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Llmbuffer

Python library for cache-optimized LLM conversation history

Details

External ID
48483607
Source
HN
Company
—
Product
Llmbuffer
Website domain
github.com
Launched
June 10, 2026
Cohort
—
Upvotes
7
Upvotes percentile
0.42008196721311475
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

I was not getting good cache utilization when including dynamic context in agent threads. After a lot of experimentation, I found a good pattern that minimizes how often long lived conversation history gets modified while still supporting dynamic context. It has flexible hooks for doing things like truncating or summarizing tool outputs when transitioning messages to the long term history. And I'm seeing >>90% of tokens hitting the cache for my agents despite including a lot of dynamic user context.There are a wide range of agent prompting strategies so I'd love to hear where this library works well and where there are patterns that don't fit well into the current API!

Enrichment

Theme
browser automation and scraping for AI
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI feature
Project type
Hobby / open-source project
Normalized one-liner
cache optimization library for llm conversations
Manually corrected
False

Could you build this?

Yes It is a lightweight Python utility library designed to structure message arrays and manage prompt prefixes to maximize LLM KV-cache reuse.

Discussion

1 comment analyzed.

Concerns raised: Order missing first tool set definition

Competitors

Other products that read as similar to this one — 115 launches clear the similarity bar, closest 8 shown.

Attention rank: #63 of 116 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 216 days after the earliest competitor.

Other launches for this product

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