🪷 स्मृति

smriti

Vedic Smriti tradition

Sacred Memory — the 18 Smritis

Multi-backend caching for LLM agents: memory, disk, Redis-compatible interface. TTL, LRU, semantic cache.

Formerly agent-cache — Part of the Vedic Arsenal: 100 production-grade Python libraries for LLM agents, each named from the Vedas, Puranas, and Mahakavyas.

The Vedic Principle

The eighteen Smritis — sacred texts of remembered law and tradition — preserved the Vedic wisdom across millennia without digital storage. Pure memory, encoded in verse, passed from guru to shishya.

smriti brings this sacred memory architecture to LLM agents. Semantic cache, exact cache, TTL-aware LRU — the agent remembers what it has learned. The TF-IDF similarity matching finds the memory that feels like the current question, even if the words differ.

Smriti means "that which is remembered." Every cached response is a memory. Every cache hit is the agent drawing on its accumulated wisdom rather than asking the universe the same question twice.

How It Works

flowchart LR
    A[Input] --> B[smriti]
    B --> C{Process}
    C -- Success --> D[Output]
    C -- Error --> E[Handle]
    E --> B
    style B fill:#6b21a8,color:#fff

Installation

pip install smriti

Or from source:

git clone https://github.com/darshjme/smriti.git
cd smriti && pip install -e .

Quick Start

from smriti import *

# See examples/ for full usage

The Vedic Arsenal

smriti is one of 100 libraries in darshjme/arsenal — each named from sacred Indian literature:

Each library solves one problem. Zero external dependencies. Pure Python 3.8+.

Contributing

1. Fork the repo
2. Create feature branch (git checkout -b fix/your-fix)
3. Add tests — zero dependencies only
4. Open a PR