Slash Your LLM API Bills by up-to 40%+ Natively.
A drop-in client proxy and token optimization engine. Strip semantic boilerplate fluff, protect vital schema parameters, and maximize context visibility in real-time.
How to Evaluate Your Prompts
Select Compression Strategy
Toggle between four custom lexer parameters at the top of the interface.
Isolate Context in Memory
Paste text or use Upload Document to stage assets.
Run and Export Assets
Click 'Optimize Context' to execute, then copy optimized streams or download files instantly.
Understanding Optimization Profiles
Conversational Trimming
Strips conversational fluff and filler syntax while preserving intent.
Knowledge-Chunk Sifting
Scans text blocks to eliminate repeated semantic descriptors and stop-words.
Loop Scratchpad Minification
Prunes verbose multi-turn tool traces while maintaining variable bindings.
Source Token Packing
Compresses leading margins, strips comments, and minifies layouts logically.
Production-Grade Developer Ecosystem
Move past the sandbox. Integrate automated token reduction directly into your infrastructure using our native engineering modules.
Python & Node.js SDKs
Deploy our open-source client libraries seamlessly. Intercept verbose RAG outputs or multi-turn agent context streams natively before hitting upstream model endpoints.
npm install @siftprompt/node
pip install siftprompt-coreDrop-In Client Proxy
Zero code refactoring required. Simply point your existing OpenAI or Anthropic client `baseURL` configurations to our secure proxy gateway to force automatic token compression instantly.
Token Spend Analytics
Leverage our paid telemetry data APIs to monitor exact token footprints, track compression ratios across models, and view real-time capital saving dashboards inside your organization console.
// Context Optimization FAQ
How do I reduce Claude Chat tokens during long developer sessions?
Claude models utilize tokens for both instructions and conversational history. By running your prompt through SiftPrompt's chat or codegen compression vectors first, you strip out up to 40% of repetitive text and system rules before submitting. This keeps your context footprint light and prevents early token expiration.
I keep running out of free messages on Claude. Can SiftPrompt help?
Yes. Claude's message limits are directly tied to the total token volume of your conversation window. When you paste large blocks of code or lengthy documentation chunks, you burn through your message allocation exponentially faster. SiftPrompt minifies your input context buffers on the fly, allowing you to fit more actual substance into every single message turn.
Does SiftPrompt support non-English document compression?
Yes. SiftPrompt includes an integrated cross-lingual optimization parser. Because LLM tokenizers handle English far more efficiently than other languages, pasting foreign-language datasets or documentation strings burns through message limits rapidly. SiftPrompt securely standardizes international text payloads into concise English arrays, protecting context depth while driving token overhead down to its absolute floor.
How do your open-source Python and Node.js SDKs handle prompt compression?
Our open-source SDKs act as local orchestrators. They calculate input string weights, communicate with our optimization layers via light JSON calls, and safely return compacted strings. If an internal network exception occurs, the libraries automatically pass your original raw text forward as a fail-safe.
Can I track financial savings using the SiftPrompt token spend analytics API?
Yes. Our paid enterprise analytics service maps incoming traffic across a telemetry dashboard framework, converting raw saved tokens directly into relative USD parameters using real-time pricing matrix tables for OpenAI, Anthropic, and Cohere.