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Showing content from https://python.langchain.com/docs/integrations/tools/memgraph/ below:

MemgraphToolkit | 🦜️🔗 LangChain

This will help you get started with the Memgraph toolkit.

Tools within MemgraphToolkit are designed for the interaction with the Memgraph database.

To be able tot follow the steps below, make sure you have a running Memgraph instance on your local host. For more details on how to run Memgraph, take a look at Memgraph docs

If you want to get automated tracing from runs of individual tools, you can also set your LangSmith API key by uncommenting below:

This toolkit lives in the langchain-memgraph package:

%pip install -qU langchain-memgraph 

Now we can instantiate our toolkit:

from langchain.chat_models import init_chat_model
from langchain_memgraph import MemgraphToolkit
from langchain_memgraph.graphs.memgraph import MemgraphLangChain

db = MemgraphLangChain(url=url, username=username, password=password)

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

toolkit = MemgraphToolkit(
    db=db,  
    llm=llm,  
)

View available tools:

Tools can be individually called by passing an arguments, for QueryMemgraphTool it would be:

from langchain_memgraph.tools import QueryMemgraphTool



tool.invoke({QueryMemgraphTool({"query": "MATCH (n) RETURN n LIMIT 5"})})

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(llm, tools)

example_query = "MATCH (n) RETURN n LIMIT 1"

events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

For more details on API visit Memgraph integration docs

• Tool conceptual guide
• Tool how-to guides

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