Chat Integration

curl -X POST 'https://api.memchat.io/integrations/chat' \
  -H 'X-API-Key: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "platform": "chatgpt",
    "chat_data": [
      {
        "timestamp": "2024-01-15T14:30:00Z",
        "role": "user",
        "content": "Tell me about machine learning applications in healthcare"
      },
      {
        "timestamp": "2024-01-15T14:30:15Z", 
        "role": "assistant",
        "content": "Machine learning has numerous applications in healthcare..."
      }
    ],
    "extract_mode": "conversations"
  }'

import requests

url = "https://api.memchat.io/integrations/chat"
headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "platform": "chatgpt",
    "chat_data": [
        {
            "timestamp": "2024-01-15T14:30:00Z",
            "role": "user", 
            "content": "Tell me about machine learning applications in healthcare"
        },
        {
            "timestamp": "2024-01-15T14:30:15Z",
            "role": "assistant",
            "content": "Machine learning has numerous applications in healthcare..."
        }
    ],
    "extract_mode": "conversations"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

const response = await fetch('https://api.memchat.io/integrations/chat', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    platform: 'chatgpt',
    chat_data: [
      {
        timestamp: '2024-01-15T14:30:00Z',
        role: 'user',
        content: 'Tell me about machine learning applications in healthcare'
      },
      {
        timestamp: '2024-01-15T14:30:15Z',
        role: 'assistant', 
        content: 'Machine learning has numerous applications in healthcare...'
      }
    ],
    extract_mode: 'conversations'
  }),
});

const result = await response.json();

{
  "integration_id": "chat_int_789abc",
  "platform": "chatgpt",
  "status": "completed",
  "processing_stats": {
    "total_messages": 24,
    "conversations_identified": 3,
    "memories_extracted": 5,
    "topics_detected": [
      "Machine Learning in Healthcare",
      "Python Programming",
      "Career Development"
    ],
    "date_range": {
      "start": "2024-01-10T09:00:00Z",
      "end": "2024-01-15T17:30:00Z"
    }
  },
  "memories": [
    {
      "memory_id": "mem_chat_001",
      "content": "Discussed machine learning applications in medical diagnosis, specifically how neural networks can improve accuracy in identifying diseases from medical imaging. Learned about specific techniques and real-world implementations.",
      "category": "learning",
      "conversation_topic": "Machine Learning in Healthcare",
      "participants": ["user", "assistant"],
      "start_time": "2024-01-15T14:30:00Z",
      "end_time": "2024-01-15T14:45:00Z"
    }
  ]
}

POST

integrations

chat

curl -X POST 'https://api.memchat.io/integrations/chat' \
  -H 'X-API-Key: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "platform": "chatgpt",
    "chat_data": [
      {
        "timestamp": "2024-01-15T14:30:00Z",
        "role": "user",
        "content": "Tell me about machine learning applications in healthcare"
      },
      {
        "timestamp": "2024-01-15T14:30:15Z", 
        "role": "assistant",
        "content": "Machine learning has numerous applications in healthcare..."
      }
    ],
    "extract_mode": "conversations"
  }'

import requests

url = "https://api.memchat.io/integrations/chat"
headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "platform": "chatgpt",
    "chat_data": [
        {
            "timestamp": "2024-01-15T14:30:00Z",
            "role": "user", 
            "content": "Tell me about machine learning applications in healthcare"
        },
        {
            "timestamp": "2024-01-15T14:30:15Z",
            "role": "assistant",
            "content": "Machine learning has numerous applications in healthcare..."
        }
    ],
    "extract_mode": "conversations"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

const response = await fetch('https://api.memchat.io/integrations/chat', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    platform: 'chatgpt',
    chat_data: [
      {
        timestamp: '2024-01-15T14:30:00Z',
        role: 'user',
        content: 'Tell me about machine learning applications in healthcare'
      },
      {
        timestamp: '2024-01-15T14:30:15Z',
        role: 'assistant', 
        content: 'Machine learning has numerous applications in healthcare...'
      }
    ],
    extract_mode: 'conversations'
  }),
});

const result = await response.json();

{
  "integration_id": "chat_int_789abc",
  "platform": "chatgpt",
  "status": "completed",
  "processing_stats": {
    "total_messages": 24,
    "conversations_identified": 3,
    "memories_extracted": 5,
    "topics_detected": [
      "Machine Learning in Healthcare",
      "Python Programming",
      "Career Development"
    ],
    "date_range": {
      "start": "2024-01-10T09:00:00Z",
      "end": "2024-01-15T17:30:00Z"
    }
  },
  "memories": [
    {
      "memory_id": "mem_chat_001",
      "content": "Discussed machine learning applications in medical diagnosis, specifically how neural networks can improve accuracy in identifying diseases from medical imaging. Learned about specific techniques and real-world implementations.",
      "category": "learning",
      "conversation_topic": "Machine Learning in Healthcare",
      "participants": ["user", "assistant"],
      "start_time": "2024-01-15T14:30:00Z",
      "end_time": "2024-01-15T14:45:00Z"
    }
  ]
}

Import chat histories from various platforms to automatically extract and store conversational memories.

