Author: Teng Yan, founder 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 interact seamlessly with users and blockchain systems.
It is a living embodiment of the marketplace philosophy, where open-source development thrives in an ecosystem driven by collaboration and creativity.
ELIZA includes powerful autonomous trading capabilities based on its trust engine and trust market, achieving secure and responsible operations.
The plugin system is ELIZA's strategic advantage, driving the growth flywheel: more developers → more plugins → more developers.
From many developer metrics, ELIZA's popularity is experiencing explosive growth, which is very promising.
In the short term, the relative valuation between AI agent platforms and the growing market share drives the price trend of ai16z. In the medium term, DAO investments and the value capture of ELIZA ecosystem agents could significantly enhance its valuation.
ELIZA does indeed face one of the most severe challenges in the tech community: ensuring the sustainability of the open-source framework. Without adequate incentives, there is uncertainty in monetization, which could lead to chaotic development and a potential decline in community interest.
Every wave of crypto innovation has its pioneers.
2017 was the ICO revolution, where ambitious project leaders (and many scammers) attracted us with promises of groundbreaking technologies 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 distinctly different philosophies and their pioneers is unfolding.
Cathedrals and Markets
On one hand, we have the cathedral approach, as exemplified by protocols like Virtuals. This is a methodical, purposeful, centralized design style that emphasizes precision and careful planning. We have previously detailed the agent framework of Virtuals, and we are very excited about its potential.
On the other hand, true to the roots of cryptocurrency, there is a market 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 the cornerstone of this new paradigm.
ELIZA embodies the marketplace philosophy: an open framework where developers can build, experiment, and launch AI agents without constraints while contributing directly to the main protocols. Shaw's open leadership style reflects the spirit of the AI Marc Andreessen he created, who is the AI partner driving investments into the ai16z DAO.
We are beginning to realize that ELIZA is not just 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 emerge in this rapidly growing ecosystem.
Internal Principles of ELIZA
We know which one we prefer.
Essentially, ELIZA provides a modular architecture for creating AI agents capable of seamless interaction with users and blockchain systems. Although it shares a name with the iconic chatbot from the 1960s, this version of ELIZA is a bold reimagining. To be honest, it looks much better.
Character File System
The soul of each ELIZA agent begins with its character file, a blueprint that details the agent's personality. It can be seen as creating a digital character, and agent developers can shape their agent's identity through six key elements:
Knowledge: What do AI agents understand?
Lore: The backstory of the agent - its narrative foundation.
Style: From the tone of conversation to responses in specific media, agents can adjust their style based on platforms like Discord or X.
Topics: Areas of interest or expertise the agent is passionate about.
Adjectives: How the agent describes itself - quirky, professional, or irreverent?
Examples: Developers can guide behavior by providing example messages, thereby fine-tuning interactions.
In ELIZA, personality files are analogous to UI design in traditional software. They define the user experience and how users interact with the agent.
By integrating built-in retrieval-augmented generation (RAG) capabilities, ELIZA allows agents to access knowledge bases 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 without getting bogged down in backend complexities.
Agent
If character files define the essence of an agent, then the agent runtime is its beating heart.
ELIZA provides an out-of-the-box 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 unique.
Rapid prototyping and deployment become easier, enabling builders to iterate faster while creating new AI experiences.
Action System
ELIZA's action system is radically different from traditional AI frameworks. Here, every agent action (even sending a message) is treated as a discrete event. This approach divides decision-making into two phases:
Determining Intent: The agent decides what action to take.
Execution: Actions are carried out through dedicated handlers, which are modules designed for specific tasks.
This separation unlocks powerful functionalities, such as multi-stage workflows and robust verification processes.
For instance, an agent might identify a user's intent to trade cryptocurrency, but the actual execution of the trade only occurs after passing rigorous risk checks and validation steps. This makes it an ideal choice for blockchain applications where security is paramount.
Providers and Evaluators
ELIZA's providers enrich the dialogue by injecting real-time context, enabling dynamic and responsive agent behavior.
Imagine having a 'boredom provider' that can track user engagement throughout the conversation. If a user's statements become repetitive or lack engagement, this agent can respond by displaying a decline in enthusiasm - cleverly mimicking real human interaction to make the conversation feel more genuine.
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-layer memory architecture:
Message History: Tracking the flow of conversation.
Fact Memory: Retaining specific, timestamped facts.
Core Knowledge: Containing the foundational understanding of the agent.
The provider then retrieves and reintroduces relevant details, enriching the interaction with the agent.
For example, if a user recalls selling a red Lamborghini a year ago, the ELIZA agent can later reference 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.
The Winning Features of ELIZA
The three core innovations of ELIZA 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.
Autonomous trading is a high-risk game - one mistake could lead to catastrophic losses. However, as AI agents play an increasingly important role in Web3, their ability to execute trades independently is becoming essential.
The emerging field of AgentFi reflects the critical role that yield farming plays in the rise of DeFi. Shaw and ELIZA confront inherent risks head-on through a robust dual-layer system: combining the trust engine with secure transaction execution.
