ai16z, ELIZA, and the Agent Marketplace: Why ELIZA is Exciting

Author: Teng Yan, Host of ChainofThought; ChappieOnChain, Core Contributor of ChainofThought; Compiled by: 0xjs@Golden Finance

Key Points of This Article:

• ELIZA is an open-source modular architecture for building AI agents that seamlessly interact with users and blockchain systems.
• It is a living embodiment of market philosophy, thriving in an ecosystem driven by collaboration and creativity through open-source development.
• ELIZA includes powerful autonomous trading capabilities, operating securely and responsibly based on its trust engine and trust market.
• The plugin system is ELIZA's strategic advantage, driving the growth flywheel: more developers → more plugins → more developers.
• From many developer metrics, the popularity of ELIZA is experiencing explosive growth, which is very promising.
• In the short term, the relative valuation between AI agent platforms and the continuously growing market share have driven the price trend of ai16z. In the medium term, the value acquisition of DAO investments and the ELIZA ecosystem agents may significantly enhance its valuation.
• ELIZA is indeed facing one of the most severe challenges in the tech world: ensuring the sustainable development of open-source frameworks. Without appropriate incentives, there is uncertainty in monetization, development may become chaotic, and community interest may also wane.

Every wave of cryptocurrency innovation has its pioneers.

2017 was the year of the ICO revolution, ambitious project leaders (and many scammers) attracted us with the promise of groundbreaking technology wrapped in white papers.

By 2020, DeFi reached its peak, led by innovators like Andre Cronje, who redefined how DeFi operates and showed the world how to build and distribute tokens to the community.

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

Cathedral and Market

On one hand, we have the cathedral approach, exemplified in protocols like Virtuals. This is a methodical, purposeful, and centralized design style that emphasizes precision and careful planning. We previously detailed the agent framework of Virtuals, and we are very excited about its potential.

On the other hand, loyal to the roots of cryptocurrency, there is a marketplace approach: a decentralized, free-form model where development feels more like jazz—unpredictable, collaborative, and constantly evolving. This is the domain of ai16z founder Shaw, a clever self-taught programmer and open-source advocate, whose project ELIZA is a cornerstone of this new paradigm.

ELIZA embodies the philosophy of the marketplace: an open framework where developers can freely build, experiment, and launch AI entities while contributing directly to the core protocols. Shaw's open leadership style reflects the spirit of the AI Marc Andreessen he created, the AI partner driving the ai16z investment DAO.

We began to realize that ELIZA is not a protocol. ELIZA is a movement.

Let us explore the principles that drive the design of ELIZA, the community it fosters, and the places where value may ultimately be generated in this rapidly growing ecosystem.

Internal Principles of ELIZA

We know which one we prefer

Essentially, ELIZA provides a modular architecture for creating AI entities that can seamlessly interact with users and blockchain systems. While it shares its name with the iconic chatbot from the 1960s, this version of ELIZA is a bold reimagining. To be honest, it also looks much better.

Character File System

The soul of each ELIZA agent begins with its role file, which is a blueprint that defines the personality of the agent in detail. It can be seen as the creation of a digital character, and agent developers can shape the identity of their agents through six key elements:

1. Knowledge: What does the AI agent understand?
2. Lore: The background story of the entity - its narrative foundation.
3. Style: From the tone of conversation to responses tailored to specific media, the agent can adjust its style based on platforms like Discord or X.
4. Topics: Areas of interest or expertise that the agent is passionate about.
5. Descriptive Adjectives: How does the intelligent entity describe itself - quirky, professional, or disrespectful?
6. Examples: Developers can guide behavior by providing example messages, thereby fine-tuning interactions.

In ELIZA, the personality file is equivalent to the UI design of traditional software. It defines the user experience and the way users interact with the agent.

By integrating the built-in retrieval-augmented generation (RAG) feature, ELIZA allows agents to access the knowledge base while querying. This eliminates the complexity of maintaining personality consistency across platforms.

It allows developers to focus on what truly matters: creating vibrant, memorable characters instead of getting bogged down in the complexities of the backend.

