Open-source platform for logging, monitoring, and debugging LLM applications. Route, debug, and analyze AI apps with comprehensive observability tools.

Overview:

Helicone is an open-source AI Gateway and LLM Observability platform designed for AI engineers. It provides a unified API to access over 100 AI models from providers like OpenAI, Anthropic, and Gemini, with intelligent routing, automatic fallbacks, and one-line code integration. The platform offers observability tools for inspecting traces and sessions, tracking cost and latency metrics, managing prompts, and testing in a built-in playground. It is designed for developers and teams building AI-powered applications, including chatbots, agents, and document processing pipelines.

Core Features:

  • AI Gateway: Access and route requests to 100+ AI models through a single API key, with automatic fallbacks.

  • LLM Observability: Log, inspect, and debug traces and sessions for agents, chatbots, and document processing pipelines.

  • Cost & Latency Tracking: Monitor and analyze performance metrics like cost, latency, and quality, with export to PostHog.

  • Prompt Management: Version prompts using production data and deploy them through the AI Gateway without code changes.

  • Playground: Test and iterate on prompts, sessions, and traces directly in the user interface.

  • Self-Hosting: Deploy via Docker for local setup or Helm for enterprise workloads, using a stack that includes ClickHouse and Minio.

Use Cases:

  • AI engineers building applications with multiple LLM providers who need a single API endpoint and automatic fallback logic.

  • Teams debugging and optimizing the performance of agent-based workflows, chatbots, or document processing pipelines.

  • Developers managing and versioning prompts across different production environments, deploying updates without code changes.

  • Organizations self-hosting an observability layer on their own infrastructure to log and analyze LLM request data.

Why It Matters:

Helicone addresses the operational complexity of working with multiple AI models by combining provider access, routing, and observability into one platform. Its self-hosted deployment option (via Docker or Helm) allows organizations to maintain direct control over request logs and analytics data, using its own managed ClickHouse and Minio storage. The project also provides an open-source API pricing database for cost queries. It positions itself as a practical tool for teams that need both a unified gateway and detailed observability without relying on a fully managed service.

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