Spots 
Picture the 3 a.m. version of this. An alert fires, you open your incident response tool, and the assistant says: "This looks like INC-214: connection pool exhaustion, fixed by raising max_connections." You go looking for INC-214 in your issue tracker. It doesn't exist.
That failure mode is why I built MemoryOps. To be clear, INC-214 is a hypothetical example of an LLM hallucination, not a captured model response: seed data in this repository spans INC-101 through INC-116, so any citation of INC-214 is invented. During an outage, a fabricated citation is worse than a generic answer because a citation reads like verified evidence. You either burn minutes verifying it, or you trust it and apply a fix that was never tested.
I reduced this risk by building persistent operational memory into the incident response lifecycle. Here is how the architecture and implementation work.
MemoryOps is an AI-powered incident response platform for DevOps and SRE teams. The frontend is built with React 19, Vite, and Tailwind CSS (providing Dashboard, Incident Creation, Investigation, and Memory Explorer views). The backend is FastAPI with SQLAlchemy over SQLite (data/incidentiq.db) as the system of record for live incident records.
Hindsight acts as the long-term persistent memory layer for resolved incident learnings, while Groq Cloud LLM (openai/gpt-oss-20b) serves as the AI reasoning engine. Nothing executes actions autonomously; the human engineer remains in full control.
MemoryOps system architecture — React frontend, FastAPI backend, SQLite database of record, Hindsight persistent memory layer, and Groq LLM reasoning engine.
The workflow begins when an engineer declares an incident via POST /api/v1/incidents. Navigating to the investigation page invokes POST /api/v1/incidents/{incident_id}/analyze. The backend recalls relevant past incidents from Hindsight, passes them alongside current symptoms to Groq LLM, and presents evidence-backed recommendations. When the incident is resolved via POST /api/v1/incidents/{incident_id}/resolve, its incident learnings are retained in Hindsight. 2. Hindsight Memory Lifecycle: RETAIN, RECALL, and REFLECT
Instead of passing massive unstructured log streams to an LLM, MemoryOps uses Hindsight to store structured experience documents. SQLite answers "what is happening now," while Hindsight answers "what did we learn from past outages." RETAIN Runs Only on Verified Resolution
An incident is not retained when merely created, during unresolved investigation, or from AI guesses. When an engineer resolves an incident, HindsightService.aretain_incident() stores a complete experience document containing ID, service, error, symptoms, severity, root cause, resolution steps, and post-mortem.
To support idempotent retention, document_id is set deterministically to incident.id (for example, INC-101). The Incident database model tracks a memory_retained boolean flag, which is flipped to True only after Hindsight confirms successful retention. RECALL Runs During Incident Investigation When an investigation is triggered, MemoryOps constructs a semantic search query from the current incident: The router parses returned memories using parse_memory_item() and supplies them as grounded context to Groq. REFLECT Synthesizes Patterns
Picture the 3 a.m.