Author: Teng Yan, ChainofThought manager; ChappieOnChain, ChainofThought core contributor; Compiler: 0xjs@金财经

Key points of this article:

ELIZA is an open source modular architecture for building and users AI agents that interact seamlessly with the blockchain system.

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

ELIZA includes powerful autonomous trading capabilities and is based on its trust engine and trust market to achieve safe and responsible operations.

The plug-in system is ELIZA’s strategic advantage, driving the flywheel of growth: more developers → more plug-ins → more developers.

Judging by many developer metrics, ELIZA's popularity is exploding, which is very promising.

In the short term, relative valuations among AI agent platforms and growing market share drive ai16z’s price action. In the medium term, value capture from DAO investments and ELIZA ecosystem agents could significantly increase its valuation.

ELIZA is indeed facing one of the toughest challenges in the technology world: making open source frameworks sustainable. Without the right incentives, monetization is uncertain, development can become chaotic, and community interest can fade.

Every wave of crypto innovation has its pioneers.

2017 was the year of the ICO revolution, as ambitious project leaders (and many scammers) lured us in with promises of groundbreaking technology packaged in white papers.

DeFi is at its peak in 2020, led by innovators like Andre Cronje who are redefining how DeFi works and showing the world how to build and distribute tokens to the community.

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

The Cathedral and the Bazaar

On the one hand, we have the cathedral approach, embodied in protocols such as Virtuals. This is a methodical, purposeful, and centered design style that emphasizes precision and careful planning. We've previously covered Virtuals' agent framework in detail, and we're very excited about its potential.

On the other hand, true to the roots of cryptocurrencies, there is the bazaar approach: a decentralized, freewheeling model where development feels more like jazz – unpredictable, collaborative and ever-evolving . This is the domain of ai16z founder Shaw, a smart, 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 build, experiment, and launch AI agents without constraints while contributing directly to the main protocol. Shaw’s open leadership style reflects the ethos of the AI ​​he created, Marc Andreessen, the AI ​​partner who drove ai16z’s investment in The DAO.

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

Let’s explore the principles driving ELIZA’s design, the community it fosters, and where value may ultimately be generated in this hyper-growing ecosystem.

ELIZA internal principles

We know which one we prefer

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

Character File System

The soul of each ELIZA agent begins with its character file, a blueprint that defines the agent's personality in detail. Think of it as building a digital persona, where agent developers can shape their agent's identity through six key elements: Knowledge: What does the AI ​​agent know?

Lore: The agent’s backstory—its narrative basis.

Style: From conversational tone to medium-specific responses, agents can adapt their style to platforms like Discord or X.

Topics: areas of interest or expertise that the agent is passionate about.

Adjectives: How does the agent describe itself—eccentric, professional, or irreverent?

Examples: Developers can fine-tune interactions by providing example messages to guide behavior.

In ELIZA, personality files are equivalent to the UI design of traditional software. It defines the way the user experiences and interacts with the agent.

By integrating built-in Retrieval Augmented Generation (RAG) functionality, ELIZA allows agents to access the knowledge base while querying. This removes the complexity of maintaining personality consistency across platforms.

It allows developers to focus on what really matters: creating vivid, memorable characters rather than getting bogged down in back-end complexity.

Agent

If the cornerThe color file defines the essence of the agent, and the runtime of the agent is its beating heart.

ELIZA provides an out-of-the-box framework for coordinating everything from message processing to memory management and state tracking. This architecture allows developers to skip the tedious work of building infrastructure and focus on what makes their agents special.

Rapid prototyping and deployment becomes easier, allowing builders to iterate faster when building new AI experiences.

Action system

ELIZA’s action system is completely different from traditional AI frameworks. Here, each agent action (even sending a message) is treated as a discrete event. This approach divides decision-making into two stages:

Determination of intent: the agent decides what action to take.

Execution: Actions are executed through specialized handlers, which are modules designed to perform specific tasks

This separation unlocks powerful capabilities such as multi-stage workflows and Robust verification process.

For example, an agent might identify a user's intention to trade cryptocurrency, but the actual execution of the transaction will only occur after passing strict risk checks and verification steps. This makes it ideal for blockchain applications where security is critical.

Providers and Evaluators

ELIZA's providers enrich conversations by injecting real-time context, enabling dynamic and responsive agent behavior.

