neruva-langgraph 0.1.0

Drop-in LangGraph integration for Neruva agent memory. Wraps BaseCheckpointSaver + BaseStore to persist graph checkpoints and cross-thread KV state into the Neruva substrate (records + KG + federated agent_context). One-line install, works with every LangGraph StateGraph.

neruva-langgraph

Drop-in LangGraph integration for Neruva agent memory. Two wrappers cover the canonical LangGraph plug points: graph checkpoints and the cross-thread KV store.

pip install neruva-langgraph

NeruvaCheckpointSaver

Persists every graph checkpoint into Neruva Records. One namespace per thread_id, so each conversation gets its own replayable timeline. Drop into any StateGraph.compile():

from langgraph.graph import StateGraph
from neruva_langgraph import NeruvaCheckpointSaver

graph = builder.compile(
    checkpointer=NeruvaCheckpointSaver(api_key="nv_..."),
)
graph.invoke(
    {"input": "hello"},
    config={"configurable": {"thread_id": "user_alice"}},
)

Realistic example -- multi-turn StateGraph with persistent checkpoints

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_anthropic import ChatAnthropic
from neruva_langgraph import NeruvaCheckpointSaver

class State(TypedDict):
    messages: list

def call_model(state: State) -> State:
    llm = ChatAnthropic(model="claude-opus-4-7")
    state["messages"].append(llm.invoke(state["messages"]))
    return state

builder = StateGraph(State)
builder.add_node("model", call_model)
builder.set_entry_point("model")
builder.add_edge("model", END)
graph = builder.compile(checkpointer=NeruvaCheckpointSaver())
graph.invoke({"messages": ["What did I tell you last week?"]},
             config={"configurable": {"thread_id": "user_alice"}})

NeruvaContextStore

LangGraph's BaseStore for cross-thread KV (e.g. user preferences, shared facts). Backed by Records + agent_recall for semantic search:

from neruva_langgraph import NeruvaContextStore

store = NeruvaContextStore(api_key="nv_...")
graph = builder.compile(
    checkpointer=NeruvaCheckpointSaver(),
    store=store,
)
store.put(("users", "alice"), "profile", {"city": "Toronto"})
hit = store.get(("users", "alice"), "profile")
print(hit)   # -> Item(value={'city': 'Toronto'}, ...)

Why Neruva instead of LangGraph's built-in checkpointers?

Feature	LangGraph defaults	Neruva
Persists across process restart	sqlite / postgres setup	Built-in (GCS-backed)
Cross-session recall	No	Yes via namespaces=[...]
Vector / semantic store search	No (KV only)	Yes via agent_recall
Auto fact extraction (KG)	No	Auto (hd_kg_extraction_prompt)
Determinism / replayability	No	Bit-identical from seed
Portability	DB-locked	.neruva zip container

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