neruva-langchain 0.1.1

Drop-in LangChain integration for Neruva agent memory. NeruvaChatMessageHistory (BaseChatMessageHistory) auto-pipes turns into Records + KG via agent_remember. NeruvaContextRetriever (BaseRetriever) federates Records + KG across one or many namespaces. Works with RunnableWithMessageHistory + RetrievalQA. Persists across restarts. Sub-100ms p95.

neruva-langchain

Drop-in LangChain integration for Neruva agent memory. Three wrappers cover the common LangChain plug points: chat history, conversation memory, and document retriever.

pip install neruva-langchain

NeruvaChatMessageHistory

Auto-records every turn into the Neruva Records substrate. Drop into any LangChain primitive that accepts a BaseChatMessageHistory:

from neruva_langchain import NeruvaChatMessageHistory

history = NeruvaChatMessageHistory(
    api_key="nv_...",          # or env NERUVA_API_KEY
    namespace="user_alice",    # one per user / session
)

history.add_user_message("My name is Alice and I live in Toronto.")
history.add_ai_message("Nice to meet you, Alice!")

# Later — even after process restart, substrate-backed:
print(history.messages)

Use with RunnableWithMessageHistory (modern pattern)

from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from neruva_langchain import NeruvaChatMessageHistory

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}"),
])
chain = prompt | ChatAnthropic(model="claude-opus-4-7")

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: NeruvaChatMessageHistory(namespace=session_id),
    input_messages_key="input",
    history_messages_key="history",
)
chain_with_history.invoke(
    {"input": "What did I tell you about my project last week?"},
    config={"configurable": {"session_id": "user_alice"}},
)

NeruvaContextRetriever

BaseRetriever for RetrievalQA chains. Returns Document objects sourced from federated agent_recall:

from neruva_langchain import NeruvaContextRetriever
from langchain.chains import RetrievalQA
from langchain_anthropic import ChatAnthropic

retriever = NeruvaContextRetriever(
    api_key="nv_...",
    namespaces=["session_a", "session_b"],   # multi-session fan-out
)
qa = RetrievalQA.from_chain_type(
    llm=ChatAnthropic(model="claude-opus-4-7"),
    retriever=retriever,
)
qa.invoke("Where does Alice work?")

Why use Neruva instead of LangChain's built-in memory?

Feature	LangChain default	Neruva
Persists across process restart	Manual setup	Built-in (GCS-backed)
Cross-session recall	No	Yes via namespaces=[...]
Fact extraction (KG)	No	Auto (hd_kg_extraction_prompt + caller LLM)
GDPR forget by user	Manual	user_id= auto-folds, one-call forget
Determinism / replayability	No	Bit-identical from seed
Portability	Pickle	.neruva zip container

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