Autonomous, multi-agent AI research — a PhD's workflow on a single-GPU budget.

An autonomous AI scientist: a multi-agent loop over literature, experiments, self-critique and write-up, with deterministic guards against reward-hacking and hallucination.

ScholarLoop runs the loop a PhD actually runs: it reads the literature, forms a grounded hypothesis, runs real ML experiments, scores them against a frozen ground-truth metric, learns from its failures, and drafts a peer-reviewed write-up — autonomously, with a deterministic harness that keeps the agents honest and impossible to reward-hack.

OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis.

OpenResearcher is a fully open agentic large language model (30B-A3B) designed for long-horizon deep research scenarios. It achieves an impressive 54.8% accuracy on BrowseComp-Plus, surpassing performance of GPT-4.1, Claude-Opus-4, Gemini-2.5-Pro, DeepSeek-R1 and Tongyi-DeepResearch. We fully open-source the training and evaluation recipe—including data, model, training methodology, and evaluation framework for everyone to progress deep research.

Agent-driven research knowledge base. Agents collect, search, and synthesize web research into a persistent, searchable wiki.

Hyperresearch turns Claude Code into a deep research agent: one that currently leads the DeepResearch-Bench RACE leaderboard (benchmarked internally). A tier-adaptive 16-step pipeline takes one prompt and produces an adversarially-audited report with full source provenance. Every source it reads lands in a persistent, searchable vault, so each session starts smarter than the last.

A self-improving RLM agent for coding workflows and long-running autonomous tasks.

Prime Agent is an open-source coding and research agent for general and long-running work.