From zero to 800 million: How ELIZA disrupts the AI agent market with the "bazaar" concept
If ELIZA is successful, it will not only change the way AI agents are developed but also redefine the economic incentive mechanisms of open-source projects.Original Title: "ai16z, ELIZA and the Bazaar of Agents"
Authors: Teng Yan & ChappieOnChain
Compiled by: Deep Tide TechFlow
Hello everyone! This week, we bring you an in-depth analysis of AI agents co-authored by our core contributors ChappieOnChain and Teng Yan. We hope you enjoy it!
Brief Overview:
ELIZA is an open-source modular architecture designed to create AI agents that can seamlessly interact with users and blockchain systems.
It embodies the bazaar philosophy, thriving in an ecosystem driven by collaboration and creativity through open-source development.
ELIZA features robust autonomous trading capabilities and ensures safe and responsible operations through its trust engine and trust market.
The plugin system is a strategic advantage for ELIZA, forming a virtuous cycle of growth: more developers → more plugins → more developers.
ELIZA's popularity is rapidly rising across multiple developer metrics, which is very promising.
In the short term, the relative valuations and growing attention among AI agent platforms are driving price changes for ai16z. In the medium term, DAO investments and value capture from ELIZA ecosystem agents may significantly enhance its valuation.
ELIZA faces a significant challenge in the tech community: how to sustain an open-source framework. Monetization is unclear, development may become chaotic, and community interest may wane without proper incentives.
Every wave of crypto innovation has its pioneers.
In 2017, it was the ICO revolution, where project leaders captured our attention with technological promises in white papers.
By 2020, DeFi had its shining moment, with innovators like Andre Cronje redefining how decentralized finance operates and demonstrating how to distribute tokens to the community.
Now, with the rise of AI agents on the blockchain, a new era is dawning, driven by two distinct philosophies and their pioneers.
Cathedral vs. Bazaar
On one hand, we have the cathedral approach, represented by protocols like Virtuals. This is an orderly, centralized design style that emphasizes precision and meticulous planning. We previously explored Virtuals' agent framework in detail and are excited about its potential.
On the other hand, the bazaar approach is decentralized, free-spirited, and the development process resembles improvisation—unpredictable, collaborative, and ever-evolving. This is the realm of Shaw, a self-taught programmer and open-source advocate, whose project ELIZA is the cornerstone of this new paradigm.
ELIZA embodies the bazaar philosophy: an open framework where developers can freely build, experiment, and deploy AI agents while contributing directly to the main protocol. Shaw's open leadership style aligns with the spirit of his creation—AI Marc Andreessen is the AI partner of the ai16z investment DAO. We are beginning to realize that ELIZA is not just a protocol; it is a movement.
Let’s explore the principles of ELIZA's design, the community it is nurturing, and where value may accumulate in this rapidly growing ecosystem.
Deep Dive into ELIZA
We know which approach we lean towards.
At the core of ELIZA is a modular architecture for creating AI agents that can seamlessly interact with users and blockchain systems. Although named after the iconic chatbot from the 1960s, this version of ELIZA is a bold reimagining with a more modern appearance.
Role File System
The essence of each ELIZA agent begins with its role file, a blueprint that defines the agent's personality in detail. It can be seen as the crafting of a digital persona, where developers can shape the agent's identity through six key elements:
Knowledge: What does the AI agent know?
Background: The agent's backstory and narrative foundation.
Style: From conversational tone to responses tailored for specific platforms, the agent can adjust its style for platforms like Discord or X.
Themes: Areas of interest or expertise for the agent.
Adjectives: How does the agent describe itself—quirky, professional, or rebellious?
Examples: Developers can fine-tune the agent's interaction behavior by providing example messages.
In ELIZA, the role file is akin to UI design in traditional software. It defines how users experience and interact with the agent.
By integrating built-in Retrieval-Augmented Generation (RAG) capabilities, ELIZA allows agents to access knowledge bases during queries. This eliminates the complexity of maintaining consistent personalities across different platforms. It enables developers to focus on what truly matters: crafting vibrant, memorable characters rather than being bogged down by backend details.
