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Overview

You can connect your existing AI harness to eigi.ai instead of rebuilding it from scratch. This works with:
  • OpenClaw
  • Harness
  • Any custom harness that exposes an OpenAI-compatible Responses API
In simple terms, you keep your own model stack running where you want, then point eigi.ai to that secure HTTPS endpoint.
This is the right setup if you already have your own orchestration, tools, prompts, guardrails, or model routing and want to use it inside eigi.ai.

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Before You Start

Make sure you have these three things ready:
  • A working harness with an OpenAI-compatible Responses API
  • A public HTTPS URL for that harness
  • A secret API key or token that protects access to the harness
If your harness is only running on your laptop or on a private server today, that is fine. You can still connect it by exposing it safely over HTTPS.

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How The Flow Works

1

Choose your harness

Pick the system you want eigi.ai to call. This can be OpenClaw, Harness, or your own custom harness as long as it exposes an OpenAI-compatible Responses API.
2

Host it where you are comfortable

Run that harness on your own laptop, another local machine, a private server, AWS, or any cloud you trust. The choice depends on your security and compliance requirements.
3

Expose it through HTTPS

Make the harness reachable on the internet through a secure HTTPS URL. Most teams do this with a public server, ngrok, or Cloudflare Tunnel.
4

Protect it with a secret

Create an API key, bearer token, or similar secret so eigi.ai can authenticate when calling your harness.
5

Add it in eigi.ai as Custom LLM

In eigi.ai Agent360, while creating or editing an agent, choose Custom LLM and enter the base URL, API key, and model name for your harness.
6

Save and test

Save the connection, run a test request, and confirm eigi.ai can reach your harness successfully.

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Step 1: Choose A Harness

Your harness is the service that receives the model request and returns the AI response. You can use:
  • OpenClaw if you already use OpenClaw workflows
  • Harness if your team already has that runtime in place
  • A custom harness if your application already exposes an OpenAI-compatible Responses API
If you are unsure whether your setup is compatible, check whether your service already accepts OpenAI-style requests and returns OpenAI-style responses.

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Step 2: Decide Where To Host It

You can host your harness in any location that matches your security needs.
Hosting optionBest forNotes
Your laptop or desktopQuick testingFastest way to validate the setup, but your machine must stay on
Another machine on your local networkInternal testingUseful when a dedicated machine is available in your office or lab
AWS, GCP, Azure, or any cloud VMProduction useBest for stable uptime and easier team access
Private company infrastructureSecurity-sensitive deploymentsGood when data must remain inside your own environment
If your team has strict security rules, start with your approved internal or cloud environment instead of a laptop-based setup.

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Step 3: Make Your Harness Reachable Over HTTPS

eigi.ai needs a secure HTTPS URL to call your harness. If your harness is already deployed on a public server with HTTPS, you can use that URL directly. If it is only running locally or on a private network, you need a secure tunnel.

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What is ngrok?

ngrok is a tunneling tool that gives your local service a temporary public URL. It is useful when:
  • You want to test quickly from your local machine
  • You do not want to deploy to the cloud yet
  • You need a short-lived public HTTPS endpoint for development

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When should you use Cloudflare Tunnel?

Cloudflare Tunnel is a better fit when:
  • You want a more stable public hostname
  • You want to use your own domain
  • You need a production-friendly tunnel option
  • Your team already uses Cloudflare for networking or security

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Choose the right option

OptionUse it whenAvoid it when
ngrokYou want the fastest local proof of conceptYou need a long-term production endpoint
Cloudflare TunnelYou want a more durable and controlled setupYou only need a quick one-time test
Direct cloud HTTPS deploymentYou already run services in the cloudYou are still validating locally

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Step 4: Install ngrok Or Cloudflare Tunnel

If you are handing this to another agent, give it your OS, local port, and tunnel choice, then ask it to either run ngrok http <PORT> after ngrok install and auth setup, or run cloudflared tunnel --url http://localhost:<PORT> after Cloudflare install, and return the final public HTTPS URL.

