Vercel's eve: an open-source agent framework built for production. Vercel just open-sourced eve, the framework it uses to run more than 100 agents internally. Its pitch is simple: an agent is a directory, tools are files, and the production plumbing comes built in. Here is what eve actually does, why it matters, and how to think about adopting it. At its Ship conference on June 17, 2026, Vercel released eve, an open-source framework for building, running, and scaling AI agents. The framing in the announcement is direct: "Agents today are where the web was before frameworks, with everyone hand-rolling the same plumbing." eve is Vercel's attempt to do for agents what Next.js did for the web, which is to standardize the repetitive infrastructure so teams can focus on what their agent actually does. That comparison is worth taking seriously, because it is the same company making it about its own most successful product. It is also worth scrutinizing, because the hardest parts of shipping an agent are rarely the parts a framework solves. This post breaks down what eve is, what comes built in, the larger bet it sits inside, and how technology leaders should think about adopting it. What eve actually is. eve is filesystem-first. An agent is a directory, and the files inside it map directly to capabilities. There is no separate wiring layer to maintain. agent/ agent.ts # model configuration instructions.md # system prompt and personality tools/ # custom capabilities (one file per tool) skills/ # markdown knowledge files subagents/ # delegated agents channels/ # Slack, Discord, HTTP, and more schedules/ # cron-style triggers The configuration surface is deliberately small. A working agent can be as little as a model declaration: import {defineAgent} from "eve"; export default defineAgent({ model: "anthropic/claude-opus-4.8",}); Tools are single TypeScript files with typed input validation and an async execute function. Skills are markdown files that teach the agent domain rules and context. Subagents are child agents the parent can delegate to, each with its own instructions and tools. The result is that an agent reads like a project rather than a tangle of glue code, and the structure is the same regardless of which team built it or what it does. eve is open source on GitHub under an Apache-2.0 license, and a project scaffolds in under a minute with npx eve@latest init. What comes built in. The configuration is the easy part. The reason eve is interesting is the set of production concerns it handles by default, the work most teams discover they need only after their prototype meets real traffic. Six capabilities stand out. * Durable execution. Conversations checkpoint at each step, so a session can survive a crash, a deploy, or a long pause and resume exactly where it stopped. This is built on Vercel's open-source Workflow SDK. * Sandboxed compute. Agent-generated code is treated as untrusted. Every agent gets its own isolated sandbox for shell commands, scripts, and file access, running on Vercel Sandbox in production and Docker locally. * Human-in-the-loop approvals. Any action can be flagged to require a human sign-off. The agent pauses there indefinitely without burning compute, which matters for anything that touches money, customer data, or production systems. * Subagents. A parent agent can hand work to specialized child agents, each scoped to its own tools and instructions, instead of stuffing every capability into one prompt. * Evals. Scored test suites validate agent behavior locally, in CI, or against a deployed agent, so a change that quietly breaks behavior gets caught before it ships. * Multi-channel deployment. One agent can run across Slack, Discord, Teams, Telegram, Twilio, GitHub, Linear, and plain HTTP without rebuilding it per surface. For observability, eve exports OpenTelemetry traces to the tools teams already run, including Datadog, Honeycomb, and Jaeger, and a local eve dev server shows every model call, tool execution, and skill load as it happens. Each of these is solvable on its own. The argument eve makes is that solving all of them, the same way, on every agent, is the part that does not carry over from one project to the next. How it connects to the rest of your Stack. An agent is only useful if it can reach real systems. eve connects through Model Context Protocol servers, any OpenAPI document, and Vercel Connect, which replaces long-lived credentials with short-lived tokens and ships with pre-built integrations for Slack, GitHub, Snowflake, Salesforce, Notion, and Linear. The model layer is configurable through Vercel's AI Gateway, with fallback providers, so the anthropic/claude-opus-4.8 in the example above is a default rather than a lock-in. The larger bet. eve did not launch alone. Vercel positioned it as the framework on top of an "Agent Stack" that bundles its existing pieces: the AI SDK, AI Gateway, Vercel Sandbox, the Workflow SDK, and the Chat SDK. CEO Guillermo Rauch used the launch to argue that the company is becoming infrastructure for the "agent era," and he brought numbers. Vercel says agent-triggered deployments grew from under 3% of commits to more than 50% in six months, and token volume through its AI Gateway rose from 2 million to 20 million. The most credible evidence is that Vercel runs eve itself. The company