LangChain and LangGraph

Wire OpenViking into your LangChain or LangGraph agent as the context backend. The SDK provides a retriever, chat history, context wrapper, agent tools, LangGraph store, and middleware — all connecting to a running OpenViking server over HTTP.

Install

pip install "openviking[langchain]"       # retriever + chat history
pip install "openviking[langgraph]"       # full LangGraph support (includes langchain)

Connection

from openviking.integrations.langchain import create_openviking_tools

tools = create_openviking_tools(
    url="http://localhost:1933",
    api_key="...",
    profile="agent",
)

When url is omitted, the adapters load connection settings from the OpenViking CLI config. Embedding and VLM providers are configured in OpenViking, not in your app.

Which adapter should I use?

I want to…	Use this
Retrieve relevant context for RAG	OpenVikingRetriever
Wrap a runnable with full session lifecycle (recall + capture + commit)	with_openviking_context()
Give the agent explicit memory tools	create_openviking_tools()
Store durable cross-thread state	OpenVikingStore
Inject context into LangGraph as middleware	OpenVikingContextMiddleware
Back LangChain chat history with OpenViking	OpenVikingChatMessageHistory

Quick examples

Retriever

from openviking.integrations.langchain import OpenVikingRetriever

retriever = OpenVikingRetriever(url="http://localhost:1933", api_key="...")
docs = retriever.invoke("What did the user decide about deployment?")

Context backend

from langchain_core.messages import AIMessage
from langchain_core.runnables import RunnableLambda
from openviking.integrations.langchain import with_openviking_context

chain = with_openviking_context(
    RunnableLambda(lambda msgs: AIMessage(content="...")),
    url="http://localhost:1933",
    api_key="...",
)

Agent tools

from openviking.integrations.langchain import create_openviking_tools

tools = create_openviking_tools(url="http://localhost:1933", profile="agent")
# Includes: viking_find, viking_search, viking_browse, viking_read,
#           viking_grep, viking_store, viking_add_resource, and more

LangGraph store

from openviking.integrations.langchain import OpenVikingStore

store = OpenVikingStore(url="http://localhost:1933", api_key="...")
store.put(("users", "ada"), "preferences", {"color": "azure"})
items = store.search(("users",), query="azure", limit=3)

LangGraph middleware

from openviking.integrations.langchain import OpenVikingContextMiddleware

middleware = OpenVikingContextMiddleware(
    url="http://localhost:1933",
    api_key="...",
    capture_on_after_agent=True,
)

Try the examples

The repository includes runnable examples that work without model credentials using an in-memory test client:

uv run --extra langgraph python examples/langchain-langgraph/langchain/rag/quick_app.py
uv run --extra langgraph python examples/langchain-langgraph/langchain/context-backend/quick_app.py
uv run --extra langgraph python examples/langchain-langgraph/langchain/message-history/quick_app.py
uv run --extra langgraph python examples/langchain-langgraph/langgraph/agent/quick_app.py
uv run --extra langgraph python examples/langchain-langgraph/langgraph/middleware/quick_app.py

For a real OpenViking server and OpenAI-compatible model flow, see the live LangGraph app.

See also

• examples/langchain-langgraph/ — full source for all examples above
• MCP Clients — for non-SDK MCP integration