Releases·Tool Release
OpsMind SRE Agent Uses Vectorize Hindsight Memory with Groq to Cut Incident Triage Time
An engineer describes OpsMind, an open-source SRE incident-triage agent pairing Vectorize Hindsight persistent memory with Groq low-latency inference on open-weight models. A four-stage loop recalls past post-mortems to turn generic LLM troubleshooting into incident-specific fixes. Benchmarks on PostgreSQL connection exhaustion show the memory-augmented agent recalling a matching prior outage and pinpointing a PgBouncer misconfiguration that a stateless model missed.
2 engagement1 sourceMon, Sep 28, 2026, 05:55 PM
AI Overview
- OpsMind pairs Vectorize Hindsight persistent memory with Groq low-latency inference on open-weight models, replacing runbook-stuffing with a 4-stage recall loop (/recall, /retain)
- Memory-augmented agent diagnosed recurring PostgreSQL connection exhaustion in seconds, spotting a Helm deployment had set PgBouncer to pool_mode = session instead of transaction
- Stateless baseline for the same incident recommended generic scaling or restarts, wasting 30-45 minutes without institutional context
- Authors warn raw-log vector search produces false positives from shared boilerplate; isolating panic line, service name, and exit code improves recall precision
- Stale long-term memory requires TTL or deprecation flags to avoid outdated remediation advice as infrastructure topology changes
- Implementation is open-sourced at github.com/Divyesh1510/opsmind-sre-agent with a full write-up on Medium
Related
Products·building
OpsMind — incident response agent that learns from past postmortems
OpsMind is an LLM-backed incident response agent for ops teams. It uses hindsight from prior incidents and postmortems so repeated pages get the same fix applied without re-diagnosis. It targets the problem of different engineers rediscovering identical fixes during on-call pages.
OpsMind is an LLM-backed incident response agent for ops teams. It uses hindsight from prior incidents and postmortems so repeated pages get the same fix applied without re-diagnosis. It targets the problem of different engineers rediscovering identical fixes during on-call pages.
2 engagement2 sourcesredditTue, Sep 29, 2026, 06:08 AM
Products·building
IncidentIQ: LLM incident troubleshooting agent with filtered memory of past incidents
A Python/Streamlit incident response prototype that recalls past incident fixes from a Hindsight memory layer, filters retrieved memories, and uses Groq LLMs to generate troubleshooting guidance. It targets on-call engineers and SRE teams, solving the problem that generic LLM responses ignore organizational history of what was tried, what failed, and what actually worked.
A Python/Streamlit incident response prototype that recalls past incident fixes from a Hindsight memory layer, filters retrieved memories, and uses Groq LLMs to generate troubleshooting guidance. It targets on-call engineers and SRE teams, solving the problem that generic LLM responses ignore organizational history of what was tried, what failed, and what actually worked.
2204 engagement4 sourcesredditMon, Sep 28, 2026, 04:23 PM
Products·building
Incident Memory Agent: AI incident investigator with vs without historical postmortem memory
An open-source project that benchmarks AI incident investigation with and without access to verified past postmortems. It uses an LLM (via Groq) to run two live investigations side by side on a sample database outage, showing how historical memory affects diagnosis quality. Aimed at DevOps teams evaluating context/memory for AI-driven troubleshooting.
An open-source project that benchmarks AI incident investigation with and without access to verified past postmortems. It uses an LLM (via Groq) to run two live investigations side by side on a sample database outage, showing how historical memory affects diagnosis quality. Aimed at DevOps teams evaluating context/memory for AI-driven troubleshooting.
1 engagement1 sourceredditTue, Sep 29, 2026, 03:55 PM
Products·launched
ResolveIQ: incident-response agent with persistent memory for diagnosing production incidents
ResolveIQ is an experiment testing whether persistent memory improves an LLM incident-response agent's diagnosis of production incidents. The agent recalls previous incidents via the Hindsight memory layer, uses them as evidence while investigating a new incident, and retains useful experience afterward. It targets on-call engineers and SRE teams evaluating memory-augmented agents.
ResolveIQ is an experiment testing whether persistent memory improves an LLM incident-response agent's diagnosis of production incidents. The agent recalls previous incidents via the Hindsight memory layer, uses them as evidence while investigating a new incident, and retains useful experience afterward. It targets on-call engineers and SRE teams evaluating memory-augmented agents.
3 engagement2 sourcesredditMon, Sep 28, 2026, 12:59 PM
Products·building
Incident response agent with operational memory via Hindsight
Hindsight is an incident-response agent that uses a memory layer to retain and apply knowledge from past production incidents when diagnosing new ones. It helps incident responders by recalling previous root causes, effective fixes, failed approaches, and operational observations, improving diagnosis and resolution.
Hindsight is an incident-response agent that uses a memory layer to retain and apply knowledge from past production incidents when diagnosing new ones. It helps incident responders by recalling previous root causes, effective fixes, failed approaches, and operational observations, improving diagnosis and resolution.
1 engagement1 sourceredditWed, Aug 12, 2026, 05:22 PM