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Documentation Index

Fetch the complete documentation index at: https://docs.memvid.com/llms.txt

Use this file to discover all available pages before exploring further.

Integrate Memvid with LangChain to build powerful RAG pipelines. The langchain adapter provides native LangChain tools for seamless integration with agents.

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Installation

npm install @memvid/sdk @langchain/core @langchain/openai @langchain/langgraph zod

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Quick Start

import { use } from '@memvid/sdk';

// Open with LangChain adapter
const mem = await use('langchain', 'knowledge.mv2');

// Access LangChain tools (compatible with createReactAgent)
const tools = mem.tools;  // Array of tool() objects

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Using Tools with Agents

import { use } from '@memvid/sdk';
import { ChatOpenAI } from '@langchain/openai';
import { createReactAgent } from '@langchain/langgraph/prebuilt';
import { HumanMessage } from '@langchain/core/messages';

// Get Memvid tools
const mem = await use('langchain', 'knowledge.mv2');
const tools = mem.tools;

// Create agent with LangGraph
const llm = new ChatOpenAI({ model: 'gpt-4o' });
const agent = createReactAgent({ llm, tools });

// Run
const inputs = { messages: [new HumanMessage('Search for authentication info')] };
const stream = await agent.stream(inputs, { streamMode: 'values' });

for await (const { messages } of stream) {
  const lastMsg = messages[messages.length - 1];
  if (lastMsg.content) {
    console.log(lastMsg.content);
  }
}

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Available Tools

The LangChain adapter provides three tools:
ToolDescription
memvid_putStore documents in memory with title, label, and text
memvid_findSearch for relevant documents by query
memvid_askAsk questions with RAG-style answer synthesis

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Using as a Retriever (Python)

from memvid_sdk import use
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

# Initialize with langchain adapter
mem = use('langchain', 'knowledge.mv2', read_only=True)

# Get the retriever
retriever = mem.as_retriever(k=5)

# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=retriever
)

result = qa_chain.run("What is the main concept?")
print(result)

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Conversational RAG (Python)

from memvid_sdk import use
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

# Initialize
mem = use('langchain', 'knowledge.mv2', read_only=True)
retriever = mem.as_retriever(k=5)

# Create conversational chain
llm = ChatOpenAI(model="gpt-4o")
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

chain = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=retriever,
    memory=memory
)

# Chat
response = chain.invoke({"question": "What are the key features?"})
print(response["answer"])

# Follow up
response = chain.invoke({"question": "Tell me more about that"})
print(response["answer"])

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Custom Search Options

from memvid_sdk import use

mem = use('langchain', 'knowledge.mv2')

# Search with specific mode
results = mem.find('authentication', mode='lex', k=10)  # Lexical only
results = mem.find('user login flow', mode='sem', k=10)  # Semantic only
results = mem.find('auth best practices', mode='auto', k=10)  # Hybrid

# With scope filtering
results = mem.find('API', scope='mv2://docs/', k=5)

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Best Practices

  1. Use read-only mode for retrieval-only applications
  2. Set appropriate k values based on your context window
  3. Use hybrid mode for best recall
  4. Close the memory when done
mem = use('langchain', 'knowledge.mv2', read_only=True)
try:
    # Do work
    results = mem.find('query', k=10)
finally:
    mem.seal()

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Next Steps

LlamaIndex

LlamaIndex integration

Python SDK

Full Python SDK documentation