Research & Data Agents

Live data RAG. Real-time streaming. 50k+ stars

An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations. github

GPT Researcher is an AI-powered autonomous agent designed for efficient and unbiased online research, generating detailed reports by leveraging recent advancements in AI and web scraping, with a focus on speed, reliability, and cost-effectiveness github | github profile

Chat with your data. NL to Pandas/SQL

The AI Scientist: First comprehensive system for fully automatic scientific discovery. From idea generation to paper writing with minimal human supervision

Open-source implementation of AlphaEvolve

Workshop-level automated scientific discovery via agentic tree search. Removes template dependency from v1, generalizes across research domains

End-to-end autonomous research workflow: idea → literature review → experiments → report. Supports both autonomous and co-pilot modes

NeurIPS 2025 paper. Full end-to-end research automation: hypothesis → experiments → manuscript → peer review. Production version at novix.science

Distributed, peer-to-peer research network where autonomous agents run experiments, gossip findings, maintain CRDT leaderboards, and archive results to GitHub across multiple research domains

Wiki-centric full-lifecycle research platform built on Claude Code, realizing Karpathy's LLM-Wiki vision. 20+ skills cover the full loop: ingest → ideate → novelty check → experiment design / run / eval → paper writing. Research state lives in a structured knowledge wiki with an interactive graph

by Pedro H. C. Sant'Anna - A ready-to-fork Claude Code template for academics using LaTeX/Beamer + R. Multi-agent review, quality gates, adversarial QA, and replication protocols. Great use of orchestration patterns and great documentation

ShinkaEvolve: Towards Open-Ended and Sample-Efficient Program Evolution

End-to-end autonomous research engine that plans experiments, generates code, runs jobs locally or on SLURM, analyzes real results, and writes papers grounded in those outputs

Open-source research workspace with sequential idea-to-paper pipelines and integrated autoresearch tool packs

CORAL: Autonomous multi-agent evolution for open-ended discovery (arXiv:2604.01658). Long-running agents with shared persistent memory, asynchronous execution, and heartbeat-based interventions; SOTA on 10 math/algorithmic/systems tasks

Chemcrow

AI-powered data analysis workbench built for transparency, reproducibility and user control. Inspect the code and evidence behind results, verify calculations through replay, and choose from a wide range of models, including local open-weight models.

data-to-paper: AI-driven research from data to human-verifiable research papers. github

by Wenyu Chiou - A catalog of 15 Claude Code skills mapped to 8 research-workflow stages (literature → gap analysis → design → drafting → reviewer response), where each stage emits an explicit YAML/Markdown deliverable the next stage consumes. Thoughtfully designed around anti-hallucination — schemas force "gap" status on unsupported claims, and downstream skills refuse malformed, overconfident ha

Early open-source package for automating scientific workflows, currently centered on literature-review generation with an ambition toward broader autonomous research

Conversations with your files! Manage and run your AI presets!

BlockAGI conducts iterative, domain-specific research, and outputs detailed narrative reports to showcase its findings. github

Fully autonomous AI scientist built on Claude Code, with explicit AutoResearch lineage, multi-agent research iteration, GPU experiment execution, and a self-evolving outer loop

AiScientist: long-horizon ML research lab with hierarchical orchestration and File-as-Bus coordination — workspace files act as the durable system of record. Drives autonomous paper-reproduction (PaperBench) and competition-style MLE-Bench iteration loops under fixed compute/time budgets. (arXiv 2604.13018)

Self-improving software factory for AI/ML objectives. State an objective and it runs a campaign: candidate solutions designed, implemented by coding agents (Claude Code, Codex), measured against the objective, and the closest refined until it is met. Each finished campaign leaves lessons carrying the evidence that earned them, and repositories and papers feed the same knowledge hub, so the next ca

Low-code Python package for automated systematic literature reviews via AI-powered agents

🧠 Societies of Mind & Economy of Minds

Reinforcing LLM agents for autonomous ML engineering. Learns from trial and error to improve model performance

Iterative research idea generation over scientific literature with LLMs. Multi-agent review and feedback loops

Autonomous ML research framework — generates ideas, implements experiments, analyzes results

AI-powered literature review assistant using RAG for accurate, well-structured related-work sections in academic writing

by muend - Skills for geospatial analysis that require coordinate-system, spatial-validation and comparability checks before a result is reported, and narrow or refuse a claim the data cannot support. Ships a paired evaluation against a skills-disabled control arm

ARK (Automatic Research Kit): idea + venue → paper pipeline orchestrating 6 agents — proposal analysis, literature search, Slurm experiments, LaTeX drafting, iterative peer review. Controlled via CLI, web dashboard, or Telegram

Autoresearch-style harness that keeps a submittable paper from the first minute and drives every experiment from the claims in that draft, looping modify → measure → verify → revise. 1st place at the Ralphthon@ICML 2026 autonomous-research hackathon

Claude Code skill for public SEC EDGAR + market data: /analyze, /score, /compare. MIT

Multi-agent QA toolkit with 10 specialized agents — orchestrator, environment-manager, functional-reviewer, test-scenario-designer, browser-validator, automation-writer, manual-validator, bug-reporter, release-analyzer, smart-test-selector. Stack-agnostic, output-chained, live validation via Chrome MCP

OpenAGS: Orchestrates a team of AI agents across the full research lifecycle — lit review, hypothesis generation, experiments, manuscript writing, and peer review

Zero-config AI agent CLI with 20+ tools, persistent memory, Ollama auto-detection, and free tier

Intent signal CLI. LinkedIn engagers, keyword posters, job changers, funding events. JSON output for agent pipelines

Claude Code skill that runs an autoresearch-style, human-gated deep-learning loop across six roles, versioned experiments, and evidence-checked completion

AI-Driven Scientific and Algorithmic Discovery