Best Alternatives to jq in 2026: 8 Tools Compared

#1

yq

Best for: YAML and JSON with jq-like syntax

Visit mikefarah.github.io

yq (Go) by Mike Farah, version 4.44 or later, handles YAML and JSON with a jq-like expression language, making it the natural alternative when configs span both formats. DevOps work runs on structured data: kubectl output, Helm values, GitHub Actions workflows, and Docker Compose files are JSON or YAML, and yq covers both with one syntax.

A warning matters: two tools share the name yq. The Python yq by Andrey Kislyuk is a thin wrapper around jq, while the Go yq has its own expression language. Installing via a package manager may give the Python one, so check the version output for the mikefarah URL before trusting examples.

yq edits YAML in place, merges configs, and audits manifests, which jq cannot do for YAML. Pick yq when YAML plus JSON leads. Pick jq when pure JSON scripts lead.

#2

gojq

Best for: Pure Go jq implementation

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gojq is a pure Go implementation of jq, offering jq-compatible syntax in a portable single binary. It suits Go environments and teams that want jq's language without a C dependency, since the Go binary runs anywhere Go runs.

The compatibility is the point: existing jq filters generally work unchanged, so migration costs nothing in script rewrites. For a team standardizing on Go tooling, gojq fits the toolchain naturally.

Ecosystem integrations trail jq, since jq's decades of adoption mean more examples, wrappers, and preinstalled availability. Pick gojq when a Go-native jq leads. Pick jq when ubiquity leads.

#3

jaq

Best for: Correct, fast jq clone in Rust

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jaq is a jq clone in Rust focused on correctness, speed, and simplicity, recommended for speed on resource-limited systems. It runs jq filters with a Rust implementation that prioritizes correct behavior where jq's edge cases can surprise.

For scripts where correctness matters more than familiarity, jaq's stricter semantics catch errors that jq tolerates silently. Speed on constrained hardware is a second benefit, since Rust binaries start fast and use little memory.

The community is smaller, and some jq features differ in detail, so test filters before migrating production scripts. Pick jaq when correctness and speed lead. Pick jq when ubiquity leads.

#4

fx

Best for: Interactive JSON viewer and processor

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fx is a dual-purpose command line tool for JSON, providing both a terminal-based viewer and a JSON processing utility. The viewer is written in Go and runs without external dependencies, while the processing tool uses JavaScript, compatible with Node.js and Deno.

For interactive exploration, fx wins over jq: browse a JSON document, expand and collapse nodes, and run JavaScript expressions against it. That exploration suits API responses that need understanding before scripting.

Scripting pipelines still favor jq, since fx's JavaScript processing adds a language dependency. Pick fx when interactive exploration leads. Pick jq when pipeline scripting leads.

#5

jless

Best for: Large-file JSON viewer

Visit jless.io

jless is a command line JSON viewer written in Rust, designed for reading, exploring, and searching large JSON files. It streams data, uses vim keys, and avoids the memory overload that hits vim or less on 100MB or larger files.

The viewer omits quotes around keys, closing delimiters, and trailing commas for a clean display, with expand and collapse for objects and arrays. Full regex search finds exactly the data needed in a large file.

jless views but does not process, so filtering and transformation still need jq or another tool. Pick jless when browsing large files leads. Pick fx when interactive processing leads.

#6

jello

Best for: Python-syntax JSON filtering

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jello filters JSON and JSON Lines data with Python syntax, letting Python developers query JSON without learning jq's language. A filter written as Python reads naturally to a Python team, and the tool integrates with Python workflows.

For a team that already thinks in Python, jello removes the context switch between Python code and jq filters. That readability matters in scripts that teammates must maintain.

Python startup and interpretation make jello slower than compiled tools on huge files, and pipeline idiom trails jq. Pick jello when Python syntax leads. Pick jq when pipeline speed leads.

#7

gron

Best for: Greppable JSON lines

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gron transforms JSON into greppable lines of the form key equals value, so standard Unix tools such as grep, sed, and awk work on JSON. A developer who knows grep can find values in JSON without learning a query language.

The workflow is simple: gron the data, grep for the key, and read the value. For quick searches in large JSON, that beats writing a jq filter, and ungron converts lines back to JSON when needed.

Expressiveness trails jq, since gron's line model cannot express complex transformations. Pick gron when grep-based searching leads. Pick jq when transformation leads.

#8

Miller

Best for: Mixed CSV, TSV, and JSON work

Visit miller.readthedocs.io

Miller, invoked as mlr, excels for mixed formats, handling CSV, TSV, and JSON interop with stats verbs. A command such as mlr with JSON input and cut for a field gives jq-like output while also handling CSV and TSV in the same pipeline.

That interop suits data work that crosses formats: logs in JSON Lines, reports in CSV, and reference data in TSV all flow through one tool. Stats verbs add aggregation that jq requires manual filters for.

JSON depth trails jq, since Miller's strength is format bridging rather than JSON transformation. Pick Miller when mixed formats lead. Pick jq when pure JSON transformation leads.