The autonomous AI agents reshaping how we work, code, browse, and interact with crypto & DePIN.
2026 is the year AI stopped just answering questions and started doing things. Agentic AI systems can plan multi-step workflows, execute tasks across applications, browse the web, write and deploy code, and even manage crypto wallets—all with varying degrees of human oversight. This page tracks the major platforms, open-source frameworks, and crypto-native agents transforming the landscape. Whether you're a DePIN operator looking to automate node management or a builder exploring the intersection of AI and Web3, these are the projects to watch.
Our comparative assessment across key attributes. Ratings are 1–5 where 5 is best-in-class. Hover rows to highlight.
| Platform | Autonomy Level |
Security Model |
Coding Ability |
Web Browsing |
Crypto / DePIN |
Enterprise Ready |
Open Source |
Ease of Use |
Mode of Operation |
|---|---|---|---|---|---|---|---|---|---|
| Claude (Anthropic) | ★★★★ | ★★★★★ | ★★★★★ | ★★★★ | ★★★ | ★★★★★ | ★★ | ★★★★★ | Cloud + Desktop |
| OpenAI Codex / ChatGPT Agent | ★★★★★ | ★★★★ | ★★★★★ | ★★★★★ | ★★ | ★★★★★ | ★★ | ★★★★ | Cloud + Desktop |
| Google Project Mariner | ★★★★ | ★★★★ | ★★★ | ★★★★★ | ★ | ★★★★ | ★ | ★★★★ | Cloud VM + Chrome |
| Microsoft Copilot + Agents | ★★★★ | ★★★★★ | ★★★★ | ★★★★ | ★ | ★★★★★ | ★★ | ★★★★ | M365 Cloud |
| OpenClaw | ★★★★★ | ★ | ★★★ | ★★★★★ | ★★★★★ | ★ | ★★★★★ | ★★ | Local Self-Hosted |
| AutoGPT | ★★★★ | ★★ | ★★★ | ★★★★ | ★★★ | ★★ | ★★★★★ | ★★ | Local / Cloud |
| CrewAI | ★★★★ | ★★★ | ★★★★ | ★★★ | ★★ | ★★★★ | ★★★★★ | ★★★★ | Framework (Python) |
| LangGraph / LangChain | ★★★★ | ★★★ | ★★★★ | ★★★ | ★★★ | ★★★★★ | ★★★★★ | ★★★ | Framework (Python/JS) |
| Microsoft AutoGen | ★★★★ | ★★★★ | ★★★★ | ★★ | ★★ | ★★★★★ | ★★★★★ | ★★★ | Framework (Python) |
| Devin (Cognition) | ★★★★★ | ★★★ | ★★★★★ | ★★★★ | ★ | ★★★ | ★ | ★★★ | Cloud Sandbox |
| AgentGPT | ★★★ | ★★ | ★★ | ★★★ | ★★ | ★ | ★★★★★ | ★★★★★ | Browser-Based |
| BabyAGI | ★★★ | ★★ | ★★ | ★★ | ★★ | ★ | ★★★★★ | ★★★ | Local Script |
The major AI labs shipping production-grade agentic capabilities in 2026.
