recallbricks-langchain 0.2.1

Enterprise-grade persistent memory for LangChain agents

RecallBricks LangChain Integration

Official LangChain integration for RecallBricks Memory Graph.

What is RecallBricks?

RecallBricks provides memory infrastructure that goes beyond vector search by understanding relationships, causality, and patterns - not just similar text.

Perfect for LangChain agents that need:

• Context across conversations
• Understanding of cause-and-effect
• Relationship-aware memory retrieval

Installation

pip install recallbricks-langchain

Quick Start

from langchain.chains.conversation.base import ConversationChain
from langchain_openai import ChatOpenAI
from recallbricks_langchain import RecallBricksMemory

# Initialize memory with RecallBricks
memory = RecallBricksMemory(
    agent_id="my-agent",
    api_key="your-recallbricks-api-key",
    user_id="user-123"  # Optional
)

# Use with any LangChain chain
llm = ChatOpenAI()
conversation = ConversationChain(
    llm=llm,
    memory=memory
)

# Your agent now has relationship-aware memory!
conversation.run("Deploy the new authentication feature")
# Later...
conversation.run("Why did the deployment fail?")
# RecallBricks provides context: "Related to the auth feature you deployed"

Features

✅ Drop-in replacement for ConversationBufferMemory ✅ Automatic relationship detection - understands causality and patterns ✅ Persistent across sessions - memories don't disappear ✅ Multi-user support - isolate memory per user ✅ Production-ready - 99.9% uptime, enterprise security

Why RecallBricks vs Standard LangChain Memory?

Feature	ConversationBufferMemory	RecallBricksMemory
Stores conversations	✅	✅
Persists across sessions	❌	✅
Understands relationships	❌	✅
Detects causality	❌	✅
Finds patterns	❌	✅
Explains connections	❌	✅

Advanced Usage

With Custom Parameters

memory = RecallBricksMemory(
    agent_id="my-agent",
    api_key="your-key",
    user_id="user-123",  # Optional
    limit=20,  # Number of memories to retrieve
    min_relevance=0.7,  # Minimum relevance score
    return_messages=True  # Return as Message objects
)

Multi-User Applications

def get_conversation_for_user(user_id: str):
    memory = RecallBricksMemory(
        agent_id="my-agent",
        api_key="your-key",
        user_id=user_id  # Isolates memory per user
    )
    return ConversationChain(llm=llm, memory=memory)

Get Your API Key

1. Sign up at recallbricks.com
2. Get your API key from the dashboard
3. Start building!

Documentation

• RecallBricks Docs
• API Reference
• LangChain Docs

Examples

Check out the examples/ directory for:

• basic_usage.py - Simple conversation example
• with_openai.py - Advanced multi-user scenarios with relationship detection

Development

Installation

# Clone the repository
git clone https://github.com/recallbricks/recallbricks-langchain.git
cd recallbricks-langchain

# Install in development mode
pip install -e .

# Install development dependencies
pip install -r requirements.txt

Running Tests

python -m pytest tests/

Support

• Email: [e-mail]
• GitHub: github.com/recallbricks

License

MIT