Integrations

Supercharge X AI with Relevance AI

X AI is an integration platform that offers advanced AI functionalities through a suite of API endpoints. By combining X AI's innovative models with Relevance AI's automation features, you can build sophisticated AI-driven applications that enhance productivity and decision-making.

Give your AI Agents X AI Superpowers

X AI provides powerful language models and embeddings that can be harnessed to create intelligent applications. Relevance AI amplifies these capabilities, enabling AI Agents to automate complex tasks and deliver insights at scale.

Advanced Cognitive Processing

Leverages X AI's state-of-the-art language models to enhance the agent's analytical and problem-solving capabilities

Multi-Modal Intelligence

Enables simultaneous processing of text, images, and structured data for comprehensive understanding and response generation

Contextual Mastery

Achieves deeper understanding of user intent through advanced natural language processing and pattern recognition

Tools

Equip AI Agents with the X AI Tools they need

Relevance AI seamlessly integrates with X AI to enhance your workflows with advanced AI capabilities.

xAI - Post Chat Completion
Generates AI chat completions with customizable parameters for controlling response characteristics like temperature, token limits, and sampling methods
xAI - Create Embedding
Converts text inputs into numerical vector embeddings with configurable dimensions and encoding formats for machine learning applications
xAI - Get Model
Retrieves information about a specific AI model from the xAI platform using model identifiers
xAI - Post Completion
Generates text completions with extensive control over output parameters, including prompt handling, sampling settings, and token management
Name
xAI API Call
Description
Make an authorized request to a xAI API
Parameters
["OAuth authentication", "HTTP method selection (GET, POST, PUT, DELETE, PATCH)", "Custom request headers", "Request body configuration", "Response handling (body, status, headers)"]
Use Case
A fintech company uses xAI API calls to automate their customer support workflow by integrating AI-powered responses into their ticketing system, enabling real-time analysis of customer inquiries and automated response generation while maintaining secure authentication through OAuth.
Quick Start

Connect X AI to Relevance AI in minutes

Security & Reliability

The X AI integration platform utilizes advanced AI capabilities through a suite of API endpoints, allowing developers to seamlessly incorporate AI-powered applications. With secure OAuth-based authentication, only authorized workflows can access the powerful features of X AI and Relevance AI.

Relevance AI automates workflow operations, handling API requests (like POST for chat completions and GET for model information) in the background, so you can focus on building your application without worrying about errors, formatting, or rate limits.

Built-in validation and type conversion ensure that your AI interactions run smoothly, even when input data varies. This integration provides access to advanced language models, embeddings for semantic search, and flexible completion APIs, all while maintaining a streamlined and efficient workflow.

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.

Get Started

Best Practices for Non-Technical Users

To get the most out of the X AI + Relevance AI integration without writing code:
  • Start with clear configurations: Ensure your OAuth credentials and API keys are correctly set up and stored securely.
  • Utilize example requests: Refer to the provided chat completion and embedding examples to understand how to structure your API calls.
  • Monitor your usage: Keep an eye on your API rate limits and adjust your request frequency to avoid throttling.
  • Test with sample data: Validate your integration using test inputs before deploying it in a production environment.
  • Implement error handling: Use try-catch blocks to manage errors gracefully and ensure your application can recover from issues.