Author: Teng Yan, ChainofThought manager; ChappieOnChain, ChainofThought core contributor; Translated by: 0xjs@ Jinse Finance
Key points:
ELIZA is an open source, modular framework for building AI agents that interact seamlessly with users and blockchain systems.
It is the living embodiment of the bazaar philosophy, where open source development thrives in an ecosystem fueled by collaboration and creativity.
ELIZA includes powerful autonomous trading capabilities, underpinned by its trust engine and trust marketplace, enabling secure and responsible operations.
The plugin system is a strategic advantage for ELIZA, driving the flywheel of growth: more developers → more plugins → more developers.
By many developer metrics, ELIZA’s popularity is exploding, which is very promising.
In the short term, relative valuations between AI agent platforms and growing market share drive ai16z's price action. In the medium term, DAO investments and value capture by ELIZA ecosystem agents could significantly increase its valuation.
ELIZA does face one of the toughest challenges in the tech world: making an open source framework sustainable. Without the right incentives, monetization is uncertain, development can become chaotic, and community interest can wane.
Every wave of crypto innovation has its pioneers.
2017 was the year of the ICO revolution, with ambitious project leaders (and plenty of scammers) enticing us with promises of groundbreaking technology packaged in white papers.
By 2020, DeFi had reached its peak, led by innovators such as Andre Cronje, who redefined how DeFi worked and showed the world how tokens could be built and distributed to the community.
Now, with the advent of AI agents on blockchains, a new era is unfolding, driven by two very different philosophies and their pioneers.
Cathedral and Market
On one hand, we have the cathedral approach, embodied in protocols like Virtuals. This is a methodical, purposeful, centralized design style that emphasizes precision and careful planning. We previously detailed Virtuals’ agent framework , and we’re very excited about its potential.
On the other hand, true to the roots of cryptocurrency, there is the bazaar approach: a decentralized, freewheeling model where development feels more like jazz—unpredictable, collaborative, and constantly 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 spirit of his AI Marc Andreessen, the AI partner who drives the ai16z investment DAO.
We began to realize that ELIZA is not a protocol. ELIZA is a movement.
Let’s explore the principles that drive ELIZA’s design, the community it fosters, and where value may ultimately be generated in this high-growth ecosystem.
ELIZA internal principle
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. While it shares its name with the iconic 1960s chatbot, this version of ELIZA is a bold reimagining. And it looks a lot better, to be honest.
Character File System
The soul of every ELIZA agent begins with its persona 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 backstory of an agent - its narrative foundation.
Style: From conversational tone to medium-specific responses, agents can adapt their style based on the platform, such as Discord or X.
Topics: Areas of interest or expertise that the agent is passionate about.
Adjectives: How would the agent describe itself—quirky, professional, or irreverent?
Examples: Developers can fine-tune interactions by providing example messages to guide behavior.
In ELIZA, the personality file is equivalent to the UI design of traditional software. It defines the user experience and the way to interact with the agent.
By integrating built-in Retrieval Augmentation Generation (RAG) capabilities, ELIZA allows agents to access the knowledge base simultaneously with queries. This eliminates the complexity of maintaining consistent personalities across platforms.
It allows developers to focus on what’s really important: creating vivid, memorable characters, rather than getting bogged down in backend complexity.
Agent
If the role file defines the essence of an agent, then the agent runtime is its beating heart.
ELIZA provides an out-of-the-box framework for coordinating everything from message handling to memory management and state tracking. This architecture allows developers to avoid the tedious work of building infrastructure and focus on what makes their agents unique.
Rapid prototyping and deployment become easier, enabling builders to iterate faster when building new AI experiences.
Action system
ELIZA’s action system is very 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 intention: 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 strong validation processes.
For example, an agent might recognize a user’s intent to trade cryptocurrency, but the actual execution of the transaction will only occur after passing rigorous 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 a user’s engagement during a conversation. If the user’s statements become repetitive or unengaging, the agent could respond by showing a decrease in enthusiasm — subtly mimicking real human interaction and making the conversation feel more real.
This creativity is further extended when the Provider works with the Evaluator, ELIZA’s reflective system, who analyzes and extracts key details from the interaction and feeds them into a multi-layered memory architecture:
Message History: Track the flow of conversations.
Factual Memory: Retains specific, time-stamped facts.
Core knowledge: Contains the basic understanding of intelligent agents.
The provider then retrieves and reintroduces relevant details, making the interaction with the agent richly contextual.
For example, if a user recalls selling their red Lamborghini a year ago, the ELIZA agent can mention this later when discussing their shiny new yellow Tesla. This interplay between memory and context improves user interaction, making the agent feel less robotic and more like a true partner.
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 Transactions and Trust Engine
Autonomous trading is a high-stakes game — a single mistake could result in catastrophic losses. However, as AI agents play an increasingly important role in Web3, their ability to execute trades independently is becoming increasingly essential.
The emerging field of AgentFi reflects the critical role that yield farming has played in the rise of DeFi. Shaw and ELIZA address the inherent risks head-on through a powerful two-layer system: a trust engine combined with secure trade execution.
