MemG Python SDK

Python SDK for MemG -- a pluggable memory layer for LLM applications.

Installation

pip install memg

# With provider extras
pip install memg[openai]
pip install memg[anthropic]
pip install memg[all]

Quick Start

Proxy Mode (simplest)

Redirect your LLM client through the MemG proxy. No extra code needed beyond wrapping:

from openai import OpenAI
from memg import MemG

client = OpenAI()
client = MemG.wrap(client, entity="user-123", mode="proxy")

# Use as normal -- MemG injects and extracts memories transparently
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "I love hiking in the mountains"}],
)

Client Mode (no proxy needed)

The SDK intercepts calls, queries the MCP server for relevant memories, injects them, and extracts new knowledge:

from openai import OpenAI
from memg import MemG

client = OpenAI()
client = MemG.wrap(client, entity="user-123", mode="client")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Remember I prefer dark roast coffee"}],
)

Direct Memory Operations

from memg import MemG

m = MemG()

# Add memories
m.add("user-123", "likes coffee")
m.add("user-123", ["works at Acme", "prefers dark mode"])

# Search
results = m.search("user-123", "coffee preferences")
for mem in results.memories:
    print(f"{mem.content} (score={mem.score})")

# List
all_mems = m.list("user-123", type="identity")

# Delete
m.delete("user-123", memory_id="some-uuid")
m.delete_all("user-123")

m.close()

Modes

Configuration

Default URLs:

• MCP server: http://localhost:8686
• Proxy: http://localhost:8787/v1

Override via constructor or wrap():

m = MemG(mcp_url="http://custom:8686", proxy_url="http://custom:8787/v1")
MemG.wrap(client, mode="proxy", proxy_url="http://custom:8787/v1")