with_memwal

with_memwal_langchain and with_memwal_openai wrap an existing LLM client with automatic memory management. Before each call relevant memories are recalled and injected; after each call the user message is analyzed for new facts (fire-and-forget).

Both integrations import their dependency lazily, install only what you use:

LangChain

Source: python-sdk/usage/with-memwal.md

$ pip install memwal[langchain]

OpenAI

Source: python-sdk/usage/with-memwal.md

$ pip install memwal[openai]

LangChain

Source: python-sdk/usage/with-memwal.md

import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from memwal import with_memwal_langchain

llm = ChatOpenAI(model="gpt-4o")
smart_llm = with_memwal_langchain(
    llm,
    key=os.environ["MEMWAL_PRIVATE_KEY"],
    account_id=os.environ["MEMWAL_ACCOUNT_ID"],
    env="prod",
    namespace="chatbot-prod",
    max_memories=5,
    min_relevance=0.3,
)

response = await smart_llm.ainvoke([HumanMessage("What are my food allergies?")])

Patches both _agenerate (async) and _generate (sync) on the model instance.

OpenAI SDK

Works with both openai.OpenAI (sync) and openai.AsyncOpenAI (async), the wrapper detects which and patches chat.completions.create accordingly.

Source: python-sdk/usage/with-memwal.md

import os
from openai import AsyncOpenAI
from memwal import with_memwal_openai

client = AsyncOpenAI()
smart_client = with_memwal_openai(
    client,
    key=os.environ["MEMWAL_PRIVATE_KEY"],
    account_id=os.environ["MEMWAL_ACCOUNT_ID"],
    env="prod",
)

response = await smart_client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What are my food allergies?"}],
)

The JS-style alias withMemWal is exported as a shortcut for with_memwal_langchain.

What it does

Before generation:

• Reads the last user message
• Runs recall() against Walrus Memory
• Filters by min_relevance (default 0.3)
• Injects matching memories as a system message before the last user message

After generation:

• If auto_save (default True), runs analyze() on the user message fire-and-forget
• Extracted facts are stored asynchronously

Options

Both wrappers accept the same keyword arguments:

Option	Default	Description
server_url	https://relayer.memory.walrus.xyz	Explicit relayer URL (wins over env)
env		Hosted relayer preset: staging for testing or prod for production
namespace	"default"	Memory namespace
max_memories	5	Max memories injected per request
auto_save	True	Auto-save new facts from the conversation
min_relevance	0.3	Minimum similarity (0–1) to include a memory
debug	False	Verbose logging through the memwal logger

When to use direct SDK calls instead

Use direct MemWal methods when you need precise control over when memory is stored, which text is analyzed, or how recall results are filtered and displayed.