Show HN: Headroom (OSS): Cuts LLM costs by 85%(github.com)
github.com
Show HN: Headroom (OSS): Cuts LLM costs by 85%
https://github.com/chopratejas/headroom
5 comments
Some results from real world data so far:
What is it?
- Context Compression (with Reversibility - this part is the difference) for LLMs
- very different than any compression or summarization tools that promise cost savings and speed!
- claude code costs / cursor costs - reduced by 50-60%
- ideal for startups and Enterprises!!
- integration with LangChain
- Memory as a first class citizen
- its OSS! So Free!
Give it a try, Its OSS - if you love it, star it. If you don't, lets make it better, together!
- Context Compression (with Reversibility - this part is the difference) for LLMs
- very different than any compression or summarization tools that promise cost savings and speed!
- claude code costs / cursor costs - reduced by 50-60%
- ideal for startups and Enterprises!!
- integration with LangChain
- Memory as a first class citizen
- its OSS! So Free!
Give it a try, Its OSS - if you love it, star it. If you don't, lets make it better, together!
Seems very useful.
I tried it on my Claude code and it was saving approximately 50%
Do you know how I can push it to save more?
Do you also have plans to make it Enterprise ready?
Not a single example of what it does or how it works
Fair enough. Trying to keep it concise here - This is how you install it:
pip install "headroom-ai[proxy]"
headroom proxy --port 8787
It will:
* Check all the data going into the LLM and apply intelligent compression based on the content type - different for JSONs, code etc.
* If the LLM is not getting what it is seeking, there is reversible compression - so the LLM will not lose accuracy
* When you think of MCP tools, code function calls etc. that fill up the context window and cause needle in haystack problems - they get eliminated.
There is also an SDK which works like this:
from langchain_openai import ChatOpenAI from headroom.integrations import HeadroomChatModel
# Wrap your model - that's it!
llm = HeadroomChatModel(ChatOpenAI(model="gpt-4o"))
# Use exactly like before response = llm.invoke("Hello!")
Ive personally used it with Claude Code and Cursor and seen the benefits.
pip install "headroom-ai[proxy]"
headroom proxy --port 8787
It will:
* Check all the data going into the LLM and apply intelligent compression based on the content type - different for JSONs, code etc.
* If the LLM is not getting what it is seeking, there is reversible compression - so the LLM will not lose accuracy
* When you think of MCP tools, code function calls etc. that fill up the context window and cause needle in haystack problems - they get eliminated.
There is also an SDK which works like this:
from langchain_openai import ChatOpenAI from headroom.integrations import HeadroomChatModel
# Wrap your model - that's it!
llm = HeadroomChatModel(ChatOpenAI(model="gpt-4o"))
# Use exactly like before response = llm.invoke("Hello!")
Ive personally used it with Claude Code and Cursor and seen the benefits.