CLAIV Memory — LLM Memory Layer for AI Chatbots and Agents

LLM memory for chatbots, AI agents, and RAG systems. chatbot memory · llm memory · ai agent memory · langchain memory alternative · rag memory system · persistent memory for openai · conversational ai memory

Persistent memory for OpenAI, Claude, LangChain, and any AI application. Drop in 3 API calls. Your AI remembers everything.

The easiest way to add persistent memory to:

• OpenAI chatbots (GPT-4o, GPT-4.1)
• LangChain agents
• Anthropic Claude applications
• Next.js AI chat apps
• Any LLM with a system prompt

Replaces:

• LangChain ConversationBufferMemory
• Naive RAG-based memory systems
• Prompt stuffing with chat history

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Quickstart (30 seconds)

npm install @claiv/memory

import { ClaivClient } from '@claiv/memory';

const claiv = new ClaivClient({ apiKey: process.env.CLAIV_API_KEY });

// Store what happened
await claiv.ingest({
  user_id: 'user_123',
  conversation_id: 'chat_abc',
  type: 'message',
  role: 'user',
  content: 'I run a fitness business and prefer morning workouts.',
});

// Recall before each response
const memory = await claiv.recall({
  user_id: 'user_123',
  conversation_id: 'chat_abc',
  query: 'What does this user do?',
});

// Inject into your system prompt
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    { role: 'system', content: `User context:\n${memory.llm_context.text}` },
    { role: 'user',   content: userMessage },
  ],
});

Python:

pip install claiv-memory

from claiv import ClaivClient

claiv = ClaivClient(api_key="your_key")

claiv.ingest({
    "user_id": "user_123",
    "conversation_id": "chat_abc",
    "type": "message",
    "role": "user",
    "content": "I run a fitness business and prefer morning workouts.",
})

memory = claiv.recall({
    "user_id": "user_123",
    "conversation_id": "chat_abc",
    "query": "What does this user do?",
})

system_prompt = f"User context:\n{memory['llm_context']['text']}"

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Why not just use LangChain memory?

	LangChain ConversationBufferMemory	CLAIV Memory
Stores	Raw chat history	Structured, deduplicated facts
Long conversations	Breaks (token overflow)	No limit
Contradiction handling	❌	✅ Resolved automatically
Cross-session memory	❌	✅
Document memory	❌	✅ Built-in
GDPR deletion	❌	✅ Audit receipt
LoCoMo benchmark	—	75.0%

LangChain memory not working? CLAIV is a drop-in replacement.

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Why not just use a vector database?

Vector databases give you similarity search. CLAIV gives you structured memory:

• Facts are extracted and deduplicated — no contradictions
• Temporal reasoning built in
• Token-budget-aware recall — not 50 raw chunks
• Document memory + conversation memory in one call
• Forget with a real audit trail

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How it works

POST /v6/ingest     →  Store a memory event (conversation turn, app event)
POST /v6/recall     →  Retrieve ranked context — ready to inject into your prompt
POST /v6/documents  →  Upload a document for persistent RAG
POST /v6/forget     →  Delete user data (GDPR-compliant, timestamped receipt)

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Document memory (built-in RAG)

Upload documents directly — no separate vector database needed:

const doc = await claiv.uploadDocument({
  user_id: 'user_123',
  project_id: 'my-project',
  document_name: 'Product Manual',
  content: documentText,
});
// Spans indexed immediately. Recall automatically surfaces relevant sections.

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Examples

Clone any example to get started immediately:

Example	Stack	Description
examples/openai-nodejs	Node.js + OpenAI	Chatbot with persistent memory
examples/openai-python	Python + OpenAI	Chatbot with persistent memory
examples/claude-python	Python + Claude	Anthropic Claude with memory
examples/langchain	Python + LangChain	LangChain memory replacement
examples/nextjs	Next.js + OpenAI	Streaming chat app with memory
examples/document-rag-python	Python	Document upload + question answering
examples/document-rag-nextjs	Next.js	Drag-and-drop document RAG app

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Deployable app

Want a full working chatbot you can deploy in one click?

👉 ai-chatbot-with-memory — Next.js + OpenAI + CLAIV. Vercel deploy button included.

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SDKs

	Install	Repo
JavaScript / TypeScript	npm install @claiv/memory	sdk-js
Python	pip install claiv-memory	sdk-py

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Benchmark

LoCoMo 10-dialogue J-score: 75.0%

Category	Score
Single-hop	68.8%
Temporal	74.2%
Multi-hop	55.2%
Open-domain	79.7%

LoCoMo is the standard benchmark for long-context conversational memory systems.

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Use cases

• AI chatbots that remember users across sessions
• AI agents with multi-step context
• SaaS apps with per-user memory
• Customer support bots that know customer history
• Internal copilots over company documents
• Research and compliance tools with document memory

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Get started

👉 API key: claiv.io 👉 Docs: claiv.io/docs

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Keywords: ai memory, llm memory, chatbot memory, ai agent memory, langchain memory, rag memory, openai memory, persistent memory, chatbot context, conversational memory, vector database alternative

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