Industry

HydraDB for

IT Operations AI

Modern IT infrastructure spans thousands of microservices, but most AI agents forget what happened across incidents the moment a session ends. HydraDB gives your SRE copilots and DevOps agents persistent, structured memory across system logs, metrics, deployment histories, and incident tickets. They can perform root cause analysis without hallucinating from outdated runbooks or missing cross-system relationships.

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90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why IT operations AI keeps failing

Platform teams building automated incident response hit the same walls: agents that apply deprecated configurations, can't trace relationships across observability silos, and lose diagnostic context during multi-hour debugging sessions.

Deprecated configurations resurface as truth

Standard vector databases treat all chunks equally. Without temporal awareness, agents retrieve outdated config guides alongside current constraints and confidently apply deprecated settings to production.

Deprecated configurations resurface as truth

Deprecated configurations resurface as truth

Cross-system relationships stay invisible

Incidents rarely have single causes. Because logs live in Datadog, tickets in Jira, and deployments in ArgoCD, agents can't connect the dots across these tools without custom correlation logic.

Cross-system relationships stay invisible

Incidents rarely have single causes. Because logs live in Datadog, tickets in Jira, and deployments in ArgoCD, agents can't connect the dots across these tools without custom correlation logic.

Diagnostic context evaporates mid-incident

Real outages span hours. Stuffing entire log streams into context windows hits token limits fast, causing "lost in the middle" behavior where critical early symptoms disappear before root cause identification.

Diagnostic context evaporates mid-incident

Infrastructure drift breaks recall

Systems evolve daily. Rebuilding vector indices for every configuration change is expensive, leading teams to accept stale context. As a result, agents debug based on infrastructure states that no longer exist.

Infrastructure drift breaks recall

// See It In Action //

An agent that actually remembers

HydraDB structures your observability data, runbooks, and incident history into a temporal graph. When an alert fires, it retrieves the exact architectural context, recent changes, and cross-system relationships without token-heavy prompt stuffing.

Live support session — HydraDB context active

Retrieval : 94ms

Critical: Database connection timeouts on payment-service. 50+ failures in 2 minutes.

PagerDuty

Production incident

Critical alert

HydraDB recalled:

Deployment: payment-service upgraded to v2.3 on Nov 12 at 14:30 UTC

Config change: Connection pool increased from 100 → 500 on Nov 12

Relationship: payment-service → depends on → billing-db (primary)

Incident history: Similar timeout pattern occurred Oct 28 after billing-db failover

The payment-service deployed v2.3 four hours ago, which increased connection pool limits to 500. However, billing-db (the primary dependency) hasn't been configured to handle this increased load. This matches the Oct 28 incident pattern after a failover. Immediate fix: Scale billing-db connection limit to 100 or roll back payment-service to v2.2.

// How Hydra Helps //

Context Infrastructure for DevOps AI

HydraDB replaces brittle RAG pipelines with a graph-native memory layer designed for dynamic, multi-vendor environments.

Persistent incident memory

Every log query, hypothesis tested, and mitigation attempted becomes structured memory. Your agent maintains context across shifts, handoffs, and follow-up investigations, never restarting diagnostic workflows from scratch.

Persistent incident memory

Persistent incident memory

Relationship-aware retrieval

HydraDB maps infrastructure dependencies natively. It connects Kubernetes events to commits and traces deployment cascades through your service mesh, turning fragmented signals into queryable context.

Relationship-aware retrieval

Temporal context

System states are versioned, not overwritten. HydraDB tracks what your infrastructure looked like before, during, and after an incident. Your agent knows which configs are current versus deprecated, achieving 90.97% accuracy on temporal reasoning tasks.

Temporal context

Structured data ingestion

HydraDB ingests records from your existing observability stack. Pull metrics from Prometheus, logs from Elasticsearch, and tickets from Jira. Map them to HydraDB's schema, and it automatically resolves entities and links related components.

Structured data ingestion

// Get Started //

Three steps to production-ready memory

HydraDB works with any LLM or framework. Replace token-heavy context stuffing with precise, structured retrieval.

