autoops-ai 0.1.0

Autonomous observability and incident intelligence for any codebase

AutoOps AI

One command to autonomously configure Splunk observability and investigate incidents for any codebase.

Install (no repo clone required)

# macOS / Linux (recommended)
pipx install autoops-ai
# or from source
curl -fsSL https://raw.githubusercontent.com/YOUR_ORG/autoops-ai/main/scripts/install.sh | bash

# Windows PowerShell
pip install autoops-ai
# or: .\scripts\install.ps1

First Run

autoops setup

Interactive wizard lets you choose:

• OpenAI
• Claude (Anthropic)
• OpenRouter
• Azure OpenAI
• Amazon Bedrock (Claude)

Credentials are saved securely in your OS keychain (macOS Keychain / Windows Credential Locker).

Configure Any Project

cd /path/to/your/app
autoops configure --repo .

AutoOps will:

1. Detect/start Docker Desktop (macOS + Windows)
2. Pull and start Splunk Enterprise (autoops-splunk)
3. Start OpenTelemetry Collector (autoops-otel-collector)
4. Scan your repo and discover architecture
5. Generate instrumentation
6. Create Splunk dashboards, saved searches, and alerts
7. Send a synthetic telemetry test event

Command Reference

Command	Description
autoops setup	Interactive LLM provider setup
autoops configure --repo .	Full project observability setup
autoops doctor	Health check (Docker, Splunk, HEC, ports, LLM)
autoops provider list|set|test|show	Manage LLM profiles
autoops splunk start|status|stop|logs|open	Manage Splunk container
autoops telemetry start|status|test	Manage OTel Collector
autoops dashboards apply|list|open	Splunk dashboards
autoops alerts apply|list|test	Splunk alerts
autoops scan --repo .	Scan repo file tree
autoops instrument --repo .	Generate instrumentation only
autoops investigate --alert <name>	Run incident investigation
autoops watch --port 9000	Webhook listener for alerts
autoops demo start|bug-on|traffic	Local demo workflow

autoops init --repo <path> is an alias for configure.

Splunk Defaults Created

Dashboards: Service Health Overview, Database Performance, Deployment Timeline, Incident Investigation, per-service health

Saved searches: error rate, latency, p95 latency, DB latency, deployments, no-data detection

Alerts: per-service error rate, checkout error rate, 5xx spike, ingestion stopped

Sample SPL to customize:

index=main sourcetype=autoops | stats count by service, path, level
index=main sourcetype=autoops path="/checkout" | timechart avg(duration_ms) p95(duration_ms)

Prerequisites

• Python 3.11+
• Docker Desktop (AutoOps detects and opens installer if missing)
• LLM API key (chosen during autoops setup)

Demo App

autoops demo start
cd demo-app && uvicorn main:app --port 8080
autoops demo bug-on
autoops demo traffic --requests 30
autoops investigate --alert checkout_error_rate

License

MIT