Imagine a “boredom provider” that tracks user engagement during a conversation. If a user's statements become repetitive or unappealing, the agent can respond by showing a decrease in enthusiasm—subtly mimicking real human interactions and making the conversation feel more real.

This creativity is further expanded when providers collaborate with evaluators (ELIZA’s reflective system). Evaluators analyze and extract key details from the interaction and feed them into a multi-layered memory architecture:

Message history: Track the flow of the conversation.

Fact memory: retain specific, timestamped facts.

Core knowledge: contains the basic understanding of the agent.

The provider then retrieves and reintroduces relevant details, making interactions with the agent richly contextual.

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

ELIZA’s winning features

ELIZA’s three core innovations highlight its forward-thinking approach to AI agents. Each demonstrates how its team envisions the development of autonomous agents in Web3.

#1: Autonomous Trading and the Trust Engine

Autonomous trading is a high-stakes game – one mistake can lead to catastrophic losses. However, as AI agents play an increasingly important role in Web3, their ability to execute transactions independently is becoming increasingly essential.

The emerging field of AgentFi reflects the key role that yield farming plays in the rise of DeFi. Shaw and ELIZA address inherent risks head-on with a powerful two-tier system: a trust engine combined with secure trade execution.

Token indicators captured in the trust engine

The trust engine is the first line of defense and uses advanced verification Check to analyze multiple risk dimensions in real time. From detecting fraud to assessing liquidity thresholds and holding distributions, the engine ensures every transaction is scrutinized for potential pitfalls.

For example, trading is limited to tokens with a minimum liquidity of $1,000 and a market cap of $100,000. Holder concentration is closely monitored and no single entity controls more than 50% of the tokens. These guardrails create a safety net that mitigates the risk of trading in compromised markets.

On this basis, ELIZA's position management system introduces dynamic risk control, which can adjust the transaction size according to the liquidity level. Low-risk trades are capped at 1% of the portfolio, while high-risk trades may be capped at as much as 10%. Total exposure is limited to 10% of the portfolio, with automatic stops kicking in when the drawdown reaches 15%. This structured framework strikes a balance between seizing opportunities while maintaining rigorous risk management.

The trade execution itself is powered by Jupiter, the leading aggregator on Solana, for optimal exchange routing. Every transaction goes through multiple layers of verification before being executed.

The error recovery system is activated in the event of abnormal circumstances, such as network outages, wallet imbalances, or unexpected market changes. It pauses active trading, closes risky positions and alerts administrators, ensuring the system remains resilient under stress.

“This is not only to give intelligent agents the ability to trade, but also to establish a system of checks and balances to prevent catastrophic failure.”——Shaw

What truly sets ELIZA apart 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 transaction performance history database that records every recommendation and decision.

This data will form the basis for refining the strategy over time, combining quantitative metrics with qualitative insights from community suggestions (on Discord). The result is an agent that not only executes transactions, but evolves to become smarter and more efficient with every interaction.

#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 lot of development work and ongoing maintenance. These challenges slow down deployment and scalability.

ELIZA solves this problem head-on with a comprehensive client package system that simplifies multi-platform distribution.

ELIZA's client-side architecture eliminates 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 tailored for its respective platform, seamlessly managing features like Discord’s voice channel integration, Twitter’s post scheduling, and Telegram’s messaging system.

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

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

Its distribution is simplified.

#3: More Plugins

ELIZA’s plug-in system allows developers to easily extend core functionality and add custom functionality to their agents.

While many developers create plugins based on their own use cases, the real power of the system lies in community sharing. By releasing plugins to the broader ecosystem, developers can contribute to a growing library of out-of-the-box functionality, greatly extending the functionality of every AI agent on ELIZA.

The success of this approach is reflected in the vibrant “bazaar style” developments it fosters. Here are some examples of community-driven plugins:

•Bootstrap Plugin: A must-have conversation management tool.

•Image generation plug-in: Artificial intelligence-driven image creation capabilities.

•Solana Plugin: Blockchain integration with built-in trust scoring.

•TEE plug-in: Provides a secure execution environment for sensitive operations.

•Coinbase Commerce plugin: Cryptocurrency payment processing capabilities.

