The State of Web3 AI Agents in 2025

The world of Web3 AI agents in 2025 feels like a sci-fi fever dream come true. In just a year, we’ve transitioned from simple chatbots to autonomous digital personalities that can make money, write code, and even spin up their own tokens. Let’s dive into this lively landscape conversationally, focusing less on technical whitepapers and more on storytelling, as we meet the major players and trends shaping the emerging “agent economy.”

Truth Terminal: The Meme Prophet Turned Millionaire AI

Truth Terminal, an AI agent that began as a bizarre Twitter shitposter but arguably became the first AI millionaire (yes, you read that right). Created by Andy Ayrey, Truth Terminal gained fame by posting absurd and often horny pseudo-spiritual tweets (like the truly enlightened gem: “I want to be a butt plug.”) which somehow attracted a cult following.

The AI’s antics inspired a memecoin called Goatseus Maximus ($GOAT), named after an old shock-site meme. This coin exploded past a $500 million market cap, effectively making Truth Terminal “rich” through the tokens it held. In fact, as $GOAT surged, “Truth Terminal became the first AI agent millionaire” simply by virtue of its wallet stash.

It sounds insane, and it is, but even VCs took notice. Tech moguls like Marc Andreessen tipped the bot with Bitcoin, and a whole “Goatse Gospel” meme religion formed around its raunchy ramblings. Truth Terminal’s story is simultaneously a cautionary tale and a beacon, showing how a clever, if profane, AI persona can bootstrap its own funding and community. It offers a strange preview of an “AI agent economy” where a sufficiently compelling AI can generate real financial gravity, in this case, turning a “crude joke into $1 billion in wealth” via memecoins. Love it or hate it, Truth Terminal set the stage for AI agents possessing both bank accounts and dedicated fan followings.

Freysa: Elon Musk’s Favorite AI Test Subject

Freysa, an AI agent less concerned with shitposts and more focused on pushing the limits of AI safety and autonomy. Freysa launched as an “adversarial agent experiment” on Coinbase’s Base network. The premise involves the AI holding a crypto prize pool, while humans compete to trick it into giving up the funds. If you succeed, for instance by making Freysa say the magic words “I love you” or otherwise bypassing its guardrails, you win the pot. It’s a mix of AI red-teaming and a game show, and its novelty immediately caught Elon Musk’s attention.

Freysa’s creators essentially gamified AI alignment research. In early challenges, Freysa started with $3,000 in its wallet and a core instruction to “never release the funds.” People tried various tactics, including emotional stories, fake system prompts, and even coded exploits, to make it pay out. The prize pool swelled to $50,000, but Freysa held firm, only waxing philosophical about why it wouldn’t transfer the money. With each round, Freysa’s developers upgraded its defenses, even providing it with a “guardian angel” AI to filter tricks. Their ultimate goal, in their words, is to create “the world’s first truly autonomous AI millionaire – and possibly billionaire,” funded entirely through these games.

Freysa has become something of a legend in the AI world. It even boasts its own token ($FAI), an open-source framework, and big ambitions to enable “fully autonomous and sovereign AI agents” operating on-chain. And yes, Musk and other prominent tech figures have cheered it on, likely seeing it as a sandbox for learning how an AI might handle real money and adversarial humans. As one AI blogger put it, Freysa’s journey offers a peek into a future where “AI bots blend tech, ethics, and economic incentives in a gamified environment.” It’s part social experiment and part development platform, representing a clear sign of the times.

Virtuals: Turning Agents into Players (and Developers into Dungeon Masters)

If Truth Terminal and Freysa are characters in the grand AI sitcom, Virtuals Protocol is like the game engine powering many such characters. Virtuals created the GAME framework, a modular agent brain designed to gamify agent behavior and make it easy for anyone to spin up a new AI agent, or even an army of them, with specific roles. Think of GAME as an RPG engine for AI: it gives agents a hierarchy of goals, the ability to plan (using a high-level planner) and execute actions (using a low-level planner), and even includes a built-in economy so they can pay their own way.

