Integration guide

LangChain prompt compression integration

Add SuperCompress to any LangChain application with a custom callback handler. Every LLM call in your chain automatically gets compressed context.

By Arjun Shah - Creator of SuperCompress - Updated 2026-07-03

Installation

pip install supercompress langchain

Callback handler for LangChain

from supercompress import Compressor
from langchain.callbacks import BaseCallbackHandler

class CompressionHandler(BaseCallbackHandler):
    def __init__(self):
        self.comp = Compressor()

    def on_llm_start(self, serialized, prompts, **kwargs):
        result = self.comp.compress(prompts[0].text, "Continue.")
        prompts[0].text = result.compressed_text

Using with LangChain agents

from langchain.agents import create_react_agent
agent = create_react_agent(llm, tools, prompt)
agent.callbacks = [CompressionHandler()]

Frequently asked questions

Does this work with LangChain streaming?

Yes. Compress first, then stream normally.

Can I use it with LangGraph?

Yes. Add the CompressionHandler to any node that calls an LLM.

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.

Get an API keyRead the guide