Unleash scientific tools for your agents.
Your agent sends a prompt and the data. ScientistWorkbench runs the analysis in an isolated Linux sandbox with a curated life-science stack, an independent reviewer checks the report against what actually ran, and you get back a summary, figures, tables and the code — addressable forever by job id.
No seats, no installs, no subscription required. One API key works for HTTP and MCP.
SciPy
pandas
statsmodels
scikit-learn
matplotlib
Biopython
RDKit
How it works
Submit
One prompt plus input files (xlsx, csv, fasta, PDFs …) over HTTP or an MCP tool call. Returns a job_id immediately.
Sandbox
A fresh Ubuntu sandbox per job with the full scientific stack below pre-installed and skills for 24 life-science databases. Nothing shared between jobs or tenants.
Reviewed report
The agent writes summary.md (Objective · Method · Results · Assumptions · Files). A separate reviewer audits every claim against the execution log.
Collect
Summary, figures, tables, scripts, transcript and reviewer findings — as JSON, as files, or inline in your agent's context. Run hundreds in parallel.
What your agents get to work with
Every job starts in a fresh Ubuntu sandbox with this stack already installed, plus a skill that teaches the agent how to query each database directly (REST, with retries and a provenance log of every query). Package managers stay available, so anything else on PyPI or apt is one install away.
Compute stack
SciPypandasstatsmodelsscikit-learnmatplotlibBiopythonRDKit
| numerics & stats | numpy, SciPy, pandas, statsmodels, scikit-learn, pyarrow |
|---|---|
| figures | matplotlib, seaborn (publication-style defaults via the figure skill) |
| sequences & phylogenetics | Biopython, MAFFT, IQ-TREE, FastTree, HMMER |
| structure | TM-align, PDB/AlphaFold fetch helpers |
| chemistry | RDKit |
| files | openpyxl, xlrd, CSV/Parquet, PDF text extraction, LibreOffice headless |
| languages | Python 3, R, shell; Node, Go, Rust, Java available |
24 life-science databases
Reachable from inside the sandbox, each with a skill page the agent reads before querying.
| proteins & genes | UniProt, Ensembl, InterPro, PDB, AlphaFold |
|---|---|
| variants & expression | gnomAD, ClinVar, dbSNP, GEO, GTEx |
| cancer & targets | DepMap, cBioPortal, Open Targets, Reactome |
| chemistry & drugs | PubChem, ChEMBL, BindingDB, openFDA |
| literature & trials | PubMed, Europe PMC, OpenAlex, bioRxiv, medRxiv, ClinicalTrials.gov |
What a job costs
Prepaid tokens, no seats. A job uses tokens in proportion to what it reads, computes and writes. Measured on the production engine:
| job | class | wall time | tokens |
|---|---|---|---|
| CRO assay QC Recompute plate statistics, refit IC50 curves, flag anomalies in a CRO xlsx deliverable | specialist (standard model) | ≈ 11 min | 185k tokens |
| Database lookup One structured question against UniProt / Open Targets / ChEMBL / DepMap / PubMed | lookup (standard model) | ≈ 4 min | 70k tokens est. |
| Research phylogeny Family selection, MAFFT + FastTree, ancestral reconstruction, structural superposition, 5 figures | research (frontier model) | ≈ 39 min | 1.11M tokens |
Built for agent frameworks, not chat
API- and MCP-native
Eight MCP tools (science_submit, science_wait, science_result …) and a plain HTTP job API. Works from Claude Code, Cursor, Claude Desktop, the Agent SDK, or curl.
Durable job ids + provenance
Every artifact has a sha256 and a stable science:<job_id>/<file> ref. Transcript, execution log and reviewer record ship with every result.
Parallel by default
Submit and poll. Each job is its own sandbox; concurrency is a setting, not a queue you wait in.
Isolation
Per-job sandboxes, per-tenant storage keys, read-only mounted inputs, no shared state, no training on your data.
Real runs
QC of a CRO IC50 deliverable (NCI-N87, two ADC candidates + benchmark + control)
specialist · standard model · 11 min · 2 figures · 173k tokens
“Take this data from the CRO and QC it and return a summary of the results.”
Proteins of unknown function in extremophiles: the MEMO1 family across the three domains of life
research · frontier model · 39 min · 5 figures · 1.11M tokens
“Give me an example of some proteins of unknown function in extremophiles. Choose the most interesting, and build a phylogenetic tree with related sequences acro…”
AI-generated analysis. Outputs may contain errors; verify numbers against the execution log and source files before relying on them. Not a substitute for review by a qualified scientist, and not medical advice: any use affecting patient care or healthcare decisions requires review by a qualified professional.