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This notebook demonstrates how to turn Hermes Agent into a fundraising research assistant, so that a single sentence produces five researched investors and five personalized email drafts sitting in your Gmail, ready for you to review and send. The agent reads your company profile, searches the live web for funds that match, pulls the messy prose into structured rows, ranks every candidate against your one-liner, writes an email that opens with something specific and true about each investor, and screens the copy before anything reaches your mailbox. By combining Hermes Agent with ZeroGPU’s edge-optimized models, this notebook walks you through a workflow where the only step that needs a capable generative model is the writing, and everything around it - extraction, ranking, classification, summarization, moderation - runs on purpose-built small models at a fraction of the cost. For the full reference, see the Hermes Agent integration guide. In this notebook, you’ll explore:
  • Hermes Agent: An open-source agent from Nous Research that runs in your terminal, a TUI, or a self-hosted web UI. It brings file, terminal, and memory toolsets, a skills system for reusable procedures, and a built-in MCP client that picks up tools from any external server at startup. Here it orchestrates the whole outreach run and decides which tool to reach for at each step.
  • ZeroGPU: An ultra-fast, compute-efficient inference provider for apps and agents. We run purpose-built small and nano language models across an edge-powered network for the high-volume, purpose-specific tasks your app or agent runs constantly. Plug in our OpenAI-compatible API and you’re live - zero GPU infrastructure, serverless, auto-scaling by default.
  • Dappier: A real-time web and licensed-data search layer exposed over MCP. It supplies the live investor news that the workflow reasons over, since ZeroGPU runs models rather than a search index.
  • Zapier MCP: A hosted MCP endpoint that turns any of Zapier’s app actions into a tool your agent can call. Here it exposes exactly one action, Gmail create draft, so the agent can write drafts and cannot send mail.
This setup not only demonstrates a practical application of agent-driven fundraising research, but also provides a flexible framework that can be adapted to other real-world scenarios requiring high-volume research, ranking, and personalized outbound writing.

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🎥 Watch the Video Guide

Video walkthrough coming soon.

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📦 Installation

This recipe assumes you already have Hermes Agent running, either locally or on a host like Hostinger. See the Hermes Agent quickstart if you do not. Everything else is one script. Download it, fill in your keys, and run it:
Open the file and set the three values at the top. Only the first is required:
Then run it:
The agent’s own reasoning now runs on ZeroGPU’s deepseek-v4-flash, and fourteen ZeroGPU tools are available to it. For what the script does line by line, see the Hermes Agent integration guide.

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🔑 Setting Up API Keys

You’ll need keys for ZeroGPU, Dappier, and Zapier. You can go to here to get an API key and Project ID from ZeroGPU. The key starts with zgpu-api- and the Project ID (UUID) is on the project settings page. The setup script writes your ZeroGPU key to Hermes’ own .env and puts the other two into their MCP URLs. If you would rather set them by hand:
For Dappier, create a key at dappier.com and pass it as a query parameter:
For Zapier, create a server at mcp.zapier.com and enable only the Gmail create-draft action. Your personal URL carries its own token:
Read that tool list before continuing. Scoping the Zapier server to a single draft action in the Zapier console is what makes this workflow unable to send mail, no matter what the agent decides to do. It is a stronger boundary than a prompt instruction, because no configuration change on the agent side can widen it.

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🧠 Install the Outreach Skill

A Hermes skill is a Markdown procedure the agent loads on demand. This one encodes the whole workflow, which ZeroGPU tool runs at each step, what the email may and may not say, and what the run has to report at the end.
If the skill does not appear, the YAML frontmatter failed to parse, usually a stray tab or an unquoted colon. There is no lint command, so the listing is the check.

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📄 Add Your Company Profile

The agent needs to know what it is pitching. Download the template, fill it in, and save it where the skill expects:
The one-liner matters more than anything else in the file. It is the string the ranking step embeds, so a vague sentence produces a vague shortlist. Save it to /data/workspace/outreach/company.md on your Hermes host. In the web UI, paste this into a new chat followed by the file contents:

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🔎 Run the Workflow

Load the skill and give it one instruction:
The agent works through the skill’s procedure. Watch the tool calls scroll:
Dappier finds the raw material. Every step that turns it into something useful is a ZeroGPU model, and each one is chosen for its job: a 205M-parameter extractor for pulling fields out of prose, a 384-dimension embedding model for ranking with no generative call at all, an 8B model for one-sentence hooks, and deepseek-v4-flash only for the writing. 🎉 ZeroGPU handled nine of the ten steps on small models, leaving the frontier-priced work to the one step that actually needed it.
If the agent finishes the research but stops short of creating drafts, tell it explicitly: Now create the Gmail drafts using the Zapier MCP tool. Agents sometimes treat an external write as needing confirmation, which is reasonable behavior when the action leaves the machine.

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💌 Check the Drafts

Open Gmail and go to Drafts. Five emails, each opening with something specific about that investor rather than a restatement of their thesis:
Now open Sent. It is empty, and it stays empty. The Zapier server exposes no send action, so the agent has no way to mail anyone. A human approves every one.

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💰 What It Cost

Every ZeroGPU tool returns a savings block, and the skill rolls them into savings.md at the end of the run:
That last pair is the number that scales. Five investors is a rounding error either way; the shape of the run is what matters: The comparison is not a benchmark. baseline_cost_usd prices the exact same token counts at a published frontier rate, so it is a like-for-like measurement of the same work done two ways. 🎉 Twenty-four model calls, five researched and drafted investors, and less than half a cent of inference.

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🌟 Highlights

This notebook has guided you through setting up and running a Hermes Agent workflow with ZeroGPU for investor research and personalized outreach. You can adapt and expand this example for various other scenarios requiring high-volume research, ranking, and personalized outbound writing. Key tools utilized in this notebook include:
  • Hermes Agent: An open-source agent from Nous Research that runs in your terminal, a TUI, or a self-hosted web UI. It brings file, terminal, and memory toolsets, a skills system for reusable procedures, and a built-in MCP client that picks up tools from any external server at startup. Here it orchestrates the whole outreach run and decides which tool to reach for at each step.
  • ZeroGPU: An ultra-fast, compute-efficient inference provider for apps and agents. We run purpose-built small and nano language models across an edge-powered network for the high-volume, purpose-specific tasks your app or agent runs constantly. Plug in our OpenAI-compatible API and you’re live - zero GPU infrastructure, serverless, auto-scaling by default.
  • Dappier: A real-time web and licensed-data search layer exposed over MCP. It supplies the live investor news that the workflow reasons over, since ZeroGPU runs models rather than a search index.
  • Zapier MCP: A hosted MCP endpoint that turns any of Zapier’s app actions into a tool your agent can call. Here it exposes exactly one action, Gmail create draft, so the agent can write drafts and cannot send mail.
This comprehensive setup allows you to adapt and expand the example for various scenarios requiring high-volume research, ranking, and personalized outbound writing.