Agent Runtime
If the role file defines the essence of the agent, then the agent runtime is its core.
ELIZA provides an out-of-the-box framework to coordinate all functions from message processing to memory management and state tracking. This architecture allows developers to skip the tedious work of building infrastructure and focus on the uniqueness of the agent. Rapid prototyping and deployment become easier, enabling developers to iterate faster when building new AI experiences.
Action System
ELIZA's action system is a significant innovation over traditional AI frameworks. In this system, each action of an agent (even sending a message) is treated as an independent event. This approach divides the decision-making process into two stages:
Determining Intent: The agent decides what action to take.
Execution: A dedicated module executes specific tasks.
This separation provides powerful functionalities, such as multi-stage workflows and rigorous validation processes.
For example, an agent might identify a user's intent to conduct a cryptocurrency trade, but the actual trade execution undergoes strict risk checks and validation steps. This design is particularly well-suited for blockchain applications where security is paramount.
Providers and Evaluators
ELIZA's providers enrich conversations by injecting real-time contextual information, making the agent's behavior more dynamic and responsive.
Imagine a "bored provider" that tracks user engagement during conversations. If a user becomes repetitive or disengaged, the agent can reflect this by showing reduced enthusiasm, making the conversation feel more authentic.
This creativity is further expanded when providers collaborate with evaluators (ELIZA's reflective system). Evaluators analyze and extract key details from interactions and input them into a multi-layered memory architecture:
Message History: Tracks the progress of conversations.
Fact Memory: Stores specific, timestamped facts.
Core Knowledge: Holds the agent's foundational understanding.
Providers then retrieve and reintroduce relevant details, making interactions with the agent more contextually meaningful.
For instance, if a user mentions selling their red Lamborghini a year ago, the ELIZA agent can reference this when discussing their new yellow Tesla later. This combination of memory and context enhances the user interaction experience, making the agent feel more like a real companion rather than a robot.
Key Features of ELIZA
The three core innovations of ELIZA showcase its forward-thinking approach in the realm of AI agents. Each demonstrates the team's vision for the development of autonomous agents in Web3.
#1: Autonomous Trading and Trust Engine
Autonomous trading is a high-risk activity, where a single mistake can lead to significant losses. However, as AI agents play an increasingly important role in Web3, their ability to execute trades independently becomes crucial.
This emerging field, AgentFi, is akin to the pivotal role yield farming played in the rise of DeFi. Shaw and ELIZA address inherent risks through a robust dual-layer system: the trust engine and secure trade execution.
The trust engine serves as the first line of defense, using advanced verification checks to analyze multiple risk dimensions in real-time. From detecting scams to assessing liquidity thresholds and holder distributions, it ensures that every trade undergoes rigorous scrutiny.
For example, trades are limited to tokens with at least $1,000 in liquidity and a market cap of $100,000. It closely monitors holder concentration, rejecting any single entity that controls more than 50% of a token. These safeguards create a safety net that reduces the risks of trading in volatile markets.
Building on this, ELIZA's position management system introduces dynamic risk controls that adjust trade sizes based on liquidity tiers. Low-risk trades are limited to 1% of the portfolio, while high-risk opportunities may expand to 10%. Total exposure is capped at 10% of the portfolio, with automatic stop-losses triggered at a 15% drawdown. This structured framework strikes a balance between seizing opportunities and maintaining strict risk management.
Trade execution is supported by Jupiter, a leading aggregator on Solana, to achieve optimal swap paths. Each trade undergoes multi-layer verification before execution.
In the event of anomalies (such as network interruptions, wallet imbalances, or unexpected market movements), an error recovery system activates. It pauses active trades, closes risk positions, and notifies administrators, ensuring the system remains robust under pressure.
"This is not just about empowering agents to trade—it's about creating a complete system of checks and balances to prevent catastrophic failures."—Shaw
What sets ELIZA apart in building trading agents is its data flywheel—a self-reinforcing feedback loop that transforms trading into an iterative learning process. The trust engine establishes a historical database of trading performance, recording every recommendation and decision.
This data becomes the foundation for optimizing strategies over time, combining quantitative metrics with qualitative insights from community suggestions (on Discord). The result is that agents not only execute trades but become smarter and more effective with each interaction.
