Introduction to GL Deep Researcher

This page focuses on using the GL Deep Researcher as a component within an RAG pipeline.

If you’re interested in a centralized, end-to-end Deep Research service in GL Ecosystem, see the GL Open DeepResearch GitBook for a deeper dive.

For documentation specific to the GL Open DeepResearch service (API, profiles, deployment), start with the GL Open DeepResearch Overview or GL Open DeepResearch Documentation.

What’s a GL Deep Researcher?

A GL Deep Researcher is a specialized component that performs structured, multi-step research within a Retrieval-Augmented Generation (RAG) pipeline. Instead of issuing a single retrieval query, it is designed to plan, execute, and refine research steps to produce a coherent, high-quality result.

GL Deep Researchers can search across multiple sources, reason over intermediate findings, and iteratively adjust their approach as new information is discovered. This makes them well-suited for tasks that require depth, comparison, or synthesis—where a single-pass retrieval would be insufficient.

By encapsulating research logic into a dedicated component, GL Deep Researchers enable RAG pipelines to move beyond basic retrieval and toward goal-driven, reasoning-aware research workflows.

Available Subclasses

The Deep Researcher module provides the following built-in implementations:

1. GoogleDeepResearcher

2. OpenAIDeepResearcher

3. ParallelDeepResearcher

4. PerplexityDeepResearcher

5. GLOpenDeepResearcher

Next Steps

1. Begin building by installing prerequisites in Prerequisites

2. Get started with Deep Researcher components: Getting Started

3. Explore more about deep researcher subclasses and features in API reference page