CRISPR-GPT

Four-agent LLM planner for CRISPR-Cas gene-editing experiments, spanning 22 individual tasks across knockout, base, prime, and epigenetic editing.

   
Affiliation Stanford University (Cong lab), Princeton, UC Berkeley, Google DeepMind (Qu et al.)
First introduced 2024-04 (arXiv v1); peer-reviewed version in Nat. Biomed. Eng. 2026-02
Lifecycle stages Experiment design, Analysis
Autonomy level Assistive (human-in-the-loop; user-proxy agent operates autonomously but user oversight is encouraged)
Domain focus Biology (CRISPR-Cas gene editing — knockout, base editing, prime editing, epigenetic editing)
Availability Closed (no full code release pending US regulatory clarity on AI in biology); welcome page on GitHub

Approach

Multi-agent LLM system with four roles:

  • Planner — decomposes user requests into a chain of state-machine tasks.
  • Task Executor — manages workflow.
  • User-Proxy — mediates user interaction.
  • Tool Providers — wrap external tools, databases, and web search via APIs.

The system implements 22 individual gene-editing tasks (sgRNA design, off-target prediction, delivery selection, protocol drafting, validation assay design) across Meta, Auto, and QA modes.

Validation

Real-world case study of non-expert researchers using CRISPR-GPT to plan and execute gene-editing experiments from scratch, as reported in the Nature Biomedical Engineering paper.

Notable results

First LLM agent system reported to span the full CRISPR experimental-design workflow across four editing modalities. Demonstrated to help non-experts plan and execute real gene-editing experiments.

Primary paper

Qu et al., “CRISPR-GPT for agentic automation of gene-editing experiments,” Nat. Biomed. Eng. 10, 245–258 (2026).

Other references

Code

Welcome page — full codebase withheld pending regulatory clarity.