Bidirectional conversion between YAML, JSON and TOML. Auto-detect source format, strict validation that pinpoints the location of a syntax error, instant swap between input and output for round-trip tests. Typical for multi-stack engineers: jump from a Kubernetes YAML manifest to wire JSON, from a pyproject.toml to the JSON of an API payload, from a .github/workflows/*.yml to its equivalent struct representation.
YAML, JSON or TOML. Auto-detect recognizes the format from the syntax (JSON starts with { or [, TOML has [section] or key = value, YAML is the fallback).
JSON, YAML or TOML. Conversion goes through an intermediate JavaScript object: lossless roundtrip for YAML/JSON, lossy admitted for TOML on edge cases (complex datetime offsets, multi-line strings).
Live conversion as you type (debounced). Convert button forces refresh. OK/Error status at the bottom with parser message.
Swap input and output with one click, useful for round-trip tests. E.g. write YAML, convert to JSON, swap, convert to TOML, swap, you should get back to the original YAML (modulo TOML edge cases).
YAML, JSON and TOML cover overlapping but distinct use cases. JSON is the universal wire format for REST APIs and for app configs where the bracket verbosity is acceptable. YAML is the de facto standard for orchestrator manifests (Kubernetes, Ansible, Helm) and for CI/CD pipelines (GitHub Actions, GitLab CI, CircleCI), thanks to its indentation-based readability. TOML is the modern config format for ecosystems that want to avoid YAML's indentation ambiguity (Cargo for Rust, pyproject.toml for Python, Hugo). Converting across the three is frequent for engineers who cross stacks within the same project.
Operational privacy. Configuration files often contain sensitive information: secrets accidentally not removed, API keys, internal backend hostnames, the structure of a deployment model. Format conversion that requires uploading the file to a third-party service is a documented anti-pattern. Here the conversion happens directly in the browser, so the config content does not leave the machine.
Strict validation with error position. When the source is invalid, the tool reports line and column of the syntax error along with the underlying parser message. Particularly useful with YAML, where a wrong indentation can silently produce an incorrect data structure; and with TOML, where the explicit types (string vs datetime vs integer) generate clear errors which are nevertheless rare for those who do not know the spec by heart.
Technical terms used on this page, briefly explained.
&) or aliases (*). For datasets with circular references (rare in configs, common in some serialized ML pipelines) you need manual workarounds or to flatten the structure upfront.yq for YAML, jq for JSON, tomlq for TOML, all with streaming processing): they handle gigabyte files without freezing the browser and integrate into automation pipelines.Maurizio Fonte, senior IT consultant with 20+ years in PHP, Laravel, unmanaged Linux infrastructure, applied cybersecurity and AI/LLM integration. Production backends, legacy code modernization, security audits, custom AI agents and MCP servers: the work behind every tool published here.
About Maurizio Fonte