Cloud memory for AI agents. 3 lines to integrate. No SDK dependencies.
Project description
🃏 HyperStack Python SDK
Cloud memory for AI agents. Zero dependencies. 3 lines to integrate.
Install
pip install hyperstack-py
Quick Start
from hyperstack import HyperStack
hs = HyperStack("hs_your_key")
# Store a memory
hs.store("project-api", "API", "FastAPI 3.12 on AWS", stack="projects", keywords=["fastapi", "python"])
# Search memories
results = hs.search("python")
# List all cards
cards = hs.list()
# Delete a card
hs.delete("project-api")
# Get usage stats
stats = hs.stats()
print(f"Saving {stats['savings_pct']}% on tokens!")
# Auto-extract from conversation text
hs.ingest("Alice is a senior engineer. We decided to use FastAPI over Django.")
Why HyperStack?
- Zero dependencies — just Python stdlib
- No LLM costs — memory ops are free
- 94% token savings — ~350 tokens vs ~6,000 per message
- 30-second setup — get key at cascadeai.dev
API Reference
| Method | Description |
|---|---|
store(slug, title, body, stack, keywords) |
Create/update a card |
search(query) |
Search cards |
list(stack=None) |
List all cards |
get(slug) |
Get one card |
delete(slug) |
Delete a card |
stats() |
Usage summary |
ingest(text) |
Auto-extract memories |
Get a free key
→ cascadeai.dev — 50 cards free, no credit card.
License
MIT © CascadeAI
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