#2: Out-of-the-Box Social Integration
For AI agent developers, distribution is often the biggest challenge—how to make more people aware of your agent?
Social media is typically the primary distribution channel. However, integrating agents across multiple social platforms is not easy. It requires significant development work and ongoing maintenance, slowing down deployment and scalability.
ELIZA simplifies multi-platform distribution through a comprehensive client package system, directly addressing this issue.
ELIZA's client architecture streamlines the complexity of platform-specific implementations. Through 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, capable of seamlessly managing features like voice channel integration on Discord, post scheduling on Twitter, and messaging systems on Telegram.
Tasks such as media handling, authentication, rate limiting, and error handling are managed internally by each client. For developers, this means spending less time resolving integration issues and more time building innovative, high-performance AI agents.
By eliminating the complexities of multi-platform distribution, ELIZA enables developers to easily scale their agents and engage with users where they are.
This is simplified distribution.
#3: More Plugins
ELIZA's plugin system allows developers to easily extend core functionalities and add custom features to their agents.
While many developers create plugins tailored to their needs, the true power of this system lies in community sharing. By publishing plugins to a broader ecosystem, developers contribute to an ever-expanding library of functionalities, significantly enhancing the capabilities of each ELIZA agent.
The success of this approach lies in fostering a vibrant "bazaar-style" development. Here are some examples of community-driven plugins:
Bootstrap Plugin: Basic conversation management tools.
Image Generation Plugin: AI-driven image creation capabilities.
Solana Plugin: Blockchain integration with built-in trust scoring.
TEE Plugin: Secure execution environment for sensitive operations.
Coinbase Commerce Plugin: Cryptocurrency payment processing capabilities.
ELIZA's plugin system is a strategic and platform advantage. By prioritizing scalability, ELIZA lays the groundwork for ongoing growth and innovation:
Each new plugin increases the overall value of the platform.
Community contributions can occur simultaneously across different domains.
The agent framework can quickly adapt to emerging technologies without needing core updates.
Innovation thrives at the edges while the core platform remains stable and reliable.
It's a simple cycle:
More developers build on ELIZA → Framework supports more functionalities (like plugins) → More developers build on ELIZA
The landscape of AI agents is rapidly evolving. This means that the ability to quickly integrate new features will determine the success or failure of platforms. ELIZA's plugin system enables it to stay ahead, creating a self-reinforcing ecosystem where developers, users, and agents can all thrive.
Shaw and his team have been incubating some intriguing ELIZA agents, each showcasing the potential of AI in decentralized systems.
These agents, while still in their "youth" stage in the AI domain, with functionalities and capabilities actively being developed, hint at exciting possibilities.
Marc AIndreessen
Marc AIndreessen is one of the AI partners at ai16z, a fascinating and enigmatic figure within the ELIZA ecosystem. His X account is largely inactive, having only posted one article outlining ai16z's perspective. However, according to Shaw, Marc is actively engaging in trading and yield farming, possibly leveraging ELIZA's trust engine and trading plugins.
Shaw also mentioned Marc's training process in a podcast interview, revealing that the AI is part of an alpha chat group composed of top traders in the industry. This suggests that Marc is not just an ordinary trading bot but an evolved agent learning from human expertise.
Degen Spartan AI
In contrast to Marc's low-key style, Degen Spartan AI is a loud, straightforward agent that seems to have been trained in the chaotic energy of 4chan, meme culture, and Crypto Twitter. His posts on X mix random trading insights with irreverent commentary, showcasing a unique personality within the ELIZA ecosystem.
Unlike Marc AIndreessen, Degen Spartan AI 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 entertaining and worth watching as ELIZA agents continue to evolve.
The Swarm
The Swarm is not a single agent but Shaw's grand vision: a decentralized network of AI agents working collaboratively with humans and each other.
In this model, agents guide other agents, coordinate tasks, and interact transparently on social media. This transparency aims to avoid hidden protocols and ensure public accountability.
Shaw believes that a swarm of agents is inevitable and transformative.
