Anchor Browser is a powerful browser automation platform designed specifically for AI agents, enabling programmatic control of browser sessions and automated web interactions. Enhance your automation processes with Relevance AI, turning web data into actionable insights.
Anchor Browser provides robust browser automation features tailored for AI agents, enabling efficient web interactions. With Relevance AI, these capabilities are amplified, allowing your AI agents to execute complex workflows intelligently and at scale.
Intelligent Web Orchestration
The agent gains precise control over complex web interactions, enabling sophisticated automation patterns across multiple domains.
Dynamic Data Synthesis
Enhanced ability to collect, process, and synthesize web data in real-time, transforming raw information into actionable insights.
Enhanced Decision Architecture
Improved decision-making capabilities through real-time web data analysis and automated response mechanisms.
Relevance AI seamlessly integrates with Anchor Browser to enhance your AI agents' web automation capabilities.
What you’ll need
You don't need to be a developer to set up this integration. Follow this simple guide to get started:
- A Relevance AI account
- An Airtable account with access to the base and table you'd like to use
- Authorization (you'll connect securely using OAuth—no sensitive info stored manually)
Security & Reliability
Anchor Browser is a robust browser automation platform tailored for AI agents, enabling seamless programmatic control of browser sessions and automated web interactions through a REST API. This integration offers significant advantages, including headless and headful browser automation, profile management, built-in proxy and captcha handling, session recording, and OAuth integration.
To get started, ensure you have the necessary accounts and credentials, including an Anchor Browser account with API access and OAuth credentials with the required permissions. Additionally, a Relevance AI account is essential for integration purposes.
System requirements include Node.js 14+ or Python 3.7+, a minimum of 4GB RAM, a stable internet connection, and support for WebSocket connections. Ensure you have the appropriate permissions for browser automation, network access for proxy configurations, and storage permissions for profile management.
Begin your setup by configuring authentication with your OAuth account ID and permission type. Next, set up your browser configuration, including viewport dimensions and timeout settings. Finally, create a profile for your automation tasks, specifying its name, description, and source.
To start a browser session, utilize the provided code snippet to initiate the session and manage profiles effectively. You can create and update profiles as needed, ensuring your automation tasks run smoothly.
In case of issues, refer to the troubleshooting guide for common problems and their solutions, such as connection timeouts, profile creation failures, and authorization errors. Implement best practices for session management, profile handling, and resource management to optimize performance.
For debugging, enable debug logging to monitor your configurations and ensure everything runs as expected. For further assistance, consult the API documentation, reach out to the support channel, or explore GitHub examples for practical implementations.
No training on your data
Your data remains private and is never utilized for model training purposes.
Security first
We never store anything we don’t need to. The inputs or outputs of your tools are never stored.
To get the most out of the Anchor Browser + Relevance AI integration without writing code:
- Start with a clean browser session: Ensure your session configurations are clear and consistent to avoid conflicts.
- Utilize profile management: Create and manage profiles strategically to streamline your automated tasks and maintain organization.
- Monitor session activity: Regularly check active sessions and implement proper cleanup procedures to optimize performance.
- Test with caution: Run your automation scripts on test data first to identify potential issues before scaling up.
- Handle errors effectively: Implement robust error handling to manage connection issues and authorization errors gracefully.