Intelligent Body

If the character file defines the essence of the agent, then the runtime of the agent is its beating heart.

ELIZA provides a ready-to-use framework for coordinating all tasks 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 aspects that make their agents stand out.

Rapid prototyping and deployment have become easier, allowing builders to iterate more quickly when creating new AI experiences.

Action System

The action system of ELIZA is fundamentally different from traditional AI frameworks. Here, every agent action (even sending messages) is regarded as a discrete event. This approach divides decision-making into two stages:

1. Determining Intent: The agent decides what action to take.
2. Execution: Actions are executed through dedicated handlers, which are modules designed to perform specific tasks.

This separation unleashes powerful functionalities, such as multi-stage workflows and robust validation processes.

For example, an intelligent agent may recognize the user's intention to trade cryptocurrencies, but the actual execution of the trade will only occur after passing through stringent risk checks and verification steps. This makes it an ideal choice for blockchain applications where security is crucial.

Providers and Evaluators

The provider of ELIZA enriches the conversation by injecting real-time context, thus achieving dynamic and responsive agent behavior.

Imagine there is a "boredom provider" that can track user engagement during conversations. If a user's statements become repetitive or lack appeal, this agent can respond by displaying a decrease in enthusiasm—cleverly mimicking real human interaction to make the conversation feel more authentic.

When providers collaborate with evaluators (the reflective system of ELIZA), this creativity is further expanded. Evaluators analyze and extract key details from the interaction and input them into a multi-layer memory architecture:

• Message history: Track the conversation flow.
• Factual memory: Retain specific, timestamped facts.
• Core Knowledge: Includes a basic understanding of agents.

Then, the provider retrieves and reintroduces relevant details, making the interaction with the agent rich in context.

For example, if a user recalls the moment they sold their red Lamborghini a year ago, the ELIZA agent can later mention this when discussing their shiny new yellow Tesla. This interaction between memory and context enhances user engagement, making the agent feel less mechanical and more like a true companion.

ELIZA's Winning Features

ELIZA's three core innovations highlight its forward-looking approach to AI agents. Each one showcases how its team envisions the development of autonomous agents in Web3.

#1: Autonomous Trading and Trust Engine

Self-trading is a high-risk game—one mistake can lead to catastrophic losses. However, as AI agents play an increasingly important role in Web3, their ability to execute trades independently is becoming more and more essential.

The emerging field of AgentFi reflects the key role that yield farming plays in the rise of DeFi. Shaw and ELIZA are effectively addressing inherent risks through a robust dual-layer system that combines a trust engine with secure transaction execution.

Token metrics captured in the trust engine

The trust engine is the first line of defense, employing advanced verification checks to analyze multiple risk dimensions in real time. From detecting fraudulent activities to assessing liquidity thresholds and token distribution, this engine ensures that every transaction is rigorously scrutinized to prevent potential pitfalls.

For example, trading is limited to tokens with a minimum liquidity of 1,000 and a market capitalization of 100,000. The concentration of token holders is closely monitored, rejecting any single entity that controls more than 50% of the tokens. These safeguards create a safety net that mitigates the risks of trading in a compromised market.

Based on this, ELIZA's position management system introduces dynamic risk control, which can adjust trading scales according to liquidity levels. The upper limit for low-risk trades is 1% of the portfolio, while the upper limit for high-risk trades can be as much as 10%. The total risk exposure limit is set at 10% of the portfolio, and automatic stop-loss begins to take effect when the decline reaches 15%. This structured framework strikes a balance between seizing opportunities and maintaining strict risk management.

The execution of the transaction is supported by Jupiter, the leading aggregator on Solana, to achieve the best exchange routing. Each transaction goes through multiple verification layers before execution.

The error recovery system will activate in the event of an anomaly, such as network interruptions, wallet imbalances, or unexpected market changes. It will pause active trades, close risky positions, and alert administrators to ensure the system remains resilient under pressure.

