smara-langchain

LangChain integration for the Smara Memory API -- persistent, decay-aware memory for AI agents.

Installation

pip install smara-langchain

Quick start

SmaraMemory -- conversation memory

Drop-in replacement for LangChain's built-in memory classes. Stores every human/AI turn as a memory in Smara and retrieves the most relevant memories on each new turn via semantic search with Ebbinghaus decay ranking.

from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain
from smara_langchain import SmaraMemory

memory = SmaraMemory(
    api_key="sm_...",
    user_id="user-42",
    top_k=5,               # retrieve top 5 memories per turn
)

chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=memory,
)

chain.invoke({"input": "I'm allergic to peanuts"})
# Memory stored in Smara automatically

chain.invoke({"input": "What should I avoid eating?"})
# Smara retrieves the peanut allergy memory as context

SmaraRetriever -- RAG over memories

Use Smara as a retriever in any LangChain RAG pipeline. Each memory is returned as a Document with the fact text and full metadata (importance, decay score, similarity, etc.).

from smara_langchain import SmaraRetriever

retriever = SmaraRetriever(
    api_key="sm_...",
    user_id="user-42",
    top_k=10,
    score_threshold=0.3,    # optional: filter low-relevance results
)

# Use with invoke()
docs = retriever.invoke("What are the user's dietary preferences?")
for doc in docs:
    print(f"{doc.page_content}  (score: {doc.metadata['score']})")

# Use in a RAG chain
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_template(
    "Use these memories about the user to answer their question.\n\n"
    "Memories:\n{context}\n\n"
    "Question: {question}"
)

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

answer = chain.invoke("What food does the user like?")

SmaraClient -- direct API access

For lower-level control you can use the client directly.

from smara_langchain import SmaraClient

client = SmaraClient(api_key="sm_...")

# Store a memory
client.store(user_id="user-42", fact="User prefers dark mode", importance=0.7)

# Search memories
results = client.search(user_id="user-42", query="UI preferences")

# Get formatted context for an LLM prompt
ctx = client.get_context(user_id="user-42", query="UI preferences", top_n=5)
print(ctx["context"])

# Delete a memory
client.delete(memory_id="some-uuid")

Async support

All methods have async counterparts -- prefix with a:

await client.astore(user_id="user-42", fact="...")
await client.asearch(user_id="user-42", query="...")
docs = await retriever.ainvoke("query")

Configuration

How Smara ranking works

Smara combines semantic similarity with Ebbinghaus decay (memories naturally fade over time but are refreshed on access) and importance scoring to produce a blended relevance score. This means frequently accessed, important, and semantically relevant memories surface first -- mimicking how human memory works.

Links

• Smara website
• API documentation
• GitHub