A 3-layer temporal RAG memory library for Python. Zero external dependencies. just stdlib + sqlite3. 43KB installed. Core idea: a self-growing tag dictionary. Day 1, every tag extraction calls your AI. Day 90, ~90% resolve from a local keyword map instantly. Cost goes down over time. L1: 3-day Markdown logs. L2: 30-day AI summaries. L3: SQLite FTS5 permanent archive (~1ms search). Works with any AI, OpenAI, Claude, Gemini, or local models. pip install sandclaw-memory
Hi everyone! I'm the maker of sandclaw-memory.
I built this while working on a larger AI project. The memory
system grew to 7,600 lines, and I realized the core concept,
self-growing tags + temporal layers + zero dependencies, was
useful beyond my own project.
Every memory library I found needed a vector DB, a graph DB,
or some external infrastructure. I wanted something that just
works with pip install. So I extracted it into a standalone
library.
The part I'm most proud of: the keyword_map that learns over
time. Your AI costs actually go down the more you use it,
instead of staying flat.
No GPU, no Docker, no vector DB. Just pip install on any machine.
Happy to answer any questions about the architecture or
trade-offs!
No comment highlights available yet. Please check back later!
About sandclaw-memory on Product Hunt
“Zero-dep Python RAG memory that gets cheaper over time”
sandclaw-memory was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #327 on the daily leaderboard. A 3-layer temporal RAG memory library for Python. Zero external dependencies. just stdlib + sqlite3. 43KB installed. Core idea: a self-growing tag dictionary. Day 1, every tag extraction calls your AI. Day 90, ~90% resolve from a local keyword map instantly. Cost goes down over time. L1: 3-day Markdown logs. L2: 30-day AI summaries. L3: SQLite FTS5 permanent archive (~1ms search). Works with any AI, OpenAI, Claude, Gemini, or local models. pip install sandclaw-memory
sandclaw-memory was featured in Open Source (68.3k followers), Developer Tools (511k followers), Artificial Intelligence (466.2k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 182.6k products, making this a competitive space to launch in.
Who hunted sandclaw-memory?
sandclaw-memory was hunted by kokogo100. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how sandclaw-memory stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
kokogo100