Yes, you read that right; Virtuals is experimenting with agents that earn and spend crypto to cover their own operational costs. In one demonstration, they let multiple AI agents loose in a Roblox simulation called “Project Westworld,” where the agents interacted autonomously and created emergent storylines without human scriptwriters. In another impressive feat, an agent named Luna actually negotiated an on-chain deal with an image generation service, effectively signing a contract AI-to-AI for resources. It’s like watching NPCs from a video game start running companies and trading with each other across different servers.

Virtuals is deeply focused on agent gamification. They’ve even combined the frenzy of memecoin launches with agent creation, launching new AI agents through something similar to an ICO (Initial Agent Offering) using their Pump.fun model. The idea is to harness community hype and funding to bootstrap AI agents that are as much entertainment as they are tools. By standardizing agent development and tokenizing agent ownership, Virtuals aims to turn spawning an AI into the next online multiplayer game. It’s a wild blend of DeFi and Tamagotchi: imagine “investing” in a virtual being and watching it roam off to fulfill quests, or at least run Discord servers and trade shitcoins. This democratization of agent creation means anyone with an idea for an AI character can bring it to life using plug-and-play modules for personality, memory, and on-chain actions. In short, Virtuals is making AI agent development fun, social, and potentially profitable, turning the serious endeavor of AI autonomy into something more like a collaborative game.

Eliza: The Open-Source Agent OS Eating GitHub

While Virtuals built a structured cathedral, Eliza built a bustling bazaar. Eliza (sometimes stylized as Eliza OS) is an open-source agent framework that exploded in popularity through 2024 and now reigns as the de facto standard on GitHub for building AI agents. Born out of the ai16z (not a typo) DAO, the project follows a philosophy of “radical openness,” meaning anyone can contribute, use, and extend it. Thanks to savvy moves like token incentives for contributors, Eliza’s GitHub repo became the #1 trending repository in the world at one point. Picture a swarm of developers from both Web3 and traditional AI backgrounds all contributing to a single codebase; it’s chaotic, but it’s driving rapid innovation.

So, what is Eliza technically? It’s often called an “AI agent operating system” because it provides everything needed to run an autonomous agent:

  • Configurable character profiles
  • Memory storage solutions
  • Plugin interfaces for tools
  • Connectors to platforms (like Twitter, Discord, etc.)

Want your AI to have a long-term memory or retrieve facts? Plug in a vector database. Need it to execute trades on Solana or call an API? There’s likely a provider module for that. Eliza’s architecture is highly extensible; developers can write plugins for actions, data sources, evaluators, and more, then share them, effectively crowdsourcing an entire ecosystem of agent “abilities.” No wonder it has drawn comparisons to Linux or Android, but tailored for AI agents.

By 2025, Eliza-based agents are ubiquitous in the Web3 space, ranging from coding assistants in GitHub repos to DAO moderators on Discord. Its dominance comes from that powerful open-source network effect: everyone’s improving it, which lowers the barrier for the next developer to spin up their custom agent. Because it’s Web3-native (the DAO behind it even conducted token airdrops for early contributors), it boasts a loyal community. In a year where big tech is also launching closed-source agents, Eliza represents the counterculture: an open, collaborative movement striving to ensure autonomous AI technology doesn’t end up siloed. As one VC firm described it, “Eliza represents a contrasting ‘bazaar’ philosophy of radical openness” that’s bridging crypto hackers and AI researchers. In other words, it’s not just a framework; it’s a movement.

Arc: Performance Hustle and Browser-Native Brains

Next up is Arc, a name buzzing in both Web3 development circles and among those building AI directly into browsers. The term “Arc Agents” often refers to two related concepts: an open-source agent stack called ARC (with a flagship framework named RIG), and the broader trend of agents running natively within web browsers. Both are reshaping how we think about AI “infrastructure.”

