Memory Management

AI agents in Allinix need to maintain context of their previous actions while executing tasks. However, they face a fundamental limitation: their memory is constrained by their context length.

ℹ️ Note: The context length limitation means agents can't retain unlimited history of their actions. This is where vector databases come in as a solution for long-term memory storage.

The Memory Challenge

When agents run multiple execution loops, they encounter two main issues:

1. Limited context window in language models

2. Need to access historical decisions and actions

Our solution leverages vector databases to store and retrieve memory externally, making it available when needed.

Vector Databases Explained

What is a Vector Database?

A vector database stores and retrieves data based on semantic similarity rather than exact matching. This makes it perfect for storing AI memory because:

• It can find relevant memories based on context

• It enables efficient similarity search across large datasets

• It works similarly to human memory retrieval

For a comprehensive understanding, you can refer to the Weaviate documentation.

Why We Chose Weaviate

Weaviate serves as our primary vector database for several compelling reasons:

Key Benefits

1. Open Source & Accessible

   • Runs locally via docker-compose

   • No API key required for local development

2. Scalable Cloud Solution

   • Managed service available

   • Automatic scaling based on workload

   • Minimal infrastructure management

3. Integration Support

   • Seamless LangChain integration

   • Rich API ecosystem

   • Active community support

ℹ️ Note: While Weaviate is our current choice, we're open to community suggestions. Feel free to create a ticket or pull request if you have alternative database recommendations.

Current Memory Implementation

Our long-term memory system is actively evolving. Here's what we've implemented so far:

graph TD
    A[Agent Action] --> B[Memory Storage]
    B --> C[Vector Embedding]
    C --> D[Weaviate DB]
    D --> E[Memory Retrieval]
    E --> F[Similar Task Filtering]

Current Features

• Task similarity detection and filtering

• Historical context retrieval

• Persistent memory across sessions

⚠️ Warning: Memory management in Allinix is still under active development. We welcome contributions and ideas from the community to enhance its capabilities.

Contributing

If you're interested in improving Allinix's memory management:

• Explore our current implementation

• Share your ideas through GitHub issues

• Submit pull requests with enhancements

• Join our community discussions

Future Development Areas

• Enhanced memory pruning algorithms

• Improved relevance scoring

• Memory compression techniques

• Multi-agent memory sharing