JSON to YAML Converter Guide: Convert Between JSON and YAML Online

1. What Are JSON and YAML?

JSON (JavaScript Object Notation) and YAML (YAML Ain't Markup Language) are two of the most widely used data serialization formats in modern software development. Both serve the same fundamental purpose -- representing structured data as text -- but they take very different approaches to syntax, readability, and feature sets. Understanding both formats and knowing how to convert between them is an essential skill for any developer working with APIs, configuration files, infrastructure-as-code, or cloud-native platforms.

JSON: The Language of APIs

JSON emerged from the JavaScript ecosystem in the early 2000s, popularized by Douglas Crockford as a lightweight alternative to XML for data interchange. Its syntax is derived directly from JavaScript object literal notation, making it instantly familiar to web developers. JSON quickly became the dominant format for REST APIs, browser-server communication, and NoSQL databases.

JSON's design philosophy prioritizes simplicity and unambiguity. It supports exactly six data types: strings (in double quotes), numbers, booleans (true/false), null, arrays (ordered lists), and objects (key-value maps). There are no comments, no special types, and no optional syntax variations. This strictness is both its greatest strength -- any conforming JSON parser will interpret any valid JSON document identically -- and its limitation for use cases where human readability and authoring ergonomics matter.

JSON is formally specified by RFC 8259 and ECMA-404. Every modern programming language includes built-in or standard-library JSON support, making it the most universally portable data format in existence.

YAML: The Language of Configuration

YAML was originally introduced in 2001 by Clark Evans, Ingy dot Net, and Oren Ben-Kiki. The name originally stood for "Yet Another Markup Language" but was later reinterpreted as the recursive acronym "YAML Ain't Markup Language," reflecting its shift in focus from document markup to data serialization.

YAML's design philosophy prioritizes human readability. It uses whitespace indentation instead of braces and brackets to denote structure, eliminates most quotation marks, and supports features specifically designed for human authors: comments, multi-line strings, anchors and aliases for reusing data, and the ability to include multiple documents in a single file. These features make YAML the format of choice for configuration files where humans are the primary readers and writers.

YAML's popularity exploded with the rise of DevOps and cloud-native computing. Today, it is the standard configuration format for Kubernetes, Docker Compose, Ansible, GitHub Actions, GitLab CI, Terraform (in HCL-to-YAML workflows), Helm charts, and countless other tools in the infrastructure-as-code ecosystem.

The current specification is YAML 1.2.2, released in 2021. A critical change in YAML 1.2 was making YAML a strict superset of JSON, meaning any valid JSON document is also a valid YAML document.

2. Syntax Comparison: JSON vs YAML

The best way to understand the differences between JSON and YAML is to see the same data represented in both formats. Here is a Kubernetes Deployment manifest expressed in each format.

JSON Representation

{
  "apiVersion": "apps/v1",
  "kind": "Deployment",
  "metadata": {
    "name": "web-server",
    "namespace": "production",
    "labels": {
      "app": "web-server",
      "version": "v2.1.0"
    }
  },
  "spec": {
    "replicas": 3,
    "selector": {
      "matchLabels": {
        "app": "web-server"
      }
    },
    "template": {
      "metadata": {
        "labels": {
          "app": "web-server",
          "version": "v2.1.0"
        }
      },
      "spec": {
        "containers": [
          {
            "name": "nginx",
            "image": "nginx:1.25-alpine",
            "ports": [
              {
                "containerPort": 80
              }
            ],
            "resources": {
              "requests": {
                "cpu": "100m",
                "memory": "128Mi"
              },
              "limits": {
                "cpu": "500m",
                "memory": "256Mi"
              }
            }
          }
        ]
      }
    }
  }
}

YAML Representation

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-server
  namespace: production
  labels:
    app: web-server
    version: v2.1.0
spec:
  replicas: 3
  selector:
    matchLabels:
      app: web-server
  template:
    metadata:
      labels:
        app: web-server
        version: v2.1.0
    spec:
      containers:
        - name: nginx
          image: nginx:1.25-alpine
          ports:
            - containerPort: 80
          resources:
            requests:
              cpu: 100m
              memory: 128Mi
            limits:
              cpu: 500m
              memory: 256Mi