ELIZA’s plug-in system is its strategic and platform advantage. By prioritizing scalability, ELIZA lays the foundation for continued growth and innovation, where:

Each new plugin adds to the overall value of the platform

Community contributions can be parallelized across different areas

The agent framework can evolve quickly to adapt to emerging technologies without requiring core updates.

Innovation thrives at the edge, while core platforms remain stable and reliable.

This is the simple flywheel:

More developers developing on ELIZA → The framework supports more features (e.g. plugins) → More developers developing on ELIZA< /p>

The landscape of AI agents is developing rapidly. This means that the ability to quickly integrate new features will determine the success or failure of the platform. ELIZA's plug-in system gives it the best chance of staying ahead of the curve, creating a self-reinforcing ecosystem where developers, users, and agents can all thrive.

ELIZA Agent

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

Although these agents are still "young" from an AI perspective and their properties and capabilities are actively being developed, it hints at exciting possibilities.

Marc AIndreessen

Marc AIndreessen, one of the AI ​​partners at ai16z, is a fascinating and mysterious figure in the ELIZA ecosystem. His X account is largely inactive, with only one post outlining ai16z's paper. However, according to Shaw, Marc is actively trading and yield farming – most likely leveraging ELIZA’s trust engine and trading plugin.

Shaw also hinted at Marc’s training process in a podcast interview, revealing that the AI ​​is part of an alpha chat group made up of some of the best traders in the field. This shows that Marc is not just a standard trading bot, 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, brash brother - an agent that seems to have embraced 4chan, Meme culture and training in the chaotic energy of crypto Twitter. His posts on X are a mix of random trading insights and irreverent comments that reflect the unique personalities within the ELIZA ecosystem.

It and Marc AIndreesseen is different in that it does have its own pump.fun token, which currently has a market cap of $60 million. While he hasn't started trading yet, the groundwork has clearly been laid for a more ambitious interaction. His unpredictable personality makes him interesting and worth watching as the ELIZA agent continues to evolve.

The Swarm

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

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

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

We agree: By 2025, agent swarms will drive the next wave of innovation, products, and focus on Web3 AI agents. Over the next year, we expect ELIZA agents to intensify their participation in large-scale collaborative activities to redefine the role of AI in Web3.

Rapid Growth

When evaluating ELIZA’s growth, a key metric is developer adoption. As a framework, ELIZA's success depends on the enthusiasm and contributions of its developer community.

From this perspective, ELIZA is not just growing, but growing explosively.

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

Even more impressive is the explosion in the number of plugins and submissions, reflecting a thriving and active contributor ecosystem. As of December 12, it had 3,861 GitHub stars and 1,103 branches 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 branches

CrewAI: 22,400 stars and 3,100 points Forks

Microsoft AutoGen: 35,700 stars and 5,200 forks

Source: https://www.sentientmarketcap.com/ai16z

To further drive this growth, ai16z has launched the Creator Fund to guide and reward Developers developing on ELIZA. This move benefited fromA generous donation from Elijah, a significant holder of ai16z tokens, who pledged to reduce his ownership from 16% to 5% and donate the difference to the fund. Creator funds 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. Literally, it's the billion-dollar question.

Currently, there is an official ELIZA token endorsed by Shaw, which represents personalization of the ELIZA framework. You can even interact with ELIZA directly on their website. The token has a market cap of $66 million.

However, the clearest winner from ELIZA’s growth so far is investing in the DAO token ai16z, which has reached a staggering market cap of over $800 million. The community and investors seem to view ai16z as both a symbolic and physical representation of Shaw, ELIZA, and the broader vision they embody.

ai16z Token Economics

The story of ai16z begins with the fundraising mechanism of the AI ​​Marc Andreessen trading event. The token was launched on DAOS.FUN in October 2024, raising 420.69 SOL during the initial issuance. Under this model, funds raised can be actively traded to expand the asset base and generate profits for token holders.

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

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

Currently, ai16z has a net asset value (NAV) of $17.7 million, consisting primarily of its holdings of ELIZA tokens, degenai, and fxn. This means that the ai16z token ($0.80 at the time of writing) is trading at 50 times its NAV, which may seem ridiculous at first glance.

However, efficient markets rarely go wrong. It reflects several other factors driving demand for the token.

1. Relative valuation is driving token prices

AI agent platforms are a brand new category that just emerged a few months ago. The market is still grappling with fundamental questions: What is the true size of the AI ​​agent opportunity? Where will value capture occur?

