Open Source

AutoResearchClaw is an open-source 23-stage autonomous research pipeline from UNC Chapel Hill that turns a research idea into a conference-ready LaTeX paper. Features: multi-agent debate for hypothesis generation, self-healing executor with Pivot/Refine decision loop, verifiable result reporting preventing hallucinations, human-in-the-loop with 7 intervention modes, and cross-run evolution. Outperforms AI Scientist v2 by 54.7% on ARC-Bench. MIT licensed.

Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a Pivot/Refine decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, AutoResearchClaw outperforms AI Scientist v2 by 54.7%.

Hermes Agent is an open-source autonomous AI agent built by Nous Research with a built-in closed learning loop — it creates skills from experience, improves them during use, persists knowledge, searches past conversations, and builds a user model across sessions. Supports 14 messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal, etc.) and 6 execution backends (local, Docker, SSH, Modal, Singularity). Features include persistent memory (FTS5 + Honcho), automated skill creation, cron scheduling, parallel sub-agent delegation, full browser automation, MCP integration, and model-agnostic provider switching via CLI. MIT licensed, first released February 2026.