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';constclaiv=newClaivClient({apiKey: process.env.CLAIV_API_KEY});// Store what happenedawaitclaiv.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 responseconstmemory=awaitclaiv.recall({user_id: 'user_123',conversation_id: 'chat_abc',query: 'What does this user do?',});// Inject into your system promptconstresponse=awaitopenai.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

fromclaivimportClaivClientclaiv=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:

constdoc=awaitclaiv.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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