Vibe Research Guide

A curated guide for LLM-agent-driven scientific research automation

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Vibe Research At The Center, With The Broader Agent-Native Stack Around It

Automate the research loop with LLM agents: literature review → idea generation → experiment execution → paper writing → peer review.

This repo is a research-first landing page for the field. The center is still Vibe Research, but the guide now gives Auto Research / AI Scientist its own top-level map and then places Claw, coding agents, connectors, and adjacent assistant ecosystems around that core.

Start here: Getting Started · Auto Research · Tools & Platforms · Claw Park

At A Glance

2026 Landscape Snapshot

Five shifts now define the field:

1. Research copilots are stronger and easier to trust: Deep Research, NotebookLM-style source-grounded reading, and scientific workspaces such as Prism are making synthesis and report-writing meaningfully faster.
2. Auto Research is becoming a recognizable system category: The AI Scientist, The AI Scientist-v2, Agent Laboratory, AI-Researcher, RD-Agent, Auto-Deep-Research, and EvoScientist now form a real system family.
3. Benchmarks are getting closer to scientific reality: ScienceAgentBench, FIRE-Bench, ResearchClawBench, SGI-Bench, and RE-Bench shift evaluation away from vague "agent capability" claims.
4. Execution substrates now determine whether research agents actually work: Claude Code, Codex, OpenHands, SWE-agent, OpenClaw, and chat bridges such as cc-connect increasingly matter because experiment loops fail on tooling long before they fail on prose.
5. A wider agent-native ecosystem is forming around research: personal assistants, registries, skills, self-evolving stacks, and companion UX are increasingly part of the same operational environment.

2026 Auto Research Signals

Several current signals make the field feel less like a loose collection of demos and more like an emerging research stack:

1. The field has moved from "can an agent summarize papers?" to "can an agent operate a research loop?"
2. System families are diverging clearly: end-to-end scientists, human-in-the-loop research copilots, R&D execution agents, deep-research assistants, and self-evolving scientist stacks now look meaningfully different.
3. Benchmarking is improving fast: scientist-aligned workflows, rediscovery tasks, expert comparison, and checklist-based evaluation are becoming standard expectations instead of afterthoughts.
4. Frameworks matter as much as flagship demos: AI-Researcher, RD-Agent, and Auto-Deep-Research show that orchestration and harness design are now first-class concerns.
5. Platformization is visible: FutureHouse Platform, Robin, BixBench, and Edison Scientific / Kosmos show how AI-scientist ideas are moving from one-off papers to persistent public or commercial surfaces.

Choose a Path

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Auto Research Stack

This is the shortest useful way to read the field in 2026:

For a dedicated overview, start here: → Auto Research

Representative Auto Research Systems

Auto Research Benchmarks

More detail: → Benchmarks

Agent-Native Landscape Beyond Research

Vibe Research remains the center of this repo. But in practice, research agents now live inside a larger operating environment: personal assistants, software-surface layers, self-improving agents, and companion UX around coding agents.

Personal Agent Assistants

These are not all "research agents", but they increasingly shape the environment research agents live in.

Agent-Native Software / CLI-Anything

This layer matters because the frontier is no longer only "which agent is best", but also "which software surfaces are now agent-operable".