LLM JSON Output Validator
Repair and validate model JSON output against a schema
Model Output
Expected Schema
Paste the model output and the schema you expect. You can optionally auto-repair malformed JSON before validation.
Repair and schema-check LLM output in one step
AI-generated JSON often arrives almost-but-not-quite valid — a stray comment, smart quotes, a truncated object — and your validator throws before it gets to check the shape. Combining repair and schema validation in one tool lets you fix the obvious mistakes first and then enforce the contract your downstream code depends on.
Use the validator when you need to
Validate Claude or GPT structured output
Run repair-then-validate on tool calls and structured responses to confirm they match the expected shape.
Catch shape regressions across model versions
Regression-test the same prompt across model versions to detect when output drifts away from the schema.
Triage a failing agent step
Drop the model's raw output in to see exactly which fields are missing, malformed, or wrong type.
How to validate model JSON
- 1
Paste the model's raw output (even if it's malformed) into the data editor.
- 2
Paste the expected JSON Schema into the schema editor.
- 3
Enable repair if needed, click Validate, and review the path-by-path errors.
Keep going
Just repair the JSON
Use this when you only need fixed JSON without schema enforcement.
Pure schema validation
Skip repair when the input is already known-good JSON.
Build the right schema
Produce a strict schema for OpenAI, Anthropic, or MCP before validating against it.
Diff two model outputs
See what changed between runs when the schema passes but the values look different.
Common LLM-validation workflows
Validate every model output before passing it to the next agent step or tool.
Repair the output and run schema validation to find which field the model is producing incorrectly.
Validate a sample of outputs from the candidate model against your production schema before rollout.
أدوات ذات صلة
الأسئلة الشائعة
It checks whether a model response, tool-call payload, or agent output is valid JSON and whether it matches the JSON Schema you expect downstream code to consume.
If the model output is malformed JSON, the tool can try to repair common issues first — such as trailing commas, comments, single quotes, or truncated objects — and then run schema validation on the repaired result.
Yes. The validator is provider-agnostic. If the model returns JSON or you expect JSON from a tool call, you can validate it here regardless of which API generated it.
No. Validation happens entirely in your browser. Nothing is uploaded or stored on JSONTech servers.