This will help you get started with the Memgraph toolkit. Tools within MemgraphToolkit are designed for the interaction with the Memgraph database.

​
Setup

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: 
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

​
Installation

This toolkit lives in the langchain-memgraph package: 
%pip install -qU langchain-memgraph

​
Instantiation

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,  # Memgraph instance
    llm=llm,  # LLM chat model for LLM operations
)

​
Tools

View available tools: 
toolkit.get_tools()

​
Invocation

Tools can be individually called by passing an arguments, for QueryMemgraphTool it would be: 
from langchain_memgraph.tools import QueryMemgraphTool

# Rest of the code omitted for brevity

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

​
Use within an agent

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()

​
API reference

For more details on API visit Memgraph integration docs