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Quickstart

A guided first run across all three interfaces. Each example is self-contained and works fully offline. Prerequisites: see Installation.

We'll use a tiny sample log throughout:

Back-off restarting failed container app in pod web-7d9f
Last State: Terminated  Reason: Error  Exit Code: 1

Save it as app.log.

1. CLI

devops-ai analyze app.log

You'll get a ranked summary, candidate root causes with confidence, read-only diagnostic commands, suggested fixes, references, and prevention tips. To get machine-readable output:

devops-ai analyze app.log --json | jq .root_causes

Explain an error by name without any input file:

devops-ai explain CrashLoopBackOff

Validate a manifest (read-only):

devops-ai validate deploy.yaml

See the full CLI guide.

2. Python SDK

from devops_ai_toolkit import AnalysisEngine

engine = AnalysisEngine()

# From a file
result = engine.analyze_file("app.log")
print(result.summary)
print("Top confidence:", result.confidence_percent, "%")

for cause in result.root_causes:
    print(f"- [{cause.confidence_percent}%] {cause.title}")
    for cmd in result.diagnostic_commands:
        print(f"    $ {cmd.command}")

# From a raw string
text_result = engine.analyze_text("ImagePullBackOff")
print(text_result.summary)

# Explain a named error
explained = engine.explain_error("OOMKilled")
print(explained.title, "-", explained.summary)

See the full SDK guide and the Output format reference.

3. REST API

Install the extra and start the server:

pip install 'devops-ai-toolkit[api]'
devops-ai serve --port 8000

Then call it:

# Health and version
curl -s localhost:8000/health  | jq
curl -s localhost:8000/version | jq

# Analyze a log
curl -s localhost:8000/analyze/log \
  -H 'content-type: application/json' \
  -d '{"content": "Back-off restarting failed container"}' | jq .summary

# Explain an error
curl -s localhost:8000/explain \
  -H 'content-type: application/json' \
  -d '{"error": "CrashLoopBackOff"}' | jq .title

Interactive docs (Swagger UI) live at http://localhost:8000/docs. See the REST API guide.

4. Add AI enrichment (optional)

All of the above is deterministic and offline. To layer an LLM narrative on top:

export DEVOPS_AI_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-...

devops-ai analyze app.log --enrich

Or in Python:

result = engine.analyze_file("app.log", enrich=True)
if result.enrichment:
    print(result.enrichment.narrative)

If no provider is configured, enrichment is skipped gracefully and you still get the full deterministic result (with a low-severity warning noting enrichment was unavailable). See AI providers.

Next steps