Key Differences at a Glance

Feature	JSON	YAML
Structure delimiters	{ } and [ ]	Indentation (spaces)
String quoting	Always required (double quotes)	Optional in most cases
Comments	Not supported	Supported (#)
Multi-line strings	Escape sequences (\n)	| (literal) and > (folded)
Multiple documents	Not supported	--- separator
Anchors/Aliases	Not supported	&anchor / *alias
Data types	6 types	6+ types (dates, binary, etc.)
Trailing commas	Not allowed	N/A (no commas)
File extensions	.json	.yaml or .yml
MIME type	application/json	application/yaml

3. How JSON-to-YAML Conversion Works

Converting between JSON and YAML is a straightforward mapping because both formats express the same underlying data model: scalars (strings, numbers, booleans, null), sequences (ordered lists), and mappings (key-value pairs). Here is how each JSON construct maps to its YAML equivalent.

Objects Become Mappings

JSON objects enclosed in { } become YAML mappings with each key-value pair on its own line. The braces are removed, and key-value pairs are represented as key: value with the colon and space serving as the delimiter.

// JSON
{"name": "nginx", "version": "1.25"}

# YAML
name: nginx
version: "1.25"

Arrays Become Sequences

JSON arrays enclosed in [ ] become YAML sequences where each element is prefixed with a dash (-) on its own line. Nested arrays and objects within arrays follow the same indentation rules.

// JSON
["alpha", "beta", "gamma"]

# YAML
- alpha
- beta
- gamma

Strings: Quoting Rules

JSON requires all strings to be enclosed in double quotes. YAML is more flexible -- most strings can be written unquoted, but quoting is required when a string contains special characters, starts with a reserved indicator, or could be misinterpreted as another data type:

• Unquoted: Simple strings like hello, web-server, nginx
• Must be quoted: Strings containing :, #, {, [, &, *, !, |, >, ', ", %, @, or leading/trailing spaces
• Should be quoted: Values that look like booleans (yes, no, true, false), numbers (3.14, 42), or null (null, ~) when you want them treated as strings

Numbers, Booleans, and Null

These scalar types map directly between formats. JSON true becomes YAML true, JSON null becomes YAML null (or ~), and numbers remain unchanged. The key difference is that YAML also treats unquoted yes, no, on, off as booleans (in YAML 1.1), which is a frequent source of bugs.

Nested Structures

Nested JSON objects and arrays are represented in YAML through increased indentation. Each level of nesting adds a consistent number of spaces (typically 2). This is where YAML's readability advantage is most apparent -- deeply nested structures that require matching braces in JSON are expressed through clean, visual indentation in YAML.

// JSON
{
  "database": {
    "primary": {
      "host": "db-primary.example.com",
      "port": 5432,
      "credentials": {
        "username": "admin",
        "password": "secret"
      }
    }
  }
}

# YAML
database:
  primary:
    host: db-primary.example.com
    port: 5432
    credentials:
      username: admin
      password: secret

4. YAML as a Superset of JSON

Since YAML 1.2, YAML is officially a strict superset of JSON. This means that any valid JSON document can be parsed by a YAML parser and will produce identical data structures. This is a deliberate design decision that makes migration between formats seamless and allows YAML tooling to handle JSON input natively.

This superset relationship has practical implications. You can paste JSON directly into a YAML file and it will parse correctly. You can mix JSON-style inline syntax with YAML block syntax in the same document. And you can incrementally convert a JSON configuration to YAML by replacing JSON constructs with their YAML equivalents one section at a time.

However, the reverse is not true. YAML has features that JSON cannot represent:

• Comments: YAML supports # inline comments that are discarded during parsing. JSON has no comment syntax at all.
• Anchors and aliases: YAML allows you to define a data fragment once (&anchor) and reference it elsewhere (*alias), avoiding repetition. JSON requires duplicating data.
• Multi-line strings: YAML's literal block scalar (|) and folded block scalar (>) handle multi-line text elegantly. JSON must use \n escape sequences within a single string.
• Complex keys: YAML mapping keys can be any data type, including sequences and mappings. JSON keys must be strings.
• Tags and custom types: YAML supports type tags (!!timestamp, !!binary) for representing dates, binary data, and custom types. JSON has no type annotation mechanism.
• Multiple documents: A single YAML file can contain multiple documents separated by ---. JSON files contain exactly one value.

