memorymesh-ai 0.2.0

Shared memory for Claude, Ollama, and GPT. Local. Private. One pip install.

MemoryMesh

Claude figured it out. Ollama forgot it. MemoryMesh fixes that.

One shared memory for all your AI tools. Local. Private. Zero cloud. Works in 60 seconds.

Install

pip install memorymesh

The problem

You spend 5 minutes explaining your stack, your preferences, your project context to Claude. It gets it. Then you switch to Ollama for a quick local task — and it knows nothing. You switch to GPT for a second opinion — nothing. Every tool starts from zero. Every single time.

The fix — three lines

import memorymesh as mm

mm.remember("user builds in FastAPI + PostgreSQL, hates ORMs")
mm.recall("tech stack")

Claude + Ollama — one shared brain

from memorymesh.connectors.claude import ClaudeWithMemory
from memorymesh.connectors.ollama import OllamaWithMemory

# Claude saves context
claude = ClaudeWithMemory()
claude.remember("Backend is FastAPI, DB is PostgreSQL, no ORMs")
response = claude.chat("How should I structure the billing module?")

# Ollama reads the SAME memory — zero setup
# Requires: ollama serve && ollama pull llama3.2
ollama = OllamaWithMemory(model="llama3.2")
response = ollama.chat("Help me with the billing queries")
# Ollama already knows: FastAPI, PostgreSQL, no ORMs

OpenAI

from memorymesh.connectors.openai import OpenAIWithMemory

gpt = OpenAIWithMemory()
response = gpt.chat("Review my database schema")
# Same shared memory as Claude and Ollama

Claude Code — MCP server

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "memorymesh": {
      "command": "memorymesh",
      "args": ["serve"]
    }
  }
}

Claude gets 5 tools:

• remember_memory — store a memory
• recall_memory — search memories
• get_context — get formatted context string
• forget_memory — delete a memory
• memory_stats — show statistics

pip install memorymesh
memorymesh serve

Smart Context (v0.2)

v0.1 as_context() returns everything matching the query. Over time, this causes context bloat.

v0.2 adds smart_context() which prunes, decays, and filters:

# Instead of:
ctx = mm.as_context("current project")

# Use:
ctx = mm.smart_context("current project")
# Automatically removes old/irrelevant memories
# Keeps your prompts clean as memory grows

# Configure:
ctx = mm.smart_context(
    "current project",
    min_relevance=0.3,  # filter below 30% relevance
    ttl_days=30,        # forget after 30 days
)

CLI

memorymesh remember "user prefers dark mode"
memorymesh recall "preferences"
memorymesh smart-recall "preferences"   # relevance-filtered
memorymesh prune                        # remove expired memories
memorymesh decay                        # apply importance decay
memorymesh list
memorymesh stats
memorymesh serve

Why local?

• Data stays at ~/.memorymesh/memory.db — plain SQLite
• No API keys for core functionality
• Works offline
• Open the DB with any SQLite viewer — it is yours forever

Limitations

• Relevance scoring is keyword-based, not semantic (semantic search with embeddings planned for v0.3)
• TTL is based on creation date, not last meaningful use
• Decay requires manual call or auto_prune=True
• FTS5 is keyword search, not semantic — recall("FastAPI") works, recall("what framework?") does not
• No sync across machines (by design — your data stays on your machine)
• Ollama connector requires ollama serve running locally

Roadmap

• Core: remember / recall / as_context
• Connectors: Claude / Ollama / OpenAI
• MCP server (Claude Code integration)
• CLI
• Semantic search with local embeddings
• Auto-learn from conversation history
• LangChain / LlamaIndex connectors

MIT - github.com/originaonxi/memorymesh