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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.

Device scan & portable packaging

Scan Claude Code, Cursor, and OpenCode data across your machine (not just the repo):

# Discover locations
memorymesh scan

# Ingest all provider messages
memorymesh pull -p myproject

# Build portable package for another machine/provider
memorymesh package build -p myproject -o ./udata.tar.gz

# Import on another machine
memorymesh package import ./udata.tar.gz -p myproject

See docs/U_DATA_PACKAGING.md and docs/STORAGE_PATHS.md.

Export chat & memory

Export everything for a project (messages, summaries, agent state) as plain text, Markdown, or JSON:

memorymesh export -p myproject --format md -o ./export.md
memorymesh export -p myproject --format txt --clipboard
memorymesh export -p myproject --format json --provider cursor

The TUI (memorymesh tui) adds [7] Export. The HTTP API accepts POST /export with {"project":"...", "format":"md", "clipboard": true}.

About

LLM Cross-provider Context / Memory Bridge Tool

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