When converting from YAML to JSON, these YAML-specific features are either lost (comments), expanded (anchors/aliases become duplicated data), or transformed (multi-line strings become escaped strings). The data values are preserved, but the authoring conveniences are not.

5. JSON and YAML in Kubernetes

Kubernetes is perhaps the most prominent system where the choice between JSON and YAML matters on a daily basis. While the Kubernetes API server accepts both formats, YAML has become the de facto standard for writing and maintaining Kubernetes manifests. Understanding why -- and knowing when JSON is the better choice -- is important for effective Kubernetes operations.

Why YAML Dominates Kubernetes Configuration

Several YAML features make it particularly well-suited for Kubernetes manifests:

• Comments for documentation: Kubernetes manifests can be complex, and inline comments explain why specific values were chosen, what resource limits were based on, or which tickets prompted a configuration change.
• Multi-document files: The --- separator allows you to define multiple related resources (a Deployment, Service, and ConfigMap) in a single file, keeping related resources together.
• Readability in code review: YAML diffs are easier to review in pull requests. Adding a label to a YAML manifest is a single-line change; in JSON, it may also change surrounding lines due to comma handling.
• Multi-line strings for embedded content: ConfigMaps, init scripts, and nginx configurations often contain multi-line text that YAML handles cleanly with | or > block scalars.

When JSON Is Better for Kubernetes

Despite YAML's dominance in human-authored manifests, JSON has legitimate Kubernetes use cases:

• Programmatic generation: When generating manifests from code, JSON is safer because it has no indentation-related pitfalls. Every JSON library produces valid output.
• API responses: kubectl get with -o json outputs JSON, and many tools that interact with the Kubernetes API work with JSON natively.
• JSON Patch operations: Kubernetes supports JSON Patch (RFC 6902) for partial updates. These patch documents must be JSON.
• Strategic merge patches: While you can write them in YAML, they are applied as JSON internally.
• Debugging: When troubleshooting parsing issues, converting a YAML manifest to JSON can reveal hidden problems like incorrect types or unexpected structure.

Converting Between Formats with kubectl

Kubernetes makes format conversion easy. You can output any resource in either format:

# Get a deployment as JSON
kubectl get deployment web-server -o json

# Get the same deployment as YAML
kubectl get deployment web-server -o yaml

# Apply a JSON file (works the same as YAML)
kubectl apply -f deployment.json

# Convert YAML to JSON using kubectl
kubectl create -f deployment.yaml --dry-run=client -o json

Multi-Document YAML in Kubernetes

One of YAML's most powerful features for Kubernetes is the ability to define multiple resources in a single file. This is commonly used to keep related resources together:

apiVersion: v1
kind: Namespace
metadata:
  name: my-app
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  namespace: my-app
spec:
  replicas: 2
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
        - name: app
          image: my-app:latest
---
apiVersion: v1
kind: Service
metadata:
  name: my-app
  namespace: my-app
spec:
  selector:
    app: my-app
  ports:
    - port: 80
      targetPort: 8080

This pattern has no JSON equivalent. If you convert this to JSON, you would need three separate files or a custom wrapper format (like a JSON array of documents).

6. JSON and YAML in CI/CD Pipelines

CI/CD platforms have overwhelmingly adopted YAML as their configuration format. Understanding YAML's role in CI/CD pipelines and knowing when JSON conversion is needed are critical skills for modern DevOps workflows.

GitHub Actions

GitHub Actions workflow files are written exclusively in YAML. The format's features align perfectly with workflow definitions:

name: Build and Deploy
on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: "20"
      - run: npm ci
      - run: npm test
      - run: npm run build

GitLab CI

GitLab CI/CD configuration uses .gitlab-ci.yml, which takes advantage of YAML anchors and aliases to reduce repetition across pipeline stages:

.default_rules: &default_rules
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
    - if: $CI_MERGE_REQUEST_ID

build:
  stage: build
  <<: *default_rules
  script:
    - npm ci
    - npm run build

test:
  stage: test
  <<: *default_rules
  script:
    - npm ci
    - npm test

The anchor &default_rules defines the rules once, and *default_rules with the merge key << reuses them in multiple jobs. This kind of DRY (Don't Repeat Yourself) pattern is impossible in JSON.

