Manage Python and R packages for notebook work. Resolve missing-package errors, check installed versions, install into the selected runtime, and restart the kernel when needed, with guidance on using separate environments for different dependency requirements.
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Use when a notebook run fails on a missing package (ImportError, ModuleNotFoundError, "there is no package called"), when you need to inspect an installed package version, or when you need to install, add, or manage Python or R packages for the notebook runtime. Covers inspect_packages, routing Python vs R through manage_packages, why in-cell %pip/!pip/install.packages() and OS installers are forbidden, restarting the kernel after an install, and when to stop and ask the user.
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The notebook runs against the session's bound runtime: the app-managed default (default-python / default-r) unless you bind another one with notebook_bind_runtime — a named environment you created, or one of the user's own detected interpreters. You never activate environments by hand, and you never install packages from inside a cell. Installs happen in the trusted main process through a single tool, manage_packages, and always land in the currently bound runtime. This page is the workflow for getting a package installed and for knowing when a package is not something you can install yourself.
A run that fails with ImportError / ModuleNotFoundError (Python) or Error in library(x): there is no package called 'x' (R) means the package is not in the environment yet. The fix is one manage_packages call, not a code change. Do not rewrite the cell to use a different library that "does roughly the same thing" — install the package the task actually needs. Do not fall back to reading data or computing results a worse way to dodge the missing import.
Use inspect_packages(language, packages) when the user asks whether a package is installed in an app-managed runtime or which version is present, or when your code depends on a version-specific feature. It reads package metadata from the session's bound app-managed runtime without importing the package or changing the environment. An installed result does not prove the import will succeed; use notebook_execute when importability itself is the question.
Inspection does not provision a missing app-managed default runtime. If it reports DEFAULT_RUNTIME_NOT_READY, use notebook_execute in that language to prepare the runtime under notebook execution approval, then retry inspect_packages.
inspect_packages intentionally rejects a user-owned external runtime because reading its metadata executes that interpreter. Use notebook_execute for an external runtime so the user sees the normal notebook execution approval.
Do not use inspection as a mandatory preflight for every install. For a clear missing-package error, call manage_packages directly; installation is the recovery action, while inspection is for explicit version and compatibility questions.
manage_packages(language="python", packages=["numpy", "pandas"]).manage_packages(language="r", packages=["ggplot2"]). R packages install from conda-forge as r-<name> automatically; pass the plain CRAN name (ggplot2, not r-ggplot2).usePip=true.channels=["bioconda"]. Leave channels off otherwise; the app supplies the right default mirror.manage_packages always returns a target receipt. When resolution succeeds, it identifies the implicit default or explicit binding and the actual runtime; selection:"unresolved" means no target could be established. After a failed bind or switch, inspect bindingChanged and target before deciding where a later install would run. Every install persists — there is no "temporary" install to undo later. Install once; it stays available in later cells and sessions on that runtime. To install into a different environment, bind or switch to it first (notebook_bind_runtime / notebook_switch_runtime); there is no per-call environment argument. The app-managed defaults are additive-only (bare name or name==version); for uninstalls, version ranges, or git/URL specs, create a named environment and install there.
manage_packages returns a compact result with ok, needsRestart, the installer method, the target receipt, verified packageChanges, and an actionable error on failure. A requested package change reports installed, updated, unchanged, or removed plus its observed before/after version when available. unchanged means only that distribution metadata in the reported target did not change; it does not prove the current Kernel can import the module. When importability or the loaded version matters, use notebook_execute to import the package and inspect its version. When needsRestart is true (always true for R, because the running kernel holds the old library state), call notebook_restart before you import or library()-load the new package, then re-run the cell. For Python, a fresh import usually sees the new package without a restart; if an earlier failed import was cached, restart and retry.
These bypass the install gate and are forbidden:
apt, brew, yum — and sudo.curl | bash, downloading and running installers, or hand-rolled subprocess installs.%pip install, !pip install, install.packages(...), remotes::install_github(...). These run inside the kernel, which has no install-network path and is sandboxed in a later phase — they do not belong in a cell.Some things are not a manage_packages install:
When you choose to select a newly created isolated environment (to remove/downgrade a package, use richer specs, or keep a project's deps separate), await manage_environments(action:"create", language, name), check created.runnable, and use its canonical created.runtimeId. Create does not select the environment; continue only when created.runnable is true. With no existing binding for that language, pass the receipt's runtimeId to notebook_bind_runtime; with an existing binding, pass it to notebook_switch_runtime. Do not use the short environment name as runtimeId, and do not call list merely to rediscover the environment you just created. Only action:"list" returns the full environment snapshot; create and remove do not return that snapshot, and instead return receipts for the environment they created or removed. The app-managed defaults cannot be removed.
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Predict structures for single proteins or protein complexes with AlphaFold2 through ColabFold. Use confidence scores and comparisons with a designed structure to assess whether a designed sequence folds as intended.
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Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.
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Predict patterns of gene expression and other genomic measurements from DNA sequences with Borzoi. Compare reference and altered sequences to estimate regulatory effects, generate predicted coverage tracks, and prioritize variants in regions that do not encode proteins.
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Predict three-dimensional structures of complexes containing proteins, RNA, DNA, and small molecules with Chai-1. Compare candidate structures using confidence scores and clash indicators, or run repeated predictions from Python to evaluate designed binders.
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Prepare reproducible setup and removal instructions for software environments on Open-Science SSH hosts, including Slurm clusters. Check available tools, define named activation settings, and validate the environment where jobs run after the user installs it.
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Create and manage focused Specialist agents and Skills through conversation. Draft agent roles, review available Skills and Connectors, configure access, and manage changes, with Skill creation and publishing handled by the application's internal skill-creator.
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