# Hermes Agent Integration: Connect to LinkedIn via Zevari

URL: https://docs.zevari.ai/api/connect/hermes-agent

> Give a Hermes Agent safe LinkedIn access through Zevari - register the MCP server in config.yaml or call the REST API, and run an approval-gated find, score, stage, approve loop.

## Overview

Hermes Agent, by Nous Research, is a self-hosted autonomous agent framework with persistent memory and a self-improving loop, VPS-hosted, configured at ~/.hermes/config.yaml (/opt/data/config.yaml in Docker), with native MCP support. This is Hermes Agent the framework, not the separately named open-weight LLM family. Zevari is the LinkedIn execution layer it calls.

## Prerequisites

A Zevari account, an API key (zvr_api_..., sent as Authorization: Bearer), workspace membership, and an exact funded LinkedIn sender seat associated with that workspace. The canonical REST reference is the bring-your-own-agent page.

- [Bring your own agent (REST + bearer)](https://docs.zevari.ai/api/connect/bring-your-own-agent)

## Connect via config.yaml (MCP)

Hermes Agent supports MCP natively. Add a zevari entry to the mcp_servers map in ~/.hermes/config.yaml (/opt/data/config.yaml in Docker) with url: "https://mcp.zevari.ai/mcp" and headers: Authorization: "Bearer ${ZEVARI_MCP_TOKEN}". ZEVARI_MCP_TOKEN is a Zevari MCP access token (zvr_mcp_...), created in Settings → Developers on the MCP access tokens tab and shown once. The agent then discovers Zevari's LinkedIn tools automatically.

## Connect via REST (bearer)

For the REST alternative, call https://api.zevari.ai with Authorization: Bearer <key> from the agent's own code.

## A working loop: find, score, stage, approve

Search posts or profiles, ICP-score the matches, draft in your voice, create a campaign, then stage each write with confirmations.requestAction. A human approves, and only then does the write execute. A persistent, self-improving Hermes Agent still never sends unattended.

## Safety and approval notes

Every LinkedIn write is staged for approval. Server-side weekly ceilings (100 on Free*, 150 on Premium, 200 on Sales Navigator), working hours, and burst caps are enforced, and no browser cookies are used. The weekly figure covers connection requests sent without a note: LinkedIn caps requests that carry a note at 3 a month on a free account, and gives Premium and Sales Navigator a far larger allowance, so Premium or Sales Navigator is the practical choice for real outbound (see zevari.ai/safety).

- [Approval gates over the API](https://docs.zevari.ai/api/connect/approval-gates)
- [Hermes Agent on zevari.ai](https://zevari.ai/hermes)

## Docs Links

- [Help Center](https://docs.zevari.ai/help): Human-facing guide to Zevari skills, workflows, agents, videos, safety, and support.
- [Workflow Guide](https://docs.zevari.ai/help/workflows): LinkedIn outreach, content, prospect research, audience analysis, campaign, inbox, and GTM workflows for Claude.
- [Prompting Guide](https://docs.zevari.ai/help/prompting-guide): Copy-ready prompts that require AI assistants to read the MCP reference before acting.
- [Workspaces and Sender Seats](https://docs.zevari.ai/help/workspaces-and-seats): Human-facing setup guide for free workspaces, payer-account billing, members, funded LinkedIn seats, and sender associations.
- [Safety Center](https://docs.zevari.ai/help/safetycenter): Human-facing guide to Zevari safety guardrails, warm-up, pauses, blocked actions, and recovery state.
- [Warm-Up](https://docs.zevari.ai/help/warm-up): Human-facing guide to LinkedIn sender warm-up and gradual activity ramping.
- [API Playbooks](https://docs.zevari.ai/api/playbooks): Ordered REST call flows for developers and AI agents using the Zevari API.
- [MoltSets + LinkedIn Workflows](https://docs.zevari.ai/api/moltsets-linkedin-workflows): Use MoltSets verified contact data with Zevari's LinkedIn enrichment, campaign, and approval-gated execution layer.
- [MCP Reference](https://docs.zevari.ai/mcp/reference): Agent-facing tool schemas, capability contracts, examples, gotchas, and recovery guidance.
- [API Reference](https://docs.zevari.ai/api/reference): Public REST API reference powered by the Zevari OpenAPI document.
- [MCP Reference JSON](https://api.zevari.ai/v1/mcp-reference.json): Machine-readable MCP tool reference.
- [LLMs Full](https://api.zevari.ai/llms-full.txt): AI-readable Zevari documentation bundle.
- [Support](mailto:issues@zevari.ai): Send bug reports, support handoffs, and feature requests.