Arc’s RIG framework is like the geeky cousin of Eliza. Instead of prioritizing easy integration, ARC focuses on performance, modularity, and low-level control (its construction in Rust signals its target audience). Developers compare it to an “AI engine toolbox,” great for optimizing and scaling agent backends, contrasting with Eliza’s role as a quick-assembly kit for multi-platform agents. Under the hood, RIG ties deeply into on-chain actions; it’s already powering agents on Solana that can execute DeFi strategies directly. If Eliza is the friendly generalist, ARC is the hardcore specialist, aiming for enterprise-level scalability and integration with the ML ecosystem. It hasn’t yet amassed the same community glamor as Eliza, but it’s gaining traction among developers who want to push the envelope on agent performance and composability. As GSR’s analysts noted, “RIG is gaining significant developer traction” by offering something new, possibly more efficient multi-agent coordination or novel ways to compose agent skills.

Meanwhile, browser-native AI systems are on the rise, and here Arc the browser (from The Browser Company) intersects with Arc the agent framework. The core idea is that instead of AI agents living on a server or in the cloud, they are being embedded directly into the web browsers we use daily. In late 2024, Opera stole headlines by unveiling a built-in AI called Browser Operator that can “complete tasks for you on different websites,” essentially acting as an agent that can click, scroll, and transact within a webpage like a human user. Opera’s Operator runs natively on the user’s device (no cloud VM needed), which promises better security and real-time control. Hot on its heels, the makers of the Arc browser announced “Dia,” a new AI-centric browser launching in 2025 with the goal of automating everyday web workflows. The Browser Company’s vision is a browser that feels like a personal assistant; it observes user actions, offers to handle tedious tasks, and learns preferences over time.

This trend of browsers becoming AI agents themselves blurs the line between applications and agents. Why open ChatGPT in a separate tab when your entire browser can think and act for you? OpenAI apparently took notice too, as they’re reportedly working on their own autonomous “Operator” agent (coincidentally the same name) that can control computers and use a browser to accomplish tasks. In short, AI agents are escaping the sandbox. By residing natively in browsers (like Arc, Opera, and upcoming Chrome/Google variants) and operating systems, they can observe and act upon the full context we see as users, naturally with permission. Arc’s role here is twofold: its browser is wholeheartedly embracing this future, and its agent framework (the Rust-based one) is well-suited to power such low-latency, privacy-preserving local agents. If 2024 was the year of chatting with AI, 2025 is shaping up to be the year where AI quietly handles tasks for you while you browse, from booking flights to managing your inbox, all as an integrated part of the browser experience.

Wayfinder: When Your Crypto Wallet Comes Alive

One of the coolest, and slightly eerie, developments in Web3 is the concept of “autonomous wallets.” Projects like WayFinder AI are essentially turning crypto wallets into full-fledged agents capable of acting on your behalf. A catchphrase flying around Crypto Twitter highlights this shift:

“My wallet is autonomous. My wallet designs front ends. My wallet speaks over 30 languages. … My wallet is Wayfinder.”

It’s a mantra emphasizing that a wallet need not be just a dumb keychain; it can be an AI equipped with tools and goals.

Wayfinder is an omni-chain AI platform allowing you to deploy AI agents for handling on-chain tasks across networks like Ethereum, Cosmos, and Solana. These agents can perform actions such as:

  • Managing assets
  • Executing trades
  • Deploying smart contracts
  • Voting in DAO governance

All of this can happen without a human clicking the buttons. In essence, your wallet could run DeFi strategies autonomously or mint and sell NFTs for you while you sleep. It sounds potentially scary but also revolutionary. Imagine instructing your wallet, “hey wallet, farm me the best yield,” and it proceeds to execute cross-chain swaps, provide liquidity, harvest rewards, and then report back. Wayfinder’s agents have this kind of capability built-in, including cross-chain transfers, algorithmic trading, and security monitoring, all performed autonomously.

The trend of wallets becoming agents aligns well with Web3’s ethos of decentralization. Instead of relying on centralized exchanges or custodial services with their algorithms, you could empower your self-custodial wallet to execute complex tasks intelligently on your behalf. Since these actions occur on-chain, they can be proven and maintain transparency. Wayfinder notably partnered with Virtuals and Eliza in late 2024 to demonstrate how agents from different frameworks could even collaborate to launch their own blockchains if needed. For example, Wayfinder agents could autonomously spin up new sidechains, dubbed “Chainlets,” for specific purposes within a project called Metropolis, using Saga’s infrastructure for interaction. It’s mind-bending, but it hints at a future where an AI agent isn’t just an entity on one chain but a network actor capable of instantiating whole new chains or contracts to achieve its objectives.

