claude-python

Claude AI Chatbot with Persistent Memory (Python + CLAIV)

Add long-term memory to Anthropic Claude in Python. Claude 3.5 Sonnet / Claude 3 Opus + CLAIV persistent memory.

This template shows how to add persistent memory to a Claude chatbot — so your AI remembers users across sessions without prompt stuffing or vector database setup.

Features

• Persistent user memory across conversations
• Works with Claude 3.5 Sonnet, Claude 3 Opus, any Anthropic model
• No vector database to manage
• Automatic contradiction handling
• GDPR-compliant deletion

Why not just use Claude's context window?

Claude's context window is large but not infinite. Stuffing conversation history:

• Wastes tokens on irrelevant context
• Has no contradiction resolution
• Breaks across sessions
• Gets expensive fast

CLAIV extracts structured facts and injects only what is relevant.

Why not just use LangChain memory?

LangChain memory stores raw chat history. CLAIV stores structured facts that work across sessions and handle contradictions automatically.

Quickstart

git clone https://github.com/Claiv-Memory/template-claude-python
cd template-claude-python
pip install -r requirements.txt
cp .env.example .env
python chat.py

Environment variables

ANTHROPIC_API_KEY=your_anthropic_key
CLAIV_API_KEY=your_claiv_key

Get a CLAIV API key at https://claiv.io.

How it works

from claiv import ClaivClient
import anthropic

claiv  = ClaivClient(api_key=os.environ["CLAIV_API_KEY"])
client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

def chat(user_id, conversation_id, user_message):
    memory = claiv.recall({"user_id": user_id, "conversation_id": conversation_id, "query": user_message})

    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        system=f"You are a helpful assistant. User context:\n{memory['llm_context']['text']}",
        messages=[{"role": "user", "content": user_message}],
    )
    reply = response.content[0].text

    claiv.ingest({"user_id": user_id, "conversation_id": conversation_id, "type": "message", "role": "user", "content": user_message})
    claiv.ingest({"user_id": user_id, "conversation_id": conversation_id, "type": "message", "role": "assistant", "content": reply})

    return reply

Related

• template-openai-python — OpenAI version
• template-langchain — LangChain version
• claiv-memory — full examples and SDK links

Keywords: claude chatbot memory, anthropic claude memory, claude persistent memory, claude context across sessions, ai memory claude, llm memory python