OnCall Memory is a hackathon demo that gives an AI assistant access to examples of earlier incidents and their outcomes. Built with FastAPI and a single-page frontend for a fictional fintech called Northwind Pay, it compares an answer generated from a new alert and logs with one that can also draw on memories stored in Hindsight. It shows how past fixes—including failed ones—could inform troubleshooting, but it is not a validated production incident-response tool.
How the OnCall Memory demo works
The project author describes a loop that connects a new alert to prior incidents, then records what happened after the engineer acts. The application provides a comparison, not an automatic remediation system.
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- An engineer submits an alert and its logs.
- The backend generates one response without retrieved incident memories and a second after querying Hindsight with the alert text and logs.
- The interface displays both responses alongside retrieved memory chunks and the source incident, so the engineer can inspect the context behind a recommendation.
- After trying a fix, the engineer marks it as worked or failed and supplies a description. The author says this outcome is retained for use in later incidents.
- A separate “Systemic Insights” action reflects over the memory bank to identify recurring patterns.
In the author’s fictional Northwind Pay demo, the Hindsight bank named northwind-oncall was seeded with 25 realistic incidents, which reportedly became about 90 memory entries. Those are project-author figures for demo data, not measures of a deployed system. Each example reportedly includes a date, affected service, error logs, root cause, fix steps, time to resolve, and whether the fix worked.
Why retrieve past incidents instead of using a stateless assistant?
A stateless assistant sees the current prompt and whatever context is supplied with it. A memory-enabled helper can also retrieve potentially relevant examples of the team’s previous actions and outcomes, adding organization-specific context to its answer. That is useful only if the retrieved material is relevant, current, and safe to consider; memory does not make a recommendation automatically correct.
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Showing the source incident is an important part of the demo’s design. An engineer can compare the earlier symptoms, diagnosis, and outcome with the current case instead of treating the generated answer as an unexplained instruction. The assistant should support investigation, while the engineer evaluates whether the old case actually applies.
What Hindsight contributes
Hindsight is the persistent memory layer in this project; it is not the incident-management workflow itself. Its documentation describes three core operations—retain, recall, and reflect—and a hierarchy of memory types: world facts, experience facts, observations, and mental models. Hindsight’s overview describes its purpose in vendor language: “Hindsight enables AI agents to remember, learn, and reason through three core operations:” Hindsight Cloud documentation.
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Retain: store incident knowledge
Retain stores information and extracts facts, entities, and temporal details. In the demo’s framing, this is how an incident and its outcome can become available as memory for a later alert. The project author says the engineer can record whether a fix worked or failed; that makes negative outcomes potentially useful rather than preserving only apparent successes.
Recall: find relevant memories
Recall searches stored memories. Hindsight’s quickstart says its recall approach runs semantic, keyword/BM25, graph, and temporal strategies in parallel. That is the documented product approach; the project article describes querying the bank with alert text and logs, but does not specify the retrieval configuration used by the demo. Hindsight Quickstart.
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Reflect: reason across memories
Reflect reasons over retrieved memories. The demo uses a separate “Systemic Insights” action to look for patterns across the bank, rather than limiting the assistant to a single similar incident. The documentation’s retain, recall, and reflect model explains the memory layer’s role; it does not establish that any particular diagnosis or pattern is accurate.
What the demo’s examples show—and what they do not
The project author’s fictional incident data includes recurring patterns such as database connection exhaustion, Redis memory problems associated with missing TTLs, Kafka consumer rebalance loops, and failures associated with automated infrastructure changes. The author presents durable fixes such as connection proxies, TTL enforcement, and pre-deploy validation as examples of insights that might emerge from retained incident experience.
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One scenario warns against rebooting a database because, in the fictional example, rebooting an RDS instance led to an 85-minute outage. That duration is part of the author’s demo scenario, not a real incident report or an independently measured operational result. The demo also describes three walkthrough scenarios: an initial alert, the same root cause appearing under different symptoms, and a warning based on a past failed fix.
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What would need to be established before production use?
The project author identifies alert ingestion, postmortem ingestion, per-team banks, and fix tracking as possible production extensions, not shipped capabilities. Before relying on a similar system for real operations, a team would need to validate it against representative incidents and make its data handling and failure modes explicit.
- Retrieval quality and provenance: Test whether the system surfaces useful prior cases and exposes enough source detail for engineers to verify why they were retrieved.
- Freshness and correction: Decide how outdated runbooks, superseded fixes, and mistaken incident records are updated or removed.
- Human review: Keep recommendations inspectable and require operational judgment before risky actions, such as restarting a database or changing infrastructure.
- Privacy and access: Incident logs may contain secrets or sensitive operational details. Define secret handling, retention, access controls, and separation between teams before storing them in a memory bank.
- Representative evaluation: Compare responses with and without memory on real, appropriately protected incident cases. Measure relevance, correctness, unsafe recommendations, and usefulness to engineers rather than inferring benefit from benchmark results for other tasks.
The project description does not establish how its demo handles governance, secrets, retention, access control, or production security. Those are design questions for any real deployment, not proven defects in this demo.
Hindsight’s repository describes self-hosting and production options, including PostgreSQL with pgvector or Oracle AI Database, operational metrics, and webhooks. Those options do not establish the topology used by OnCall Memory. Likewise, the Hindsight repository and an ACL Anthology paper report results on memory benchmarks such as LongMemEval, but those results assess memory tasks—not incident diagnosis accuracy or the operational value of this demo. Hindsight repository; ACL Anthology.
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How to judge an incident assistant with memory
When comparing a memory-enabled helper with a stateless assistant or conventional runbooks, examine the capabilities that affect whether its advice can be trusted and maintained:
- Does it capture actions and outcomes, including failed fixes?
- Can engineers see which source incidents informed a response?
- Can maintainers correct, expire, or delete stale memories?
- Are data access and team boundaries appropriate for the operational information being stored?
- Has the system been evaluated on representative incidents, with results reported separately from general memory benchmarks?
The OnCall Memory article does not provide measured comparisons on these points. Its contribution is a concrete demo pattern: retain incident experience, retrieve it for a new alert, expose the supporting examples, and capture the outcome for possible future use.
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