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.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.