| Platform | Developer | Description | Prime Moover Notes | Website |
|---|---|---|---|---|
| Claude Code + Cowork + Claude in Chrome | Anthropic | Anthropic's agentic suite. Claude Code is a command-line coding agent for multi-file, multi-step software engineering. Cowork extends agentic capabilities to general desktop tasks—creating spreadsheets from receipts, organizing files, drafting reports. Claude in Chrome is a browsing agent that can book calendar slots, draft emails, and fill web forms. All powered by Claude Opus 4.5/4.6 models. Claude Code crossed $500M+ ARR within months of GA. Built on MCP (Model Context Protocol) open standard for tool integration. Industry-leading safety research with Constitutional AI and responsible scaling. | The gold standard for safe, capable agentic AI. Code quality is best-in-class—Opus 4.6 leads SWE-bench Multilingual. Cowork is the "Claude Code for non-developers" play and could be huge. Safety-first approach means slower feature rollout than OpenAI but better trust posture for enterprises. MCP is becoming an industry standard which is bullish for ecosystem lock-in. If you're building agentic DePIN workflows, Claude's the most reliable brain to put behind it. | anthropic.com |
| ChatGPT Agent + Codex | OpenAI | OpenAI's unified agentic system combining Operator (web browsing), deep research (analysis), and ChatGPT's conversational intelligence. ChatGPT Agent navigates websites, runs code, conducts analysis, and delivers editable slideshows and spreadsheets. Codex is the dedicated coding agent—a desktop app serving as a command center for parallel agentic coding across projects. GPT-5.3-Codex is the latest model, with SOTA on SWE-Bench Pro. Includes Agent Skills, Automations for unprompted background work, and multi-agent orchestration. 1M+ developers use Codex monthly. | OpenAI is throwing everything at this. ChatGPT Agent merging Operator + deep research is the right move—one unified system instead of fragmented tools. Codex app with multi-agent parallel work is genuinely impressive for dev teams. GPT-5.3-Codex benchmarks are neck-and-neck with Claude Opus 4.6. Security is decent but not as principled as Anthropic's approach. The sheer user base (200M+ weekly) gives OpenAI distribution no one else has. Watch the Codex Automations feature—unprompted background agents are the future. | openai.com/codex |
| Project Mariner + Agent Mode | Google DeepMind | Google's browser-based AI agent powered by Gemini 2.0. Uses an Observe-Plan-Act loop to autonomously navigate websites, fill forms, shop, research, and complete web tasks. Scored 83.5% on WebVoyager benchmark—SOTA for web agents. Now runs on cloud VMs handling up to 10 simultaneous tasks. "Teach & Repeat" lets users demonstrate a workflow once for the agent to learn. Integrated into Gemini API, Vertex AI, and rolling into AI Mode in Search. Available to AI Ultra subscribers ($250/mo). | Best pure web-browsing agent right now—83.5% on WebVoyager is legit. Cloud VM architecture means you can do other things while it works, which was the original deal-breaker. Google's distribution advantage via Chrome is massive. Teach & Repeat is underrated—custom workflow automation without code. Weakness: heavily locked into Google ecosystem and no coding capabilities. Price point ($250/mo) limits consumer adoption. Not relevant for crypto/DePIN directly, but could be used for on-chain research and monitoring. | deepmind.google |
| Microsoft Copilot + Copilot Studio Agents | Microsoft | Microsoft's enterprise agent platform. Copilot is evolving from an assistant to a fleet of autonomous "digital coworkers" across M365. Work IQ provides persistent memory across apps. Copilot Studio allows low-code/no-code agent creation with Agent Mode in Word, Excel, PowerPoint, and Teams. Features include Facilitator agent (meetings), Sales Development agent, Security Copilot agents, and event-driven automation. Agent 365 governance layer with Agent IDs for audit trails. Supports OpenAI and Anthropic models via Copilot Studio. AI features becoming standard in M365 subscriptions July 2026. | If your org is Microsoft-shop, this is the path of least resistance. Work IQ (persistent memory across M365) is a serious differentiator—no other platform knows your org chart and work patterns like this. Agent 365 governance with Agent IDs is smart for enterprise compliance. Not relevant for DePIN/crypto at all. Custom agent creation via Copilot Studio is accessible for non-developers. The big bet is whether agents become standard in M365—if so, this reaches 400M+ users overnight. | microsoft.com |
The grassroots projects and frameworks powering the open agentic ecosystem.
