AI Memory for Chatbots, Agents, and LLM Apps

Your AI forgets everything. CLAIV fixes that.

If you're building with OpenAI, Claude, or LangChain — you've seen this:

• Your chatbot forgets users between sessions
• Your AI agent loses context after a few steps
• Your app relies on fragile chat history or prompt stuffing
• Your RAG setup is inconsistent and unreliable

👉 This is not a model problem — it's a memory problem

CLAIV is a drop-in memory API for AI applications.

It gives your AI:

• 🧠 Persistent user memory across conversations
• 📚 Reliable document understanding (not just chunk retrieval)
• ⏱️ Temporal awareness — what happened and when
• 🎯 Context injection ready for LLM prompts
• 🧹 Controlled forgetting — GDPR-safe deletion

❌ Without memory

User:  My name is Alex and I run a fitness business.

...later...

AI:    How can I help you today?

✅ With CLAIV

AI:    How is your fitness business going, Alex?

1. Send events to POST /v6/ingest
2. CLAIV extracts structured memory asynchronously
3. Call POST /v6/recall before each LLM response
4. Inject llm_context.text into your system prompt

That's it.

pip install claiv-memory

from claiv import ClaivClient

client = ClaivClient(api_key="your_key")

client.ingest({
    "user_id": "user_123",
    "conversation_id": "conv_abc",
    "type": "message",
    "role": "user",
    "content": "I'm building an AI agent for compliance review."
})

context = client.recall({
    "user_id": "user_123",
    "conversation_id": "conv_abc",
    "query": "What is the user building?"
})

print(context["llm_context"]["text"])

System prompt:

User memory:
{{ llm_context.text }}

JavaScript / TypeScript

npm install @claiv/memory

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

const client = new ClaivClient({ apiKey: 'your_key' });

await client.ingest({
  user_id: 'user_123',
  conversation_id: 'conv_abc',
  type: 'message',
  role: 'user',
  content: "I'm building an AI agent for compliance review.",
});

const context = await client.recall({
  user_id: 'user_123',
  conversation_id: 'conv_abc',
  query: 'What is the user building?',
});

Upload documents and CLAIV will:

• Parse structure into sections and spans
• Embed content with pgvector
• Retrieve relevant sections automatically
• Inject them into your LLM context

doc = client.upload_document({
    "user_id": "user_123",
    "project_id": "proj_abc",
    "document_name": "Product Manual",
    "content": open("manual.md").read(),
})

print(f"Indexed {doc['spans_created']} spans across {len(doc['sections'])} sections")

Supports:

• Knowledge copilots over internal documentation
• Compliance and research tools
• Support assistants that reason over policies and manuals

POST /v6/ingest      →  Store memory events
POST /v6/recall      →  Retrieve context
POST /v6/documents   →  Upload documents
POST /v6/forget      →  Delete memory

Full reference → claiv.io/docs

LoCoMo 10-dialogue J-score: 75.0%

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

• AI chatbots with persistent memory
• AI agents with multi-step workflows
• SaaS apps with per-user context
• Customer support systems
• Internal knowledge copilots
• Research and compliance tools

• Ingest is async — memory may not appear instantly
• Always include user_id and conversation_id
• Inject llm_context.text into your system prompt

• API key authentication with tenant isolation
• Strict schema validation on all endpoints
• Scoped deletion via POST /v6/forget

👉 Get API key: claiv.io

👉 Docs: claiv.io/docs