Discover AI agent projects built on OpenClaw for bioinformatics, biomedicine, data science, and multi-agent learning — from lightweight assistants to self-evolving research systems.
From the full-featured core platform to ultra-lightweight edge agents, from self-evolving research systems to multi-omics pipelines.
The core platform. Personal AI assistant connecting 20+ messaging apps (WhatsApp, Telegram, Slack, Discord, Signal, iMessage, Teams, Matrix, Feishu, LINE). Voice support on macOS/iOS/Android with live Canvas rendering.

Ultra-lightweight OpenClaw in Python. 99% less code, full agent capabilities. Supports MCP, multi-model (OpenAI, Anthropic, Gemini, Ollama), memory, cron, sub-agents. Install: pip install nanobot-ai.

Fast, small, and fully autonomous AI assistant infrastructure. Deploy anywhere, swap anything. Written in Rust with extreme performance focus — the infrastructure layer for self-hosted AI agents.

Go-based agent running on $10 hardware. <10MB RAM, <1s boot time. 99% less memory than OpenClaw. Supports x86_64, ARM64, MIPS, RISC-V, and LoongArch architectures.

Lightweight alternative to OpenClaw that runs in containers for security. Connects to WhatsApp, Telegram, Slack, Discord, Gmail. Built on Anthropic Agents SDK with memory and scheduled jobs.

Agentic AI assistant built with Rust. Runs on <1MB RAM targeting $2-5 MCU hardware like ESP32. The smallest member of the Claw family for sensor-level embedded AI.

Edge-Cloud Collaborative AI Assistant built on OpenClaw. Three-tier privacy system (Passthrough/Desensitization/Local) with dual-engine edge detector. Keeps sensitive data local while routing 80% of requests to cost-effective cloud models. By THUNLP, RUC, OpenBMB.

OpenClaw reimagined in pure Python. Autonomous AI agent with memory, RAG, skills, web dashboard, voice input, and multi-channel support (Telegram, etc.).

Desktop app providing a graphical interface for OpenClaw AI agents. Turns CLI-based AI orchestration into a visual desktop experience without using the terminal.

Self-learning agent that evolves from every conversation. LoRA-based continual learning + reinforcement learning, no GPU cluster needed. Supports OpenClaw, NanoBot, PicoClaw backends.

OpenClaw for Teams. Multi-agent collaboration with persistent identity, long-term memory, and autonomous 'Aware' consciousness system. 6 trigger types: cron, once, interval, poll, on_message, webhook.
Self-evolving multi-agent AI research system. 6 sub-agents (plan, research, code, debug, analyze, write) for end-to-end scientific discovery. #1 on DeepResearch Bench II. ICAIS 2025 Best Paper Award.

AI research assistant for biomedicine. Built on OpenClaw + Claude Code with 140 K-Dense Scientific Skills. Covers RNA-seq, drug discovery, clinical analysis. Integrated RStudio & JupyterLab.

Operating layer for LabOS — Stanford-Princeton AI Co-Scientists project. Automated scientific co-discovery workflows for laboratory research.

The first bioinformatics-native AI agent skill library. Local-first, reproducible, built on OpenClaw. Covers genomics, population genetics, and equity-focused bioinformatics workflows.

AI-powered bioinformatics research assistant built on OpenClaw. Integrates PubMed literature search and PyMOL molecular visualization for streamlined biological research workflows.

Multi-omics AI analysis tool from TianGz Lab. Focused on genomics, transcriptomics, and proteomics data processing with intelligent agent workflows.

Early-stage medical AI project exploring agent-based approaches for clinical decision support and medical data analysis.

Light, practical, memory-enabled AI orchestrator for science. Built on NanoClaw. Adds OpenAI/OpenRouter providers, Discord/WhatsApp channels, runtime skills, durable session memory, and real-time agent execution dashboard.

Open Multi-Agent Interactive Classroom by Tsinghua University. One-click immersive AI classroom with AI teachers and peers. Slides, quizzes, whiteboard, PBL. Published in JCST'26.

An AI agent for fully automated multi-omic analyses. Supports RNA-seq, scRNA-seq, spatial transcriptomics, WGS/WES, and ChIP-seq with automated code repair.

