eve

An open-source marketing-agent team from Vercel Labs built on the eve framework. Users submit launch planning, copywriting, or SEO requests through Slack or the eve terminal TUI; a lead agent loads the shared brand-context document, writes a self-contained brief, routes it to one of five specialists—product marketing, content marketing, social media, SEO, or email—and returns the resulting deliverable. The lead does not write deliverables itself, and specialists work independently without shared conversation history; the product marketer alone maintains the brand-context document, which the other specialists read at the start of a task. Newsletter work passes from the content marketer to the email specialist for inbox adaptation and sending. Outputs are delivered through Notion, Typefully, Resend, or stored audit artifacts. The system pauses irreversible operations—including Resend sends and deletes, Typefully deletes and scheduled publishing, and Notion page moves—for approval in Slack or the terminal; its email specialist is restricted to an allowlist of Resend tools. Deployment provisions Notion, Resend, and Slack connectors and a Vercel Blob store, and requests a Typefully API key. The implementation uses TypeScript, Vercel Connect, MCP connections for Notion, Typefully, and Resend, Vercel AI Gateway for model access, and Vercel Sandbox for reference files and shell commands. Email campaigns require a verified Resend sending domain and at least one segment configured in Resend.

Comp AI CRM is an open-source, self-hostable customer relationship management system for AI agents, developed by Comp AI and hosted by Try Comp AI. Its agent runs as an independent durable deployment on its own schedule and database-backed work queue rather than waiting for browser requests: it selects records to investigate, researches contacts and companies, spends a research budget, schedules follow-ups and rechecks, records observed evidence, and sends weak or ambiguous matches to a human instead of writing them directly to records. It supports durable sessions, contact and company records, an Agent tab for viewing work and answering questions, authored tools for reading CRM history, searching records, identifying contacts, researching people, enriching companies, recording facts and scheduling rechecks, and versioned Markdown skills. The agent uses file-based tools and Markdown skills on Vercel's eve durable-agent framework, while its queue leases due tasks with PostgreSQL row locking so concurrent workers claim disjoint work and expired leases release tasks from failed runs. A restricted shell sandbox provides bash, grep, glob and a workspace without network egress, database credentials or direct database access. Optional sources include mailbox history, company brand data, LinkedIn and Perplexity web research.

Viserys is a self-contained pack of Markdown engineering workflows, reviewer personas, validation scripts, commands, and lifecycle hooks for AI coding agents. Its workflow routes development through DEFINE, PLAN, BUILD, VERIFY, REVIEW, and SHIP stages, with 28 skills covering requirements clarification, specification and task breakdown, implementation, test-driven development, debugging, browser testing, review, security, performance, migrations, CI/CD, documentation, observability, and release preparation. The pack also includes four reviewer personas, shared checklists, evaluation cases and fixtures, and validators for skills, commands, artifact paths, reference links, and versions. The repository includes an OpenCode agent definition that registers the skills and routes requests through the lifecycle workflow. The Markdown skills can also be supplied to skill-aware agents such as Claude Code, Cursor, and other agents, or read directly as standalone process documents.

An open-source AI software-development factory provided as a Vercel template and implemented by Foreman. It takes tasks from GitHub issues, GitHub mentions, Linear Agent Sessions, or a local developer TUI and moves them through four isolated agent stations: a Classifier triages task type, priority, complexity, and actionability; an Analyst creates a plan and acceptance criteria from a live repository checkout; an Implementer executes the plan in a sandbox, runs the repository's checks, and pushes a branch; and a Reviewer independently evaluates the resulting diff with evidence. The pipeline produces a reviewed draft pull request for human approval, after which people mark it ready and merge it. Foreman maintains shared repository notes between runs, can diagnose failing CI on its own factory pull requests, and posts progress to the originating GitHub or Linear task. The deployment flow configures GitHub and Linear connectors, Vercel Blob storage, a target repository, and an issue label.