Zevari Docs - Start Here
Connect any AI agent to LinkedIn safely. Choose your path by client or goal, run the quickstart, and find the API and MCP reference.
What is Zevari
Zevari is the safe LinkedIn execution layer for AI agents. Connect Claude, Codex, ChatGPT, OpenClaw, Hermes Agent, or your own code over MCP or a REST API; the agent can search, score, draft, and run campaigns, and every write is staged for your approval. No browser cookies, no bans.
Choose your path
- Quickstart (5 minutes)
Connect Claude over MCP and send your first approval-gated request.
- Connect an agent over MCP
Claude, Codex, ChatGPT, OpenClaw, Hermes Agent.
- Connect over REST (bring your own)
Any runtime, cron, or custom code via a bearer token.
- Look up an endpoint
The REST API and MCP reference.
Most-used pages
- API overview
- Approvals
- Connect an email provider
- Set up reply email
- Track a booking calendar
- Help Center
Human-facing guide to Zevari skills, workflows, agents, videos, safety, and support.
- Workflow Guide
LinkedIn outreach, content, prospect research, audience analysis, campaign, inbox, and GTM workflows for Claude.
- Prompting Guide
Copy-ready prompts that require AI assistants to read the MCP reference before acting.
- Workspaces and Sender Seats
Human-facing setup guide for workspaces, members, billing, add-on LinkedIn sender seats, and teammate LinkedIn connections.
- Safety Center
Human-facing guide to Zevari safety guardrails, warm-up, pauses, blocked actions, and recovery state.
- Warm-Up
Human-facing guide to LinkedIn sender warm-up and gradual activity ramping.
- API Playbooks
Ordered REST call flows for developers and AI agents using the Zevari API.
- MoltSets + LinkedIn Workflows
Use MoltSets verified contact data with Zevari's LinkedIn enrichment, campaign, and approval-gated execution layer.
- MCP Reference
Agent-facing tool schemas, capability contracts, examples, gotchas, and recovery guidance.
- API Reference
Public REST API reference powered by the Zevari OpenAPI document.
- MCP Reference JSON
Machine-readable MCP tool reference.
- LLMs Full
AI-readable Zevari documentation bundle.
- Support
Send bug reports, support handoffs, and feature requests.