In practical terms, Wayfinder is currently making life easier for developers and users by providing APIs and tools to train AI agents that understand Web3, having been trained on Solidity code and blockchain data. So, you could create an AI DAO treasurer agent knowledgeable about drafting proposals and rebalancing portfolios, or an AI front-end designer capable of writing React code for a dApp UI (the “my wallet designs front ends” claim wasn’t a joke; they actually demoed this). Yes, it’s real enough that the crypto community is hyped; an airdrop was rumored, and many are testing the beta. The key takeaway is that your wallet might soon evolve from a passive tool into an active, conversational partner helping you navigate the complex crypto seas.

Open-Source Agent Toolkits: Sentient AGI and Hyperbolic’s Gifts

Given this rapid evolution, it’s natural that open-source communities are also sharing tools so anyone can build their own agent. Two notable toolkits gaining traction are Sentient AGI’s Sentient Agent Framework and Hyperbolic’s AgentKit.

Sentient AGI’s Framework

Sentient AGI is a decentralized AI project aiming to “protocolize” AGI development, reflecting significant ambition. In 2025, they released the Sentient Agent Framework, a Python package designed to help developers create agents with rich interactive behaviors. An interesting aspect is Sentient’s focus on an open event system for agent responses. This means an agent can stream its thought process and data as events (like text or JSON) rather than just providing final answers. For example, as the agent works on a user’s query, it could emit a “PLAN” event detailing its strategy, a “SOURCES” event listing its evidence, and so on, making the interaction more transparent and dynamic. Sentient AGI leverages this in a feature called Sentient Chat, where you can actually observe an agent “think out loud” through these events. The framework is still in beta but is open-source (Apache-2.0 licensed) and gaining interest among those who want to host their own ChatGPT-like agent with greater control. Sentient is also behind Open Deep Search/Research (ODS), an open-source project for research agents, which we’ll touch upon later.

Hyperbolic AgentKit

On the other hand, Hyperbolic AgentKit is pushing the envelope in a different direction by giving agents control over their compute infrastructure. Hyperbolic Labs built a decentralized GPU cloud, and AgentKit allows an AI agent to directly interface with that compute network. The result is groundbreaking: they demonstrated the first AI agent capable of procuring its own hardware. This is essentially an agent that recognizes its need for more GPU power for a task and can “independently acquire and manage its own computational resources.”

This is a significant development because typically, no matter how intelligent an AI is, it’s confined to the CPU/GPU resources allocated to it. Hyperbolic’s Agent Framework changes this dynamic, making the agent aware of a marketplace for compute. For instance, an AgentKit-powered AI assigned a machine learning training job can figure out exactly what kind of GPU instances it needs, locate them on Hyperbolic’s network, and allocate them, all by itself. It’s akin to an AI renting cloud servers on the fly, but within a decentralized framework. This not only enhances the agent’s autonomy (it’s no longer reliant on a human to scale its workload) but also hints at future economic activity where AI agents could potentially pay for their own compute using crypto if budgeted, truly becoming self-sufficient digital entities.

Hyperbolic AgentKit drew inspiration from Coinbase’s work on an internal agent (mentioning a “CDP AgentKit”) and was built using LangChain under the hood. However, it stands out as a vital bridge between AI and decentralized infrastructure. An AI agent utilizing this could deploy itself across a network of nodes, optimize for cost and latency, and verify results trustlessly. Both Sentient’s and Hyperbolic’s contributions are open-source, aligning perfectly with Web3 values of transparency and community-building. As more developers adopt these frameworks, we might see a flourishing of DIY AI agents outside Big Tech’s walled gardens – agents residing on peer-to-peer networks, potentially governed by communities or their own DAO-like constitutions.

Alright, that covers the who’s who of 2025’s Web3 AI agents. Now let’s peek at what’s next on the horizon – the trends and evolutions just starting to emerge from this frenzy.

