JSON Repair fixes common invalid JSON locally, then can use an AI provider for complex cases. Review the recovered JSON before you reuse, export, or deploy it.
Repair JSON works best between validation and formatting. Start with the raw broken payload, let the tool try deterministic local repair first, use AI-assisted repair only if needed, then move the recovered JSON into validation, formatting, or export.
Recover broken text into usable JSON
Repair JSON is most valuable when the payload is too broken to use anywhere else
A lot of broken JSON is not just ugly - it is completely unusable. The repair step matters because it gets that payload back into a valid JSON shape so validation, formatting, comparison, and export are possible again.
Broken input
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{
2
name:"Project X",
3
'id':1024,
4
items:[
5
"A",
6
"B",
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]
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}
Repaired output
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{
2
"name":"Project X",
3
"id":1024,
4
"items":[
5
"A",
6
"B"
7
]
8
}
If the JSON is too broken to parse at all, starting with Repair JSON is often faster than starting with manual validation.
3 repair examples that reflect real workflows
Repair JSON is usually used for messy real-world payloads, not classroom examples. These examples match common issues in legacy systems, config cleanup, and corrupted logs.
Legacy API
Fix missing quotes and mixed quoting styles quickly
Useful for old systems, script-generated payloads, or manually assembled responses that almost look like JSON but are not actually valid.
損壞的 JSON
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{
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orderId: 'SO-1024',
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customer:{
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name: 'Maeve'
5
}
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}
已修復的 JSON
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{
2
"orderId":"SO-1024",
3
"customer":{
4
"name":"Maeve"
5
}
6
}
This category is usually fixed by local repair alone, without needing AI.
Config Cleanup
Strip comments and trailing commas so config parses again
Teams often write JavaScript object style config and only realize later that strict JSON does not allow comments or trailing commas.
損壞的 JSON
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{
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"env":"prod",
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"retry":3, // retry count
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"features":{
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"betaCheckout":false,
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}
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}
已修復的 JSON
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{
2
"env":"prod",
3
"retry":3,
4
"features":{
5
"betaCheckout":false
6
}
7
}
Repair JSON is ideal for pulling almost-JSON config back into a strict JSON form.
Complex Logs
Use AI only when deterministic repair is not enough
When a payload includes truncation, escape damage, and multiple bracket errors at once, deterministic local rules may not be enough. That is where AI repair becomes useful.
After this kind of repair, validate the result again before trusting it in downstream workflows.
01
Tutorial Step
步驟 1:貼上損壞的 JSON
The repair tool needs the original context more than a half-cleaned version. Logs, old API responses, and broken config files are often easier to repair when you keep the raw text intact instead of manually deleting large sections first.
複製無效或損壞的 JSON(例如來自日誌、舊 API 或設定檔)。
將其貼到左側編輯區;也可以拖曳檔案或使用「匯入」按鈕。
不必擔心鍵名未加引號或尾隨逗號等錯誤——本工具就是為此而設計。
If the validator already showed a long list of syntax failures, switching here usually saves more time than fixing them one by one.
Keeping the raw payload intact gives both local repair and AI repair a better shot at reconstructing the intended structure.
02
Tutorial Step
步驟 2:自動修復流程
Not every repair uses AI. Most broken JSON is fixed by deterministic local rules, so the real goal here is to understand the repair order instead of assuming every issue needs an AI rewrite.
當 JSON 無效時,會出現「修復」按鈕(也可手動點擊)。
工具會先嘗試快速的本地修復,立即修復語法問題。
若本地修復不足,系統會自動升級至 AI 修復引擎以推斷意圖並修復結構。
The progress indicator helps you see whether the page is doing local repair, AI repair, or final validation of the repaired result.
If the input is too large for AI repair, split it into smaller logical chunks instead of retrying the same oversized payload.
03
Tutorial Step
步驟 3:檢視修復後的 JSON
A successful repair is not only about making the JSON parse again. You also need to confirm that the result still matches the intended business structure, especially for nested objects, arrays, and partially corrupted logs.
右側面板會顯示已修復且有效的 JSON。
我們會自動美化格式,方便您確認資料結構與值。
查看有效性指示,確認其符合標準 JSON 語法。
If the repaired result still looks suspicious, return to the validator or original source instead of pushing it downstream blindly.
For logs and configuration, pay special attention to booleans, numbers-as-strings, and datetime fields after repair.
04
Tutorial Step
步驟 4:使用乾淨的 JSON
Repair is the recovery step, not the final destination. Once the JSON is usable again, you should immediately move it into the right next tool depending on whether you need validation, readability, schema generation, or field-level review.
點選「複製」將修復後的 JSON 複製到剪貼簿。
下載為 `.json` 檔案以便備份。
若想繼續手動編輯,可使用「套用」將結果移回輸入區。
If you need row-and-column review, filtering, or bulk edits, continue into the table editor.
If the repaired JSON is now stable, it is also a much better input for schema generation, type generation, and transformation steps.
A more reliable repair workflow
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Paste the raw broken JSON into Repair JSON before doing broad manual cleanup.
2
Let local repair run first and use that result when it is enough, rather than forcing every case through AI.
3
If the structure is still too broken, split the payload or let AI repair handle the more complex reconstruction.
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Validate the repaired output next, then move it to the formatter, table editor, or schema tools depending on your goal.
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If the result matters for team review or rollback, download the repaired version instead of only copying it to the clipboard.
Repair JSON is most useful not because it saves one comma fix, but because it recovers data that would otherwise be blocked from the rest of your JSON workflow.
Practical repair tips
If the payload only has a few syntax problems, repair is fast. If large sections are missing, repair can recover syntax but not invent the original business data.
For very large payloads, split the JSON into logical chunks before repair. Success rates are usually better that way.
Do not ship or store the repaired result blindly. Validate it first, then decide whether it also needs formatting.
If you need to inspect fields one by one after repair, the table editor is often easier than scanning a long JSON blob.
Use manual fixes when key sections are missing, the recovered structure does not match the source intent, or the data needs domain-specific decisions that an automatic repair cannot make.