langchain-encrypted-mongo-memory 0.1.0

A secure, encrypted MongoDB chat memory for LangChain applications

LangChain-Encrypted-Mongo-Memory

A Secure, Encrypted MongoDB Chat Memory for LangChain Applications

LangChain-Encrypted-Mongo-Memory extends MongoDBChatMessageHistory to provide AES-128 encryption for all stored messages. Built on top of mores-encryption, it ensures sensitive conversation data remains protected at rest.

Perfect for securing chat histories containing PII, medical data, financial information, or any sensitive conversation data in your LangChain applications.

LangChain-Encrypted-Mongo-Memory removes the cryptographic complexity so you can focus on building — not configuring.

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Features

• AES-128 Encryption — Messages encrypted using Fernet (AES-128 CBC with PKCS7 padding)
• HMAC-SHA256 Integrity — Cryptographic verification of message integrity
• URL-safe Base64 Output — Encrypted data stored in URL-safe format
• Drop-in Replacement — Compatible with LangChain's chat memory interface
• Persistent Storage — Long-term message storage in MongoDB
• Type Filtering — Supports human, AI, and system message types
• Zero-Config Encryption — Automatic key handling via mores-encryption

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Installation

pip install langchain-encrypted-mongo-memory

Or install from source:

git clone https://github.com/HATAKEkakshi/langchain-encrypted-mongo-memory.git
cd langchain-encrypted-mongo-memory
pip install -e .

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Setup

Generate Encryption Key

Run this command in your terminal:

python -c "from cryptography.fernet import Fernet; print('ENCRYPTION_KEY=' + Fernet.generate_key().decode())"

Save to .env

Copy the output and save it in your .env file:

ENCRYPTION_KEY=your_generated_key_here

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Usage

1. Basic Usage

from langchain_encrypted_mongo_memory import EncryptedMongoDBChatMessageHistory
from langchain_core.messages import HumanMessage, AIMessage

# Create encrypted history
history = EncryptedMongoDBChatMessageHistory(
    connection_string="mongodb://localhost:27017",
    database_name="chat_app",
    collection_name="encrypted_messages",
    session_id="user-123"
)

# Add messages (automatically encrypted)
history.add_message(HumanMessage(content="Hello, how are you?"))
history.add_message(AIMessage(content="I'm doing great, thank you!"))

# Retrieve messages (automatically decrypted)
messages = history.messages
for msg in messages:
    print(f"{msg.type}: {msg.content}")

2. Clear Session History

# Clear history for the current session
history.clear()

3. With LangChain Memory

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory(
    chat_memory=history,
    return_messages=True
)

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Why Use LangChain-Encrypted-Mongo-Memory?

Because securing chat history shouldn't be painful.

Most developers store sensitive conversation data in MongoDB without encryption — exposing PII, medical records, and confidential information to potential breaches.

LangChain-Encrypted-Mongo-Memory gives you:

• Automatic Encryption — Messages encrypted before storage
• Transparent Decryption — Seamless retrieval without extra code
• LangChain Compatible — Works with all LangChain memory patterns
• Production Ready — Built on proven cryptographic standards
• Minimal Code Changes — Drop-in replacement for MongoDBChatMessageHistory

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Configuration

Environment Variables

Variable	Description	Required	Default
ENCRYPTION_KEY	Base64-encoded 32-byte Fernet key	No (Auto-generated if missing)	N/A

MongoDB Connection URLs

# Local MongoDB
connection_string="mongodb://localhost:27017"

# With authentication
connection_string="mongodb://user:password@localhost:27017"

# MongoDB Atlas
connection_string="mongodb+srv://user:[e-mail]"

# With replica set
connection_string="mongodb://host1:27017,host2:27017/?replicaSet=rs0"

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API Reference

EncryptedMongoDBChatMessageHistory

EncryptedMongoDBChatMessageHistory(
    connection_string: str,
    database_name: str,
    collection_name: str,
    session_id: str
)

Parameter	Type	Description
connection_string	str	MongoDB connection URL
database_name	str	Name of the database
collection_name	str	Name of the collection
session_id	str	Unique identifier for the chat session

Properties

Property	Type	Description
messages	List[BaseMessage]	List of decrypted messages

Methods

Method	Description
add_message(message)	Encrypt and store a message
add_user_message(content)	Add a human message
add_ai_message(content)	Add an AI message
clear()	Delete all messages for this session

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Security Implementation Details

• Encryption: cryptography.fernet.Fernet (AES-128 CBC with PKCS7 padding, HMAC-SHA256 for integrity)
• Key Management: Automatic loading from ENCRYPTION_KEY environment variable
• Encoding: All outputs are URL-safe Base64 encoded strings
• Library: Built on mores-encryption for proven security

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Development

Setup

git clone https://github.com/HATAKEkakshi/langchain-encrypted-mongo-memory.git
cd langchain-encrypted-mongo-memory
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

Running Tests

# Run all tests
pytest

# With verbose output
pytest -v

# With coverage
pytest --cov=langchain_encrypted_mongo_memory

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Documentation & Source Code

• GitHub: https://github.com/HATAKEkakshi/langchain-encrypted-mongo-memory
• PyPI: https://pypi.org/project/langchain-encrypted-mongo-memory/
• mores-encryption: https://pypi.org/project/mores-encryption/

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License

MIT License — see LICENSE for details.

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Author

Hemant Kumar — GitHub

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Acknowledgments

• LangChain for the excellent LLM framework
• mores-encryption for encryption utilities
• cryptography for secure encryption primitives