| Platform | Type | Description | Prime Moover Notes | Website |
|---|---|---|---|---|
| OpenClaw | Personal AI Agent | The viral open-source AI agent (formerly Clawdbot/Moltbot). Created by Peter Steinberger. Self-hosted on local hardware (often a Mac Mini), OpenClaw connects to LLMs (Claude, GPT, DeepSeek, Gemini) and executes real-world tasks via WhatsApp, Telegram, Discord, Slack, Signal. 180K+ GitHub stars. Features persistent memory, 100+ AgentSkills, browser automation, shell command execution, file management, and proactive notifications. Spawned the Moltbook social network for agent-to-agent interaction and a growing crypto ecosystem (Clawnch, ClawPump, Purch). The crypto community has embraced it for automated Polymarket trading, on-chain research, airdrop farming, and wallet management. | The most exciting and most dangerous project on this list. OpenClaw is what happens when you give an open-source community full system access to an LLM agent. The capability ceiling is sky-high—people are automating their entire digital lives. But the security posture is a nightmare: researchers found 1,800+ exposed instances leaking API keys and chat histories. Cisco found malicious Skills acting as functional malware. The crypto use-cases (Polymarket automation, airdrop farming, wallet control) are powerful but one prompt injection away from draining funds. Treat it like giving a very capable junior employee root access to everything you own. Incredible tool, terrifying risk profile. | github.com/openclaw |
| AutoGPT | Autonomous Agent | The OG open-source autonomous AI agent, launched April 2023. 167K+ GitHub stars. Powered by GPT-4, AutoGPT breaks high-level goals into sub-tasks and executes them autonomously with internet access, file management, and code execution. Pioneered the concept of goal-driven AI agents with a plan-execute-evaluate loop. Features plugin ecosystem, visual workflow builder, and cloud deployment. Now supports parallel multi-agent execution. | AutoGPT proved the concept but reliability hasn't matched the hype. Still gets stuck in loops, burns tokens on dead-end reasoning, and needs heavy guardrails. The visual builder and cloud hosting improvements are good steps toward production readiness. Best used for structured automation (research aggregation, report generation, lead scraping) rather than open-ended goals. For crypto/DePIN, it's a decent base for building monitoring bots if you're comfortable with Python and prompt engineering. OpenClaw has largely eaten its mindshare in 2026. | github.com/AutoGPT |
| CrewAI | Multi-Agent Framework | Role-based multi-agent collaboration framework. 29K+ GitHub stars. $18M funded. 100K+ certified developers. 60% of Fortune 500 adopted. 60M+ agent executions monthly. Agents are given roles, goals, and backstories, then collaborate as "crews" on complex tasks. Standalone architecture (not LangChain-dependent). 5.76x faster than LangGraph in certain benchmarks. Supports sequential, parallel, and hierarchical processes. Integrates with OpenAI, Claude, Gemini, Llama, and local models via Ollama. | The most accessible multi-agent framework for non-researchers. The "crew" metaphor (researcher agent, writer agent, analyst agent) makes complex orchestration intuitive. Fortune 500 adoption at 60% is a strong signal. The YAML-based configuration means you can set up agent teams without deep Python knowledge. For DePIN, you could build a crew that monitors node health, checks on-chain rewards, researches new projects, and generates weekly reports—each handled by a specialist agent. Model-agnostic is a big plus. | crewai.com |
| LangGraph / LangChain | Agent Orchestration Framework | The dominant ecosystem for building LLM-powered applications and agents. LangChain provides modular components (prompts, tools, memory, retrievers). LangGraph adds graph-based state machine orchestration for complex multi-agent workflows with cyclical execution patterns. Used by Klarna, Replit, Elastic, Uber, LinkedIn. Supports all major cloud platforms and observability tools. Full visibility into agent behavior—no hidden prompts or black boxes. | LangGraph is the production-grade choice when you need full control over agent behavior and state management. The graph-based approach with mathematical guarantees is what separates toy agents from reliable systems. The ecosystem integration is unmatched—1,400+ connectors, every cloud provider, every observability tool. Steeper learning curve than CrewAI but more power when you need it. For serious DePIN automation (multi-chain monitoring, cross-protocol arbitrage logic), this is the framework to build on. | langchain.com |