Docker Compose

Docker Compose files use YAML to define multi-container applications. Comments are particularly valuable here for documenting port mappings, environment variables, and volume mounts:

services:
  web:
    image: nginx:alpine
    ports:
      - "8080:80"  # Map host 8080 to container 80
    volumes:
      - ./html:/usr/share/nginx/html:ro
    depends_on:
      - api

  api:
    build: ./api
    environment:
      - DATABASE_URL=postgres://db:5432/myapp
      - REDIS_URL=redis://cache:6379
    depends_on:
      - db
      - cache

  db:
    image: postgres:16-alpine
    volumes:
      - pgdata:/var/lib/postgresql/data
    environment:
      POSTGRES_DB: myapp
      POSTGRES_PASSWORD: secret  # Use secrets in production

  cache:
    image: redis:7-alpine

volumes:
  pgdata:

When CI/CD Systems Need JSON

Although CI/CD configurations are written in YAML, there are situations where JSON conversion is needed:

• API interactions: GitHub Actions and GitLab CI APIs accept and return JSON. Automating pipeline creation or modification often requires JSON.
• Dynamic configuration: Generating pipeline configurations programmatically (e.g., matrix builds from a script) is easier and safer with JSON.
• Validation and linting: Some validation tools work with JSON schemas. Converting your YAML to JSON lets you validate it against a JSON Schema definition.
• Debugging: When a YAML pipeline config has subtle parsing issues, converting to JSON can reveal structural problems that are invisible in YAML.

7. Configuration File Use Cases

Beyond Kubernetes and CI/CD, JSON and YAML each have established territories in the broader configuration landscape. Understanding these conventions helps you choose the right format for your project.

Where JSON Is the Standard

• package.json (Node.js): npm and the Node.js ecosystem standardized on JSON for package manifests. This includes package.json, tsconfig.json, package-lock.json, and .eslintrc.json.
• Web APIs: REST APIs almost universally use JSON for request and response bodies. The Content-Type: application/json header is ubiquitous.
• VS Code settings: Visual Studio Code uses JSON (with comment extensions, JSONC) for all configuration: settings.json, launch.json, tasks.json, extensions.json.
• Terraform state: Terraform stores its state in JSON format, and plan output is also JSON.
• CloudFormation: While AWS CloudFormation supports both JSON and YAML, JSON was the original format and remains widely used.

Where YAML Is the Standard

• Ansible: Ansible playbooks, roles, inventories, and variable files all use YAML. The entire Ansible ecosystem is built around YAML authoring.
• Helm charts: Kubernetes Helm chart templates, values files, and Chart.yaml metadata all use YAML.
• OpenAPI/Swagger: While OpenAPI specifications can be written in either format, YAML is strongly preferred in practice because the specifications are large and deeply nested, benefiting greatly from YAML's readability.
• Spring Boot: Java Spring Boot applications commonly use application.yml for configuration, taking advantage of YAML's hierarchical syntax.
• Jekyll and Hugo: Static site generators use YAML front matter in content files and YAML configuration files.
• Ruby on Rails: Rails uses YAML for database configuration (database.yml), internationalization files, fixtures, and credentials.

Choosing Between Formats

When starting a new project and you have a choice, consider these guidelines:

• Choose JSON when: The configuration is primarily read and written by machines, you need strict parsing guarantees, or you want maximum cross-language compatibility.
• Choose YAML when: The configuration is primarily authored and read by humans, you need comments for documentation, the configuration has deeply nested structures, or you are working within an ecosystem that already uses YAML.
• Consider TOML when: You want a human-readable format with unambiguous syntax and no indentation sensitivity. TOML is popular for Rust (Cargo.toml), Python (pyproject.toml), and Go module configuration.

8. Common Pitfalls and Gotchas

YAML's flexibility and implicit type coercion are the source of many subtle bugs. Being aware of these pitfalls helps you write correct YAML and understand why converting between JSON and YAML sometimes produces unexpected results.

