Brevit Playground
Brevit semantically compresses data for LLM prompts to reduce tokens while keeping meaning and structure. Try brevity() (auto strategy) and optimize() (explicit) and compare formats.
Quick facts
Token reduction
Often 40–60% depending on structure
Auto mode
brevity() selects the best strategy
Works with
JSON, text, tabular arrays, more
Install
npm i brevit
Mode
Intent (optional)
Compare formats
Token counts are estimated.
JSON: 0
YAML: 0
Brevit: 0
JSON
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YAML
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Brevit
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Test with OpenAI (via Puter)
Runs in your browser using Puter’s client-side API (no OpenAI key stored in this app). See Puter’s tutorial: Free, Unlimited OpenAI API.
Model
Prompt / Task
Tip: run Brevit first to compare AI response quality on the compressed input.
Original response
est. input tokens: 56
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Brevit response
est. input tokens: 0
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Installation & Features
Installation
Features & Functions
Core Methods
- brevity() — Auto mode that selects the best compression strategy automatically
- optimize() — Explicit optimization with optional intent parameter
Supported Data Types
- • JSON objects and arrays
- • Plain text and structured text
- • Tabular data (arrays of objects)
- • Nested structures
- • Mixed content types
Benefits
- • 40–60% token reduction on average
- • Preserves semantic meaning and structure
- • Works with all major LLM providers
- • Reduces API costs and latency
- • Improves context window utilization
Use Cases
- • LLM prompt optimization
- • API request payload compression
- • RAG (Retrieval-Augmented Generation) systems
- • Chatbot context management
- • Data preprocessing for AI pipelines