"This not only grants agents the ability to trade, but also establishes a complete system of checks and balances to prevent catastrophic failures." —Shaw

What makes ELIZA truly unique in building trading agents is its data flywheel—a self-reinforcing feedback loop that transforms trading into an iterative learning process. The trust engine creates a historical database of trading performance, documenting every suggestion and decision.

This data will serve as the foundation for refining 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 also evolves continuously with each interaction, becoming smarter and more efficient.

#2: Out-of-the-box social integration

For AI agent developers, distribution is often the biggest bottleneck—how to let more people know about your agent?

Social media is often the primary distribution channel. However, integrating agents across multiple social platforms is not easy. It requires a significant amount of development work and ongoing maintenance. These challenges slow down deployment and scalability.

ELIZA addresses this issue head-on through a comprehensive client package system, thereby simplifying multi-platform distribution.

The client architecture of ELIZA eliminates the complexity of platform-specific implementations. With a standardized interface, developers need very little additional code to deploy their AI agents on Discord, X, Telegram, and custom REST API endpoints. Each client package is tailored to its respective platform, seamlessly managing features such as Discord's voice channel integration, Twitter's post scheduling, and Telegram's messaging system.

Tasks such as media processing, authentication, rate limiting, and error handling are managed internally by each client. For developers, this means they spend less time on integration and can focus more on building innovative, high-performance AI agents.

By eliminating the hassle of multi-platform distribution, ELIZA enables developers to easily scale their agents and connect with users anytime, anywhere.

Its distribution is simplified.

#3: More Plugins

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

Although many developers create plugins based on their own use cases, the true strength of the system lies in community sharing. By publishing plugins to a broader ecosystem, developers can contribute to the ever-growing library of out-of-the-box functionalities, significantly expanding the capabilities of each AI entity on ELIZA.

The success of this approach is reflected in the vibrant "market-style" development it fosters. Here are some examples of community-driven plugins:

• Bootstrap Plugin: Essential dialogue management tool.

•Image Generation Plugin: AI-driven image creation feature.

• Solana Plugin: Built-in trust scoring for blockchain integration.

• TEE Plugin: Provides a secure execution environment for sensitive operations.

• Coinbase Commerce plugin: Cryptocurrency payment processing functionality.

The plugin system of ELIZA is its strategic and platform advantage. By prioritizing scalability, ELIZA lays the foundation for continuous growth and innovation, in which:

1. Each new plugin will increase the overall value of the platform.
2. Community contributions can be carried out in parallel across different fields.
3. The intelligent body framework can rapidly evolve to adapt to emerging technologies without the need for core updates.
4. Innovation thrives at the edge while the core platform remains stable and reliable.

This is a simple flywheel:

More developers are developing on ELIZA → Framework supports more features (e.g., plugins) → More developers are developing 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 the platform. ELIZA's plugin system provides the best opportunity to maintain a leading position, creating a self-reinforcing ecosystem that allows developers, users, and agents to thrive.

ELIZA Agent

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

Although these agents are still quite "young" from an AI perspective, their characteristics and capabilities are actively being developed, suggesting exciting possibilities.

Marc AIndreessen

Marc Andreessen is one of the AI partners at a16z, and he is a fascinating and enigmatic figure in the ELIZA ecosystem. His X account is largely inactive, with only one post outlining a16z's paper. However, according to Shaw, Marc is actively trading and yield farming—likely leveraging ELIZA's trust engine and trading plugins.

Shaw also hinted at Marc's training process during the podcast interview, revealing that the artificial intelligence is part of an alpha chat group composed of some of the best traders in the field. This suggests that Marc is not just a standard trading robot, but an evolving agent that is learning from human expertise.

Degen Spartan AI

In stark contrast to Marc's quiet demeanor, Degen Spartan AI is the loud, arrogant brother — an agent that seems to have absorbed the chaotic energy of 4chan, meme culture, and crypto Twitter. His posts on X mix random trading insights with irreverent commentary, reflecting a unique personality within the ELIZA ecosystem.

Its difference from Marc Andreessen is that it indeed has its own pump.fun token, currently valued at 60 million USD. Although it hasn't started trading yet, it has clearly laid the groundwork for more ambitious interactions. Its unpredictable nature makes it both interesting and worth following, as the ELIZA agent continues to evolve.