The Norway Problem

This is perhaps the most infamous YAML gotcha. In YAML 1.1, the unquoted values NO, no, No, and many other strings are interpreted as boolean false. This means a list of country codes can silently lose data:

# YAML 1.1 interpretation
countries:
  - GB    # string "GB"
  - IE    # string "IE"
  - FR    # string "FR"
  - NO    # boolean false (!)
  - DK    # string "DK"

Converting this YAML to JSON would produce "NO": false instead of "NO": "NO". The fix is simple: quote the value ("NO"). YAML 1.2 significantly reduced this problem by only treating true and false (lowercase) as booleans, but many parsers still use YAML 1.1 rules by default.

Indentation Errors

YAML's reliance on indentation means that a single misplaced space can silently change the meaning of a document. Unlike Python, YAML does not always produce an error for inconsistent indentation -- sometimes it just produces a different data structure:

# Intended: "ports" is a key under "spec"
spec:
  ports:
    - port: 80

# Bug: "ports" is a sibling of "spec" (wrong indentation)
spec:
ports:
  - port: 80

JSON's explicit braces make this class of bug impossible. When debugging YAML indentation issues, converting to JSON reveals the actual structure the parser sees.

Tabs vs Spaces

YAML explicitly forbids tab characters for indentation -- only spaces are allowed. Mixing tabs and spaces is a common error that produces confusing parse errors. Most editors can be configured to insert spaces when the Tab key is pressed, and YAML linters will flag tab characters.

Unquoted Special Values

Several common strings are parsed as non-string types in YAML when left unquoted:

YAML Input	Parsed As	Expected
version: 1.0	Float 1.0	String "1.0"
version: 1.2.0	String "1.2.0"	String "1.2.0"
port: 0080	Octal 64	String "0080"
time: 12:30	Sexagesimal 750	String "12:30"
enabled: yes	Boolean true	String "yes"
value: null	Null	String "null"
date: 2026-03-12	Date object	String "2026-03-12"

The solution is to quote any value that should remain a string: version: "1.0", port: "0080", enabled: "yes".

Multi-line String Confusion

YAML provides multiple ways to handle multi-line strings, and each behaves differently:

• | (literal block scalar): Preserves newlines exactly as written. Trailing newlines are kept.
• |- (literal block scalar, strip): Same as | but removes the final trailing newline.
• |+ (literal block scalar, keep): Same as | but keeps all trailing newlines.
• > (folded block scalar): Replaces single newlines with spaces (folds lines). Double newlines become single newlines.
• >- (folded block scalar, strip): Same as > but removes the final trailing newline.

Choosing the wrong style can result in unexpected whitespace in the converted JSON string. When converting YAML with multi-line strings to JSON, always verify the resulting string contains the correct newline characters.

9. Programmatic Conversion in Different Languages

Most programming languages have mature libraries for both JSON and YAML, making programmatic conversion straightforward.

Python

Python's json module is built-in, and PyYAML is the most popular YAML library:

import json
import yaml

# JSON to YAML
with open('config.json') as f:
    data = json.load(f)

with open('config.yaml', 'w') as f:
    yaml.dump(data, f, default_flow_style=False, sort_keys=False)

# YAML to JSON
with open('config.yaml') as f:
    data = yaml.safe_load(f)

with open('config.json', 'w') as f:
    json.dump(data, f, indent=2)

Always use yaml.safe_load() instead of yaml.load(). The latter can execute arbitrary Python code from YAML tags, making it a security risk when processing untrusted input.

JavaScript / Node.js

Node.js has built-in JSON support, and the js-yaml package is the standard YAML library:

const fs = require('fs');
const yaml = require('js-yaml');

// JSON to YAML
const jsonData = JSON.parse(fs.readFileSync('config.json', 'utf8'));
const yamlOutput = yaml.dump(jsonData, { lineWidth: -1, noRefs: true });
fs.writeFileSync('config.yaml', yamlOutput);

// YAML to JSON
const yamlData = yaml.load(fs.readFileSync('config.yaml', 'utf8'));
const jsonOutput = JSON.stringify(yamlData, null, 2);
fs.writeFileSync('config.json', jsonOutput);

Go

Go has the standard library encoding/json and the popular gopkg.in/yaml.v3 package:

package main

import (
    "encoding/json"
    "fmt"
    "os"

    "gopkg.in/yaml.v3"
)

func main() {
    // Read JSON file
    jsonData, _ := os.ReadFile("config.json")

