Unify provides a hackable LLMOps platform for building custom interfaces for AI observability, including logging, evaluations, guardrails, and tracing. It targets developers and researchers building LLM applications, enabling them to probe, analyze, and iterate on their data with a focus on product development rather than LLM intricacies.

How It Works

Unify operates on a "log your data, add an interface" philosophy, leveraging four core building blocks: tables, views, plots, and an editor (upcoming). This modular approach allows users to create tailored interfaces for their specific LLM product requirements, drawing inspiration from observability tools like PostHog and Grafana, LLM-specific tools like LangSmith, and versatile platforms like Notion.

Quick Start & Requirements

• Install via pip install unifyai.
• Requires Python.
• Quickstart video and documentation are available.

Highlighted Details

• Fully hackable LLMOps.
• Supports logging, evals, guardrails, labelling, tracing, agents, human-in-the-loop, and hyperparam sweeps.
• Inspired by PostHog, Grafana, LangSmith, BrainTrust, Weave, Notion, and Airtable.
• Focuses on simplifying LLM app development by abstracting away jargon.

Maintenance & Community

• Under active development.
• Feedback is welcomed.
• Discord channel available for questions.

Licensing & Compatibility

• License not explicitly stated in the README.

Limitations & Caveats

The project is in beta and under active development, with an editor component still forthcoming. The specific license is not detailed, which may impact commercial use or closed-source integration.