Connect your observability stack

Pull alerts, metrics, and tickets from your existing tools using their native APIs.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Use your platform APIs to retrieve incident data
alert = pagerduty.get_incident("INC-4821")
metrics = datadog.get_metrics("service:payment", last="1h")
ticket = jira.get_issue("OPS-1923")

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Use your platform APIs to retrieve incident data
alert = pagerduty.get_incident("INC-4821")
metrics = datadog.get_metrics("service:payment", last="1h")
ticket = jira.get_issue("OPS-1923")

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Use your platform APIs to retrieve incident data
alert = pagerduty.get_incident("INC-4821")
metrics = datadog.get_metrics("service:payment", last="1h")
ticket = jira.get_issue("OPS-1923")

Connect your observability stack

Pull alerts, metrics, and tickets from your existing tools using their native APIs.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Use your platform APIs to retrieve incident data
alert = pagerduty.get_incident("INC-4821")
metrics = datadog.get_metrics("service:payment", last="1h")
ticket = jira.get_issue("OPS-1923")

Connect your observability stack

Pull alerts, metrics, and tickets from your existing tools using their native APIs.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Use your platform APIs to retrieve incident data
alert = pagerduty.get_incident("INC-4821")
metrics = datadog.get_metrics("service:payment", last="1h")
ticket = jira.get_issue("OPS-1923")

Ingest and structure operational context

Map your data to HydraDB's typed schema for automatic entity resolution and relationship linking.

import json

# Structure and ingest the incident context
client.context.ingest(
    type="knowledge",
    tenant_id="platform_team",
    sub_tenant_id="production",
    app_knowledge=json.dumps([
        {
            "id": f"alert_{alert.id}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": alert.title,
            "type": "incident",
            "content": {"text": alert.description},
            "metadata": {
                "service": "payment-service",
                "severity": alert.urgency,
                "timestamp": alert.created_at
            }
        },
        {
            "id": f"config_{ticket.key}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": "Connection pool configuration change",
            "type": "config_change",
            "content": {"text": ticket.description},
            "metadata": {
                "component": "billing-db",
                "change_date": "2024-11-12",
                "old_value": "100",
                "new_value": "500"
            }
        }
    ])
)

import json

# Structure and ingest the incident context
client.context.ingest(
    type="knowledge",
    tenant_id="platform_team",
    sub_tenant_id="production",
    app_knowledge=json.dumps([
        {
            "id": f"alert_{alert.id}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": alert.title,
            "type": "incident",
            "content": {"text": alert.description},
            "metadata": {
                "service": "payment-service",
                "severity": alert.urgency,
                "timestamp": alert.created_at
            }
        },
        {
            "id": f"config_{ticket.key}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": "Connection pool configuration change",
            "type": "config_change",
            "content": {"text": ticket.description},
            "metadata": {
                "component": "billing-db",
                "change_date": "2024-11-12",
                "old_value": "100",
                "new_value": "500"
            }
        }
    ])
)

import json

# Structure and ingest the incident context
client.context.ingest(
    type="knowledge",
    tenant_id="platform_team",
    sub_tenant_id="production",
    app_knowledge=json.dumps([
        {
            "id": f"alert_{alert.id}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": alert.title,
            "type": "incident",
            "content": {"text": alert.description},
            "metadata": {
                "service": "payment-service",
                "severity": alert.urgency,
                "timestamp": alert.created_at
            }
        },
        {
            "id": f"config_{ticket.key}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": "Connection pool configuration change",
            "type": "config_change",
            "content": {"text": ticket.description},
            "metadata": {
                "component": "billing-db",
                "change_date": "2024-11-12",
                "old_value": "100",
                "new_value": "500"
            }
        }
    ])
)

Ingest and structure operational context

Map your data to HydraDB's typed schema for automatic entity resolution and relationship linking.

import json

# Structure and ingest the incident context
client.context.ingest(
    type="knowledge",
    tenant_id="platform_team",
    sub_tenant_id="production",
    app_knowledge=json.dumps([
        {
            "id": f"alert_{alert.id}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": alert.title,
            "type": "incident",
            "content": {"text": alert.description},
            "metadata": {
                "service": "payment-service",
                "severity": alert.urgency,
                "timestamp": alert.created_at
            }
        },
        {
            "id": f"config_{ticket.key}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": "Connection pool configuration change",
            "type": "config_change",
            "content": {"text": ticket.description},
            "metadata": {
                "component": "billing-db",
                "change_date": "2024-11-12",
                "old_value": "100",
                "new_value": "500"
            }
        }
    ])
)

Ingest and structure operational context

Map your data to HydraDB's typed schema for automatic entity resolution and relationship linking.

import json

# Structure and ingest the incident context
client.context.ingest(
    type="knowledge",
    tenant_id="platform_team",
    sub_tenant_id="production",
    app_knowledge=json.dumps([
        {
            "id": f"alert_{alert.id}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": alert.title,
            "type": "incident",
            "content": {"text": alert.description},
            "metadata": {
                "service": "payment-service",
                "severity": alert.urgency,
                "timestamp": alert.created_at
            }
        },
        {
            "id": f"config_{ticket.key}",
            "tenant_id": "platform_team",
            "sub_tenant_id": "production",
            "title": "Connection pool configuration change",
            "type": "config_change",
            "content": {"text": ticket.description},
            "metadata": {
                "component": "billing-db",
                "change_date": "2024-11-12",
                "old_value": "100",
                "new_value": "500"
            }
        }
    ])
)

Retrieve precise context before diagnosis

Query HydraDB for temporal context and cross-system relationships before your LLM responds.

