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Validate, debug, auto-repair, and enforce JSON Schema and Zod rules on raw completion text from OpenAI, Claude, DeepSeek, and Llama 3. Fix markdown fences, missing closing braces, trailing commas, and unescaped quotes with 100% browser-local privacy.
Your JSON schemas, LLM outputs, RAG completions, and API keys stay 100% in local browser memory.
Select a real-world scenario to test schema validation and JSON auto-repair instantly.
Paste your target JSON Schema (or select an e-commerce, RAG, or AI agent preset) and paste the raw text completion returned by your LLM model.
Our client-side engine automatically strips markdown ```json fences, balances unclosed brackets, removes trailing commas, and highlights schema type mismatches.
Copy clean RFC 8259 JSON, download .json files, or copy production-ready Python, TypeScript Zod, or LangChain guardrail code snippets into your application.
Zero network transmission. Your proprietary prompt completions, customer data, and JSON schemas remain 100% inside local browser memory.
Intelligently repairs markdown ```json fences, balances unclosed quotes/brackets, strips JavaScript comments, and removes trailing commas.
Pinpoints exact JSON path errors (`$.items[0].price`), missing required properties, data type mismatches, and enum violations in real time.
Validates against W3C JSON Schema Draft 7, Draft 2020-12, or OpenAI function `parameters` specs.
Export copy-paste validation guardrails for Python, TypeScript / Zod, and LangChain `StructuredOutputParser`.
Instant starter presets for E-Commerce Order Extraction, RAG Citations, AI Agent Tool Outputs, and Support Ticket Classification.
Our LLM Structured Output & JSON Schema Validator is open source under HiMat Technologies. Inspect auto-repair regex patterns, contribute schemas, or star the repository on GitHub.
Unlike remote validation APIs that log LLM responses and prompt contexts, HiMat's LLM Structured Output & JSON Schema Validator executes 100% in local browser memory. Neither your raw LLM outputs, RAG completions, customer data, nor API keys leave your device.
Large Language Models generate text token-by-token. Model context truncation, streaming network timeouts, or instruction drift often cause LLMs to output Markdown fences (```json), unescaped quotes inside strings, trailing commas, or incomplete closing braces.
Our client-side repair engine strips Markdown code blocks, trims conversational preambles, strips JS comments (`//`), replaces invalid single quotes with standard double quotes, removes trailing commas, and balances unclosed object/array brackets automatically.
No. The HiMat LLM Structured Output Validator operates 100% locally in your web browser. Neither your schemas, prompt text, completions, nor customer data ever leave your machine.
Yes! The built-in Guardrail Code tab generates ready-to-run Python, TypeScript Zod, and LangChain parsing snippets that automatically clean and validate raw LLM responses before passing them to backend databases or APIs.
Yes, 100% free with no account registration required, no rate limits, and zero advertisements.
HiMat Technologies builds robust multi-agent systems, deterministic output guardrails, RAG platforms, and custom LLM integrations backed by 21-day fixed-scope AI pilots.
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