langchain-dkg 0.1.7

LangChain memory and retriever backed by OriginTrail DKG v10 Working Memory

langchain-dkg

LangChain memory and retriever backed by OriginTrail DKG v10 Working Memory.

Give any LangChain agent persistent, verifiable, queryable memory — every conversation turn stored as a cryptographically-linked Knowledge Asset on the Decentralized Knowledge Graph.

Install

pip install langchain-dkg

Requires a running DKG v10 node. Install with:

npm install -g @origintrail-official/dkg
dkg init && dkg start
export DKG_TOKEN=$(dkg auth show)

Quick start

from langchain_dkg import DKGChatMessageHistory, DKGMemory, DKGRetriever
from langchain_core.messages import HumanMessage, AIMessage

# Store and retrieve conversation turns
history = DKGChatMessageHistory(context_graph_id="my-project")
history.add_message(HumanMessage(content="What is a Knowledge Asset?"))
history.add_message(AIMessage(content="An ownable container of structured knowledge on the DKG."))

messages = history.messages  # tri-modal semantic search

With a LangChain chain (modern LCEL style)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_dkg import DKGMemory

llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}"),
])

chain_with_memory = DKGMemory.wrap_chain(
    prompt | llm,
    context_graph_id="my-project",
)

response = chain_with_memory.invoke(
    {"input": "What is DKG?"},
    config={"configurable": {"session_id": "user-42"}},
)

RAG retrieval via SPARQL

from langchain_dkg import DKGRetriever
from langchain.chains import RetrievalQA

retriever = DKGRetriever(limit=10)
chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

Components

Class	LangChain base	Purpose
DKGChatMessageHistory	BaseChatMessageHistory	Stores turns in DKG WM; retrieves via tri-modal search
DKGMemory	—	Factory for RunnableWithMessageHistory with DKG backend
DKGRetriever	BaseRetriever	SPARQL retriever — returns triples as Document objects
DKGClient	—	Low-level async HTTP client for the DKG v10 API

Memory layers

DKG v10 has three memory layers:

Layer	Scope	Cost	Use
Working Memory (wm)	Private to your node	Free	Default for conversation history
Shared Working Memory (swm)	Gossip-replicated	Free	Team-visible context
Verified Memory	On-chain, permanent	TRAC	Auditable, publishable knowledge

By default, turns are written to Shared Working Memory (swm). Use layer="wm" for private-only storage.

Explicit promotion to Shared Memory:

turn_uri = history.get_turn_uri("**Human:** Summarize this meeting")
await history.promote_to_shared(turn_uri)

Configuration

Env var	Default	Description
DKG_TOKEN	—	Bearer token from dkg auth show
DKG_BASE_URL	http://localhost:9200	DKG node API URL

Or pass token= / base_url= directly to DKGClient.

Session isolation

Each session_id passed to chain_with_memory.invoke(config={"configurable": {"session_id": "..."}}) becomes a sessionUri in DKG, linking turns together within the shared Context Graph.

Development

pip install -e ".[dev]"
pytest tests/unit/                                    # unit tests (no node required)
DKG_TOKEN=$(dkg auth show) pytest tests/integration/  # integration tests
python examples/research_agent.py                     # demo script

License

MIT