Getting Started

This guide will walk you through setting up your Hindsight Cloud account and creating your first memory bank.

Prerequisites

• A Hindsight Cloud account (sign up at ui.hindsight.vectorize.io)
• Basic understanding of REST APIs or familiarity with Python/TypeScript

Step 1: Sign Up and Log In

1. Navigate to ui.hindsight.vectorize.io
2. Click Sign Up and create your account
3. Verify your email address
4. Log in to access your dashboard

Step 2: Create an Organization

When you first log in, you'll be prompted to create an organization. Organizations are the top-level container for all your resources:

• Memory banks
• Team members
• API keys
• Billing information

Enter a name for your organization and click Create.

Step 3: Create Your First Memory Bank

1. From the dashboard, click Create Memory Bank
2. Enter a name for your memory bank (e.g., "My AI Assistant")

Step 4: Get Your API Key

1. Click the Connect button in the top navigation bar
2. Click Create API Key
3. Give your key a descriptive name
4. Choose an expiration period (or select "Never")
5. Important: Copy your API key immediately—it won't be shown again!

Step 5: Make Your First API Call

Using an agent framework or AI tool?

Most teams connect through a pre-built integration: Claude Code, Cursor, LangGraph, CrewAI, the OpenAI Agents SDK, n8n and many more. Give the integration your API key and it handles memory for you. See Integrations, or browse all integrations.

Python

pip install hindsight-client

from hindsight_client import Hindsight

client = Hindsight(
    base_url="https://api.hindsight.vectorize.io",
    api_key="your-api-key"
)

# Create a memory bank
bank = client.create_bank(
    bank_id="my-assistant",
    name="My Assistant"
)

# Store a memory
client.retain(
    bank_id="my-assistant",
    content="The user's name is Alice and she prefers Python."
)

# Retrieve relevant memories
result = client.recall(
    bank_id="my-assistant",
    query="What is the user's name?"
)

for memory in result.results:
    print(memory.text)

# Create a mental model - a pre-computed reflection that stays current
result = client.create_mental_model(
    bank_id="my-assistant",
    name="User Profile",
    source_query="What do we know about this user?"
)
print(f"Creating mental model (operation: {result.operation_id})")

# List mental models
models = client.list_mental_models(bank_id="my-assistant")
for m in models.items:
    print(f"{m.name}: {m.content}")

client.close()

TypeScript

npm install @vectorize-io/hindsight-client

import { HindsightClient } from '@vectorize-io/hindsight-client';

const client = new HindsightClient({
  baseUrl: 'https://api.hindsight.vectorize.io',
  apiKey: 'your-api-key'
});

// Create a memory bank
const bank = await client.createBank('my-assistant', {
  name: 'My Assistant'
});

// Store a memory
await client.retain(
  'my-assistant',
  "The user's name is Alice and she prefers Python."
);

// Retrieve relevant memories
const result = await client.recall(
  'my-assistant',
  "What is the user's name?"
);

result.results.forEach(memory => {
  console.log(memory.text);
});

// Create a mental model - a pre-computed reflection that stays current
const mmResult = await client.createMentalModel(
  'my-assistant',
  'User Profile',
  'What do we know about this user?'
);
console.log(`Creating mental model (operation: ${mmResult.operation_id})`);

// List mental models
const models = await client.listMentalModels('my-assistant');
models.items.forEach(m => {
  console.log(`${m.name}: ${m.content}`);
});

cURL

# Store a memory
curl -X POST https://api.hindsight.vectorize.io/v1/default/banks/{bank_id}/memories \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"items": [{"content": "The user'\''s name is Alice and she prefers Python."}]}'

# Retrieve memories
curl -X POST https://api.hindsight.vectorize.io/v1/default/banks/{bank_id}/memories/recall \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"query": "What is the user'\''s name?"}'

# Create a mental model - a pre-computed reflection that stays current
curl -X POST https://api.hindsight.vectorize.io/v1/default/banks/{bank_id}/mental-models \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "User Profile",
    "source_query": "What do we know about this user?"
  }'

Next Steps

• Connect your agent framework or AI tool with a pre-built integration
• Learn about Memory Operations in depth
• Create Mental Models to curate pre-computed reflections from your memories
• Set up Team Management for collaboration
• Configure Billing and purchase credits
• Explore the API Integration guide for advanced usage