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Documentation Index

Fetch the complete documentation index at: https://docs.bentolabs.ai/llms.txt

Use this file to discover all available pages before exploring further.

Deep Search is the analyst loop for training a new signal. You describe a failure mode in plain English, Bento surfaces candidate runs that look like it, you label them match or no-match, and you turn those labels into a live classifier. It’s how a one-line description becomes a signal that detects the pattern from then on. A session is one training run. You open it with a natural-language query that names the failure mode you’re hunting. scope decides which side of the run to match on: both (the default), user, or assistant. Bento ranks your trajectories by how similar each is to that description, and you work down the ranked list, giving each candidate a label of match, no_match, or skip. Training reads the match labels; those positive examples are what the signal learns to detect.

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Commands

bentolabs deep-search --help
CommandWhat it does
deep-search create-sessionOpen a session against a plain-English description. Mints the session_id.
deep-search list-candidates <session-id>The ranked candidate trajectories. Labeled runs drop off.
deep-search label-candidate <session-id>Record your verdict on one trajectory.
deep-search create-classifier-signal <session-id>Train a signal from the session’s match labels.
deep-search get-session <session-id>Re-read where a session stands (status, iteration, signal_id).

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Train a signal

Open a session. query is required; scope defaults to both. The response carries the id you pass to every command below: 
bentolabs deep-search create-session --data '{"query": "agent retries a failed payment with no backoff", "scope": "assistant"}'
Pull the candidates, ranked by similarity to the query: 
bentolabs deep-search list-candidates <session-id> --output table
Label one as match, no_match, or skip. Re-labeling the same trajectory overwrites your prior verdict: 
bentolabs deep-search label-candidate <session-id> --data '{"trajectory_id": "<trajectory-id>", "label": "match"}'
Repeat for each candidate. Labeled runs drop out, so re-list for a fresh page. When you’ve collected enough match examples, train the signal. name is required, prompt optional. The response returns the new signal_id: 
bentolabs deep-search create-classifier-signal <session-id> --data '{"name": "Payment retry storm"}'
Training needs at least one match label to learn from. A session with no positive examples returns a 400 asking you to label more.
Check where the session stands at any point: 
bentolabs deep-search get-session <session-id>
The new detector lives under signals from here. The CLI trains the detector; binding it into a firing alert rule is dashboard-only.

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See also

Signals

Where a trained detector lives. Read it, watch its events, tune it.

Trajectories

The analyzed runs Deep Search ranks and you label.

Issues

The tracked problems a signal helps surface and group.