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OpsOrch

Unified API for Incidents, Logs, Metrics, and Tickets

Details

External ID
46196926
Source
HN
Company
—
Product
OpsOrch
Website domain
opsorch.com
Launched
Dec. 8, 2025
Cohort
—
Upvotes
6
Upvotes percentile
0.2652671755725191
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

I built OpsOrch, an open-source orchestration layer that gives you one unified API for incidents, logs, metrics, tickets, messaging, and service metadata. It sits on top of the tools you already use (PagerDuty, Jira, Elasticsearch, Prometheus, Slack, etc.) and normalizes everything into a single schema.OpsOrch doesn’t store your operational data. It just brokers requests through pluggable adapters (Go or JSON-RPC) and returns unified structures. On top of this, there’s an optional MCP server that exposes all capabilities as typed tools for LLM agents.Why? Most incident workflows today require jumping across 5+ vendor UIs and APIs, each with its own query language and auth model. OpsOrch aims to be the small, transparent “glue layer” that removes that complexity without forcing a migration.What’s available now:Core orchestration service (Go, Apache-2.0)Adapters: PagerDuty, Jira, Prometheus, Elasticsearch, Slack, plus mock providersMCP server exposing incidents/logs/metrics/tickets/services as agent toolsNo vendor lock-in, no data gravityRepos: https://github.com/OpsOrch/opsorch-corehttps://github.com/OpsOrch/opsorch-mcp(+ adapters in the OpsOrch org)Would love feedback on architecture, adapter model, security concerns, and which integrations you’d want next.

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Observability & eval
Audience
B2B
AI stance
Not AI
Project type
Commercial product
Normalized one-liner
incident management and monitoring platform
Manually corrected
False

Could you build this?

Partial The front-end dashboard and workflow triggers are vibe-codeable, but creating a unified schema and resilient connector architecture across dozens of third-party monitoring, incident, and ticketing systems is complex engineering.

What it would actually take: The platform requires a Go or Node.js backend with an extensible adapter pattern normalizing data models across PagerDuty, Jira, Prometheus, Slack, and Elasticsearch. The hard parts are maintaining bi-directional syncing, rate limits, webhooks, and state consistency across heterogeneous third-party APIs while handling enterprise auth and RBAC.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 45 launches clear the similarity bar, closest 8 shown.

Attention rank: #37 of 46 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 37 days after the earliest competitor.

Other launches for this product

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