Who you are authenticated as: account email, plan (isPro / pioneer), this month's free-run quota, and — with a hosted token — the token name and days until it expires. Call this first when setup guidance appears or to check which credential a session uses.

List Nimbus pipeline node types (data, preprocessing, features, models...).

Use get_node_schema(node_type) for one node's full config schema and ports.

Full config JSON schema + input/output ports for one node type.

List built-in starter pipelines (MI/P300/SSVEP...). get_template(id) for the graph.

Full template incl. the 'train' execGraph needed by run_pipeline/validate_pipeline.

Curated public EEG datasets (MOABB packs) available to pipelines.

Public benchmark leaderboard: pipeline rankings per dataset.

Rankings are per-dataset under the canonical within_session protocol (see protocol). Within each dataset, rows are sorted desc by meanAccuracyPct (95% CI in ciLoPct/ciHiPct). Use pipelineId as the template id hint for get_template when building a pipeline. updated marks each dataset's most recent run; packFingerprint identifies the exact dataset pack the scores came from.

Validate a pipeline graph before running. ExecGraphSnapshot: {nodes: [{id, type, config}], connections: [{from, to}]}. Build it from get_template(id).train or from scratch using list_nodes().

Validate one node's config object against its schema (get_node_schema).

Start a pipeline run (NON-BLOCKING). Returns executionId — poll with get_execution() until status is completed/failed, then get_results(). layout is optional canvas positions ({nodes: {id: {x, y}}}); a grid is synthesized when omitted.

Cancel a running execution.

Execution status summary (status: running/completed/failed/cancelled).

Recent executions. Optional status filter (running/completed/failed/cancelled).

Metrics for a completed run. Trimmed by default (accuracy, kappa, ITR, confusion matrix, per-class); full=True returns the complete result object.

Run 1-25 pipelines as ONE paced experiment (NON-BLOCKING). Returns an experimentId immediately; a background thread submits at most 2 runs at a time (min(max_concurrent, 2)), retries queue-full up to 3 times per run, and polls each execution to completion. Poll get_experiment() for per-run status and, once finished, aggregated metrics.

Experiment snapshot: status (running/completed/failed), per-run rows ({name, executionId, status, error?, metrics?}) and, once finished, aggregates {metric: {mean, std, best: {name, value}}} over completed runs only (std = population; None below 2 values).

Trained artifacts (models/filters, e.g. *.pkl) saved by an execution.

Download one artifact file to NIMBUS_EXPORT_DIR/executions// and return its path.

Export the pipeline as a standalone runnable Python bundle (zip saved locally).

EEG devices supported by this backend (OpenBCI, Muse, BrainBit, LSL, PiEEG...).

Test a device connection WITHOUT starting a stream (safe, no confirm needed).

Connect an EEG device and START a live streaming session on the user's head. Requires confirm=True; call test_device first. Track with stream_status(). Idle watchdog: if no stream_status()/get_live_session() poll happens for idle_timeout_sec (default 900), the session is stopped and the device disconnected automatically — an abandoned stream never keeps running on the user's head. Any poll resets the timer; idle_timeout_sec=0 disables the watchdog.

Live snapshot of a streaming session (running, deviceConnected). Polling this also feeds the idle watchdog: each call resets the session's idle timer (see start_stream's idle_timeout_sec).

Stop a streaming session and disconnect the device (always safe to call). Also removes the session from the idle watchdog so it cannot fire after an explicit stop.

Live snapshot of a streaming session: latest prediction + recent window, signal quality (meanChannelQuality, snrDb, artifactProbability), indicators, running stats. Poll this while a session runs. Live telemetry requires a DEPLOYED model session (hub deploy / playback with a classifier); modelless hardware streams have no telemetry — use stream_status for those. Expect low confidence during filter/ASR warm-up (first seconds); quality < 0.5 or high artifactProbability means the signal is poor. 404 => session not active in this backend. Each poll also feeds the idle watchdog (see start_stream's idle_timeout_sec), keeping an actively watched session alive.

Upload an EEG file (.edf/.bdf/.mat/.csv/.txt/.tsv/.h5/.hdf5, <=500MB) to the backend and get the registered path for a custom_data node.

sampling_rate (Hz, e.g. 250.0) is REQUIRED for plain CSV/TSV/TXT files without embedded metadata — the backend silently assumes 250 Hz otherwise, which mis-times epochs, filters and spectral features. format overrides extension-based detection (auto, mat, csv, tsv, txt, edf, bdf, h5, hdf5).

Exploratory summary of a public EEG dataset (MOABB pack): channels, sampling rate, trial/class balance, per-channel µV stats, band powers and a PSD overview.

Look at the data BEFORE building pipelines: class balance drives stratification choices (imbalanced classes skew accuracy), and flatlined channels mean a montage/reference problem worth fixing first. subject is REQUIRED (the backend 400s without it) — get the subject list via list_datasets, e.g. "S01"; a comma-list like "S01,S03" loads a cohort. mode: training | evaluation | all. Units note: values are ASSUMED volts by the loader — a µV-native file reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed.

Exploratory summary of an EEG file (.edf/.bdf/.mat/.csv/.tsv/.txt/.h5): channels, sampling rate, trial/class balance, per-channel µV stats, band powers and a PSD overview.

The path shape picks the source: ABSOLUTE path → read the file from disk (only on a LOCAL backend: desktop app / MCP local mode — no upload needed); RELATIVE path (the one upload_data returns) → describe the uploaded file, which works on ANY backend (hosted or local).

Look at the data BEFORE building pipelines: class balance drives stratification choices, and flatlined channels mean a montage/reference problem worth fixing first. Units note: values are ASSUMED volts by the loader — a µV-native CSV reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed. On a hosted backend absolute paths are refused and this returns guidance (upload the file first or switch to a local backend).

Create a project (container for one pipeline document). Returns projectId.

List projects owned by the current principal (agent work included).

Save a pipeline graph into a project (visible on the studio canvas). Handles revision conflicts automatically (one retry).

Load a project's saved pipeline (train graph + meta) for editing/re-running.