Orq.ai now supports the latest OpenAI models: o3, o4-mini, GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano.

To ensure fair usage and consistent performance, we’ve introduced rate limits for all Orq.ai APIs on a per-account basis. This helps prevent server overload, reduces risk of abuse, and keeps costs manageable.

You can now enable Agentic RAG in your deployments to improve the relevance of retrieved context and the overall output quality.

We’ve added a new Threads view to help you make sense of multi-step conversations at a glance.

We’ve added Jina AI as a new provider, bringing a suite of high-performance models to the platform:

Previously, creating and managing a knowledge base required using the Orq UI. Users had to manually create a knowledge base, upload one file at a time, and define a chunking strategy per datasource. While simple, this process was time-consuming, especially for developers looking to scale or automate their workflows.

We’ve added a new feature called the Hub — a library where you can browse and reuse common evaluators and prompts for your projects.

AI workflows can feel like a black box—when something goes wrong, it’s hard to know why. Tracing changes that by giving you full visibility into every step of your workflow. Instead of guessing why an LLM output is wrong, you can quickly check every step in the workflow—saving time and reducing frustration.

When building AI features, ensuring high-quality and reliable outputs is crucial. Orq.ai allows you to implement custom evaluators in Python, giving you full control over how AI-generated content is assessed and validated.

Previously, once an entity was created, it was locked in place—you couldn’t move it to another project or directory. Now, you finally can.