AgentVectorDB (AVDB)

The Cognitive Core for Your AI Agents - Powered by LanceDB

📚 Superagentic AI Introduction

Superagentic AI Website

📚 Project Documentation

• Getting Started
• API Reference
• Examples
• FAQ

🌟 Overview

AgentVectorDB (AVDB) is a specialized memory management system developed by Superagentic AI. Built on top of LanceDB's powerful vector database capabilities, it provides optimized cognitive architecture for AI agents.

🤝 Built with LanceDB

We extend LanceDB's robust foundation with agent-specific features:

• Agent memory patterns
• Importance scoring
• Context management
• Cognitive state handling

✨ Key Features

Core Capabilities

• 📝 Persistent Storage: File-based, no server required
• 🔍 Semantic Search: Efficient ANN search with filtering
• ⚡ Async Support: High-performance async/await API
• 🎯 Agent-Optimized: Purpose-built for AI systems

Advanced Features

• 🔄 Memory Lifecycle: Complete CRUD operations
• 📊 Batch Processing: Efficient bulk operations
• 🧹 Smart Pruning: Intelligent memory management
• 🔧 Flexible Schema: Dynamic Pydantic schemas
• ⏱️ Time Tracking: Automatic timestamps

📦 Installation

# Basic installation
pip install agentvectordb

# With all extras (recommended)
pip install "agentvectordb[all]"

# Development installation
git clone https://github.com/superagenticai/agentvectordb.git
cd agentvectordb
pip install -e ".[dev]"  
#For windows if you encounter UnicodeDecodeError set $env:PYTHONUTF8=1

🚀 Quick Start

from agentvectordb import AgentVectorDBStore
from agentvectordb.embeddings import DefaultTextEmbeddingFunction

# Initialize store
store = AgentVectorDBStore(db_path="./agent_db")
ef = DefaultTextEmbeddingFunction(dimension=384)

# Create collection
memories = store.get_or_create_collection(
    name="agent_memories",
    embedding_function=ef
)

# Add memories (minimum 8 recommended)
initial_memories = [
    {
        "content": "User prefers dark mode",
        "type": "preference",
        "importance_score": 0.8
    },
    # Add more memories...
]

# Add batch
memories.add_batch(initial_memories)

# Query memories
results = memories.query(
    query_text="user preferences",
    k=2
)

🛠️ API Overview

Store Classes

AgentVectorDBStore

store = AgentVectorDBStore(db_path="./db")

Methods:

• get_or_create_collection()
• list_collections()
• delete_collection()

AsyncAgentVectorDBStore

store = AsyncAgentVectorDBStore(db_path="./db")

Collection Classes

AgentMemoryCollection

Methods:

• add(): Add single memory
• add_batch(): Add multiple memories
• query(): Search memories
• prune_memories(): Remove old memories
• delete(): Remove memories
• count(): Get collection size

📚 Advanced Usage

Custom Embedding Functions

from agentvectordb.embeddings import BaseEmbeddingFunction

class CustomEmbedder(BaseEmbeddingFunction):
    def __init__(self, dimension=384):
        super().__init__(dimension=dimension)
    
    def embed(self, texts):
        # Your embedding logic here
        return vectors

Memory Management

# Prune old memories
pruned = collection.prune_memories(
    max_age_seconds=7*24*3600,  # 7 days
    min_importance_score=0.3
)

# Complex queries
results = collection.query(
    query_text="important task",
    filter_sql="type = 'task' AND importance_score > 0.8",
    k=5
)

Async Operations

async def handle_memories():
    store = AsyncAgentVectorDBStore(db_path="./async_db")
    collection = await store.get_or_create_collection(
        name="async_memories",
        embedding_function=ef
    )
    
    await collection.add_batch(memories)
    results = await collection.query(
        query_text="search term",
        k=5
    )

🎯 Use Cases

• Personal AI assistants
• Customer service bots
• Research agents
• Task automation agents
• Knowledge management systems
• Learning systems

🔄 Memory Types

• Episodic memories
• Semantic knowledge
• Procedural information
• Short-term observations
• Long-term knowledge

🛣️ Roadmap

Upcoming features:

• Enhanced filter builders
• Reflection/summarization helpers
• Schema evolution support
• Memory consolidation
• Extended embedding support
• Performance optimizations

🤝 Contributing

We welcome contributions! See our Contributing Guide.

📄 License

Licensed under Apache 2.0 - same as LanceDB. See LICENSE.

🙋‍♂️ Support

• GitHub Issues: Technical support
• Email: support@super-agentic.ai
• Enterprise: enterprise@super-agentic.ai

🙏 Acknowledgments

Built with ❤️ by Superagentic AI using LanceDB

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AgentVectorDB: Empower Your AI Agents with Memory!