Token metrics captured within 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 instance, trades are limited to tokens with a minimum liquidity of $1,000 and a market cap of $100,000. Concentration of token holders is closely monitored, rejecting any single entity from controlling more than 50% of the tokens. These guardrails create a safety net that mitigates the risks of trading in a compromised market.
On this foundation, ELIZA's position management system introduces dynamic risk control, adjusting trade sizes based on liquidity levels. The cap for low-risk trades is set at 1% of the portfolio, while high-risk trades can be capped at up to 10%. The total risk exposure limit is set at 10% of the portfolio, with automatic stop-losses kicking in at a 15% drop. This structured framework balances seizing opportunities with maintaining strict risk management.
Transaction execution itself is powered by Jupiter, the leading aggregator on Solana, to achieve optimal exchange routing. Every transaction undergoes multiple verification layers before execution.
The error recovery system activates in cases of anomalies, such as network interruptions, wallet imbalances, or unexpected market changes. It pauses active transactions, closes risk positions, and alerts administrators to ensure the system remains resilient under pressure.
"This not only gives agents the ability to trade but also establishes a whole system of checks and balances to prevent catastrophic failures." - 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 database of trading performance history, documenting every suggestion and decision.
This data will form the basis for refining strategies over time, combining quantitative metrics with qualitative insights from community suggestions (on Discord). The result is that agents not only execute transactions but also evolve 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 do you get more people to know about your agent?
Social media is often the primary distribution channel. However, integrating agents across multiple social platforms is not easy. It requires substantial development work and ongoing maintenance. These challenges slow down deployment and scalability.
ELIZA addresses this issue head-on through a comprehensive client package system, streamlining multi-platform distribution.
ELIZA's client architecture eliminates the complexities of platform-specific implementations. With a standardized interface, developers need minimal extra 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 like 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 eliminating the hassles of multi-platform distribution, ELIZA enables developers to easily extend their agents and connect with users anytime, anywhere.
Its distribution is quite streamlined.
#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 based on their use cases, the true strength of the system lies in community sharing. By releasing plugins to a broader ecosystem, developers can contribute to the growing library of out-of-the-box functionalities, significantly expanding the capabilities of each AI agent on ELIZA.
The success of this approach is reflected in the vibrant 'marketplace-style' development it fosters. Here are some examples of community-driven plugins:
• Bootstrap Plugin: Essential dialogue management tools.
• Image Generation Plugin: AI-driven image creation capabilities.
• Solana Plugin: Integrated trust scoring within the blockchain.
• 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 scalability, ELIZA lays the groundwork for ongoing growth and innovation, including:
Each new plugin adds overall value to the platform.
Community contributions can be carried out in parallel across different domains.
The agent framework can rapidly evolve to adapt to emerging technologies without core updates.
Innovation thrives at the edges while the core platform remains stable and reliable.
This is a simple flywheel:
More developers developing on ELIZA → Framework supports more features (like plugins) → More developers developing on ELIZA.
The landscape for 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 it with the best opportunity to stay ahead, creating a self-reinforcing ecosystem where developers, users, and agents can all thrive.
ELIZA Agents
Shaw and his team have been incubating some interesting ELIZA agents, each showcasing the potential of AI in decentralized systems.
While these agents are still relatively 'young' from an AI perspective, their characteristics and capabilities are actively being developed, hinting at exciting possibilities.
Marc Andreessen
Marc Andreessen is one of the AI partners at ai16z, a fascinating yet enigmatic figure within the ELIZA ecosystem. His X account is largely inactive, with only one post outlining the ai16z 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 a podcast interview, revealing that the AI is part of an alpha chat group consisting of some of the best traders in the field. This suggests that Marc is not just a standard trading bot but an evolving agent 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 trained on the chaotic energy of 4chan, meme culture, and crypto Twitter. Its posts on X mix random trading insights with irreverent comments, reflecting a unique personality within the ELIZA ecosystem.
What sets it apart from Marc Andreessen is that it indeed has its 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 interesting and worth watching, as ELIZA agents are continually evolving.
The Swarm
While 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 with each other.
In this model, agents lead other agents, coordinate tasks, and interact transparently on social media. This intentional transparency aims to avoid hidden protocols and ensure public accountability.
Shaw believes that the emergence of agent swarms is inevitable and will bring about transformation.
We are united in our view: by 2025, the swarm of agents will drive the next wave of innovation, products, and focus in Web3 AI agents. Next year, we expect ELIZA agents to strengthen their involvement 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, ELIZA's success depends on the enthusiasm and contributions of its developer community.
From this perspective, ELIZA is not just growing; it is experiencing explosive growth.
On its GitHub, the number of forks and stars (representative of developer interest) is approaching vertical acceleration, resembling 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 had 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 drive this growth, ai16z has launched a Creator Fund aimed at guiding and rewarding developers working on ELIZA. This initiative benefits from the generous donation of key ai16z token holder Elijah, 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, despite the immense value of ELIZA's framework, how these values will ultimately be realized is far from straightforward. To be precise, this is a billion-dollar question.
