Why Choose Venice AI
Venice AI is an AI inference service platform built around privacy as its core principle. Unlike traditional cloud-based large language models, Venice promises not to store user conversation data, not to use user input for model training, and to provide end-to-end privacy protection. For teams that prioritize data security and privacy compliance, Venice is a choice worth serious consideration.
OpenClaw natively supports Venice AI as a model provider, and you can integrate Venice's model services just as easily as configuring any other provider. Venice currently offers inference services for several open-source models, including Llama 3.3 70B, delivering inference quality close to commercial models while maintaining privacy.
Obtaining a Venice AI API Key
First, you need to register an account on the Venice AI website and obtain an API key:
- Visit the Venice AI official website, register, and log in to your account.
- Navigate to the account settings page and find the API key management section.
- Create a new API key and save it securely — it will only be displayed once.
Venice offers free credits for developers to test with. For production use, it is recommended to choose an appropriate paid plan based on your actual usage volume.
Quick Configuration with OpenClaw Onboard
OpenClaw provides an interactive guided configuration tool that helps you quickly set up Venice AI:
openclaw onboard
During the guided flow, select Venice as the model provider and enter your API key when prompted. The tool will automatically generate a correctly formatted configuration file.
Manual Configuration of openclaw.json
If you prefer to manually edit the configuration file, add the following to openclaw.json:
{
"agents": {
"defaults": {
"model": {
"primary": "venice/llama-3.3-70b"
}
}
}
}
The model format used here is provider/model-name, i.e., venice/llama-3.3-70b. Venice also supports other models, and you can replace the model name as needed.
Configuring API Authentication
In the authentication configuration section, add your Venice API key:
{
"providers": {
"venice": {
"auth": [
{
"key": "your-venice-api-key"
}
]
}
}
}
OpenClaw supports a multi-account authentication mechanism. You can add multiple API keys to the auth array. When a key encounters rate limiting or service errors, the system will automatically switch to the next available key and initiate cooldown tracking for the failed key to avoid repeated requests in a short period.
{
"providers": {
"venice": {
"auth": [
{ "key": "key-A", "profile": "primary-account" },
{ "key": "key-B", "profile": "backup-account" }
]
}
}
}
Recommended Venice Models
The models available on the Venice platform are primarily based on open-source large language models. Here are some recommended options:
- venice/llama-3.3-70b: Based on Meta's Llama 3.3 70B inference service, with excellent overall capabilities suitable for most general-purpose scenarios.
- venice/llama-3.1-405b: A larger-parameter model with stronger reasoning capabilities, suitable for complex tasks.
When using Venice models, all inference processes are completed in a privacy-protected environment. Venice does not log your input or output content.
Configuring Failover
To improve service reliability, it is recommended to configure a failover chain for Venice. When Venice is unavailable, the system can automatically switch to another provider:
{
"agents": {
"defaults": {
"model": {
"primary": "venice/llama-3.3-70b",
"fallback": "openrouter/meta-llama/llama-3.3-70b-instruct"
}
}
}
}
This way, even if Venice is temporarily unavailable, user conversation requests will not be interrupted but will be automatically forwarded to the same model series on OpenRouter.
Privacy Advantages and Usage Recommendations
Venice AI is particularly well-suited for the following scenarios:
- Handling conversations containing sensitive information, such as legal consultations or medical Q&A.
- Enterprise deployments subject to strict data compliance requirements.
- Teams that want to use open-source models but do not want to deploy their own GPU clusters.
Note that Venice offers fewer model choices compared to OpenAI or Anthropic, and inference speed may vary slightly. It is recommended to thoroughly test model performance in your specific use case before production deployment.
Verifying the Configuration
After completing the configuration, you can send a test message to verify that Venice AI is working properly. If everything is configured correctly, you should receive a response from the Venice model. Checking OpenClaw's log output can confirm whether requests are being correctly routed to the Venice provider.
By following the steps above, you have successfully integrated Venice AI privacy inference service with OpenClaw. Enjoy a privacy-first AI conversation experience.