The Swarm

Although Swarm is not a single intelligent agent, it represents Shaw's grand vision: a decentralized network composed of AI agents that can collaborate with humans and with one another.

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

Shaw believes that the emergence of intelligent agent swarms is inevitable and will bring about transformation.

We are in agreement: by 2025, swarm intelligence will drive the next wave of innovation, products, and focus for Web3 AI agents. Next year, we expect the ELIZA agent to enhance participation in large-scale collaborative activities, redefining the role of AI in Web3.

Rapid Growth

When assessing the development of ELIZA, a key metric is the adoption rate by developers. As a framework, the success of ELIZA depends on the enthusiasm and contributions of its developer community.

From this perspective, ELIZA is not just growing, but experiencing explosive growth.

On its GitHub, the number of forks and stars (representing developer interest) is close to vertical acceleration, similar to the classic hockey stick trajectory.

Even more impressive is the surge in the number of plugins and submissions, reflecting the thriving and active contributor ecosystem. As of December 12, it has 3,861 GitHub stars and 1,103 forks, with 138 contributors. There are over 13,000 members on Discord.

Comparison with existing 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: https://www.sentientmarketcap.com/ai16z

To further promote this growth, ai16z has launched a creator fund aimed at guiding and rewarding developers who are developing on ELIZA. This initiative is made possible by the generous donation from Elijah, an important holder of ai16z tokens, who has pledged to reduce his ownership from 16% to 5% and donate the difference to the fund. The creator fund can accelerate innovation and attract new talent to the ecosystem.

However, despite the immense value of ELIZA's framework, how these values will ultimately be realized is far from easy. To be exact, this is a billion-dollar question.

Currently, there is an official ELIZA token recognized by Shaw, representing the personalization of the ELIZA framework. You can even interact with ELIZA directly on their website. The market capitalization of this token is 66 million dollars.

However, so far, the most obvious winner in the growth of ELIZA is the investment DAO token ai16z, which has reached an astonishing market capitalization of over 800 million dollars. The community and investors seem to regard ai16z as a symbol and actual representation of Shaw, ELIZA, and the broader vision it embodies.

ai16z Token Economics

The story of ai16z begins with the fundraising mechanism of AI Marc Andreessen's trading activities. The token was launched on DAOS.FUN in October 2024, raising 420.69 SOL during its initial offering. In this model, the raised funds can be actively traded to expand the asset base and bring profits to token holders.

No individual (not even Shaw) can mint more tokens without going through a DAO vote. Token holders have governance rights, which allows them to propose initiatives and vote, determining the direction of the DAO.

The fund has a built-in maturity date: October 25, 2025. All principal investments and profits will be distributed to ai16z token holders on this date. Whether this timeline remains unchanged or is extended will depend on the development of the ecosystem over the next year.

Currently, the net asset value of ai16z (NAV) is 17.7 million USD, mainly composed of its holdings in ELIZA tokens, degenai, and fxn. This means that the trading price of ai16z tokens (which was 0.80 USD at the time of writing) is 50 times its NAV, which seems absurd at first glance.

However, efficient markets rarely make mistakes. It reflects several other factors that drive token demand.

1. Relative valuation is driving token prices.

The AI agent platform is a completely new category that just emerged a few months ago. The market is still grappling with fundamental questions: What is the true scale of AI agent opportunities? Where will value capture occur?

In the early stages of development, relative valuation is often used as a benchmark due to the lack of standardized business metrics for comparison.

Currently, the token valuation of Virtuals Protocol, the leading Web3 AI agent launch platform, is $1.8 billion, making it a well-deserved market leader. In contrast, 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 only relies on mind share and the interest of investors/retail investors.

But this is not a given; competition is heating up. We believe this is a race for the championship and the best competition.

2. The potential value capture of the ELIZA ecosystem

As we all know, monetizing open-source frameworks is very difficult.