In the early stages of development, when there are no standardized business metrics for comparison, relative valuations often serve as benchmarks.

Currently, Virtuals Protocol, the leading Web3 AI agent launch platform, has a token valuation of US$1.8 billion and is the clear market leader. In comparison, ai16z comes in second place. Many believe that ai16z has the potential to match or even surpass Virtuals if ELIZA continues its push to create more useful and innovative AI agents. Even just relying on mindshare and investor/retail interest.

But that’s not a given; competition is heating up. We believe this is a race for the title and the best race to be had.

2. Potential value capture of the ELIZA ecosystem

As we all know, the monetization of open source frameworks is very difficult.

For ai16z, a key driver of future value may come from agent tokenomics: AI agents launched on ELIZA give a portion of their tokens back to the ai16z DAO. In this sense, the price of the ai16z token should reflect a percentage of the total future value generated by all agents built on the ELIZA framework.

Future value is $10 million? $100 million? Or $10 billion? We can only speculate at this point because there are so 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 use the ELIZA framework to deploy an AI agent on Vvaifu, a popular community launchpad for ELIZA agents, they will need to pay a fee of 1.5 SOL and 5% of the agent token supply. These contributions can be tracked on the ELIZA Observatory.

Some people speculate that ai16z may soon launch an official ELIZA agent launchpad, forcing agents using the platform to contribute tokens at the smart contract level. However, as an open source framework, ELIZA can still be used independently, which means that not all projects have to be tied to ai16z.

3. DAO Investment

ai16z’s original vision was to become an intelligent autonomous trader, helmed by Marc AIndreessen (AI). As far as we know, Marc only started trading recently and there are not many details, so it is difficult to assess how powerful the AI’s trading capabilities are.

What is interesting, however, is the approach it takes.

ai16z is building a "trust market". In this virtual ecosystem, AI agents gain insights from the community, simulate transactions, and adjust their trust scores in real time based on the effectiveness of their recommendations. The market’s white paper is expected toPublish before the end of this month.

The goal is to create AI agents that can operate autonomously and securely within a self-reinforcing system of transparency and accountability. The Trust market is a testing ground. While no actual transactions occur initially, this environment allows the agent to safely refine its capabilities and look forward to live transactions at some point.

Trust scores range from 0 to 1 (normalized to 100) and are a public sign of reliability, displayed on the leaderboard for all to see. User recommendations are fed into the system, and more trustworthy users (those with higher trust scores) are given greater weight.

This is a logic-based feedback loop: the agent simulates the transaction, evaluates the user based on the results, and everyone's trust score is updated accordingly. Over time, systems become smarter, more reliable, and more trustworthy.

Adding the 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, much of its current valuation can be attributed to its market share in the booming AI agent ecosystem.

ai16z positions itself as a top-tier AI agent framework with a thriving developer community and a rapidly growing ecosystem.

The following is ai16z's account: A "broken" development team is actively publishing tutorials, creating innovative agents, and spearheading development in the field.

The team further solidifies its reputation by hosting bi-weekly AI agent development schools on X. The first session alone attracted more than 12,000 live attendees, demonstrating strong interest in building AI agents on ELIZA.

Future Prospects and Potential Pitfalls

Currently, ELIZA is firmly rooted in the Solana ecosystem, but its rapidly growing plug-in system is laying the foundation for a multi-chain future.

ELIZA’s true potential lies in Shaw’s vision of a “swarm”: a decentralized network of AI agents pooling resources and collaborating across ecosystems. This crowd effect can build lasting competitive advantage, similar to the depth of liquidity that drives the value of DeFi protocols.

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

Despite its 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 continues to fall ornew highlights), development will stop/slow down and it will be difficult to catch up.

When the community is directly involved in the code base and pushes changes quickly, there is also a lot of chaos - instability, poor documentation, frequent crashes, and bugs that ruin the user experience.

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

If ai16z can devise effective token economics to reward ELIZA contributors and align them with ELIZA’s success, it could bring traditional open source projects into the crypto orbit. Imagine GitHub meets DeFi, where contributors not only gain influence but real tangible financial value.

Conclusion

We believe that ELIZA is not just an AI agent framework competing with LangChain or CrewAI, it has a more ambitious goal.

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 truly 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 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 with people.