open-researcher-guide

name	open-researcher-guide
description	Open pipeline for generating deep research trajectories with LLMs
metadata	{"openclaw":{"emoji":"🔬","category":"research","subcategory":"deep-research","keywords":["OpenResearcher","deep research","research trajectory","open pipeline","literature synthesis","LLM research"],"source":"https://github.com/GAIR-NLP/OpenResearcher"}}

OpenResearcher Guide

Overview

OpenResearcher is a fully open pipeline for long-horizon deep research trajectory synthesis. It breaks complex research questions into sub-questions, iteratively searches and reads literature, builds internal knowledge representations, and synthesizes comprehensive answers. Unlike single-shot approaches, it models the researcher's thought process — reading, questioning, connecting, and refining understanding over multiple rounds.

Pipeline Stages

1. Question Decomposition

from open_researcher import OpenResearcher

researcher = OpenResearcher(llm_provider="anthropic")

# Complex research question
result = researcher.research(
    "How do retrieval-augmented generation systems handle "
    "knowledge conflicts between parametric and retrieved knowledge, "
    "and what are the current mitigation strategies?"
)

# Automatically decomposes into sub-questions:
# SQ1: What types of knowledge conflicts occur in RAG?
# SQ2: How are conflicts detected?
# SQ3: What resolution strategies exist?