Using Attixa with LangChain: Real Agent Memory in Minutes

LangChain has revolutionized how we build AI applications, but one area where it often falls short is in persistent memory. This tutorial shows how to integrate Attixa with LangChain to create agents that maintain rich, contextual memory across conversations and tasks.

Why Attixa + LangChain?

The combination of LangChain's powerful agent framework and Attixa's sophisticated memory system provides several key benefits:

1. Persistent Context: Maintain conversation history and context across sessions
2. Structured Memory: Store and retrieve information with rich metadata
3. Dynamic Learning: Adapt to user preferences and patterns over time
4. Scalable Storage: Handle growing conversation histories efficiently

Quick Start Integration

Here's how to get started with Attixa and LangChain:

from langchain.agents import AgentExecutor
from langchain.chat_models import ChatOpenAI
from attixa import MemorySystem
from attixa.langchain import AttixaMemory

# Initialize Attixa memory
memory = MemorySystem()
attixa_memory = AttixaMemory(memory_system=memory)

# Create a LangChain agent with Attixa memory
agent = AgentExecutor.from_agent_and_tools(
    agent=ChatOpenAI(temperature=0),
    tools=[...],  # Your tools here
    memory=attixa_memory,
    verbose=True
)

# Use the agent with persistent memory
response = agent.run("What was our last conversation about?")

Memory Integration Patterns

1. Conversation History

# Store conversation context
async def store_conversation(agent, message, response):
    await attixa_memory.store(
        content={
            "message": message,
            "response": response,
            "timestamp": datetime.now()
        },
        context={
            "conversation_id": agent.conversation_id,
            "user_id": agent.user_id
        }
    )

2. Tool Usage Memory

# Remember tool usage patterns
async def remember_tool_usage(agent, tool_name, result):
    await attixa_memory.store(
        content={
            "tool": tool_name,
            "result": result,
            "success": result["success"]
        },
        context={
            "task": agent.current_task,
            "user_preferences": agent.user_preferences
        }
    )

3. Context Preservation

# Maintain context across sessions
async def load_context(agent, conversation_id):
    context = await attixa_memory.retrieve(
        query="conversation context",
        filters={
            "conversation_id": conversation_id
        }
    )
    agent.context = context

Advanced Integration Features

1. Custom Memory Classes

class CustomAttixaMemory(AttixaMemory):
    async def store(self, content, context):
        # Add custom preprocessing
        processed = await self.preprocess(content)
        await super().store(processed, context)
    
    async def retrieve(self, query, filters):
        # Add custom retrieval logic
        results = await super().retrieve(query, filters)
        return await self.postprocess(results)

2. Memory Chains

# Chain multiple memory operations
async def process_conversation(agent, message):
    # Store current message
    await attixa_memory.store(message)
    
    # Retrieve relevant context
    context = await attixa_memory.retrieve(
        query="relevant context",
        filters={"user_id": agent.user_id}
    )
    
    # Update agent state
    agent.update_context(context)

3. Memory Optimization

# Optimize memory usage
async def optimize_memory(agent):
    # Clean up old memories
    await attixa_memory.cleanup(
        filters={
            "timestamp": {"$lt": datetime.now() - timedelta(days=30)}
        }
    )
    
    # Compress frequent patterns
    await attixa_memory.compress_patterns()

Best Practices

1. Memory Management

   • Set appropriate retention periods
   • Implement memory cleanup routines
   • Monitor memory usage

2. Context Handling

   • Maintain conversation boundaries
   • Preserve important context
   • Clean up irrelevant information

3. Performance Optimization

   • Batch memory operations
   • Cache frequently accessed data
   • Optimize retrieval patterns

Real-World Examples

Here are some practical applications of Attixa + LangChain:

• Customer Support Agents: Maintain context across multiple conversations
• Research Assistants: Remember and connect related information
• Personal Assistants: Learn from user preferences and patterns

Next Steps

Ready to enhance your LangChain agents with powerful memory? Check out our documentation or try our LangChain integration guide.