AGIFlow
AGIFlow is an LLM QA and Observability platform designed for product teams and developers. Our goal is to streamline LLM and Agentic Workflow development with real-time tracing and a visual debugger, enabling you to test prompt and model performance while continuously monitoring performance in production.
We understand the challenges of bringing your LLM app to production and aim to provide a scalable, trustworthy infrastructure to support you every step of the way.
Key Features
AGIFlow is designed to be scalable from day one, allowing you to host your own solution from $0 with our customizable platform. Developed by full-stack engineers, AGIFlow aims to deliver a seamless ML-ops experience, providing an end-to-end solution to bring your LLM apps to life with these features:
End-to-End Tracing
Whether you're building a chat app, copilot, or workflow solution powered by LLM, understanding how users interact with your LLM, identifying issues in real-time, and providing customer support are critical for any production app.
Unlike other LLM-ops solutions, AGIFlow provides both frontend and backend SDKs to enable real-time analytics on user interactions with LLM tracing, giving you a complete picture of your application's performance. These SDKs are seamlessly integrated to offer the best developer experience, while also being agnostic to work well with other technologies such as Azure Insights, Prometheus, and more.
Custom LLM Evaluations and Testing
AGIFlow supports both batch and real-time LLM evaluations and testing, fully customizable to your business domain and industry needs.
To empower LLM apps, we offer Model and Prompt Registries, and Dataset management built-in so you can easily benchmark and A/B test different prompts and models without writing a single line of code. When your product goes live, you can simply switch on the Evaluations and Guardrails plugin with your own models and custom prompt templates to continuously monitor your LLM app.
Simplified User Feedback Integration
Our frontend SDKs include a feedback widget that can be easily enabled with a single line of code. During development, QA, and pre-production, this widget provides insights into LLM behavior for QA and business experts, allowing seamless feedback collection without the need for back-and-forth communication.
You can also enable the widget to collect high-quality user feedback in production, or use our SDK to update user feedback from either frontend or backend, which can be used for Reinforcement Learning with Human Feedback fine-tuning.
AGIFlow is here to provide a comprehensive, scalable, and user-friendly solution to bring your LLM applications to production smoothly and efficiently. Join us on the journey to revolutionize your LLM operations and achieve seamless integration and performance monitoring.