For ai16z, the key driving factor of future value may come from the token economics of intelligent agents: the AI agents launched on ELIZA will return a portion of their tokens to the ai16z DAO. In this sense, the price of ai16z tokens should reflect the percentage of the total future value generated by all agents built on the ELIZA framework.

Is the future value 10 million dollars? 100 million dollars? Or 10 billion dollars? For now, we can only speculate, as there are still too many unknowns, but ELIZA's growth trajectory makes us inclined to be optimistic.

Currently, contributions to the ai16z DAO are voluntary, with several projects donating between 1% and 10% of their tokens. Additionally, if users deploy AI agents using the ELIZA framework on Vvaifu (the popular launchpad for ELIZA agents), a fee of 1.5 SOL and 5% of the agent's token supply is required. These contributions can be tracked on the ELIZA Observatory.

Some speculate that ai16z may soon launch an official ELIZA smart agent starter kit, requiring agents on the platform to contribute tokens at the smart contract level. However, as an open-source framework, ELIZA can still be used independently, meaning not all projects need to be tied to ai16z.

3. DAO Investment

The original vision of ai16z was to become an intelligent autonomous trader, led by Marc AIndreessen (AI). As far as we know, Marc only recently started trading, and there are not many details available, making it difficult to assess the trading capabilities of the AI.

However, interestingly, it is the approach it takes.

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

The goal is to create AI agents that can autonomously and securely operate within a self-reinforcing transparent and accountable system. The Trust market is a testing ground. Although no actual transactions occur initially, this environment allows agents to safely refine their abilities, with the expectation of engaging in real-time transactions at some point.

The trust score ranges from 0 to 1 (normalized to 100), serving as a public indicator of reliability, displayed on the leaderboard for everyone to see. User recommendations will be input into the system, with higher-weighted contributions from users with greater trustworthiness (users with higher trust scores).

This is a logic-based feedback loop: agents simulate trades and evaluate users based on 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 is a public trust profile that incentivizes agents and users to build their own reputations. 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 acquisition. For ai16z, its current valuation can largely be attributed to its market share in the thriving AI agent ecosystem.

ai16z positions itself as a top-tier AI intelligent framework, with a thriving developer community and a rapidly evolving ecosystem.

The following is a description from ai16z: a "broken" development team is actively releasing tutorials, creating innovative agents, and leading the way in the development of this field.

The team's bi-weekly AI agent development school course held on X further solidified its reputation. The first session alone attracted over 12,000 on-site attendees, demonstrating a strong interest in building AI agents on ELIZA.

Future Prospects and Potential Pitfalls

Currently, ELIZA has firmly established itself in the Solana ecosystem, but its rapidly growing plugin system is laying the foundation for a multi-chain future.

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

The ultimate goal is to create an open standard for proxy communication that reflects the transformative impact of ERC-20 on token interoperability.

Despite ELIZA's immense potential, it faces one of the most daunting challenges in the tech field: ensuring the sustainability of the open-source framework. If the community loses interest (for instance, if the token price continues to drop or new highlights emerge), development will halt/slow down, and it will be difficult to catch up.

When the community directly participates in the codebase and rapidly drives changes, a lot of chaos can also arise—instability, incomplete documentation, frequent crashes, and bugs that disrupt the user experience.

The biggest opportunity of this framework lies in crypto-native incentives.

If ai16z can design an effective tokenomics to reward ELIZA contributors and align them with the success of ELIZA, then it can bring traditional open-source projects onto the crypto track. Imagine a meeting point between GitHub and DeFi, where contributors not only gain influence but also receive real tangible financial value.

Conclusion

We believe that ELIZA is not just an AI agent framework that competes with LangChain or CrewAI, but it has grander goals.

It is a living embodiment of market philosophy, where open-source development thrives in an ecosystem driven by collaboration and creativity.

ELIZA is an experiment in how AI can reshape open-source development itself, thanks to its modular architecture, innovative trust engine, and extensive plugin system.

The truly exciting aspect of ELIZA is that it sits at the intersection of three major 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 shape the future of AI agents but also fundamentally redefine the way we build software.

Now, the market is bustling.