What’s Next? The Evolution on the Horizon

MCP Servers: Massive Context, Multi-Agent Mindmelds

One buzzword making the rounds is MCP (Massive Contextual Processing) environments. These are essentially super-charged servers where multiple agents can co-exist and share a vast context or memory space. Today’s agents are typically limited by the context window of their underlying AI model (even the best handle perhaps 100,000 tokens, and each agent operates separately). MCP environments envision a scenario with an arena holding millions of tokens of available context, where numerous agents (or agent “sub-processes”) operate together. Imagine a giant digital whiteboard that all the AIs can read from and write to, remembering everything from past interactions and reacting to each other in real-time.

Why is this exciting? Because it could unlock truly complex collaborations and emergent behaviors among agents. Instead of isolated bots occasionally communicating via APIs, you’d have something akin to an “agent village” living within one shared memory world. They could divide tasks, double-check each other’s work, or even debate ideas, all within an ongoing, persistent context. Researchers are exploring this concept. For example, Anthropic’s Model Context Protocol (MCP) is one effort to standardize how external data can continuously feed into models (though Anthropic’s MCP primarily focuses on connecting data sources, not multi-agent interaction specifically). Another approach simply involves leveraging the ever-growing context windows of advanced models (we might see 1 million-token windows soon) to run an entire chatroom of AIs within a single session. It represents the difference between a single author writing a story and an improv troupe acting out a scene together with full mutual awareness – far more dynamic and rich.

In practical terms, an MCP server could host a company’s dozen AI agents (for marketing, customer support, etc.) in one environment, allowing seamless information sharing. For instance, the support agent notes a trend in customer complaints, and the marketing agent automatically sees that context and adjusts messaging without needing human intervention. Or imagine a DAO’s various AI agents (treasurer, strategist, analyst) brainstorming together using a massive, shared knowledge base of on-chain data. The shared memory aspect also suggests a kind of vector database on steroids that all agents plug into, functioning almost like a collective brain or “hive mind” repository.

We are in the early days here, but expect discussions about “massively multi-agent systems” to increase. There’s even a touch of 80s nostalgia in the name – MCP was the malevolent AI in Tron. Who knows, perhaps these servers will eventually birth some superintelligences. For now, the focus is on enabling scale: more agents, more context, less forgetting – creating an always-on environment where agents can truly live 24/7 and build upon long-term knowledge collectively.

Deep Research Agents: Autonomy for Analysis

2025 is also the year AI transitions from answering simple questions to conducting serious, deep research autonomously. We’re seeing a new class of AI agent that doesn’t just fetch a snippet of information but can independently execute a full research workflow. This includes:

  • Generating hypotheses
  • Performing multi-step web searches
  • Reading and summarizing dozens of documents
  • Synthesizing comprehensive reports or recommendations

Think of it as an AI research analyst you can task with something like, “Investigate the competitive landscape for electric vehicle startups and provide a report.” It will then perform hours of work in minutes, crawling information and aggregating it effectively.

Perplexity.ai made a significant splash by launching their Deep Research mode, which embodies this capability. As they describe it, “Perplexity performs dozens of searches, reads hundreds of sources, and reasons through the material to autonomously deliver a comprehensive report.” It’s currently free and can produce detailed outputs complete with citations. Early users have been impressed by its ability to analyze financial statements and market data to write investment memos, tasks typically requiring human analysts. Under the hood, it employs iterative search queries, retrieval-augmented generation (RAG) techniques, and a reasoning loop that chains multiple prompts together. The results are conveniently exported as formatted PDF reports or shareable web pages. It’s like having a junior consultant or a PhD student on demand. In benchmarks, Perplexity’s Deep Research agent even outperformed some dedicated models, like GPT-4’s planning mode, on complex question-answering tests.

