• Helicone Adapter - Screen-Thumbnail
  • Helicone Adapter - Screen-Thumbnail
  • Helicone Adapter - Screen-Thumbnail
  • Helicone Adapter - Screen-Thumbnail
  • Helicone Adapter - Screen-Thumbnail
  • Helicone Adapter - Screen-Thumbnail
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Asset Version: 1.0.0
Last Published: May 28, 2026
The Helicone AI Gateway Adapter allows Volt MX applications to connect with multiple AI providers through a single REST interface. It supports features like chat completion, text generation, request routing, logging, analytics, cost tracking, and performance monitoring for AI applications.
Requirements
  • HCL Volt MX Foundry
Devices
  • phone
  • tablet
  • desktop
Platforms
  • iOS
  • android
  • windows

Features:

  • Multiple Model Access:
    Access a wide range of AI models from providers like OpenAI, Anthropic, Google, and open models such as Llama and Mistral through a single unified API gateway.
  • Unified Text Generation & Chat:
    Generate high-quality text, perform chat completions, summarization, and conversational AI interactions using an OpenAI-compatible API format.
  • Smart Model Routing:
    Dynamically route requests to different models or providers based on cost, latency, or performance requirements for optimized results.
  • AI Request Logging & Observability:
    Track and monitor all API requests and responses with detailed logs, enabling debugging, auditing, and performance analysis.
  • Cost Tracking & Usage Analytics:
    Monitor token usage, request counts, and overall costs across multiple providers to optimize budget and usage.
  • Caching & Performance Optimization:
    Improve response time and reduce costs by caching repeated requests and reusing responses when applicable.
  • Failover & Reliability Handling:
    Ensure high availability with automatic retries and fallback to alternative providers if a request fails.
  • OpenAI-Compatible REST API:
    Easily integrate with existing applications using standard HTTP requests and JSON responses without major code changes.
  • Prompt Testing & Experimentation:
    Test and compare prompts across multiple models to evaluate output quality and refine AI responses.
  • Scalable & Developer-Friendly:
    Provides a flexible and scalable infrastructure for building AI-powered applications without managing multiple APIs separately.