# Get structured incident context
context = client.query(
    tenant_id="platform_team",
    sub_tenant_id="production",
    query="What recent changes could cause database connection timeouts in payment-service?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Generate root cause analysis with full context
analysis = llm.generate(
    system_prompt=build_diagnostic_prompt(context.data),
    user_query="Identify root cause and recommend immediate mitigation"
)

# Get structured incident context
context = client.query(
    tenant_id="platform_team",
    sub_tenant_id="production",
    query="What recent changes could cause database connection timeouts in payment-service?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Generate root cause analysis with full context
analysis = llm.generate(
    system_prompt=build_diagnostic_prompt(context.data),
    user_query="Identify root cause and recommend immediate mitigation"
)

# Get structured incident context
context = client.query(
    tenant_id="platform_team",
    sub_tenant_id="production",
    query="What recent changes could cause database connection timeouts in payment-service?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Generate root cause analysis with full context
analysis = llm.generate(
    system_prompt=build_diagnostic_prompt(context.data),
    user_query="Identify root cause and recommend immediate mitigation"
)

Retrieve precise context before diagnosis

Query HydraDB for temporal context and cross-system relationships before your LLM responds.

# Get structured incident context
context = client.query(
    tenant_id="platform_team",
    sub_tenant_id="production",
    query="What recent changes could cause database connection timeouts in payment-service?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Generate root cause analysis with full context
analysis = llm.generate(
    system_prompt=build_diagnostic_prompt(context.data),
    user_query="Identify root cause and recommend immediate mitigation"
)

Retrieve precise context before diagnosis

Query HydraDB for temporal context and cross-system relationships before your LLM responds.

# Get structured incident context
context = client.query(
    tenant_id="platform_team",
    sub_tenant_id="production",
    query="What recent changes could cause database connection timeouts in payment-service?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Generate root cause analysis with full context
analysis = llm.generate(
    system_prompt=build_diagnostic_prompt(context.data),
    user_query="Identify root cause and recommend immediate mitigation"
)

// Why HydraDB //

HydraDB vs. Standard Vector Search for IT Operations

HydraDB gives IT operations AI persistent diagnostic history, relationship-aware retrieval, temporal context, and structured ingestion where standard vector search treats every query as isolated semantic lookup.

Features

HydraDB

Standard Vector Search

Cross-session memory

Maintains diagnostic history across incidents and team handoffs

Treats every query as isolated, losing prior investigation context

Relationship-aware retrieval

Links commits, deployments, metrics, and tickets into unified context

Returns similar text chunks without understanding system dependencies

Temporal context

Tracks config evolution with versioned graph (97.4% accuracy)

Mixes current and deprecated documentation randomly

Infrastructure state tracking

Understands migrations, rollbacks, and dependency changes over time

Requires complex metadata filtering for any state awareness

Integration method

Structured record ingestion with automatic entity resolution

Manual chunking and embedding of disparate data sources

Best fit

Automated root cause analysis and persistent debugging

Semantic search over static documentation

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Infrastructure state tracking

Integration method

Best fit

HydraDB

Maintains diagnostic history across incidents and team handoffs

Links commits, deployments, metrics, and tickets into unified context

Tracks config evolution with versioned graph (97.4% accuracy)

Understands migrations, rollbacks, and dependency changes over time

Structured record ingestion with automatic entity resolution

Automated root cause analysis and persistent debugging

Standard Vector Search

Treats every query as isolated, losing prior investigation context

Returns similar text chunks without understanding system dependencies

Mixes current and deprecated documentation randomly

Requires complex metadata filtering for any state awareness

Manual chunking and embedding of disparate data sources

Semantic search over static documentation

// What Teams Are Saying //

Trusted by teams building DevOps AI

HydraDB cut our MTTR by 40%. Our incident response agents now understand the full deployment history and dependency graph. No more guessing which config change three weeks ago is suddenly causing today's outage.

Alex Chen

Principal SRE, Enterprise SaaS Platform

We tried building memory for our DevOps copilot with vector search alone. Total failure. HydraDB's temporal graph finally gave our agents the ability to trace a production issue back through multiple system changes without losing context.