Building a Local AI Email Assistant with OpenClaw and Himalaya

Managing an inbox is practically a full-time job. Between endless notifications, useless newsletters, and urgent client requests, you waste a massive amount of time just figuring out what actually needs your attention.

I wanted an assistant to read my emails, filter the noise, and draft replies. The catch? I absolutely refuse to give full IMAP access to my Google Workspace or hand over my data to a random AI startup.

The solution is to run everything locally using OpenClaw and Himalaya (a CLI email client).

Why Himalaya?

If you want an AI agent to interact with your email securely, you need a robust command-line interface. Himalaya is an incredible open-source project: it lets you manage your emails straight from the terminal using standard IMAP and SMTP.

No complex APIs to wrap, no OAuth tokens to refresh constantly. You configure it once, and your terminal becomes a full-fledged email client. This means OpenClaw, which excels at utilizing CLI tools, can operate it flawlessly out of the box.

Setting up the Environment

First, let's get Himalaya installed. On a Mac, Homebrew makes this trivial:

brew install himalaya

Next, you need to set up your account by creating the ~/.config/himalaya/config.toml file. You'll add your IMAP and SMTP server details here. I highly recommend generating an "App Password" if you use Gmail or similar providers, keeping your main password safe.

Creating the OpenClaw Skill

Now for the fun part. We need to teach OpenClaw how to use Himalaya. OpenClaw relies on "Skills", which are essentially Markdown files containing clear instructions and tool contexts.

I set up a skill file in ~/agents/skills/himalaya/SKILL.md (which is now a standard OpenClaw skill) telling the agent:

1. Use himalaya list to view recent emails.
2. Use himalaya read <id> to parse the content.
3. Use himalaya reply <id> to draft a response.

The beauty of OpenClaw is that I don't need to write glue code in Python. I just write natural language instructions, and the agent figures out how to chain the bash commands.

The Daily Workflow

Now, in the morning, I just open my terminal and ask my agent:

"Check my inbox. Summarize the important emails, ignore the newsletters, and draft replies for client inquiries offering my availability for Thursday afternoon."

Here is what happens under the hood:

1. The agent runs himalaya list to fetch the unread message queue.
2. It reads the relevant threads one by one.
3. It evaluates the context using the LLM model I've configured (usually Claude 3.5 Sonnet or a capable local model).
4. It executes himalaya reply to compile drafts and saves them to the IMAP server.

All I have to do is open my preferred email client (or my phone), review the AI-generated drafts, tweak if necessary, and hit "Send".

Security Considerations

The most important aspect of this setup is that I retain full control.

The AI agent never sends emails automatically. The golden rule here is to only allow it to create drafts. You never want to risk an LLM hallucination being sent directly to a key client. Furthermore, by running OpenClaw on your own server or Mac, your credentials never leave your machine.

Conclusion

Integrating Himalaya with a local AI agent has completely eliminated my inbox fatigue. It is a perfect example of how artificial intelligence does not have to live in the cloud or require expensive SaaS subscriptions. Often, the most powerful tools are built by combining classic, reliable CLI utilities with a bit of "intelligence".

If you run OpenClaw, give it a try. You might never manage your email the old way again.