Chatbot optimization
Token compression for AI chatbots
Every chatbot conversation accumulates history that gets sent on every turn. Token compression removes the low-value messages while keeping the evidence needed for the next response.
Why chatbots need compression
A typical support chatbot conversation has 10-20 messages. By turn 10, the full history may be 3,000-5,000 tokens. At turn 20, it could be 8,000+ tokens. Most of this history is irrelevant to the latest customer question.
Compressing the conversation history before each LLM call saves 60-85% on input tokens while keeping all answer-relevant context.
Integration example
from supercompress import Compressor
comp = Compressor()
async def chat_response(messages, user_query):
history = format_history(messages)
compressed = comp.compress(history, user_query)
return await llm.chat(compressed.compressed_text, user_query)Frequently asked questions
Does compression change the chatbot personality?
No. System prompts and instructions are kept intact.
Can I use this with Dialogflow or Rasa?
Yes. Compression happens before the LLM call in your middleware.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.