How I Built RecallOps — An AI Agent That Never Forgets a Server Incident

#python #ai #machinelearning #devops

How I Built RecallOps — An AI Agent That Never Forgets a Server Incident

Picture this: It's 2AM. Your production server is down. Users are screaming.
And your engineer is frantically searching through old Slack messages trying
to remember what fixed this exact same issue three weeks ago.

That's the problem I set out to solve with RecallOps.

What is RecallOps?

RecallOps is an AI-powered DevOps incident response agent that remembers
every past incident and its resolution. When a similar problem happens again,
it instantly recalls what worked before and suggests a fix — in seconds.

The secret weapon? Hindsight — an agent memory system by Vectorize that
lets AI agents remember, recall, and learn from past interactions.

The Problem with Traditional Incident Response

Most engineering teams handle incidents the same way:

Engineer gets paged at 2AM
Spends 30-60 minutes debugging from scratch
Fixes the issue
Writes a post-mortem nobody reads
Same issue happens 3 weeks later — repeat

Static runbooks get outdated. Wikis are never updated. Slack messages get
buried. The institutional knowledge lives in people's heads and disappears
when they leave.

RecallOps fixes this by building a living, learning knowledge base
automatically.

How It Works

The architecture is surprisingly simple:
Engineer reports incident
↓
RecallOps searches Hindsight memory for similar past incidents
↓
Groq LLM analyzes + generates solution using past context
↓
Agent suggests root cause, fix, and prevention steps
↓
Resolution saved back to memory
↓
Agent gets smarter with every incident!

The Tech Stack

Hindsight — Agent memory (retain & recall)
Groq + LLama 3.3 — Fast LLM inference
Streamlit — Simple chat UI
Python + Requests — Backend logic

Building the Memory Layer

The core of RecallOps is how it uses Hindsight memory. Here's the retain function:

def remember_incident(incident, resolution):
    response = requests.post(
        f"{HINDSIGHT_BASE_URL}/banks/{BANK_ID}/memories",
        headers=HEADERS,
        json={
            "items": [
                {
                    "content": f"Incident: {incident}\nResolution: {resolution}",
                    "context": "devops incident"
                }
            ]
        }
    )

When an incident is saved, Hindsight doesn't just store the raw text. It:

Extracts structured facts from the content
Identifies entities (PostgreSQL, Nginx, Redis etc.)
Builds a knowledge graph linking related incidents
Creates embeddings for semantic search

And the recall function:

def recall_similar(incident):
    response = requests.post(
        f"{HINDSIGHT_BASE_URL}/banks/{BANK_ID}/memories/recall",
        headers=HEADERS,
        json={
            "query": incident,
            "budget": "low"
        }
    )

The Before vs After

Without RecallOps:

Engineer gets a 502 Bad Gateway alert. Spends 45 minutes checking
configs, reading logs, googling solutions.

With RecallOps:

Engineer types the incident. RecallOps instantly recalls: "Last time
this happened, Nginx upstream was down. Run: systemctl restart gunicorn".
Fixed in 2 minutes.

What I Learned

1. Memory is what separates useful AI from toy AI.
A chatbot that starts from scratch every time is useless for operational work.
Persistent memory changes everything.

2. Simple beats complex.
RecallOps does one thing brilliantly — remember and recall incidents. That
focus made the demo immediately understandable to anyone.

3. The value compounds over time.
Interaction 1: generic response. Interaction 10: personalized.
Interaction 100: feels like it truly knows your infrastructure.