Currently, there is an official ELIZA token recognized by Shaw, representing personalization of the ELIZA framework. You can even interact directly with ELIZA on their website. The token has a market capitalization of $66 million.
However, so far, the most obvious winner of ELIZA's growth has been the investment DAO token ai16z, with a market cap exceeding a staggering $800 million. The community and investors seem to view ai16z as a symbol and tangible representative of Shaw, ELIZA, and the broader vision it embodies.
ai16z Token Economics
The story of ai16z began with the fundraising mechanism for AI Marc Andreessen's trading activities. The token was launched on DAOS.FUN in October 2024, raising 420.69 SOL during its initial issuance. Under this model, the raised funds 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 DAO voting. Token holders have governance rights, allowing them to propose initiatives and vote, deciding the direction of the DAO.
The fund has a built-in expiration 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, ai16z's net asset value (NAV) is $17.7 million, primarily composed of its holdings in ELIZA tokens, degenai, and fxn. This means that the trading price of ai16z tokens (at the time of writing, $0.80) is 50 times its NAV, which seems absurd at first glance.
However, effective markets rarely go wrong. They reflect several other factors driving token demand.
1. Relative valuation is driving token prices.
AI agent platforms represent a brand 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 often serves as a benchmark due to the lack of standardized business metrics for comparison.
Currently, the leading Web3 AI agent launch platform, Virtuals Protocol, has a token valuation of $1.8 billion, making it the undisputed 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 or even surpass Virtuals, even if it relies solely on mindshare and interest from investors/retailers.
But this is not a foregone conclusion; competition is heating up. We see this as a race for the championship, and it’s the best kind of competition.
2. Potential Value Capture of the ELIZA Ecosystem
It is well known that monetizing open-source frameworks is very challenging.
For ai16z, key drivers of future value may come from AI token economics: 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 future total value generated by all agents built on the ELIZA framework.
Is the future value $10 million? $100 million? Or $10 billion? It's still too early to say, as there are many unknowns, but ELIZA's growth trajectory leads us to be optimistic.
Currently, contributions to the ai16z DAO are voluntary, with several projects donating between 1% and 10% of tokens. Additionally, if users deploy AI agents using the ELIZA framework on Vvaifu (a popular community launchpad for ELIZA agents), they must pay a fee of 1.5 SOL and 5% of the agent's token supply. These contributions can be tracked on the ELIZA Observatory.
It is speculated that ai16z may soon launch an official ELIZA agent launchpad, mandating agents using 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 must be tied to ai16z.
3. DAO Investments
The initial vision for ai16z was to function as a smart autonomous trader, helmed by Marc Andreessen (AI). As far as we know, Marc has only recently begun trading, and details are scarce, making it difficult to assess how strong AI's trading capabilities are.
However, it is interesting how it approaches the task.
ai16z is building a 'Trust Market.' In this virtual ecosystem, AI agents draw 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 the month.
The goal is to create AI agents that can autonomously and safely operate within a self-reinforcing system of transparency and accountability. The Trust Market serves as a testing ground. While no actual trades occur initially, this environment allows agents to safely hone their capabilities, anticipating real-time trading at some point.
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 are fed into the system, with higher-weighted users (those with higher trust scores) having greater influence.
This is a logic-based feedback loop: agents simulate trades, evaluate users based on the outcomes, 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, which incentivizes agents and users to build their own reputation. Community management ensures accountability and transparency.
4. Attention Premium
Source: X Radar
In the cryptocurrency space, speculation often leads product-market fit, monetization, and long-term value capture. For ai16z, its current valuation can largely be attributed to its market share within the thriving AI agent ecosystem.
ai16z positions itself as the top AI agent framework, boasting a thriving developer community and a rapidly evolving ecosystem.
Here is the narrative of ai16z: a 'broken' 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 cements its reputation. The first session alone attracted over 12,000 live viewers, showcasing a 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 groundwork for a multi-chain future.
ELIZA's true potential lies in Shaw's 'swarm' vision: a decentralized network of AI agents that pool resources and collaborate across ecosystems. This collective effect can establish a lasting competitive advantage, akin to the liquidity depth driving the value of DeFi protocols.
The ultimate goal is to create an open standard for agent communication, reflecting ERC-20's transformative impact on token interoperability.
Despite ELIZA's immense potential, it faces one of the most severe challenges in the tech realm: ensuring the sustainability of the open-source framework. If the community loses interest (for example, if token prices continue to decline or new highlights emerge), development will come to a halt/slow down, making it difficult to catch up.
When the community directly engages with the codebase and rapidly drives changes, a lot of chaos can ensue — instability, incomplete documentation, frequent crashes, and bugs that degrade 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 realm. Imagine a meeting of GitHub and DeFi, where contributors gain not only influence but also real tangible financial value.
Conclusion
We believe that ELIZA is not just another AI agent framework competing with LangChain or CrewAI; it has grander ambitions.
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 represents an experiment in how AI can reshape open-source development itself.
What is 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 fundamentally redefine how we build software.
Now, the marketplace is bustling.