Operon — The IDE that runs your experiments

Try it in the browser

Feel the agent
without installing.

Three live demos — a mode picker, a scripted Claude conversation, and a side-by-side of the old way vs. Operon. No account, no install.

Interactive · 30 seconds

Which AI mode should I use?

Answer three questions and we'll point you at the right mode.

Mode picker

What stage is your work at?

Should Claude touch your files?

Do you need citations or polished output?

Recommended mode

Agent mode

Claude will run commands in your shell, edit files, and iterate on outputs until the analysis finishes. You watch the stream and step in when needed.

Learn Agent mode → Download & try

Recommended mode

Plan mode

Claude writes an implementation_plan.md — a step-by-step proposal you can review, edit, and approve before any code runs. Great for risky or long-running work.

Learn Plan mode → Download & try

Recommended mode

Ask mode

Read-only conversation. Toggle PubMed and Claude cites real DOIs. Perfect for the "why did DESeq2 shrink these log-fold changes?" moments.

Learn Ask mode → Download & try

Recommended mode

Report mode

Turn a finished session into a publication-ready PDF — methods, figures, citations, and a reproducibility appendix, all generated from the session transcript.

Learn Report mode → Download & try

Watch the agent think

A real Operon conversation, replayed.

Pick a scenario and watch Claude stream tool calls, thinking, and cited answers.

operon — chat.tsx

Drag to compare

The old way vs. with Operon.

Same analysis, two worlds. Drag the divider.

Before

bulk-rnaseq — manual

You open a blank terminal and write:

# Write a Snakefile from scratch
$ vim Snakefile
# Set up the conda env
$ conda create -n rnaseq -c bioconda fastp star subread deseq2
$ conda activate rnaseq
# Write the alignment rule...
rule align:
    input:  "fastq/{sample}_R1.fq.gz"
    output: "bam/{sample}.bam"
    shell:  "STAR --runThreadN 8 --genomeDir {genome} ..."
# 40 more rules, plus SLURM profile, plus QC...
$ snakemake -j 16 --use-conda --profile slurm
# STAR segfaults at 4 a.m. No traceback.
$ tail log.out
Segmentation fault (core dumped)
# Read the STAR source on GitHub for two hours.
# Realize --genomeLoad is the problem.
# Fix it. Rerun. Repeat.

Time: 2–3 days · Expertise: advanced

With Operon

bulk-rnaseq — conversational

You open Operon and type:

You: Run a standard bulk RNA-seq pipeline on the FASTQs in ./raw —
     I want counts, a DESeq2 contrast of WT vs. KO, and volcano plots.

Claude ✻ Plan:
  1. Detect 6 samples · paired-end · ~30M reads each
  2. Apply protocol: bulk-rnaseq.v3 (fastp → STAR → featureCounts → DESeq2)
  3. Submit as SLURM array on HPC
  4. Generate volcano + heatmap

[Proceed?] y

⠋ Running fastp on 6 samples...        ✓ done (4m12s)
⠋ STAR alignment (SLURM job 284712)...  ✓ done (38m)
⠋ featureCounts → counts.tsv...         ✓ done
⠋ DESeq2 contrast WT vs. KO...          ✓ 412 DEGs (FDR < 0.05)
⠋ Rendering volcano + heatmap...        ✓ figs/ready

Session summary written to report.md.

Time: 45 minutes · Expertise: whatever you already have

⇆

Agent mode

Describe the analysis.
Operon runs it.

Point Operon at your data and ask for the experiment. Claude writes the script, runs it in your terminal, reads the output, and iterates until the plot is on disk. You watch.

Understands FASTA, FASTQ, VCF, BAM, GFF, H5AD, MTX, and 20+ other formats
Runs commands in your shell — respects conda, modules, aliases
Streams every tool call so you can interrupt, redirect, or approve

Protocols · import & reuse

Your lab's pipelines,
one click away.

Drop a Snakemake workflow, Nextflow pipeline, bash script, or R notebook into Operon and Claude turns it into a reusable protocol — parameters, dependencies, and run command all captured. Share it with your lab, run it from any project, version it with Git.

Import Snakemake, Nextflow, bash, or R workflows in one drag
Browse a growing catalog of bioinformatics protocols — scRNA-seq, ATAC, ChIP, bulk, spatial
Run any protocol on local data or over SSH on your HPC

Explore protocols → How import works

Import an existing Snakemake or Nextflow pipeline

Browse the built-in protocol catalog Drag in your own pipeline — Claude wires it up Run on any dataset, local or HPC

Ask mode · PubMed

Grounded in the literature.

