helicone and anchoring-ai
These are complements: Helicone provides monitoring and evaluation infrastructure for LLM applications, while Anchoring AI provides a no-code platform for building and hosting those applications, so teams would use both together in their LLM development workflow.
About helicone
Helicone/helicone
🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓
Operates as a reverse proxy AI gateway that intercepts requests to 100+ LLM providers through a unified OpenAI-compatible API, enabling intelligent routing and automatic fallbacks. Built on a microservices architecture with a Cloudflare Workers proxy layer for request interception, Express-based collection server (Jawn), ClickHouse for analytics, and Supabase for application data. Integrates with OpenAI, Anthropic, Gemini, LangChain, Vercel AI SDK, and supports self-hosting via Docker or Helm with optional async logging through OpenLLMetry.
About anchoring-ai
AnchoringAI/anchoring-ai
An open-source no-code tool for teams to collaborate on building, evaluating, and hosting applications leveraging GPT and other large language models. You could easily build and share LLM-powered apps, manage your budget and run batch jobs.
Features include prompt chain management with drag-and-drop composition, optimized response caching to reduce API costs, and modular extensibility for custom models and datasets. The architecture uses a Node.js/React frontend with a Python backend (Flask/SQLAlchemy), MySQL for persistence, Redis for caching and task queuing, and Celery for asynchronous batch processing. It integrates directly with Langchain for Python-based prompt chains and supports multiple LLM providers beyond GPT.
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