AI CompBio agent that autonomously analyzes biological data and generates new insights. From Stanford Zou Group. Published in Nature Methods. Specializes in scRNA-seq with self-debugging and hypothesis generation.

An AI agent for spatial biology by Genentech. Processes multimodal spatial genomics data for research insights.

LLM agent for CRISPR genome engineering. Assists with guide RNA design, experiment planning, and protocol optimization.

AI agent system for various bioinformatics tasks with specialized agent coordination. From the same group as POPGENAGENT.

LLM-driven multi-agent framework for automated scRNA-seq analysis. Three-role architecture: Planner, Executor, and Evaluator for single-cell RNA sequencing workflows.

Tool-aware, reproducible, report-oriented AI workflows for population genomics. Template-driven execution with publication-quality figures, full provenance tracking, and cost-aware LLM routing.

Computational AI Research Interface for Bioinformatics, Omics, and Unifying Agents. Multi-agent bioinformatics system by OpenTechBio.

A virtual lab of LLM agents for science research. Multi-agent system for nanobody design with principal investigator agent coordination.

Molecular dynamics simulations with an LLM agent. Automates simulation setup, execution, and analysis using natural language.

Agentic framework for computational chemistry and materials science workflows. From Argonne National Laboratory (ALCF). Orchestrates chemistry simulation pipelines.

Multi-agent platform for pharmaceutical R&D. Covers drug discovery, drug development, and clinical pipeline automation. Built by VirtualPatientEngine.

Microsoft R&D automation platform. AI-driven data and model research — automates hypothesis generation, experiment design, and iterative improvement for industrial and scientific R&D.

Fully autonomous, self-evolving research framework: from a one-line idea to a conference-level paper. 23-stage pipeline with multi-agent debate, self-healing error correction, citation verification, and hardware-adaptive execution. Compatible with OpenClaw and MetaClaw.

Autonomous scientific innovation system from HKUDS. NeurIPS 2025 paper. End-to-end research automation: literature review, hypothesis generation, experiment design, and paper writing. Production version at novix.science.

Sakana AI's autonomous scientific discovery system. Workshop-level paper generation via agentic tree search. End-to-end: idea → experiment → paper. Successor to the 10K-star AI-Scientist v1.

AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing. Full research pipeline from question to publication-ready report.

AI Research IDE with built-in "AI Doctors" assistants. Full research lifecycle: Survey → Ideation → Experiment → Publication → Promotion. 100+ research skills, auto pipeline planning, real-time task tracking, LaTeX rendering, Git integration. PWA-ready.

Autonomous Generalist Scientist — AI agent targeting all scientific fields. Broad-scope autonomous research from hypothesis to verification.

Open-source adaptation of Google DeepMind's AI Co-Scientist. Generates, reviews, ranks, and evolves research hypotheses using multi-agent architecture.

Fully autonomous multimodal research agent. Combines vision and text understanding for scientific discovery. Chinese academic origin with bilingual support.

AI scientist framework for autonomous deep research in biological sciences. Multi-agent system combining literature analysis with data scientist agents for iterative scientific discovery.

LLM-based AI agent for closed-loop design of genetic perturbation experiments. Autonomous reasoning for biological discovery.

Scientific equation discovery with agentic AI. Published at ICLR 2026. Symbolic regression via AI agents from GAIR Lab (Shanghai Jiao Tong University).

Self-evolving AI agent framework with 5-layer safety gatekeeper. Agents observe failures, propose fixes, and safely apply them. Built on HKUDS/nanobot.
A rapidly growing family of AI agent projects powering scientific research worldwide.
Total GitHub Stars
Core Projects
Programming Languages
How the Claw ecosystem empowers researchers across disciplines:
EvoScientist's research sub-agent scans, summarizes, and synthesizes papers — generating comprehensive literature reviews autonomously.
OmicsClaw and MedgeClaw process RNA-seq, proteomics, and clinical data through intelligent pipelines with RStudio and JupyterLab integration.
OpenMAIC transforms any topic into a multi-agent classroom with AI teachers, quizzes, whiteboard illustrations, and project-based learning.
From hypothesis to publication — EvoScientist's 6-agent pipeline plans, codes, analyzes, and writes research papers with human-on-the-loop oversight.
Common questions about the OpenClaw science ecosystem.
Find the right AI agent for your research workflow.