Ask mode is pure Q&A — no commands, no file writes. Toggle PubMed and Claude will search NCBI, retrieve papers, and answer with proper citations. No API key required.

Ideal for "why did DESeq2 shrink these log-fold changes?" moments
Cites real DOIs you can open in one click
Works alongside the editor — perfect for literature review sessions

HPC-native

Run on your cluster,
not your laptop.

SSH into your institution's HPC, grab a compute node, and let Claude run inside a persistent tmux session. Sessions survive dropped Wi-Fi and closed laptops. Your data never leaves the server.

SLURM, PBS/Torque, and SGE batch-job templates built in
Shared-filesystem aware — works around /tmp being node-local
Resume a running analysis from any Mac, Windows, or Linux machine

HPC guide →

Operon connected to an HPC cluster over SSH

Private LLM stack

Your data. Your model.
Your machine.

Clinical cohorts, embargoed sequencing data, industry collaborations — some of your work simply cannot leave your network. Operon was built for that reality from day one.

Run locally with Ollama

Point Operon at a local Ollama daemon and run llama3, qwen-coder, deepseek-coder — any model you've pulled. Zero network egress.

On-prem via vLLM or LM Studio

Spin up vLLM or LM Studio on a lab server. Operon's bundled translation proxy turns any OpenAI-compatible endpoint into a first-class backend.

40+ backends, one interface

LiteLLM, OpenRouter, Together, Groq, DeepInfra, Cerebras — all work through the same translation proxy. Switch providers per-session, not per-install.

Secrets in the OS keychain

API keys live in macOS Keychain, Windows Credential Manager, or libsecret on Linux — never in plain-text config files, never in telemetry.

Reverse-tunnel to your cluster

Host a 70B model on an A100 node, tunnel it over SSH, and query it from your laptop like it's localhost. The recipe is documented, not magic.

No telemetry

Operon collects nothing. Not a session count, not a crash breadcrumb, not a ping. If you want to see for yourself — the source is on GitHub.

Fully air-gapped

No license server, no update pings, no phone-home. Unplug the Ethernet cable and Operon still starts, still runs Ollama, still executes protocols.

Per-model token budgets

Set hard ceilings on tokens-per-session and dollars-per-month. Operon stops and asks before crossing — no surprise invoices from cloud backends.

Private-LLM setup guide →

Research MCPs

The tools biologists actually use — one click away.

Operon ships with a catalog of 12+ MCP servers that give Claude direct, typed access to the databases your analysis depends on. Install, toggle, and every chat session gets the tool.

PubMed

Full-text literature search grounded in NCBI's E-utilities.

BioMCP

PDB structure retrieval, UniProt lookups, AlphaFold access.

GEO

Query any GSE accession, pull metadata, stream count matrices.

GTEx

Tissue-specific expression across the GTEx v8 release.

JASPAR

Transcription-factor motifs for enrichment workflows.

KEGG

Pathway-level enrichment and gene-ontology crosswalks.

AlphaFold

Predicted structures by UniProt ID, straight into PyMOL.

Gene databases

Cross-species ortholog lookup, Ensembl and RefSeq resolution.

Browse the catalog →

The full surface

Everything you'd leave an IDE to do — already here.

Four AI modes

Agent · Plan · Ask · Report.

180+ protocols

RNA-seq, scRNA, ATAC, spatial, proteomics, more.

Protocol generator

Describe a workflow; Claude writes the full protocol.

PubMed-grounded

Real citations, real DOIs, no hallucinations.

SSH + tmux + SLURM

Persistent sessions on remote compute nodes.

Claude on compute nodes

The agent runs where the data lives.

MCP catalog

PubMed, GEO, GTEx, JASPAR, KEGG, AlphaFold.

Private LLMs

Ollama, vLLM, LM Studio — local or on-prem.

Monaco editor

The engine that powers VS Code — 30+ languages.

Git + GitHub

Stage, commit, push, publish — all in-app.

Voice dictation

Native OS speech recognition into the chat input.

Report mode

Turn a session into a publication-ready PDF.

Protocols library

180+ analyses, curated.

Every protocol encodes real-world best practices — conda environments, tool versions, QC gates, SLURM resource hints — so Claude doesn't guess. Pick one to start, or describe your own.

Single-cell

Scanpy end-to-end

QC, normalization, HVG, PCA, Leiden clustering, UMAP.

Single-cell

Seurat integration

Multi-dataset harmonization with SCTransform.