says it operates more than 100 agents in production on the framework, including a data analyst that fields over 30,000 Slack questions a month, a support agent it credits with resolving 92% of tickets autonomously, and a revenue-operations agent built by non-engineers in six weeks. Treat the specific figures as vendor claims, because they are. But a framework its maker depends on at that scale is a different proposition from one shipped as a demo. How to think about adopting it. eve is a strong piece of engineering, and for teams already on Vercel it lowers the cost of getting an agent to production in a real way. The decision to use it, though, sits downstream of harder questions that no framework answers for you. The first is whether the agent should exist at all. The documented failure mode for agentic AI is not bad plumbing. It is automating a workflow that was never worth automating, or handing autonomy to a process that needed a form. eve makes building faster, which makes building the wrong thing faster too. The second is lock-in, and it cuts both ways. The framework is open source and the model layer is portable, both genuine. But the production story, durable execution, sandboxing, and the dashboard, is most seamless on Vercel's own platform. That is a reasonable trade for many teams and a dealbreaker for a few. It is a decision to make deliberately, with eyes open, not one to back into because the scaffolding was convenient. The third is governance. The approval gates, eval suites, and OpenTelemetry traces eve provides are the right primitives. They are not a policy. Who approves what, what an agent is allowed to touch, and how you prove after the fact that it behaved are organizational decisions the framework can enforce but cannot make. How this maps to how Honra works. At Honra Honra Llc. sit on the client's side of these decisions. Honra Llc. do not resell frameworks or take a margin on the tools a client adopts, so its read on eve is the same one Honra Llc. would give privately: it is a serious option, well suited to teams already invested in Vercel, and the framework choice is the smallest of the choices in front of you. The work Honra Llc. care about is the work eve does not do. Deciding which processes deserve an agent and which deserve a fix elsewhere. Setting the governance and approval model before the first agent ships, not after an incident. Building the thing directly when that is the right call, with the same judgment Honra Llc. bring to advising on it, and working alongside the implementers a client already has when it is not. eve removes a real category of busywork from that build. It does not remove the judgment, and that is the part worth getting right. Key takeaway. eve is Vercel making a credible claim that the production infrastructure of AI agents can be standardized the way web infrastructure was, backed by a framework it runs at scale on itself. For teams building agents, especially on Vercel, it is worth a serious look. Just keep the order of operations straight: decide whether the agent should exist, how it will be governed, and what you are willing to depend on, and only then decide which framework builds it.

next-aeo: what Profound's new NPM package means for GEO practitioners. A new NPM package called next-aeo - released this week by Profound - puts Answer Engine Optimization (AEO) structured data directly into Next.js application code. For developers building GEO-optimized sites, this is a meaningful shift in how structured data gets deployed. Here's what it does, what it doesn't do, and what it means for GEO strategy. What is next-aeo? next-aeo is a Next.js-focused NPM package that generates AEO-structured JSON-LD schema markup directly from your application code. Instead of managing JSON-LD as static strings in your CMS or manually updating schema blocks when content changes, next-aeo lets developers define structured data as typed React components that stay synchronized with page content. The practical effect: when a developer adds an FAQ section to a Next.js page, the corresponding FAQPage schema gets generated automatically in the correct format. When a product description changes, the structured data updates with it. Schema stays current without a separate CMS workflow. Profound has also integrated next-aeo into the Vercel Marketplace, enabling one-click Agent Analytics setup for sites already deployed on Vercel. For teams already in that ecosystem, the barrier to deploying AEO schema drops significantly. Why this matters for GEO. The connection between structured data and AI citation is now well-documented. AI systems - including Google's AI Overviews, ChatGPT, and Perplexity - preferentially cite content that includes explicit machine-readable signals: FAQPage schema, Article schema with proper author and publisher entities, BreadcrumbList for topical hierarchy, and HowTo or Q&A markup for process-based content. next-aeo lowers the technical barrier to deploying this schema at scale, especially for teams that: * Build and maintain multiple Next.js blog or content routes * Update content frequently, which creates schema drift when JSON-LD is managed manually * Are scaling programmatic SEO or programmatic GEO - generating hundreds or thousands of pages where manual schema management isn't feasible If schema