Agentic Memory 🧠

A novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way.

Introduction 🌟

Large Language Model (LLM) agents have demonstrated remarkable capabilities in handling complex real-world tasks through external tool usage. However, to effectively leverage historical experiences, they require sophisticated memory systems. Traditional memory systems, while providing basic storage and retrieval functionality, often lack advanced memory organization capabilities.

Our project introduces an innovative Agentic Memory system that revolutionizes how LLM agents manage and utilize their memories:

Note: This repository provides a memory system to facilitate agent construction. If you want to reproduce the results presented in our paper, please refer to: https://github.com/WujiangXu/AgenticMemory

For more details, please refer to our paper: A-MEM: Agentic Memory for LLM Agents

Key Features ✨

• 🔄 Dynamic memory organization based on Zettelkasten principles
• 🔍 Intelligent indexing and linking of memories via ChromaDB
• 📝 Comprehensive note generation with structured attributes
• 🌐 Interconnected knowledge networks
• 🧬 Continuous memory evolution and refinement
• 🤖 Agent-driven decision making for adaptive memory management

Framework 🏗️

How It Works 🛠️

When a new memory is added to the system:

1. Generates comprehensive notes with structured attributes
2. Creates contextual descriptions and tags
3. Analyzes historical memories for relevant connections
4. Establishes meaningful links based on similarities
5. Enables dynamic memory evolution and updates

Results 📊

Empirical experiments conducted on six foundation models demonstrate superior performance compared to existing SOTA baselines.

Getting Started 🚀

1. Clone the repository:

git clone https://github.com/agiresearch/A-mem.git
cd A-mem

2. Install dependencies: Create and activate a virtual environment (recommended):

python -m venv .venv
source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

Install the package:

pip install .

For development, you can install it in editable mode:

pip install -e .

3. Usage Examples 💡

Here's how to use the Agentic Memory system for basic operations:

from agentic_memory.memory_system import AgenticMemorySystem

# Initialize the memory system 🚀
memory_system = AgenticMemorySystem(
    model_name='all-MiniLM-L6-v2',  # Embedding model for ChromaDB
    llm_backend="openai",           # LLM backend (openai/ollama)
    llm_model="gpt-4o-mini"         # LLM model name
)

# Add Memories ➕
# Simple addition
memory_id = memory_system.add_note("Deep learning neural networks")

# Addition with metadata
memory_id = memory_system.add_note(
    content="Machine learning project notes",
    tags=["ml", "project"],
    category="Research",
    timestamp="202503021500"  # YYYYMMDDHHmm format
)

# Read (Retrieve) Memories 📖
# Get memory by ID
memory = memory_system.read(memory_id)
print(f"Content: {memory.content}")
print(f"Tags: {memory.tags}")
print(f"Context: {memory.context}")
print(f"Keywords: {memory.keywords}")

# Search memories
results = memory_system.search_agentic("neural networks", k=5)
for result in results:
    print(f"ID: {result['id']}")
    print(f"Content: {result['content']}")
    print(f"Tags: {result['tags']}")
    print("---")

# Update Memories 🔄
memory_system.update(memory_id, content="Updated content about deep learning")

# Delete Memories ❌
memory_system.delete(memory_id)

# Memory Evolution 🧬
# The system automatically evolves memories by:
# 1. Finding semantic relationships using ChromaDB
# 2. Updating metadata and context
# 3. Creating connections between related memories
# This happens automatically when adding or updating memories!

Advanced Features 🌟

1. ChromaDB Vector Storage 📦

   • Efficient vector embedding storage and retrieval
   • Fast semantic similarity search
   • Automatic metadata handling
   • Persistent memory storage

2. Memory Evolution 🧬

   • Automatically analyzes content relationships
   • Updates tags and context based on related memories
   • Creates semantic connections between memories

3. Flexible Metadata 📋

   • Custom tags and categories
   • Automatic keyword extraction
   • Context generation
   • Timestamp tracking

4. Multiple LLM Backends 🤖

   • OpenAI (GPT-4, GPT-3.5)
   • Ollama (for local deployment)

Best Practices 💪

1. Memory Creation ✨:

   • Provide clear, specific content
   • Add relevant tags for better organization
   • Let the system handle context and keyword generation

2. Memory Retrieval 🔍:

   • Use specific search queries
   • Adjust 'k' parameter based on needed results
   • Consider both exact and semantic matches

3. Memory Evolution 🧬:

   • Allow automatic evolution to organize memories
   • Review generated connections periodically
   • Use consistent tagging conventions

4. Error Handling ⚠️:

   • Always check return values
   • Handle potential KeyError for non-existent memories
   • Use try-except blocks for LLM operations

Citation 📚

If you use this code in your research, please cite our work:

@article{xu2025mem,
  title={A-mem: Agentic memory for llm agents},
  author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao and Zhang, Yongfeng},
  journal={arXiv preprint arXiv:2502.12110},
  year={2025}
}

License 📄

This project is licensed under the MIT License. See LICENSE for details.