Mem0Middleware

Two construction paths:

1. Models — pass an AgentScope chat_model and embedding_model (optionally with a custom mem0_config as base for vector store / history DB / etc.). The middleware builds an OSS AsyncMemory internally, wired so mem0's memory extraction and embedding both go through your AgentScope models.
2. Client — pass a pre-built mem0.AsyncMemory / mem0.AsyncMemoryClient when you want full control (e.g. hosted Platform, sharing one mem0 across agents). When client is given it takes precedence and the other backend kwargs are ignored.

Three control patterns are available via the mode parameter ("static_control" / "agent_control" / "both"); see the constructor's mode arg for what each does.

Example (build OSS internally)::

from agentscope.middleware import Mem0Middleware
from agentscope.tool import Toolkit

mw = Mem0Middleware(
    user_id="alice",
    chat_model=my_chat_model,
    embedding_model=my_embedding_model,
    mode="both",
)
agent = Agent(
    ...,
    toolkit=Toolkit(tools=await mw.list_tools()),
    middlewares=[mw],
)

Example (hosted Platform with pre-built client)::

from agentscope.middleware import Mem0Middleware
from agentscope.tool import Toolkit
from mem0 import AsyncMemoryClient

mw = Mem0Middleware(
    user_id="alice",
    client=AsyncMemoryClient(api_key="m0-..."),
    mode="both",
)
agent = Agent(
    ...,
    toolkit=Toolkit(tools=await mw.list_tools()),
    middlewares=[mw],
)