Making AI Agent Configurations Stable with an LLM Gateway

When building AI agents, one recurring problem is configuration churn.

LLMs evolve quickly: models are upgraded, providers change APIs, and endpoints get deprecated.
But agent configurations usually move much slower. Over time, this mismatch leads to fragile setups and frequent config changes.

In this article, I’m sharing an approach I came across while exploring ways to keep agent configurations stable: using an LLM gateway as an abstraction layer, with Clawdbot as the concrete example and Vivgrid as the gateway.

The Core Idea

Instead of binding an AI agent directly to a specific LLM provider or model, you route all model requests through an LLM gateway.

This way:

• Agent logic stays stable

• Model upgrades happen behind the scenes

• Configuration files change far less often

Vivgrid acts as that gateway, while Clawdbot remains focused on agent behavior rather than model infrastructure.

What This Setup Achieves

After this configuration, Clawdbot will:

• Use a single, stable endpoint for model access

• Avoid vendor lock-in

• Stay operational as underlying models evolve

• Reduce the need for frequent config edits and restarts

Prerequisites

Before starting, make sure you have:

• Clawdbot installed

• A Vivgrid API key

• Completed Clawdbot onboarding

Run:

clawdbot onboar

Step 1: Configure Vivgrid as a Model Provider

Clawdbot defines model providers in its configuration file.

Config file location:

~/.clawdbot/clawdbot.json

Add Vivgrid under models.providers:

"providers": {
  "vivgrid": {
    "baseUrl": "https://api.vivgrid.com/v1",
    "apiKey": "viv-clawdbot-xxxxxxxxxxxxxxxxxxxx",
    "api": "openai-completions",
    "models": [
      {
        "id": "managed",
        "name": "managed",
        "contextWindow": 128000
      }
    ]
  }
}

This tells Clawdbot to route all model requests through Vivgrid, instead of calling a specific LLM provider directly.

Step 2: Set Vivgrid as the Primary Model

Next, update the default model used by the agent:

"model": {
  "primary": "vivgrid/managed"
}

This is the key change.

Clawdbot is no longer tied to a specific model like GPT-4 or Claude.
Instead, it relies on Vivgrid’s managed routing layer, which can be updated independently.

Step 3: Restart and Verify

Restart the Clawdbot daemon:

clawdbot daemon restart

Then follow the logs to verify requests are flowing correctly:

clawdbot logs --follow

If everything is set up correctly, you should see model requests being routed through Vivgrid.

Why This Works Well in Production

Using an LLM gateway creates a clean separation of responsibilities:

• Clawdbot handles agent behavior and logic

• Vivgrid handles model selection, routing, and upgrades

This approach makes it easier to:

• Upgrade models without touching agent config

• Reduce downtime caused by model migrations

• Keep agent systems stable as LLMs evolve rapidly

Handling Long Configuration Files

If your full clawdbot.json is lengthy, it’s usually better not to paste the entire file into the article.

A common approach is:

• Keep only the essential snippets inline

• Link to the complete configuration via GitHub or Gist for reference

This keeps the article readable while still being reproducible.

Final Thoughts

LLMs will continue to change faster than most agent systems.

Introducing an LLM gateway is a practical way to decouple agents from model infrastructure, reduce configuration churn, and build more stable AI systems.

Curious how others handle model upgrades and vendor lock-in when building AI agents in production.
Would love to hear different approaches in the comments.