    // Parse JSON into a generic structure
    var data interface{}
    json.Unmarshal(jsonData, &data)

    // Convert to YAML
    yamlData, _ := yaml.Marshal(data)
    fmt.Println(string(yamlData))
}

Command-Line Tools

Several command-line tools make quick conversions easy:

# Using yq (https://github.com/mikefarah/yq)
# JSON to YAML
yq -P input.json

# YAML to JSON
yq -o=json input.yaml

# Using Python one-liner (JSON to YAML)
python3 -c "import sys,json,yaml; yaml.dump(json.load(sys.stdin),sys.stdout,default_flow_style=False)" < input.json

# Using Python one-liner (YAML to JSON)
python3 -c "import sys,json,yaml; json.dump(yaml.safe_load(sys.stdin),sys.stdout,indent=2)" < input.yaml

# Using Ruby one-liner (JSON to YAML)
ruby -ryaml -rjson -e 'puts YAML.dump(JSON.parse(STDIN.read))' < input.json

10. Best Practices

Follow these guidelines to work effectively with JSON and YAML and avoid common conversion problems.

Always Quote Ambiguous Values in YAML

When authoring YAML, quote any value that could be misinterpreted as a non-string type. This includes version numbers ("1.0"), boolean-like strings ("yes", "no", "on", "off"), and strings that look like dates or numbers. When in doubt, quote it.

Use Consistent Indentation

Choose 2 spaces (the most common convention) and stick with it throughout your project. Configure your editor to insert spaces instead of tabs for YAML files. Use a .editorconfig file to enforce this across your team:

# .editorconfig
[*.{yml,yaml}]
indent_style = space
indent_size = 2
insert_final_newline = true

Use a YAML Linter

Tools like yamllint catch common errors before they cause problems. Configure it to enforce consistent style and catch ambiguous values:

# .yamllint.yml
extends: default
rules:
  line-length:
    max: 120
  truthy:
    check-keys: true
  indentation:
    spaces: 2
    indent-sequences: true

Validate Converted Output

After converting between formats, validate that the converted output matches the original data. This is especially important when converting YAML to JSON, as implicit type coercion may have changed string values to booleans, numbers, or nulls. Compare the parsed data structures, not just the text representations.

Prefer JSON for Machine Communication

Use JSON for API payloads, inter-service communication, and any context where data is primarily consumed by machines. JSON's strict syntax eliminates ambiguity, and its parsing performance is generally superior to YAML.

Prefer YAML for Human-Authored Configuration

Use YAML for files that humans will regularly read, edit, and review in version control. The ability to add comments, avoid excessive quoting, and express structure through indentation makes YAML more maintainable for configuration management.

Use safe_load in YAML Parsers

Always use safe loading functions when parsing YAML from untrusted sources. YAML's tag system allows arbitrary object instantiation in some parsers, which can lead to remote code execution vulnerabilities. Use yaml.safe_load() in Python, avoid yaml.load() without a Loader argument, and prefer YAML parsers that default to safe behavior.

Document Your Format Choice

When a project uses both JSON and YAML (which is common), document the convention. Specify which format is used for which purpose, and provide conversion instructions for team members who may need to convert between formats.

11. Using Our Free JSON/YAML Converter Tool

Our free JSON/YAML Converter tool makes it easy to convert between JSON and YAML directly in your browser. No data is sent to any server -- all processing happens locally on your machine.

JSON to YAML Mode

Paste your JSON data and instantly get clean, properly formatted YAML output. The converter handles nested objects, arrays, strings with special characters, and all JSON data types. The output uses consistent 2-space indentation and applies appropriate quoting rules for YAML values.

YAML to JSON Mode

Paste your YAML configuration and get valid, well-formatted JSON output. The converter correctly handles YAML-specific features like multi-line strings, anchors, aliases, and implicit type coercion. Error messages help you identify and fix YAML syntax issues before conversion.

Key Features

• 100% client-side: Your data never leaves your browser
• Bidirectional conversion: Convert JSON to YAML and YAML to JSON with a single click
• Syntax highlighting: Both input and output are syntax-highlighted for easy reading
• Error detection: Clear feedback for invalid JSON or YAML input
• One-click copy: Copy the converted output to your clipboard instantly
• Configurable indentation: Choose your preferred indentation level for the output