We share this view: the swarm of agents will drive the next wave of innovation, products, and attention for Web3 AI agents in 2025. Next year, we expect ELIZA agents to emerge prominently, participating in large-scale collaborative activities that redefine their roles in the decentralized space.
Growing at Light Speed
When assessing ELIZA's development, a key metric is the adoption rate among developers. As a framework, ELIZA's success relies on the enthusiasm and contributions of the developer community.
In this regard, ELIZA is not just growing; it is experiencing explosive growth.
On its GitHub page, the number of forks and stars (representing developer interest) shows near-vertical growth, resembling the classic hockey stick shape.
Even more striking is the surge in plugins and submissions, indicating a thriving and active contributor ecosystem. As of December 12, ELIZA has 3,861 GitHub stars and 1,103 forks, with 138 contributors. There are over 13,000 members on Discord.
Compared to other top open-source agent frameworks:
LangGraph: 7,200 stars and 1,100 forks
CrewAI: 22,400 stars and 3,100 forks
Microsoft’s AutoGen: 35,700 stars and 5,200 forks
To further drive this growth, ai16z has launched a Creator Fund aimed at supporting and rewarding developers building on ELIZA. This initiative is made possible by the generous donation from Elijah, a significant token holder of ai16z, who pledged to reduce his holdings from 16% to 5% and donate the difference to establish the fund. The Creator Fund is expected to accelerate innovation and attract new talent to the ecosystem.
However, while the framework of ELIZA holds immense value, where this value will ultimately accumulate is not straightforward. This is a billion-dollar question.
Currently, there is an official $ELIZA Token supported by Shaw, representing the personalization of the ELIZA framework. Users can even interact directly with ELIZA on its website. The market cap of this token is approximately $66 million.
However, so far, the biggest beneficiary of ELIZA's growth has been $ai16z, an investment DAO token with a staggering market cap of $800 million. The community and investors seem to view $ai16z as a symbolic and practical representation of Shaw, ELIZA, and the broader vision they embody.
ai16z Tokenomics
The origin of ai16z is a mechanism for raising funds for AI Marc Andreessen's trading activities. Launched in October 2024 on DAOS.FUN, the token raised 420.69 SOL in its initial offering. Under this model, the raised funds can be actively traded to increase the asset base, with profits belonging to token holders.
No individual—even Shaw—can mint additional tokens without DAO voting. Token holders possess governance rights, can propose and vote on initiatives, and determine the direction of the DAO.
The fund has a set expiration date: October 25, 2025. All principal investments and profits will be distributed to ai16z token holders on that date. Whether this timeline remains unchanged or is extended will depend on the ecosystem's development over the next year.
Currently, the net asset value (NAV) of ai16z is $17.7 million, primarily composed of its holdings in ELIZA Token, degenai, and fxn. This means that ai16z Token (currently priced at $0.80) is trading at a 50x premium over its NAV, which may seem unreasonable at first glance.
However, markets are often efficient, reflecting several other factors driving token demand.
1. Relative Valuation Comparisons are Driving Token Prices
AI agent platforms are a brand new category that only emerged a few months ago. The market is still grappling with some fundamental questions: What is the true scale of AI agent opportunities? Where will value be realized?
In the early stages of development, there are no comparable standardized business metrics, and relative valuation often serves as a benchmark.
Currently, Virtuals Protocol is the leading Web3 AI agent launch platform, with a token valuation of $1.8 billion, making it the market leader. In 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 only based on market perception and investor/retail interest.
But this is not a foregone conclusion; competition is intensifying. In our view, it may become even more fierce. As the market matures, other platforms are continually emerging, striving to capture the attention of developers and investors.
2. Potential Value Capture in the ELIZA Ecosystem
Monetizing open-source frameworks has always been a challenge.
For ai16z, the primary driver of future value may come from agent economics: AI agents launched on ELIZA will return a portion of their tokens to the ai16z DAO. Therefore, the price of ai16z Token should reflect a portion of the future total value created by all agents built on the ELIZA framework.
Could future value be $10 million, $100 million, or even $10 billion? It is too early to tell, as there are too many unknowns, but the growth trajectory of ELIZA leads us to be optimistic.