stays current with content automatically, the citation signal stays consistent. Consistent, accurate structured data is one of the clearest factors in becoming a reliable AI citation source. The GEO stack is becoming developer infrastructure. The launch of next-aeo is part of a broader pattern: GEO tooling is moving from marketing workflows into developer infrastructure. This is significant. Historically, SEO tools lived in the marketing stack - keyword research platforms, content optimization dashboards, rank trackers. GEO is following a similar early adoption curve, but with one key difference: structured data, entity definitions, and schema markup are fundamentally code-level concerns. They live in the application layer, not in a content editor. The teams who will build durable GEO authority over the next 18 months are likely the ones who treat schema and entity optimization as first-class engineering concerns - not as a post-launch checklist item. Packages like next-aeo accelerate this shift. What next-aeo doesn't do. To be clear about scope: next-aeo handles structured data generation - the technical layer. It does not: * Track whether AI systems are citing your content. Generating correct schema is necessary but not sufficient. You still need a way to monitor which LLMs mention your brand, on which topics, and with what frequency. That's a separate measurement problem. * Optimize content strategy for GEO. Schema tells AI systems what your content is about. It doesn't determine whether the content itself is the best answer to the question. Content depth, authority signals, and topical cluster coverage still drive which pages get cited. * Handle non-Next.js environments. The package is explicitly a Next.js tool. WordPress, plain React, or other frameworks require different approaches. How MeetGEO approaches the Full GEO stack. MeetGEO's approach treats schema as one component of a complete GEO system: Layer 1 - Entity Definition: Every site needs a clear @graph that defines the Organization, its key people, its products, and its topical authority. This establishes the entity record AI systems pull from. Layer 2 - Content Architecture: Posts are structured to answer questions directly, with explicit FAQ sections and answer-first formatting. Each post targets a specific query pattern an AI might generate. Layer 3 - Schema Markup: Full @graph schema on every page - Article, FAQPage, BreadcrumbList, WebPage, Organization - ensures AI systems can accurately extract and attribute content. Layer 4 - Citation Monitoring: MeetGEO tracks which queries trigger AI mentions of a brand, where competitors are being cited instead, and how citation frequency changes over time. The next-aeo package is a useful tool for Layer 3 if you're building on Next.js. But GEO practitioners need all four layers working together to build and maintain AI search visibility. What to do this week. If you're a GEO practitioner evaluating next-aeo: * Audit your current schema coverage. Before adding a new tool, confirm which pages have no schema, partial schema, or outdated schema. A schema audit will tell you where the highest-impact gaps are. * Prioritize FAQ schema. FAQPage is the schema type most directly connected to AI citation patterns. If you're deploying schema selectively, start there. * Verify entity definitions. Check that your Organization, author, and product entities are correctly defined with @id references that create a consistent knowledge graph across your site. * Start tracking citations. Schema without citation monitoring is like publishing content without checking rankings. You need to know whether the structured data is working. The next-aeo package makes one part of this easier. The GEO strategy work - what to publish, how to structure it, and how to measure AI visibility - remains the same regardless of which technical tools you use. Frequently asked questions. What is next-aeo? next-aeo is an NPM package by Profound that generates AEO-compliant JSON-LD structured data directly from Next.js application code, keeping schema synchronized with page content automatically. What is AEO (Answer Engine Optimization)? AEO is the practice of optimizing content to be surfaced by AI-powered answer engines - including Google AI Overviews, ChatGPT, and Perplexity. It focuses on structured data, direct answers, and content that AI systems can accurately extract and cite. Does next-aeo replace a GEO platform like MeetGEO? No. next-aeo handles structured data generation at the code level. A GEO platform like MeetGEO tracks AI citation frequency, identifies where competitors are being cited instead of your brand, and guides content strategy based on actual AI search behavior. Is structured data required to get cited by AI? Structured data significantly increases the likelihood of AI citation by providing explicit, machine-readable signals about your content. While not the only factor, it is consistently one of the highest-impact technical optimizations for GEO. How do I know if my schema is working for GEO? Schema validation tools can confirm technical correctness, but the real test is whether AI systems are citing your content. Citation tracking - monitoring how often your brand appears in AI-generated answers to target queries - is the metric that measures GEO schema effectiveness.