LLM Context Trimmer & Token Pruner
Trim 20-50% of wasted tokens from source code, docs, and datasets by stripping fluff, redundant whitespace, and markdown bloat.
LLM Context & Token Trimmer
Trim bloated prompt context, strip comments, excessive whitespace, and old turns to slash token counts by 30%–60% and save API costs.
=== SECTION 1: OVERVIEW & CORE PRINCIPLES === The platform provides a high-throughput event-driven microservices architecture. Every incoming request must carry a valid Bearer token in the Authorization header. ### Message Exchange Protocol: 1. Client sends POST request to /v1/events. 2. Gateway verifies HMAC signature. 3. Worker thread dispatches event to Kafka queue. User: How do I handle 429 Too Many Requests? Assistant: When receiving a 429 status, inspect the 'Retry-After' header and use exponential backoff with jitter. User: Can you show me an example? Assistant: Yes, here is an example in JavaScript using setTimeout.
Overview
Free client-side LLM context trimmer. Strip code comments, collapse excessive whitespace, drop badges/images, truncate log lines, and view real-time token savings and cost reductions.
LLM Context Trimmer & Token Pruner Guide
While modern LLMs boast massive context windows, bloated context inflates API costs and degrades reasoning recall ("lost in the middle").
Source code files and markdown documentation often contain redundant whitespace, verbose comments, and badge links that consume expensive tokens needlessly.
This client-side utility prunes 20% to 50% of wasted tokens while preserving semantic intent.
How to Prune and Compress Prompt Context
Fast & IntuitivePaste Source Content
Paste your code, raw logs, or markdown documentation into the input panel.
Toggle Pruning Options
Enable comment stripping, whitespace collapsing, and badge removal.
Inspect Savings & Copy
Check the real-time token reduction percentage and copy the clean context.
Key Highlights & Advantages
Zero server uploads. Everything processes securely within your local browser memory.
Immediate results with no file upload or download queues.
No registration, no paywalls, and no hidden quotas.
Seamlessly optimized for mobile smartphones, tablets, and desktop workstations.
All data processing runs natively via W3C compliant browser hardware acceleration.
- Strip SVG badges and image tags from GitHub READMEs before asking LLMs to summarize libraries.
- Collapse repetitive log timestamps to keep error traces within strict token limits.
Frequently Asked Questions
2 Q&AsWill removing comments hurt code generation accuracy?
Usually not. Clean identifiers allow LLMs to comprehend logic without bulky license headers or inline comments.
How is token count estimated?
Using standard BPE tokenization approximations (~4 characters or 0.75 words per token).
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