AI & LLM 4 Tools Available 100% Client-Side

AI & LLM Engineering

Professional browser-native utilities for prompt engineering, token counting, structured outputs, and RAG vector chunking. All tools operate entirely in your web browser with zero server roundtrips, offline caching, and strict data privacy.

Available Utilities in this Category

Showing all 4 utilities

Essential

Universal LLM Token Counter, Colorizer & Context Estimator

Universal token counter and cost calculator for OpenAI, Anthropic, Gemini, and Llama 3.

✓ Multi-model subword token estimation supporting OpenAI GPT 5.6 (Luna/Sol/Terra/5.5), Claude (Opus 5/Sonnet 5/Fable 5/Haiku 4.5), Gemini 3 (3.7/3.6/3.5/3.1), Grok (4.6/4.5), Qwen (3.8 Max/Flash), DeepSeek (V4 Flash/Pro), Z.ai (GLM 5.3/5.2), and Moonshot (Kimi K3/K2.7)

✓ Interactive token stream colorizer revealing exact subword token splits, prefixes, whitespace handling, and byte-pair boundaries

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Popular

Interactive System Prompt, Meta-Prompt & Persona Studio

Interactive studio to generate structured, production-ready system prompts and meta-prompts.

✓ Modular Prompt Architecture: Build system prompts with structured sections for Role & Persona, Core Objectives, Operating Rules, Constraints, and Output Format

✓ Model-Specific Presets: Auto-format prompts using Anthropic Claude XML tags (<instructions>, <context>), OpenAI developer messages, or DeepSeek/Llama markdown conventions

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Essential

LLM Function Calling & Structured Outputs Schema Generator

Generate strict JSON Schemas, OpenAI Structured Outputs, and function calling tools.

✓ Multi-Target Schema Synthesis: Generate compliant schemas for OpenAI Structured Outputs (response_format: json_schema), OpenAI Tools, Anthropic Claude Tools (input_schema), and Gemini FunctionDeclarations

✓ Strict Mode Compliance: Automatically enforces additionalProperties: false, complete required arrays, and non-empty property descriptions required for deterministic LLM constrained decoding

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New

RAG Document Text Chunker & Token Overlap Splitter

Split documents and code for RAG with recursive, token-aware chunking and overlap.

✓ Multiple Chunking Strategies: Recursive Character Splitting, Token-Length Windowing, Paragraph/Sentence Boundary Splitting, and Markdown Header hierarchy segmentation

✓ Configurable Overlap Percentage: Set chunk overlap (0% to 50% / 10–200 tokens) to maintain contextual coherence across chunk seams and eliminate split-context retrieval failures

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