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wisp-science_1.8.0_aarch64.dmgOPEN SOURCE · LOCAL-FIRST · SCIENTIFIC AGENT WORKSPACEVOL. 01 / v1.8.0
Wisp ScienceFrom research question to reproducible artifact.
Wisp Science is an open-source, local-first scientific agent workspace for desktop and CLI. It connects papers, local data, Python/R, scientific databases, and agent workflows in traceable projects.
Bring your own model. Keep project state local.
RESEARCH RECORD · WS-014Analysis trace preserved
QUESTION
Which pathways distinguish the treatment-responsive cell population?
responsive population
01Evidence
12 papers · 3 local datasets
02Method
Scanpy workflow · persistent Python
03Result
Marker table · UMAP · methods note
LATEST RELEASE
Install Wisp Science and begin with a local project.
Release files are served by GitHub. Package checksums are published here so each download can be verified before use.
Current releasev1.8.0
Published Aug 30, 2026. macOS packages are signed and notarized; Windows packages remain unsigned and may trigger SmartScreen.
Read release notesSMART DOWNLOAD
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Real workflows, not a concept mockup.
Move from a research question to evidence, analysis, tables, and reproducible artifacts in one local project.
01 / 05
WISP SCIENCE
Used by researchers from universities and research teams
Cornell University
University of Michigan
UCLA
Yale University
Tsinghua University
Zhejiang University
Washington University in St. Louis
Saint Louis University
Shanghai Jiao Tong University
Peking University
Chinese Academy of Sciences
WHAT STAYS WITH YOUR PROJECT
Keep sources, analysis, and results in one research workspace.
Work with papers and local files, run Python or R, query scientific databases, and keep code, figures, and decisions together. Your project stays on this computer; Wisp Science connects to an outside service only when you use one you configured.
What Wisp Science keeps together
- 01SOURCES
Papers and local files
Bring the material for one question into a project without losing track of the original files.
- 02PROCESS
Questions, plans, and decisions
Keep the reasoning behind each step beside the task, so you can review or continue the work later.
- 03ANALYSIS
Python, R, and research SKILLs
Run analysis and reusable workflows in the same project instead of moving data and context between separate tools.
- 04RESULTS
Code, tables, figures, and history
Return to the code, outputs, and conversation that produced a result when you need to check or share it.
BEFORE DATA LEAVES THIS COMPUTERLocal project files, explicit outside connections.
Project files, session history, and generated results are stored locally. If a task uses a model, database, ACP agent, or remote compute environment, the information needed for that step is handled under that service’s settings and credentials. Review sensitive material before enabling an outside connection.
A TRACEABLE RESEARCH LOOP
Move from discovery to communication without losing the method.
Each stage remains connected to its evidence, code, tools, and generated outputs, so the final report is a record of the work—not just an answer.
- 01
Discover
Search papers and biological databases, inspect local files, and assemble evidence around the question.
- 02
Analyze
Run persistent Python or R, shell, MCP tools, and domain SKILL workflows while preserving project-scoped state.
- 03
Verify
Review plans before execution, inspect intermediate files, and trace every artifact back to inputs and code.
- 04
Communicate
Export figures, tables, methods, citations, and narrative reports from one coherent research record.
REPRODUCIBILITY, BUILT INTO THE SESSION
Evidence, code, and context—kept in the same research record.
Tables, figures, code blocks, LaTeX equations, and file paths can become artifacts. Each stays connected to its producing conversation, code, logs, inputs, and environment details.
@ file references
Attach the file already open or any file in the current project without breaking the research context.
Plan approval
Let the agent propose a multi-step method, pause for review, and only continue after approval.
Artifact provenance
Open tables, figures, code, formulas, and file references together with the context and inputs that produced them.
PROVENANCE LEDGERWS / TRACE-014
01Input
counts_matrix.h5adsha256 verified02Method
scanpy_marker_workflow29 steps logged03Figure
umap_response.pngcode attached04Report
methods_and_results.mdcitations linkedREPRODUCIBLE
RESEARCH PLATES
One workspace, different scientific questions.
Compose database retrieval, local computation, domain workflows, and structured writing around the task at hand.
PLATE 0101 / 04
Single-cell analysis
Run Scanpy or scVI-style workflows, annotate populations, and export UMAPs, marker tables, and methods notes.
PLATE 0202 / 04
Protein structure
Fetch sequences and structures, combine AlphaFold-, Boltz-, or OpenFold-style skills, and draft structured interpretation.
PLATE 0303 / 04
Cheminformatics
Search ChEMBL or PubChem, compare activity data, calculate properties, and prepare SAR-style tables.
PLATE 0404 / 04
Literature and writing
Search PubMed or Semantic Scholar, inspect PDFs, draft discussion sections, and keep citations with the text.
DEMO ATLAS
Inspect a complete agent trace before using API credits.
Bundled read-only sessions expose the path from question to evidence and exported output for common life-science tasks.
Raw counts → hit calling → visualization report
Sequence analysis → mutation proposals → activity reasoning
Metagenomic search → cross-database functional annotation
Tumor immunology literature → evidence map → hypotheses
FIELD NOTES FROM THE BENCH
How researchers move work forward.
Not a vague “analyze this” prompt, but a concrete research task advanced with evidence, code, and a traceable record.
01 / 06
Start by QCing this single-cell dataset, then help me identify those clusters. Keep every parameter, figure, and rationale—I need to walk through it at lab meeting tomorrow.
02 / 06
Map the preclinical studies on this target from the past five years. Don’t just give me a conclusion—show the sources, conflicting findings, and evidence that is still missing.
03 / 06
Could these mutations affect protein stability? Check the databases and literature first, then give me a locally reproducible analysis plan without moving the raw data off my computer.
04 / 06
I added 20 papers to the project. Organize the controls by experimental system, flag inconsistent numbers, and make every finding traceable to the original text.
05 / 06
Don’t interpret these metabolomics results yet. Check missing values, batch effects, and outliers first, then turn the recommended statistical steps into a rerunnable script.
06 / 06
Search ChEMBL and PubChem for these candidate molecules, compare activity, selectivity, and known risks, then identify the three most valuable experiments to run next.
Anonymous early-user feedback. Identities are summarized, wording has been lightly edited for length and clarity, and portraits are illustrative.
QUESTIONS BEFORE YOU BEGIN
What Wisp Science is—and where its boundaries are.
01Is Wisp Science a new model?
No. It is an open-source desktop and CLI application that uses the model provider and credentials you configure.
02How is it different from a generic AI assistant?
It can execute local Python, shell and file tools, call MCP databases, follow domain SKILL workflows, and retain artifact provenance.
03Does research data stay private?
Project files, sessions, artifacts, and settings remain local. Prompts and responses still pass through your configured model provider.
04Is it production-stable?
Wisp Science is an active preview for local scientific workflows. Review critical methods and outputs, and check release notes for current signing and update status.