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Start with the memory bank as the unit of scope
Hindsight organizes data into isolated memory banks. According to the Hindsight developer documentation on memory banks, a bank holds memories, documents, entities, relationships, and directives. Memories come in three types: world facts, experiences, and derived observations. Bank configuration controls entity labels and observation consolidation.
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For an incident dashboard, treat the bank as a boundary and an operational scope. It is not a generic event-stream partition. Every panel should state which bank it describes and over what time window, because a healthy aggregate can hide one bank whose consolidation has stopped.
Map each operator question to a signal
An incident view should answer four questions. The table below pairs each question with the documented source that can answer it and the limit that a reader should keep in mind.
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| Operator question | Signal to show | Where it is documented | What it cannot tell you |
|---|---|---|---|
| Can the service serve traffic? | Readiness and liveness, shown as separate indicators | Hindsight HTTP API reference, health checks | Readiness does not say whether ingestion is current or consolidation is keeping up. |
| Is ingestion or consolidation backing up or failing? | Pending and failed operations, pending and failed consolidation, last memory write, last consolidation, and the ingestion time series | Hindsight HTTP API reference, bank statistics and time series endpoints | Counts show volume, not cause. The documentation does not publish universal alert thresholds. |
| Which retrieval path returned the relevant memories? | Recall inputs and outputs for the affected bank, plus trace or debugging detail where the deployment exposes it | Hindsight developer documentation on recall, and the Recall debugging view | A single score or result list does not explain every outcome. |
| Did an event alert arrive late or more than once? | Webhook event timeline with operation IDs, timestamps, and delivery history | Hindsight developer documentation on webhooks, and the delivery-history endpoint in the API reference | Delivery is at least once, so duplicates are expected behavior rather than proof of duplicated work. |
Service health: keep readiness and liveness apart
Readiness answers whether traffic can be served
The API reference describes readiness as a check that verifies database reachability and reports whether the API can serve traffic. Use it for routing and load-balancer decisions.
Liveness answers whether the process is stuck
Liveness checks whether the process can serve a request without touching the database. Use it to detect a process that is no longer responding at all.
Keeping the two separate matters during a database incident. A database outage should fail readiness and drop the instance from rotation, while liveness stays green. If you wire database connectivity failures into liveness-driven restarts, you will restart processes that are healthy and still cannot reach the database, which adds churn without fixing the cause. Show the API and worker health as separate indicators as well, so an operator can tell which component is failing.
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Memory-bank work state: volume, status, and freshness
The bank statistics API exposes the values an operator needs to judge progress. Display them together rather than one at a time:
- Node and link counts, documents, and fact-type and link-type breakdowns, which describe volume and shape.
- Pending and failed operations, and pending and failed consolidation, which describe status.
- Total observations, which describe the consolidated output so far.
- Timestamps for the last memory write and the last consolidation, which describe freshness.
Read the values in combination. A growing pending-consolidation count while the last memory write keeps advancing and the last consolidation timestamp stays old suggests that consolidation is falling behind incoming work. A zero failed count does not prove health on its own, especially if the timestamps are old. Add the memory-ingestion time series beside these panels so you can see whether incoming work has stopped, is delayed, or is moving through while consolidation lags behind.
Set alert thresholds from your own normal workload and service objectives. The documentation supplies the fields, not the cut-off values.
Retrieval diagnostics: reproduce the recall before explaining it
When an answer is missing, unexpected, or stale, you need enough context to reproduce the recall. Capture the following for each investigated query:
- Bank scope.
- Query text, or a redacted representation if the text is sensitive.
- The query-time anchor, where relevant. The recall API accepts a query timestamp, which matters when a question is about what was true at a given moment.
- Requested fact types.
- Returned memories and their associated entities.
- Source facts and chunks, when you request them.
Recall combines semantic similarity, keyword (BM25) matching, graph traversal, and temporal retrieval. Those paths fail differently. A miss caused by missing terms or weak entity connections looks different from one caused by a misread time expression or a semantic mismatch. The Recall debugging view in Hindsight’s documentation is designed for testing retrieval approaches and inspecting traces, so use it to see which path contributed. If your deployment does not expose traces, record what the query returned and say that the path is unknown rather than guessing.
Operations dashboards are often viewed by many people, and memory content can be private. Decide who may open the retrieval panel and what gets redacted before you add it. The documentation does not establish an access or redaction policy, so that policy is yours to define.
Event timeline: webhooks, retries, and duplicates
Webhooks can report memory events, including consolidation completion with a status and counts of observations created or updated. Delivery is at least once, and failed deliveries are retried. Two consequences follow for the timeline:
- Deduplicate events on the operation identifier before counting them. A repeated delivery of the same event should appear once in the timeline, marked as a redelivery if your receiver records delivery attempts.
- Compare each event’s timestamp with the operation timestamps from the bank statistics. A late alert may be a retry, not a late operation. A missing event may mean a failed delivery that has not yet been retried, so check the delivery-history endpoint before concluding that the operation never completed.
Store the event’s operation ID, event time, receipt time, status, and consolidation outcome. Without the receipt time, you cannot tell a slow pipeline from a slow delivery.
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- Service row: API and worker health as separate indicators, with readiness and liveness shown independently.
- Work row: operation counts and status, the ingestion time series, pending and failed consolidation, last memory write, and last consolidation.
- Retrieval row: the exact bank, query, and time context for a selected recall, with whatever retrieval-path or trace information the deployment exposes.
- Event row: a deduplicated webhook timeline showing status, event time, operation ID, retry or delivery state, and consolidation outcome.
- Scope and freshness: the bank and time window for each panel, with stale telemetry marked as stale instead of shown as current.
This layout is a design recommendation built on the documented API and event behavior. It is not a description of a feature Hindsight ships.
Cardinality and scale
The monitoring guide warns that adding bank or tenant identifiers to metrics is only appropriate when a deployment has few banks or tenants, because high cardinality can cause unbounded memory growth in the monitoring backend. Keep aggregate metrics as the default. Enable high-cardinality labels only when the number of banks is bounded and your metrics backend can carry the series.
If you need per-bank views in a large deployment, query bank statistics through the API for those panels instead of multiplying every metric series by a bank identifier. This is an implementation inference from the warning, not an architecture the vendor prescribes.
Monitoring stack and what to compare
The monitoring guide includes prebuilt Grafana dashboard JSON for Hindsight operations, LLM metrics, and API-service monitoring. Treat these files as a starting point to import and adapt. The local monitoring stack is described as development-only. For production, use a monitoring system you deploy separately or a hosted service. The guide names Grafana Cloud, Datadog, and New Relic as options. It does not compare their performance or pricing, so choose among them on your own evidence.
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- Deployment model: whether you run the monitoring stack yourself or use a hosted platform.
- Signal coverage: health endpoints, bank statistics, ingestion series, traces, and webhook delivery state. Confirm which of these your chosen platform can ingest or display.
- Diagnostic depth: aggregate metrics against bank-level and API-level detail.
- Cardinality tolerance: how many series your bank count can produce before per-bank labels become a problem.
- Custom work: how much of the layout above you must build, versus what the prebuilt Grafana files already cover.
The API reference page used for this article is labeled version 0.10.2. Endpoint names and behavior can change between releases, so check the current Hindsight documentation before you copy specific paths or fields into production configuration.
Scope points to the sources you should check: the Hindsight developer documentation on memory banks, recall, monitoring, and webhooks, and the Hindsight HTTP API reference for health checks, bank statistics, time series, and delivery history.
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