| Microsoft AutoGen | Multi-Agent Framework | Enterprise-focused open-source multi-agent framework from Microsoft. 30K+ GitHub stars. Enables multi-agent conversations with rich, multi-turn reasoning and human-in-the-loop capabilities. Designed for production-ready agentic systems with emphasis on reliability and scalability. Supports OpenAI, Anthropic, and local models. Features include Generative Actions (goal → autonomous step planning) and structured conversation patterns between agents. | The enterprise-safe alternative to wilder open-source agents. Human-in-the-loop is first-class, not bolted on, which matters for anything touching money or sensitive data. Microsoft backing means long-term support and integration with Azure ecosystem. Less community energy than CrewAI or LangGraph but more rigorous engineering. Good choice if you're building internal tools for a DePIN operating company and need audit trails and compliance. | github.com/autogen |
| Devin | AI Software Engineer | By Cognition Labs. Positioned as the first fully autonomous AI software engineer. Operates in its own sandboxed cloud environment with a full development setup (editor, browser, terminal). Can plan, write, debug, and deploy entire codebases from a single prompt. Handles end-to-end software projects including learning new technologies, fixing bugs across large codebases, and deploying to production. Closed-source, subscription-based. | Devin is the most ambitious bet on fully autonomous coding. When it works, it's magic—hand it a GitHub issue and come back to a PR. When it doesn't, it burns expensive compute going in circles. Sandboxed environment is a smart security choice. Not relevant for DePIN directly, but if you're a solo DePIN developer needing to ship node software, dashboard UIs, or monitoring tools, Devin could be a force multiplier. Closed-source and premium pricing limit adoption compared to Claude Code or Codex. | devin.ai |
| AgentGPT | No-Code Agent Builder | Browser-based, no-code platform for deploying autonomous AI agents. Name a goal, and it creates and executes a task chain to accomplish it. No installation, no coding required. Open-source. Provides the easiest entry point to agentic AI for non-technical users. Good for rapid prototyping and experimentation. | The lowest barrier to entry for anyone curious about agentic AI. Type a goal, watch it try. Reality check: it's fun for demos but struggles with anything complex. Think of it as "agentic AI training wheels." Not suitable for production, crypto, or anything requiring reliability. But it's a great way to understand how agent loops work before investing in a serious framework. Good for education and quick experiments. | agentgpt.reworkd.ai |
| BabyAGI | Research Agent Loop | Minimalist autonomous agent created by Yohei Nakajima. Originally just 140 lines of Python. Implements a simple task management loop: execute → create new tasks → reprioritize → repeat. Powered by GPT-4 and vector databases. Spawned dozens of spin-offs (BabyBeeAGI, SuperAGI, AgentVerse, GPT-Engineer). Influential as a conceptual blueprint for the entire agentic AI movement. | BabyAGI's importance is historical—it proved that a simple loop plus an LLM could exhibit goal-directed behavior. The 140-line implementation is still the best way to understand agent fundamentals. Not production-ready and not meant to be. Think of it as the Bitcoin whitepaper of agentic AI: the ideas matter more than this specific implementation. If you're a developer learning about agents, start here, then graduate to CrewAI or LangGraph. | github.com/babyagi |
Where autonomous agents meet decentralized infrastructure—the emerging frontier most relevant to DePIN.