curl -X POST 'https://api.memchat.io/integrations/chat' \
  -H 'X-API-Key: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "platform": "chatgpt",
    "chat_data": [
      {
        "timestamp": "2024-01-15T14:30:00Z",
        "role": "user",
        "content": "Tell me about machine learning applications in healthcare"
      },
      {
        "timestamp": "2024-01-15T14:30:15Z", 
        "role": "assistant",
        "content": "Machine learning has numerous applications in healthcare..."
      }
    ],
    "extract_mode": "conversations"
  }'

import requests

url = "https://api.memchat.io/integrations/chat"
headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "platform": "chatgpt",
    "chat_data": [
        {
            "timestamp": "2024-01-15T14:30:00Z",
            "role": "user", 
            "content": "Tell me about machine learning applications in healthcare"
        },
        {
            "timestamp": "2024-01-15T14:30:15Z",
            "role": "assistant",
            "content": "Machine learning has numerous applications in healthcare..."
        }
    ],
    "extract_mode": "conversations"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

const response = await fetch('https://api.memchat.io/integrations/chat', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    platform: 'chatgpt',
    chat_data: [
      {
        timestamp: '2024-01-15T14:30:00Z',
        role: 'user',
        content: 'Tell me about machine learning applications in healthcare'
      },
      {
        timestamp: '2024-01-15T14:30:15Z',
        role: 'assistant', 
        content: 'Machine learning has numerous applications in healthcare...'
      }
    ],
    extract_mode: 'conversations'
  }),
});

const result = await response.json();

Request Body

string

required

Chat platform source. Supported: chatgpt, claude, telegram, whatsapp, slack, discord

array

required

Array of chat messages

Show Message Properties

string

required

ISO 8601 timestamp of the message

string

required

Message role: user, assistant, system, or participant name

string

required

Message content/text

string

Unique identifier for the message

object

Additional message metadata

string

default:"conversations"

Extraction strategy. Options: conversations, insights, topics, all

object

Settings for conversation processing

Show Conversation Settings

number

default:"50"

Minimum message length to consider

boolean

default:"true"

Whether to group related messages by topic

boolean

default:"false"

Whether to include system messages

number

default:"3600"

Seconds gap to separate conversations

boolean

default:"true"

Whether to automatically categorize extracted memories

Response

string

Unique identifier for the chat integration

string

Chat platform that was processed

string

Processing status: processing, completed, error

object

Statistics about the processing

Show Processing Statistics

number

Total number of messages processed

number

Number of distinct conversations found

number

Number of memories created

array

Main topics identified in conversations

object

Date range of the chat history

array

List of extracted memories

Show Memory Properties

string

Unique identifier for the memory

string

Extracted conversation content

string

Assigned category

string

Main topic of the conversation

array

Participants in the conversation

string

When the conversation started

string

When the conversation ended

{
  "integration_id": "chat_int_789abc",
  "platform": "chatgpt",
  "status": "completed",
  "processing_stats": {
    "total_messages": 24,
    "conversations_identified": 3,
    "memories_extracted": 5,
    "topics_detected": [
      "Machine Learning in Healthcare",
      "Python Programming",
      "Career Development"
    ],
    "date_range": {
      "start": "2024-01-10T09:00:00Z",
      "end": "2024-01-15T17:30:00Z"
    }
  },
  "memories": [
    {
      "memory_id": "mem_chat_001",
      "content": "Discussed machine learning applications in medical diagnosis, specifically how neural networks can improve accuracy in identifying diseases from medical imaging. Learned about specific techniques and real-world implementations.",
      "category": "learning",
      "conversation_topic": "Machine Learning in Healthcare",
      "participants": ["user", "assistant"],
      "start_time": "2024-01-15T14:30:00Z",
      "end_time": "2024-01-15T14:45:00Z"
    }
  ]
}

Extraction Modes

Conversations

Groups related messages into conversation memories, preserving context and flow.

Insights

Extracts key insights, learnings, and important information from conversations.

Topics

Creates memories organized by topics discussed across conversations.

All

Combines all extraction modes for comprehensive memory creation.

Supported Platforms

ChatGPT

Export conversations from ChatGPT
Identifies learning topics and insights
Categories: primarily learning, productivity

Claude

Claude conversation exports
Focus on analytical discussions
Categories: learning, productivity, interests

Telegram/WhatsApp

Personal and group conversations
Relationship and social memories
Categories: relationships, interests, travel

Slack/Discord

Professional and community discussions
Work-related and interest-based memories
Categories: career, relationships, interests

Privacy and Security

Data Processing: Chat data is processed securely and not stored permanently
Participant Privacy: Names and identifiers can be anonymized
Content Filtering: Sensitive content is automatically filtered out
Retention: Original chat data is deleted after processing

Getting Started

Memory Management

User Profiles

Integrations

API Keys

Request Body

Response

Extraction Modes

Supported Platforms

Privacy and Security

​Request Body

​Response

​Extraction Modes

​Supported Platforms

​Privacy and Security

Request Body

Response

Extraction Modes

Supported Platforms

Privacy and Security