Interactive Runbook Tools Compared: Finding the Right Fit

The search for the perfect interactive runbook tool is a journey every DevOps team takes. You want documentation that’s both readable and executable—but the options all have tradeoffs.

This comparison helps you evaluate interactive runbook tools for your context. For runbook writing fundamentals, see how to write a runbook.

What Makes a Runbook “Interactive”?

Interactive runbooks go beyond static documentation:

• Executable: Run commands directly from the document
• Stateful: Pass output from one step to the next
• Readable: Understandable without special tools
• Shareable: Work across your team without complex setup

The Contenders

Jupyter Notebooks

Originally built for data science, Jupyter has been adopted for operational tasks.

How it works: Cells contain code or markdown. Execute cells individually, see output inline.

# Jupyter cell example
import subprocess
result = subprocess.run(['kubectl', 'get', 'pods'], capture_output=True, text=True)
print(result.stdout)

Strengths:

• Rich output (tables, charts, images)
• Strong Python ecosystem
• Good for data-heavy operations
• GitHub renders notebooks

Weaknesses:

• JSON format (not human-readable raw)
• Python-centric (shell requires workarounds)
• Heavy runtime requirements
• Merge conflicts are painful

Best for: Data engineering teams, Python-heavy workflows

VS Code Notebooks

Microsoft’s take on the notebook format with pluggable kernels.

How it works: Similar to Jupyter but integrated into VS Code. Requires specific extensions for different languages.

Strengths:

• Familiar VS Code environment
• Extensible kernel system
• Good debugging integration
• TypeScript/JavaScript friendly

Weaknesses:

• Requires VS Code (not portable)
• JSON format (not human-readable)
• Extension ecosystem fragmented
• Stability issues with Insiders builds

Best for: Teams already deep in VS Code ecosystem

Emacs Org-Mode

The original literate programming environment.

How it works: Plain text format with code blocks. Execute blocks with keyboard shortcuts, results appear inline.

#+BEGIN_SRC shell
kubectl get pods
#+END_SRC

#+RESULTS:
: NAME                   READY   STATUS    RESTARTS   AGE
: api-7d4f8b6c9-x2k4j   1/1     Running   0          2d

Strengths:

• Plain text (fully readable without tools)
• Polyglot (supports many languages)
• Mature and stable (decades of development)
• Powerful chaining between blocks
• Works offline

Weaknesses:

• Steep learning curve (Emacs)
• Not widely adopted
• Team adoption challenges
• No web interface

Best for: Individual power users, Emacs enthusiasts

Runme

A VS Code extension that makes markdown executable.

How it works: Recognizes code blocks in markdown files and adds run buttons.

Strengths:

• Markdown-based (human-readable)
• Works with existing markdown files
• VS Code integration
• Low barrier to start

Weaknesses:

• Requires VS Code
• Limited state passing between blocks
• Relatively new project

Best for: Teams wanting to enhance existing markdown docs

Comparison Matrix

Criteria	Jupyter	VS Code Notebooks	Org-Mode	Runme
Format	JSON	JSON	Plain text	Markdown
Human readable	❌	❌	✅	✅
Git-friendly	⚠️	⚠️	✅	✅
Setup complexity	Medium	Medium	High	Low
Team adoption	Medium	Medium	Hard	Easy
Shell support	Workaround	Plugin	Native	Native
State passing	✅	✅	✅	Limited
Offline support	✅	✅	✅	✅
Web interface	✅ (JupyterHub)	❌	❌	❌
Learning curve	Medium	Low	High	Low

Key Selection Criteria

1. Readability as Plain Text

Can you read and execute the runbook without special software?

This matters because:

• During incidents, you might not have your usual tools
• New team members need quick access
• Static documentation should work as fallback

Winners: Org-mode, Markdown-based tools

2. Ease of Installation

How much setup is required to use runbooks?

Tool	Setup Required
Jupyter	Python, pip, jupyter, kernels
VS Code Notebooks	VS Code, extensions per language
Org-Mode	Emacs, configuration
Markdown-based	Minimal (often just a viewer)

3. Team Adoption Friction

Will your whole team actually use it?

Consider:

• Existing tool familiarity
• Cross-platform support
• Documentation culture
• Training requirements

4. Technology Support

Can it run your stack’s commands?

Tool	Shell	Python	REST	Other
Jupyter	Via subprocess	Native	Via requests	Kernels available
VS Code	Via extension	Via extension	Via extension	Varies
Org-Mode	Native	Native	Via curl	Most languages
Markdown	Native	Via shebang	Via curl	Via shell

The Missing Piece

All these tools solve the execution problem differently, but most share common gaps:

1. Remote execution: Running commands on remote servers
2. Team collaboration: Sharing sessions and results
3. Git-native workflow: Fitting into existing dev practices
4. Web access: Running from a browser without local setup

Stew’s Approach

Stew is designed around the criteria that matter most:

Markdown-first: Write in plain markdown. No special format to learn.

# Database Health Check

## Check connections
​```bash
psql -h db.internal -c "SELECT count(*) FROM pg_stat_activity;"
​```

## Check replication
​```bash
psql -h db.internal -c "SELECT * FROM pg_stat_replication;"
​```

Human-readable always: Your runbooks work as documentation even without Stew.

Git-native: Standard markdown files in your repo. Normal diffs, normal PRs.

Browser and terminal: Run in the web or from your terminal—your choice.

SSH built-in: Execute commands on remote servers, not just locally.

Team-ready: Share runbooks and results across your team.

The goal isn’t to replace notebooks—it’s to make runbooks as natural as the markdown you already write.

Join the waitlist and see how Stew approaches interactive runbooks.