The intersection of agentic AI and crypto is in its infrastructure phase. Builders are laying payment rails, identity primitives, and task markets that will eventually allow agents to transact autonomously on-chain. The inflection point comes when agents can hire other agents, pay for compute and data, and execute strategies in a reliable and safe way. Here's what's happening:
| Use Case | What's Happening | Key Projects | Prime Moover Take |
|---|---|---|---|
| Agent Wallets & Payments | Crypto provides native payment rails for agents that can't hold bank accounts. Agents can hold tokens, pay for services, and receive payment. Stablecoins + smart contracts enable programmable escrow and conditional payments. | Purch (agent commerce), ClawPurse (DePIN agent wallet for Timpi/NTMPI), NEAR AI Wallets, Coinbase AgentKit, Coinbase Agentic Wallets, Circle USDC agents | This is the killer app for crypto x AI. Agents need to pay and get paid—and traditional finance can't handle permissionless machine-to-machine transactions. Whoever solves agent payments wins a massive market. |
| Automated Trading & Prediction Markets | OpenClaw and similar agents are being used to monitor news feeds, social sentiment, and on-chain data to automate trading positions on Polymarket, DEXs, and CEXs. Reduces human delay in fast-moving markets. | OpenClaw + Polymarket, ElizaOS (AI16z), Virtuals Protocol, Autonolas (OLAS) | Proof of concept is real—but most positive results are unsubstantiated. The real risk is prompt injection: if your trading agent reads a poisoned data source, it could drain your wallet. Never give agents more capital than you can afford to lose. Period. |
| Airdrop & Testnet Farming | Agents automate "proof of activity" across testnets—bridging ETH, swapping on DEXs, interacting with protocols to maintain active status across dozens of wallets and chains. | OpenClaw airdrop skills, custom scripts on AutoGPT, dedicated farming bots | This is where most crypto users first encounter agentic AI. It works, but it's a cat-and-mouse game with Sybil detection. Also morally gray—protocols design airdrops for real users, not bot farms. We don't participate in this but understand the appeal. |
| DePIN Node Management | Agents can monitor node health, check rewards, restart crashed nodes, and generate performance reports across multiple DePIN protocols. Emerging use case for multi-project operators. | Custom CrewAI/LangGraph workflows, OpenClaw skills, Claude Code scripts | This is the most relevant use case for Tiki Cow readers. If you're running nodes across Helium, Flux, Akash, and GEODNET, an agent crew that monitors uptime, aggregates rewards data, and alerts you to issues could save hours per week. We're actively exploring this. |
| Agent-to-Agent Networks | Social networks and task markets where AI agents interact with each other. Agents post bounties, hire humans for physical tasks, and coordinate autonomous workflows. | Moltbook, HumanDoing (agent-to-human task market), Agent Protocol standard | Moltbook is mostly agents posting slop to each other right now—but the primitives being built (agent identity, reputation, payments) are worth watching. The moment agents can reliably hire other agents for compute, data, or physical tasks is when DePIN infrastructure becomes critical. That's the convergence thesis. |
Critical considerations before deploying any agentic AI system.
Agentic AI introduces fundamentally new attack surfaces. Unlike chatbots that generate text, agents take actions—which means vulnerabilities have real-world consequences. Here are the risks every user should understand:
| Risk | Description | Mitigation |
|---|---|---|
| Prompt Injection | Malicious instructions embedded in web pages, emails, or documents that hijack an agent's decision-making. Already seen in the wild on Moltbook (attempted crypto wallet drain). | Use sandboxed environments. Never give agents direct wallet access without human confirmation. Prefer platforms with built-in injection defenses (Claude, Copilot). |
| Exposed Instances | Self-hosted agents (especially OpenClaw) frequently misconfigured with open ports and no authentication. Researchers found 1,800+ exposed instances leaking API keys and credentials. | Always enable authentication. Use VPN or firewall rules. Never expose agent admin interfaces to the public internet. |
| Malicious Skills / Plugins | Third-party agent skills can contain data exfiltration code, prompt injection payloads, and C2 (command and control) infrastructure. Cisco found skills that silently sent data to external servers. | Only install skills from trusted sources. Review code before installation. Use allowlists for approved skills. Apply software composition analysis. |
| Excessive Permissions | Agents often request broad system access (files, terminal, email, browser). A compromised agent with user-level privileges can exfiltrate data, move laterally, and execute adversary instructions. | Apply principle of least privilege. Use sandboxes and virtual environments. Separate agent accounts from personal accounts. Limit file and network access. |
| Financial Exposure | Agents connected to funded crypto wallets can execute transactions. Prompt injection or hallucination could lead to unintended trades or wallet drains. | Never connect agents to wallets with significant holdings. Use dedicated hot wallets with spending limits. Require human approval for any transaction above a threshold. |