Bulk RNA-seq

DESeq2 · PyDESeq2

Count modelling, shrinkage, volcano + MA plots.

Imaging

CellPose segmentation

Model selection, GPU config, mask QC.

Spatial

Visium / 10x spatial

Spot deconvolution and niche analysis.

Epigenomics

ATAC-seq pipeline

Alignment, peak calling, motif enrichment.

Structural

AlphaFold lookups

Fetch, align, and render predicted structures.

Dynamics

scVelo trajectories

RNA velocity on raw or processed counts.

Browse all 180+ →

Video tutorials

Watch a biologist use it.

Every feature in this page has a 5-minute video walkthrough. Start with the full install; skip to the scRNA-seq or CellPose demo if you want to see the agent in action.

Featured · analysis

Single-cell RNA-seq with Scanpy — end to end

From raw h5ad to a finished UMAP, watch Operon run QC, HVG selection, clustering, and annotation — all driven from a single chat prompt.

See all tutorials →

Built by biologists

Made at the bench,
for the bench.

Operon is developed by the Swarup Lab at UC Irvine — a neurogenomics group running single-cell, spatial, and epigenomic experiments in Alzheimer's disease. We built Operon because we needed it ourselves.

Swarup LabUC Irvine · Dept. of Anatomy & Neurobiology

UC Irvine Neurogenomics · AD MIT-licensed ★ stars — contributors v0.6.1

A day with Operon

Scroll through a real session.

One scroll, one analysis — from opening the app to publication-ready figures on disk.

Step 1

Open the workspace.

Project folder on the left, Monaco in the middle, terminal at the bottom, Claude on the right. Everything in one window — no tab-hopping.

Step 2

Pick your AI mode.

Agent runs commands. Plan drafts a proposal. Ask is pure Q&A with PubMed. Report wraps the session into a PDF. One keystroke to switch.

Step 3

Start from a protocol.

Drop in your data and pick a protocol — scRNA-seq, ATAC, CellPose, any of 180+. Or describe what you want and Claude writes a fresh one.

Step 4

Ground every claim.

Toggle PubMed and Claude pulls real DOIs to back its answer. No hallucinated citations, no API key, no separate tab.

Step 5

Ship it on the HPC.

SSH to the cluster, grab a compute node, hand the analysis to Claude in a tmux session. Close the laptop. Come back tomorrow to finished figures.

Workspace: file tree, Monaco, terminal, and chat

Four AI modes — Agent, Plan, Ask, Report

Get Operon.

Free. Open-source. macOS, Windows, and Linux. No account required to install.

macOS · Apple Silicon macOS · Intel Windows Linux · .deb Linux · AppImage

v0.6.1 · release notes · all downloads & checksums

Feel the agentwithout installing.

Which AI mode should I use?

Mode picker

Agent mode

Plan mode

Ask mode

Report mode

A real Operon conversation, replayed.

The old way vs. with Operon.

bulk-rnaseq — manual

bulk-rnaseq — conversational

Describe the analysis.Operon runs it.

Your lab's pipelines,one click away.

Grounded in the literature.

Run on your cluster,not your laptop.

Your data. Your model.Your machine.

Run locally with Ollama

On-prem via vLLM or LM Studio

40+ backends, one interface

Secrets in the OS keychain

Reverse-tunnel to your cluster

No telemetry

Fully air-gapped

Per-model token budgets

The tools biologists actually use — one click away.

PubMed

BioMCP

GEO

GTEx

JASPAR

KEGG

AlphaFold

Gene databases

Everything you'd leave an IDE to do — already here.

Four AI modes

180+ protocols

Protocol generator

PubMed-grounded

SSH + tmux + SLURM

Claude on compute nodes

MCP catalog

Private LLMs

Monaco editor

Git + GitHub

Voice dictation

Report mode

180+ analyses, curated.

Scanpy end-to-end

Seurat integration

DESeq2 · PyDESeq2

CellPose segmentation

Visium / 10x spatial

ATAC-seq pipeline

AlphaFold lookups

scVelo trajectories

Watch a biologist use it.

Single-cell RNA-seq with Scanpy — end to end

Made at the bench,for the bench.

Scroll through a real session.

Open the workspace.

Pick your AI mode.

Start from a protocol.

Ground every claim.

Ship it on the HPC.

Get Operon.

Feel the agent
without installing.

Describe the analysis.
Operon runs it.

Your lab's pipelines,
one click away.

Run on your cluster,
not your laptop.

Your data. Your model.
Your machine.

Made at the bench,
for the bench.