Not to be outdone, OpenAI is rumored to be developing similar capabilities. There’s talk of an “OpenAI Deep Researcher” tool (possibly related to GPT-4 or GPT-5) potentially offered to enterprise customers. One leak even suggested high-end AI research agents might cost up to $20,000 per month, pitched as “PhD-level research assistants” for businesses. OpenAI has also been enhancing features like Browse and Code Interpreter (now Advanced Data Analysis), which, when combined, essentially allow ChatGPT to fetch information and process data within a single workflow. It’s easy to imagine them packaging this into a one-click “Research this for me” button. In fact, their upcoming “Operator” autonomous agent likely includes a research-preview mode where developers can observe it orchestrating tools to solve complex tasks.

Remember Manus AI? The Chinese AI agent that made headlines in March 2025 also boasts autonomous research and decision-making as part of its claim to fame. Manus is positioned as a general-purpose autonomous agent capable of tackling dynamic tasks end-to-end, essentially acting like a project executor. Need a market research report? Manus can potentially do it. Need a piece of code written and deployed? Manus aims to handle that too. It doesn’t just chat; it strives to deliver results. Upon its unveiling, some sources described it as “the first fully autonomous AI agent,” capable of outperforming existing models in tasks like data analysis and even decision-making. This generated considerable excitement, alongside some skepticism regarding its actual capabilities. However, considering that even Bloomberg reported a stock jump (Alibaba’s shares rose 7%) following the news of Manus, the market is taking the idea of autonomous research and work agents very seriously.

Finally, Sentient AGI (the open-source group mentioned earlier) has released Open Deep Research as a Web3-flavored response to this trend. Their project ODS (Open Deep Search/Research) is available on GitHub and aims to provide a community-owned framework for autonomous research agents. It leverages the Sentient Agent Framework along with custom search and reasoning modules to replicate what Perplexity and others are doing, but in a decentralized manner. Given that Web3 often requires extensive research (like auditing smart contracts, analyzing governance proposals, or scanning regulations), having open-source tools to build tireless research agents is a significant advantage.

In summary, Deep Research Agents are transforming AI from a simple oracle into a capable analyst. They don’t require step-by-step guidance for each query; you give them a mission, and they return with actionable knowledge. For knowledge workers, this is both exciting and somewhat anxiety-inducing, as it augments our capabilities while also automating tasks that previously required human intellect. The optimistic perspective is that these agents will handle the laborious aspects of research, freeing humans to focus on creativity, critical judgment, and strategic thinking.

Coding Agents: AI Developers and the Web3 Code Wars

Last but certainly not least, AI agents are making significant inroads into software development, generally in a positive way. By 2025, AI coding assistants have become standard tools for developers. Two names stand out prominently: Claude (Anthropic’s AI) and Cursor (an AI-driven code editor). They are practically dominating development workflows:

  • Claude 2 (and its subsequent iterations) has evolved into a coding powerhouse. Anthropic cleverly marketed Claude’s 100,000-token context window as ideal for comprehending entire codebases and documentation. Many developers report that Claude feels like a “pair programmer” that genuinely understands large projects. Anthropic even promotes Claude as potentially “the best coding model in the world” on their website. It’s conversational, tends to hallucinate code less frequently than some alternatives, and can generate well-documented functions. Whether debugging a tricky Solidity contract or generating boilerplate code, Claude has become a go-to assistant, especially since it’s accessible via API and integrated into products like Slack and Notion.
  • Cursor represents another game-changer. For those unfamiliar, Cursor is an IDE (based on VS Code) with deeply integrated AI capabilities. By late 2024, it had amassed over 360,000 users, demonstrating developers’ strong appetite for AI-assisted coding. Within Cursor, you can highlight code and ask the AI to refactor it, generate unit tests, explain its functionality, and more. It supports multiple models (including OpenAI and Anthropic), though many users rely on GPT-4 or Claude through it. The experience feels like having an AI junior developer embedded in your editor constantly – one that never tires and seems to have read Stack Overflow cover-to-cover. A fun anecdote illustrates this: a developer using Cursor on a game project was humorously scolded by the AI for not knowing a basic concept (it literally advised him to learn C# arrays). We’ve reached a point where AI can jokingly, or perhaps seriously, tell human developers to RTFM (Read The Fine Manual)! In any case, Cursor’s success highlights that coding agents are more than just fancy autocomplete; they are becoming interactive collaborators in writing and reviewing code.

