Framework Integrations

# LangChain with tools
from memvid_sdk import use
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

mem = use('langchain', 'knowledge.mv2')
agent = create_react_agent(ChatOpenAI(model="gpt-4o"), mem.tools)
result = agent.invoke({"messages": [{"role": "user", "content": "What is..."}]})

# Or use find() + ask() directly
results = mem.find("search query", k=5)
answer = mem.ask("What is the main concept?")

# LangChain Agent
from langgraph.prebuilt import create_react_agent
mem = use('langchain', 'knowledge.mv2')
agent = create_react_agent(model, mem.tools)

# AutoGen
mem = use('autogen', 'knowledge.mv2')
assistant = AssistantAgent("helper", llm_config={"tools": mem.tools})

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

const mem = await use('vercel-ai', 'knowledge.mv2');

export async function POST(req: Request) {
  const result = await streamText({
    model: openai('gpt-4o'),
    tools: mem.tools,
    messages: await req.json(),
  });
  return result.toDataStreamResponse();
}

# OpenAI
mem = use('openai', 'knowledge.mv2')
response = client.chat.completions.create(
    model="gpt-4o",
    tools=mem.tools,
    messages=[...]
)

# Google Gemini
mem = use('google-adk', 'knowledge.mv2')
chat = client.chats.create(
    model="gemini-2.0-flash",
    config=types.GenerateContentConfig(tools=[mem.tools])
)

mem = use('semantic-kernel', 'knowledge.mv2')
kernel.add_plugin(mem.tools, "memvid")

curl -X POST https://api.memvid.com/v1/memories/{MEMORY_ID}/ask \
  -H "Authorization: Bearer mv2_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"question":"What does this process do?"}'

const res = await fetch("https://api.memvid.com/v1/memories/{MEMORY_ID}/find", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.MEMVID_API_KEY}` },
  body: JSON.stringify({ query: "search query", topK: 5 }),
});