How to Manage Multiple OpenClaw Nodes for Different Projects

If you’re running OpenClaw for multiple distinct projects, perhaps one for your personal blog, another for a client’s e-commerce site, and a third for an internal dev tool, you’ve likely encountered the problem of configuration sprawl. Juggling different API keys, model preferences, and rate limits in a single .openclaw/config.json can quickly become unmanageable. The official documentation often steers you towards a monolithic configuration, but for practical multi-project use, a more compartmentalized approach is essential. Here’s how to manage multiple OpenClaw nodes effectively, ensuring each project operates within its own defined parameters without stepping on the others’ toes.

Affiliate Disclosure: As an Amazon Associate, we earn from qualifying purchases. This means we may earn a small commission when you click our links and make a purchase on Amazon. This comes at no extra cost to you and helps support our site.

Understanding OpenClaw’s Configuration Loading

OpenClaw, by default, looks for its configuration file in ~/.openclaw/config.json. This is great for a single, personal setup. However, when you need separate configurations, simply changing this file manually before each project run is cumbersome and prone to errors. The key insight here is that OpenClaw respects the OPENCLAW_CONFIG_PATH environment variable. By setting this variable, you can point OpenClaw to a completely different configuration file. This allows you to create project-specific configuration directories and files, effectively isolating each project’s settings.

Let’s say you have three projects: myblog, client-shop, and dev-tool. Instead of modifying ~/.openclaw/config.json repeatedly, you’ll create separate configuration directories for each. For instance:

mkdir -p ~/openclaw-configs/myblog
mkdir -p ~/openclaw-configs/client-shop
mkdir -p ~/openclaw-configs/dev-tool

Inside each of these directories, you’ll place a config.json file tailored to that specific project. For example, ~/openclaw-configs/myblog/config.json might look like this:

{
  "api_keys": {
    "openai": "sk-YOUR_OPENAI_KEY_FOR_BLOG",
    "anthropic": "sk-ant-api03-YOUR_ANTHROPIC_KEY_FOR_BLOG"
  },
  "default_model": "gpt-3.5-turbo",
  "temperature": 0.7,
  "max_tokens": 1024,
  "system_prompt": "You are a helpful assistant for my blog project."
}

And ~/openclaw-configs/client-shop/config.json would have different keys, models, and prompts:

{
  "api_keys": {
    "openai": "sk-YOUR_OPENAI_KEY_FOR_CLIENT",
    "anthropic": "sk-ant-api03-YOUR_ANTHROPIC_KEY_FOR_CLIENT"
  },
  "default_model": "claude-haiku-4-5",
  "temperature": 0.2,
  "max_tokens": 512,
  "system_prompt": "You are a concise assistant for an e-commerce product description generator."
}

Notice the model choice for the client project. While the docs often default to more powerful, expensive models, for tasks like product descriptions, claude-haiku-4-5 is often 10x cheaper and perfectly adequate, especially when generating many descriptions. This kind of optimization is crucial when managing client budgets.

Launching OpenClaw with Project-Specific Configurations

Now that you have your separate configurations, the next step is to tell OpenClaw which one to use. This is where the OPENCLAW_CONFIG_PATH environment variable comes in. You can set this variable directly when running your OpenClaw commands or, more practically, wrap your commands in small shell scripts.

For the myblog project, you would execute:

OPENCLAW_CONFIG_PATH="~/openclaw-configs/myblog/config.json" openclaw generate "Draft a short post about managing OpenClaw configs."

For the client-shop project:

OPENCLAW_CONFIG_PATH="~/openclaw-configs/client-shop/config.json" openclaw generate "Write a catchy product description for a 'smart coffee mug'."

While direct command-line setting works, for more complex workflows or scripts, it’s better to encapsulate this logic. Create a simple wrapper script for each project, for instance, ~/bin/openclaw-myblog:

#!/bin/bash
export OPENCLAW_CONFIG_PATH="~/openclaw-configs/myblog/config.json"
openclaw "$@"

Make it executable: chmod +x ~/bin/openclaw-myblog. Then, from anywhere, you can run:

openclaw-myblog generate "List three SEO keywords for a blog post about serverless functions."

This approach keeps your environment clean and prevents accidental cross-project configuration bleed. It’s also incredibly useful for CI/CD pipelines where you want to ensure a specific OpenClaw instance is always used for a given build or deployment task.

Managing Logs and Cache Separately

Beyond just configuration, you might also want to isolate logs and cache files for each project. By default, OpenClaw often places these in ~/.openclaw/logs and ~/.openclaw/cache. However, the config.json allows you to specify log_dir and cache_dir. This means you can extend your project-specific configuration to manage these as well.

For example, in ~/openclaw-configs/myblog/config.json, you might add:

{
  "api_keys": {
    "openai": "sk-YOUR_OPENAI_KEY_FOR_BLOG",
    "anthropic": "sk-ant-api03-YOUR_ANTHROPIC_KEY_FOR_BLOG"
  },
  "default_model": "gpt-3.5-turbo",
  "temperature": 0.7,
  "max_tokens": 1024,
  "system_prompt": "You are a helpful assistant for my blog project.",
  "log_dir": "~/openclaw-configs/myblog/logs",
  "cache_dir": "~/openclaw-configs/myblog/cache"
}

This ensures that each project’s interactions, including API calls, responses, and cached data, are kept entirely separate. This is critical for debugging, cost analysis, and ensuring data privacy between client projects.

Limitations and Practical Considerations

This multi-node management strategy using OPENCLAW_CONFIG_PATH is highly effective, but it does come with some limitations. This approach primarily manages the configuration and runtime environment of the OpenClaw CLI or any application that correctly honors the environment variable. It doesn’t, for instance, create isolated virtual environments for Python dependencies if you’re using OpenClaw as a library within a larger Python project. For that, you’d still need to rely on tools like venv or conda.

Furthermore, while this setup is robust for most VPS environments (like Hetzner, DigitalOcean, etc.) with at least 1GB of RAM, running multiple OpenClaw instances *simultaneously* on a resource-constrained device like a Raspberry Pi might struggle, especially if you’re hitting powerful models like GPT-4 or Claude Opus. The individual OpenClaw processes are relatively lightweight, but the combined memory footprint of multiple Python interpreters and potentially large model outputs can add up. Ensure your system has adequate RAM and CPU cores if you plan to run several OpenClaw operations concurrently.

Finally, remember to secure your API keys. Storing them directly in config.json files is common for personal use, but for production environments or shared systems, consider using a proper secret management system (like environment variables loaded from a secure vault, or services like AWS Secrets Manager/Azure Key Vault) and reference those in your config.json where OpenClaw can resolve them.

To begin managing your OpenClaw projects separately, create a dedicated configuration file for each project, such as ~/openclaw-configs/myblog/config.json, and then execute your OpenClaw commands using the OPENCLAW_CONFIG_PATH environment variable: OPENCLAW_CONFIG_PATH="~/openclaw-configs/myblog/config.json" openclaw generate "My prompt here."