ai-research
Autoresearch on your machine
Start the local dashboard, then give each research direction its own agent. They work in parallel, in isolated worktrees, using your existing tools and compute.
To spin up remote compute while doing Autoresearch, use OpenResearch's compute marketplace, which aggregates offerings from multiple providers.
A deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data.
Ask a question. mole decomposes it, searches, reads sources, extracts claims, checks each claim against the text it came from, looks for contradictions between them, and writes an answer with citations. Every model call is reserved against a budget before it happens and settled after, so the ceiling you set is the ceiling it hits.
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.
Related contents:
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.
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.