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Option A: Install ngrok

Install ngrok from the official site:
  1. Go to ngrok.com/download
  2. Download the version for your operating system
  3. Install it using the instructions shown by ngrok
  4. Create an ngrok account if required
  5. Add your ngrok auth token following ngrok’s setup instructions
Once your harness is running locally on a port such as 3000 or 8000, start the HTTPS tunnel: 
ngrok http 8000
ngrok will give you a public HTTPS URL such as: 
https://example-name.ngrok-free.app
Use that HTTPS URL as your harness base URL in eigi.ai.

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Option B: Install Cloudflare Tunnel

Install Cloudflare Tunnel from the official Cloudflare documentation:
  1. Go to developers.cloudflare.com/cloudflare-one/connections/connect-networks/downloads
  2. Install cloudflared for your operating system
  3. Sign in to Cloudflare if your setup requires account authorization
  4. Create a tunnel and map it to your local harness port
For a quick local tunnel, Cloudflare commonly uses a command like this: 
cloudflared tunnel --url http://localhost:8000
Cloudflare will provide a public HTTPS hostname you can use as your harness base URL.
Use only one public URL for the final configuration. If your ngrok or Cloudflare URL changes, update the saved base URL in eigi.ai.

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Step 5: Create A Secret For Authentication

Do not expose your harness without protection. Create a secret value that eigi.ai will send with each request. Depending on your harness, this can be:
  • An API key in a header
  • A bearer token
  • A gateway secret managed by your own reverse proxy
Recommended approach:
  1. Generate a strong random secret
  2. Save it in your harness configuration or proxy
  3. Configure your harness to reject requests that do not include that secret
  4. Copy the same secret into eigi.ai when you configure the Custom LLM settings for your agent
Never put this secret in frontend code, screenshots, shared docs, or public repositories.

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Step 6: Add The Harness In eigi.ai

Once your harness has a public HTTPS URL and an API secret, open eigi.ai and add it as a Custom LLM while creating or editing your agent in Agent360. In Agent360:
  1. Open Create Agent or edit an existing agent
  2. Go to the LLM configuration section
  3. Choose Custom LLM as the provider
  4. Enter your harness details
  5. Save the agent
Enter these values:
  • Base URL: the public HTTPS address for your harness
  • API Key: the secret token eigi.ai should send for authentication
  • Model Name: the model identifier your harness expects
Examples:
FieldExample
Base URLhttps://your-harness.example.com
API Keysk_live_example_secret
Model Namegpt-4.1, gpt-4o, or your custom routed model name
You can do this from:
  • The eigi.ai dashboard
  • Our MCP-based workflow
  • Our CLI-based workflow
The important part is the same in every case: save the base URL, API key, and model name for your external harness in the agent’s Custom LLM configuration.

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Step 7: Test The Connection

After saving the Custom LLM configuration:
  1. Send a simple test request
  2. Confirm the harness responds successfully
  3. Check that the returned response format is valid
  4. Verify that authentication is working correctly
If the test works, your harness is now connected to eigi.ai.

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Quick Checklist

  • I chose a harness that supports an OpenAI-compatible Responses API
  • I hosted it on a machine I trust
  • I exposed it through a public HTTPS URL
  • I protected it with an API key or token
  • I added the base URL, API key, and model name in eigi.ai
  • I ran a successful test

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Common Problems

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eigi.ai cannot reach the harness

Check that:
  • The URL is public and starts with https://
  • Your tunnel or server is still running
  • Your firewall allows inbound traffic
  • The saved base URL is correct

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Authentication fails

Check that:
  • The secret in eigi.ai matches the secret expected by your harness
  • Your harness is reading the correct header
  • The secret has not expired or been rotated

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The harness responds, but the model call fails

Check that:
  • The model name in eigi.ai matches the model name your harness expects
  • Your harness supports the required OpenAI-compatible Responses API behavior
  • Your harness is healthy and can reach its own underlying model provider
If you are not sure where to begin, use this order:
  1. Start with your harness on a local machine
  2. Expose it temporarily with ngrok
  3. Validate the connection from eigi.ai
  4. Move it to Cloudflare Tunnel or a cloud HTTPS deployment for long-term use
This keeps the first setup simple while still giving you a clean path to a more stable production deployment.