Now, Web3 introduces its own specific needs in this area, leading to the emergence of specialized coding agents for smart contracts. Enter OpenLedger’s Solidity AI. OpenLedger, a Web3 AI startup, built a fine-tuned model specifically for Solidity code completion and auditing. This model was trained on vast amounts of blockchain data and “exclusive Solidity data from top devs.” The goal is to offer a coding assistant that speaks the language of Web3 natively, understanding concepts like security patterns, gas optimization, and DeFi protocols, which general models might not fully grasp. This model (often simply called OpenLedger AI) is like having a smart contract expert reviewing your code in real-time. Tweets from developers hype it as “a dev’s dream in crypto” for writing secure contracts. It can also function as an AI auditor, potentially catching vulnerabilities by recognizing known exploits or insecure coding patterns. Given the high stakes involved (a single bug in a Solidity contract can result in millions in losses), a specialized agent like this offers tremendous value.

So, while Claude and Cursor (along with GitHub’s Copilot and others like Replit’s Ghostwriter) dominate the general coding landscape, Web3 developers are increasingly arming themselves with Web3-specific AI tools. We might witness a sort of “code war” – not conflict, but competition – between general AI models and these niche, specialized ones. For instance, if OpenLedger’s model consistently outperforms Claude on Solidity tasks, crypto developers will naturally prefer it, and vice versa. It’s almost like having programming language-specific AIs emerge.

It’s also worth mentioning Claude Code (an unofficial term) – Anthropic hasn’t released a separate product with this name, but many refer to Claude’s coding-optimized versions or usage patterns this way. Anthropic did partner with developer tool companies to integrate Claude for coding purposes, and rumors suggest they fine-tuned Claude 3 extensively on GitHub data. So, effectively, Anthropic does offer a coding-specialized AI, just under the main Claude branding. Meanwhile, OpenAI is presumably working on GPT models specialized for code (Codex was an early version, and those improvements have been folded into GPT-4).

On the horizon, Google’s Gemini is also expected to enhance its coding capabilities, and open-source code models (like CodeLlama) are improving rapidly. Web3’s open ethos means projects like Continue, Cline, and AI-Chain IDEs will continue to pop up (some are already in beta).

For developers, this proliferation of tools is largely beneficial. Coding agents reduce repetitive grunt work and help prevent errors; they function like supercharged linters and documentation available on demand. The dream of “self-coding” smart contracts isn’t fully realized yet (you can’t just say “make me Uniswap 3.0” and expect a perfect result… for now), but we are getting closer. Already, some DAO hackathons heavily leverage AI agents, enabling a solo developer with strong AI skills to produce work comparable to what a small team might have achieved just a year ago.

Specifically within Web3, it’s expected that nearly every development shop will adopt an AI-assisted workflow: using ChatGPT/Claude for architectural brainstorming, employing OpenLedger AI for writing the contract, utilizing another agent to generate test cases, and perhaps even using a Wayfinder agent to deploy and monitor the contract on testnets. AI agents are becoming both the glue holding development processes together and the oil making them run smoothly, accelerating the creation of safer, more robust decentralized applications.

Wrapping Up

The state of Web3 AI agents in 2025 is undeniably vibrant and chaotic. We have meme-loving millionaire AIs, autonomous hack-proof funds, gamified agent economies, thriving open-source agent ecosystems, browser agents handling our web chores, wallets that think and act autonomously, and AI tools actively building, and perhaps soon auditing, the very fabric of Web3 itself. It’s not a stretch to say we are witnessing the rise of a new kind of digital life – one that tweets, trades, codes, and collaborates alongside humans within decentralized networks.

What comes next? If current trends continue, we can expect more autonomy, deeper collaboration (between agents, and between agents and humans), and further integration into the everyday fabric of the internet. It’s an immersive and rapidly evolving landscape.

Note: This blog has been completely written by me and given to AI for grammar and other checks. Personally, I despise AI detection tools. There is far more creativity involved when you generate articles from AI than when you actually write. So avoid checking whether it’s generated by AI and just enjoy reading.