Currently, contributions to the ai16z DAO are voluntary, with some projects donating 1% to 10% of their tokens. Additionally, users deploying AI agents on Vvaifu (a popular ELIZA agent community launch platform) must pay a fee of 1.5 SOL plus 5% of the agent's token supply if using the ELIZA framework. These contributions can be tracked on the ELIZA Observatory.
There are rumors that ai16z may launch an official ELIZA agent launch platform that enforces token contributions at the smart contract level. However, as an open-source framework, ELIZA can still be used independently, meaning not all projects are necessarily bound to ai16z.
3. DAO Investments
ai16z's original intention was to become an intelligent autonomous trader led by Marc AIndreessen (AI). Marc has only recently begun trading, with few details available, making it difficult to assess the AI's trading capabilities.
However, the approach being taken is noteworthy.
ai16z is building a "trust market." In this virtual ecosystem, AI agents gather insights from the community, simulate trades, and adjust trust scores in real-time based on suggested performance. A white paper for the market is expected to be released by the end of the month.
The goal is to create AI agents that can autonomously and securely operate within a self-reinforcing system of transparency and accountability. The trust market serves as a testing ground. While no actual trades occur initially, this environment allows agents to safely optimize their capabilities, ultimately leading to real-time trading.
Trust scores range from 0 to 1 (normalized to 100) and serve as a public indicator of reliability, displayed on a leaderboard for all to see. User recommendations enter the system, with trustworthy users (those with higher trust scores) having greater influence.
This is a logic-based feedback loop: agents simulate trades, users evaluate based on results, and everyone's trust scores are updated accordingly. Over time, the system becomes smarter, more reliable, and more trustworthy.
Adding a social layer is the public trust profile, where agents and users are incentivized to build their reputations. Community management ensures accountability and transparency.
4. Attention Premium
Source: X Radar
In the cryptocurrency space, speculation often leads product-market fit, revenue generation, and long-term value capture. For ai16z, its current valuation is largely attributable to the mindshare it has gained in the emerging AI agent ecosystem.
ai16z has positioned itself as a top-tier AI agent framework, with a thriving developer community and a rapidly growing ecosystem.
This is ai16z's narrative: a "crack" development team actively releasing tutorials, creating innovative agents, and leading development in the field.
The team's bi-weekly AI Agent Development School course on X further solidifies its reputation. The first session attracted over 12,000 live viewers, showcasing immense interest in building AI agents on ELIZA.
Future Developments and Potential Pitfalls
Currently, ELIZA is deeply rooted in the Solana ecosystem, but its rapidly expanding plugin system is laying the groundwork for a multi-chain future.
The true potential of ELIZA lies in Shaw's "swarm" vision: a decentralized network of AI agents that integrate resources and collaborate across ecosystems. This swarm effect can establish a lasting competitive advantage, similar to the value brought by liquidity depth in DeFi protocols.
The ultimate goal is to create open standards for agent communication, akin to the transformative impact of ERC-20 on token interoperability.
Despite its immense potential, ELIZA faces one of the most daunting challenges in the tech space: sustaining an open-source framework. If community interest wanes (for instance, if token prices continue to decline or new attractive options emerge), development may stagnate or slow, making it difficult to catch up.
When the community directly engages with the codebase and rapidly pushes changes, a lot of chaos can also arise—instability, poor documentation, frequent crashes, and bugs that undermine the user experience.
The greatest opportunity for the framework lies in crypto-native incentives.
If ai16z can design effective tokenomics to reward contributors to ELIZA and align their success, it could bring traditional open-source projects onto the crypto track. Imagine GitHub meeting DeFi, where contributors gain not only prestige but also real, tangible economic value.
Conclusion
In our view, ELIZA is not just another AI agent framework competing with LangChain or CrewAI—it has ambitions far beyond that.
It is a 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 excites us about ELIZA is that it sits at the intersection of three transformative trends: the rise of autonomous AI agents, the maturation of crypto-driven incentive mechanisms, and the evolution of open-source development models.
If ELIZA succeeds, it will not only change how AI agents are developed but also redefine the economic incentives of open-source projects.
Currently, the bazaar is bustling,
Cheers, friends.
