Metadata-Version: 2.4
Name: autoops-ai
Version: 0.1.0
Summary: Autonomous observability and incident intelligence for any codebase
Author: AutoOps AI
License: MIT
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: anthropic>=0.40.0
Requires-Dist: boto3>=1.34.0
Requires-Dist: httpx>=0.27.0
Requires-Dist: jinja2>=3.1.0
Requires-Dist: keyring>=25.0.0
Requires-Dist: mcp>=1.0.0
Requires-Dist: openai>=1.40.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: questionary>=2.0.0
Requires-Dist: requests>=2.31.0
Requires-Dist: rich>=13.0.0
Requires-Dist: typer>=0.12.0
Provides-Extra: dev
Requires-Dist: pytest>=8.0.0; extra == "dev"
Requires-Dist: pytest-asyncio>=0.23.0; extra == "dev"
Requires-Dist: pytest-mock>=3.12.0; extra == "dev"

# AutoOps AI

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

![Architecture](architecture.png)

## Install (no repo clone required)

```bash
# 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

```bash
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

```bash
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

```bash
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