Token metrics captured in the trust engine
The Trust Engine is the first line of defense, using advanced validation checks to analyze multiple risk dimensions in real time. From detecting fraud to assessing liquidity thresholds and coin distribution, the engine ensures that 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. Token holder concentration is closely monitored, denying any single entity control of more than 50% of tokens. These guardrails create a safety net that mitigates the risk of trading in impaired 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 transactions are capped at 1% of the portfolio, while high-risk transactions may be capped at up to 10%. The total risk exposure is limited to 10% of the portfolio, and when the decline reaches 15%, the automatic stop loss takes effect. This structured framework strikes a balance between seizing opportunities and maintaining strict risk management.
The trade execution itself is powered by Jupiter, the leading aggregator on Solana, for optimal exchange routing. Each trade goes through multiple layers of validation before execution.
The error recovery system kicks in when something unusual happens, such as a network outage, wallet imbalance, or unexpected market changes. It pauses active trading, closes risky positions, and alerts administrators, ensuring the system remains resilient under pressure.
“It’s not just about giving the agent the ability to trade, it’s about putting in place a whole system of checks and balances to prevent catastrophic failure.” — Shaw
What really differentiates ELIZA 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 will become the basis for refining strategies over time, combining quantitative metrics with qualitative insights from community suggestions (on Discord). The result is an agent that not only executes trades, but also evolves 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 make more people understand your agent?
Social media is often the primary distribution channel. However, integrating agents across multiple social platforms is not easy. It requires significant development work and ongoing maintenance. These challenges slow down deployment and scalability.
ELIZA addresses this problem head-on with a comprehensive client package system that simplifies multi-platform distribution.
ELIZA's client-side architecture removes the complexity of platform-specific implementations. With standardized interfaces, 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 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 more time focusing on building innovative, high-performance AI agents.
By removing the hassle of multi-platform distribution, ELIZA enables developers to easily scale their agents and reach users anywhere, anytime.
Its distribution is simplified.
#3: More plugins
ELIZA’s plugin system allows developers to easily extend core functionality and add custom features to their agents.
While many developers will create plugins for their own use cases, the real power of the system lies in community sharing. By releasing plugins to the wider ecosystem, developers can contribute to the growing library of out-of-the-box functionality, greatly expanding the capabilities of each AI agent on ELIZA.
The success of this approach is reflected in the vibrant "bazaar-style" development it fosters. Here are some examples of community-driven plugins:
• Bootstrap plugin: Essential conversation management tool.
•Image Generation Plugin: AI-driven image creation capabilities.
• Solana plugin: blockchain integration with built-in trust scoring.
• TEE plugin: Provides a secure execution environment for sensitive operations.
• Coinbase Commerce plugin: Cryptocurrency payment processing capabilities.
ELIZA’s plugin system is its strategic and platform advantage. By prioritizing extensibility, ELIZA lays the foundation for continued growth and innovation, including:
Each new plugin increases the overall value of the platform
Community contributions can be made in parallel 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.
Here is a simple flywheel:
More developers develop on ELIZA → The framework supports more features (such as plugins) → More developers develop on ELIZA
The AI agent landscape is evolving at a rapid pace. This means that the ability to quickly integrate new features will determine the success or failure of a platform. ELIZA’s plugin system gives it the best chance to stay 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 a number of interesting ELIZA agents, each of which demonstrates the potential of AI in decentralized systems.
While these agents are still “young” from an AI perspective and their features and capabilities are still being actively developed, they hint at exciting possibilities.
Marc A Indreessen
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 the podcast interview, revealing that the AI is part of an alpha chat group that consists of some of the best traders in the field. This suggests 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 boisterous, brash brother — an agent seemingly 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, reflecting the unique personalities in the ELIZA ecosystem.
It differs from Marc AIndreesseen in that it does have its 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 groundwork for more ambitious interactions. His unpredictable personality makes him both interesting and worth keeping an eye on as the ELIZA agent continues to evolve.
The Swarm
Although 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 intended to avoid hidden protocols and ensure public accountability.
Shaw believes the emergence of swarms of intelligent agents is inevitable and transformative.
We agree that agent swarms will drive the next wave of innovation, products, and attention for Web3 AI agents by 2025. Next year, we expect ELIZA agents to increase their participation in large-scale collaborative activities, redefining the role of AI in Web3.
Rapid growth
When evaluating the growth of ELIZA, the key metric is developer adoption. As a framework, ELIZA's success depends on the passion and contributions of its developer community.
In this respect, ELIZA isn’t just growing, it’s exploding.
On its GitHub, the number of forks and stars (representing developer interest) approaches vertical acceleration, resembling a classic hockey stick trajectory.
Even more impressive is the surge in plugins and commits, reflecting a thriving and active contributor ecosystem. As of December 12, it has 3,861 GitHub stars and 1,103 forks, with 138 contributors. There are more than 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 forks
Microsoft's AutoGen: 35,700 stars and 5,200 forks
Source: https://www.sentientmarketcap.com/ai16z
To further fuel this growth, ai16z launched the Creator Fund, which aims to guide and reward developers building on ELIZA. This move was made possible by the generosity of Elijah, a significant holder of ai16z tokens, who 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, while ELIZA’s framework has enormous value, how that value will ultimately be realized is far from straightforward. To be precise, this is the billion-dollar question.
