Deepiri MemoryMesh 🧠

Drop-in memory layer for ANY AI agent

from memorymesh import Memory

mem = Memory()

mem.store("user likes rust")
mem.query("what does the user like?")
# → ["user likes rust"]

Install

pip install memorymesh

For semantic embeddings (recommended):

pip install memorymesh[embeddings]

Usage

from memorymesh import Memory

mem = Memory()                    # uses ~/.memorymesh/memory.db
mem.store("user prefers dark mode")
mem.store("working on async refactor")

# Query by semantic similarity
mem.query("theme preference")
# → ["user prefers dark mode"]

mem.query("async work", top_k=5)
# → ["working on async refactor", ...]

API

Method	Description
Memory(db_path=None, embedder="auto")	Create memory. Set embedder="fallback" to skip embeddings model.
mem.store(content)	Store a memory (deduped).
mem.query(query, top_k=3)	Query by semantic similarity.
mem.all()	List all memories.

Philosophy

• Dead simple – 2 methods: store() and query()
• Plug-and-play – works out of the box, no config
• Local-first – SQLite + optional sentence-transformers
• Zero deps – falls back to deterministic hashing if no model

Works with any AI agent, tool, or assistant.