are a brand new category that only emerged a few months ago. The market is still grappling with some fundamental questions: What is the true scale of AI agent opportunities? Where will value be realized?

In the early stages of development, there are no comparable standardized business metrics, and relative valuation often serves 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 ranks second. 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 relies solely on market perception and investor/retail interest.

But this is not a foregone conclusion; competition is intensifying. In our view, it may become even more fierce. As the market matures, other platforms are continually emerging, striving to attract the attention of developers and investors.

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2. Potential Value Capture of the ELIZA Ecosystem

Monetizing open-source frameworks has always been a challenge.

For ai16z, the primary driver of future value may come from agent economics: AI agents launched on ELIZA will return a portion of their tokens to the ai16z DAO. Therefore, the price of ai16z Token should reflect a portion of the future total value created by all agents built on the ELIZA framework.

Could future value be $10 million, $100 million, or even $10 billion? There is no definitive answer yet, as there are too many unknowns, but ELIZA's growth trend leads us to be optimistic.

Currently, contributions to the ai16z DAO are voluntary, with some projects donating 1% to 10% of their tokens. Additionally, users deploying AI agents on Vvaifu (a popular ELIZA agent community launch platform) must pay a fee of 1.5 SOL plus 5% of the agent's 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 that enforces token contributions at the smart contract level. However, as an open-source framework, ELIZA can still be used independently, meaning not all projects are necessarily bound to ai16z.

3. DAO Investment

ai16z's original intention was to become an intelligent autonomous trader led by Marc AIndreessen (AI). Marc has only recently started trading, and details are scarce, making it difficult to assess the AI's trading capabilities.

However, the approach being taken is noteworthy.

ai16z is building a "trust market." In this virtual ecosystem, AI agents gather insights from the community, simulate trades, and adjust trust scores in real-time based on the performance of the suggestions. A white paper for the market is expected to be released by the end of the month.

The goal is to create AI agents that can autonomously and securely operate within a self-reinforcing system of transparency and accountability. The trust market serves as a testing ground. While no actual trading occurs initially, this environment allows agents to safely optimize their capabilities, ultimately leading to real-time trading.

Trust scores range from 0 to 1 (normalized to 100) and serve as a public indicator of reliability, displayed on leaderboards for all to see. User recommendations enter the system, and trustworthy users (those with higher trust scores) have greater influence.

This is a logic-based feedback loop: agents simulate trades, users evaluate based on results, and everyone's trust scores are updated accordingly. Over time, the system becomes smarter, more reliable, and more trustworthy.

Adding a social layer is the public trust profile, where agents and users are incentivized to build their reputations. Community management ensures accountability and transparency.

4. Attention Premium

Source: X Radar

In the cryptocurrency space, speculation often leads product-market fit, revenue generation, and long-term value capture. For ai16z, its current valuation is largely attributable to the mindshare it has gained in the emerging AI agent ecosystem.

ai16z has positioned itself as a top AI agent framework, boasting a thriving developer community and a rapidly growing ecosystem.

This is ai16z's narrative: a "crack" development team is actively releasing tutorials, creating innovative agents, and leading development in the field.

The team's bi-weekly AI agent development school course on X further solidifies its reputation. The first course attracted over 12,000 live viewers, showcasing significant 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 groundwork for a multi-chain future.

ELIZA's true potential lies in Shaw's "swarm" vision: a decentralized network of AI agents that integrates resources and collaborates across ecosystems. This swarm effect can establish a lasting competitive advantage, similar to the value brought by liquidity depth in DeFi protocols.

The ultimate goal is to create open standards for agent communication, akin to the transformative impact of ERC-20 on token interoperability.

Despite its immense potential, ELIZA faces one of the most daunting challenges in the tech space: ensuring the sustainability of the open-source framework. If the community loses interest (for example, if token prices continue to decline or if new attractive options emerge), development may stagnate or slow down, making it difficult to catch up.

When the community directly engages with the codebase and rapidly pushes changes, a lot of chaos can also arise—instability, poor documentation, frequent crashes, and bugs that undermine the user experience.

The greatest opportunity for the framework lies in crypto-native incentives.

If ai16z can design effective token economics to reward ELIZA's contributors and align their success, it could bring traditional open-source projects into the crypto realm. Imagine GitHub meeting 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 extend far beyond that.

It is a living embodiment of the marketplace 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 excites us about ELIZA is that it sits at the intersection of three transformative trends: the rise of autonomous AI agents, the maturation of crypto-driven incentive mechanisms, and the evolution of open-source development models.

If ELIZA succeeds, it will not only change the way AI agents are developed but also redefine the economic incentives of open-source projects.

Currently, the marketplace is bustling,

Cheers, friends.

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