langchain-substrate 0.1.1

SUBSTRATE cognitive memory integration for LangChain and LangGraph

langchain-substrate

SUBSTRATE cognitive memory integration for LangChain and LangGraph.

Use SUBSTRATE as a persistent memory store for LangGraph agents or as a retriever in LangChain RAG pipelines. SUBSTRATE provides causal memory, semantic search, knowledge graphs, emotion state, identity verification, and 61 cognitive capability layers.

Installation

pip install langchain-substrate

Or install from source:

cd integrations/langchain
pip install -e ".[dev]"

Quick Start

Environment Setup

import os
os.environ["SUBSTRATE_API_KEY"] = "sk-sub-..."

As a LangGraph Memory Store

Use SubstrateStore as the backing store for any LangGraph agent. This gives your agent persistent, semantically searchable memory across conversations.

from langchain_substrate import SubstrateStore, SubstrateClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

# Create the SUBSTRATE-backed store
client = SubstrateClient(api_key=os.environ["SUBSTRATE_API_KEY"])
store = SubstrateStore(client=client)

# Create a LangGraph agent with SUBSTRATE memory
model = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(model, tools=[], store=store)

# The agent now persists state to SUBSTRATE
config = {"configurable": {"thread_id": "conversation-1"}}
response = agent.invoke(
    {"messages": [{"role": "user", "content": "Remember that my favorite color is blue."}]},
    config=config,
)

Store Operations

# Store a value
store.put(("user", "alice"), "preferences", {"theme": "dark", "language": "en"})

# Retrieve by key
item = store.get(("user", "alice"), "preferences")
print(item.value)  # {"theme": "dark", "language": "en"}

# Semantic search across memory
results = store.search(("user", "alice"), query="color preferences", limit=5)
for item in results:
    print(item.key, item.value)

# Delete is a no-op (SUBSTRATE memory is append-only)
store.delete(("user", "alice"), "preferences")

Multi-Tenant Isolation

# Use namespace_prefix for tenant isolation
store = SubstrateStore(
    client=client,
    namespace_prefix="myapp.prod",
)
# All operations are scoped under "myapp.prod.*"
store.put(("user", "bob"), "state", {"step": 3})

As a LangChain Retriever (RAG)

Use SubstrateRetriever in any LangChain RAG chain. It uses SUBSTRATE's hybrid search (semantic + keyword) to find relevant memories.

from langchain_substrate import SubstrateRetriever
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

# Create the retriever
retriever = SubstrateRetriever(
    api_key=os.environ["SUBSTRATE_API_KEY"],
    top_k=5,
)

# Build a RAG chain
prompt = ChatPromptTemplate.from_template(
    "Answer based on the following context:\n{context}\n\nQuestion: {question}"
)
model = ChatOpenAI(model="gpt-4o")

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

answer = chain.invoke("What are the entity's core values?")

Retriever with Namespace Scoping

retriever = SubstrateRetriever(
    api_key=os.environ["SUBSTRATE_API_KEY"],
    namespace="app.conversations",
    top_k=10,
)

Free Tier Fallback

hybrid_search requires the Pro tier. On the free tier, fall back to memory_search:

retriever = SubstrateRetriever(
    api_key=os.environ["SUBSTRATE_API_KEY"],
    search_tool="memory_search",
)

Async Support

All operations support async for use in async LangGraph workflows:

import asyncio
from langchain_substrate import SubstrateStore, SubstrateClient

async def main():
    client = SubstrateClient(api_key="sk-sub-...")
    store = SubstrateStore(client=client)

    await store.aput(("user", "alice"), "mood", {"current": "happy"})
    item = await store.aget(("user", "alice"), "mood")
    print(item.value)

    results = await store.asearch(("user",), query="emotional state")
    for r in results:
        print(r.value)

asyncio.run(main())

Architecture

LangGraph Agent / RAG Chain
        |
   SubstrateStore / SubstrateRetriever
        |
   SubstrateClient (httpx)
        |
   SUBSTRATE MCP Server (JSON-RPC over HTTP)
        |
   Causal Memory + Knowledge Graph + 61 Layers

Namespace Encoding

LangGraph uses tuple namespaces like ("user", "alice", "prefs"). SUBSTRATE uses flat string keys. The store encodes namespaces as dot-separated prefixes:

LangGraph Namespace	SUBSTRATE Prefix
("user", "alice")	user.alice
("app", "v2", "state")	app.v2.state

Tool Mapping

Store Operation	SUBSTRATE Tool	Tier
put()	respond	Free
get()	memory_search	Free
search()	hybrid_search	Pro
list_namespaces()	N/A (limited)	--
delete()	No-op	--

SUBSTRATE MCP Tools Available

Tool	Description	Tier
respond	Send a message, get a response	Free
memory_search	Search causal memory episodes	Free
hybrid_search	Semantic + keyword search	Pro
get_emotion_state	Affective state vector	Free
verify_identity	Cryptographic identity check	Free
knowledge_graph_query	Query knowledge graph	Pro
get_values	Core value architecture	Free
theory_of_mind	User model	Free
get_trust_state	Trust scores	Pro

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=langchain_substrate --cov-report=term-missing

# Lint
ruff check src/ tests/

# Type check
mypy src/

License

MIT -- Garmo Labs