Currently, there is an official ELIZA token endorsed by Shaw that represents the personalization of the ELIZA framework. You can even interact with ELIZA directly on their website. The market cap of this token is $66 million.
However, the clearest winner of ELIZA’s growth so far has been investment 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 a symbolic and de facto representation of Shaw, ELIZA, and the broader vision it embodies.
ai16z token economics
The story of ai16z begins with the fundraising mechanism of the AI Marc Andreessen trading activity. The token was launched on DAOS.FUN in October 2024 and raised 420.69 SOL during the initial offering. In this model, the funds raised 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 a vote from the DAO. Token holders have governance rights, which allow 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 invested and profits will be distributed to ai16z token holders on this date. Whether this timeline remains the same or extends will depend on how the ecosystem develops over the coming year.
Currently, ai16z’s net asset value (NAV) is $17.7 million, primarily made up of its ELIZA tokens, degenai, and fxn holdings. This means that ai16z tokens ($0.80 at the time of writing) are trading at 50x their NAV, which seems absurd at first glance.
However, efficient markets are rarely wrong. It reflects several other factors driving demand for tokens.
1. Relative valuations 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 fundamental questions: What is the true size of the AI agent opportunity? Where will value capture occur?
In the early stages of development, relative valuations are often used as benchmarks because there are no standardized business metrics to compare against.
Currently, Virtuals Protocol, the leading Web3 AI agent startup, is the undisputed market leader with a token valuation of $1.8 billion. 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 based solely on mind share and investor/retail interest.
But it's not a given; the competition is heating up. We believe it's a race to the top and a race to be the best.
2. Potential value capture of the ELIZA ecosystem
Monetization of open source frameworks is notoriously difficult.
For ai16z, the key driver of future value may come from agent token economics: AI agents launched on ELIZA give a portion of their tokens back to the ai16z DAO. In this sense, the price of ai16z tokens should reflect a percentage of the total future value generated by all agents built on the ELIZA framework.
Is it worth $10 million? $100 million? $10 billion? We can only guess at this point because there are so many unknowns, but ELIZA's growth trajectory makes us optimistic.
Currently, contributions to the ai16z DAO are voluntary, with several projects donating between 1% and 10% of their tokens. Additionally, if users deploy an AI agent on Vvaifu (a popular community launchpad for ELIZA agents) using the ELIZA framework, they will be required to pay a fee of 1.5 SOL plus 5% of the agent token supply. These contributions can be tracked on the ELIZA Observatory.
There is speculation that ai16z may soon launch an official ELIZA agent launchpad that forces agents using the platform to make token contributions at the smart contract level. However, as an open source framework, ELIZA can still be used independently, meaning that not all projects are necessarily tied to ai16z.
3. DAO Investment
ai16z was originally envisioned to be an intelligent autonomous trader, with Marc AIndreessen (AI) at the helm. As far as we know, Marc has only recently started trading and there are few details, so it is difficult to assess how strong 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 white paper for the market is expected to be released by 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 proving ground. While no actual trading occurs initially, this environment allows agents to safely perfect their capabilities with the expectation of live trading at some point.
Trust scores range from 0 to 1 (normalized to 100) and are a public sign of reliability that is displayed on a leaderboard for all to see. User recommendations are fed into the system, with more credible users (those with higher trust scores) being weighted more heavily.
It’s a logic-based feedback loop: Agents simulate transactions, users are evaluated based on the outcomes, 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 where agents and users are incentivized to build their own reputation. Community management ensures accountability and transparency
4. Attention Premium
Source: X Radar
In the cryptocurrency space, speculation often precedes product-market fit, revenue generation, and long-term value capture. 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 AI agent framework with a thriving developer community and a rapidly growing ecosystem.
Here is ai16z’s account: A “broken” development team is actively publishing tutorials, creating innovative agents, and leading the way in the field.
The team further solidified its reputation with the biweekly AI Agent Development School courses held at X. The first course alone attracted over 12,000 live attendees, demonstrating the 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 plugin system is laying the foundation for a multi-chain future.
The real potential of ELIZA lies in Shaw’s vision of a “swarm”: a decentralized network of AI agents that pool resources and collaborate across ecosystems. This swarm effect can build a 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 proxy communication, mirroring the transformative impact of ERC-20 on token interoperability.
Although ELIZA has great potential, it 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 a new bright spot comes along), development will stop/slow down and it will be hard 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 this framework lies in crypto-native incentives.
If ai16z can design 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 gain not only influence but also real tangible financial value.
Conclusion
We believe that ELIZA is not just an AI agent framework that competes with LangChain or CrewAI, it has a much more ambitious goal.
It is the living embodiment of the bazaar philosophy, where open source development thrives in an ecosystem fueled 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 really 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 shape the future of AI agents, but will fundamentally redefine the way we build software.
Now, the market is bustling with people.
