https://github.com/ksm26/llms-as-operating-systems-agent-memory
This repository introduces the Letta framework, empowering developers to build LLM-based agents with long-term, persistent memory and advanced reasoning capabilities. It leverages concepts from MemGPT to optimize context usage and enable multi-agent collaboration for real-world applications like research, HR, and task management.
https://github.com/ksm26/llms-as-operating-systems-agent-memory
advanced-reasoning agent-memory ai-applications ai-framework ai-innovation ai-research ai-with-memory artificial-intelligence collaborative-agents context-optimization intelligent-agents letta-framework llm-innovation llms memgpt memory-agents memory-enhanced-agents memory-management multi-agent-systems persistent-memory
Last synced: 1 day ago
JSON representation
This repository introduces the Letta framework, empowering developers to build LLM-based agents with long-term, persistent memory and advanced reasoning capabilities. It leverages concepts from MemGPT to optimize context usage and enable multi-agent collaboration for real-world applications like research, HR, and task management.
- Host: GitHub
- URL: https://github.com/ksm26/llms-as-operating-systems-agent-memory
- Owner: ksm26
- Created: 2025-01-07T18:18:12.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-17T18:20:42.000Z (9 months ago)
- Last Synced: 2025-03-28T16:18:49.384Z (6 months ago)
- Topics: advanced-reasoning, agent-memory, ai-applications, ai-framework, ai-innovation, ai-research, ai-with-memory, artificial-intelligence, collaborative-agents, context-optimization, intelligent-agents, letta-framework, llm-innovation, llms, memgpt, memory-agents, memory-enhanced-agents, memory-management, multi-agent-systems, persistent-memory
- Language: Jupyter Notebook
- Homepage: https://www.deeplearning.ai/short-courses/llms-as-operating-systems-agent-memory/
- Size: 43 KB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 💻 [LLMs as Operating Systems: Agent Memory](https://www.deeplearning.ai/short-courses/llms-as-operating-systems-agent-memory/)
Welcome to the "LLMs as Operating Systems: Agent Memory" course! 🧠 Learn how to build agents with long-term, persistent memory using **Letta**, an open-source framework for memory-enhanced LLM agents. This course is taught by **Charles Packer** and **Sarah Wooders**, co-founders of Letta, and is based on the innovative ideas presented in the MemGPT research paper.
## 📘 Course Summary
This course equips you with the skills to create AI agents that manage and edit memory autonomously, optimizing context usage for real-world applications like research and HR. Learn how to leverage **Letta** to add persistent, long-term memory to your LLM agents, enabling advanced reasoning and adaptability.
### **What You’ll Learn**
1. 🔄 **Agent Memory Management**: Build agents with self-editing memory, utilizing tool-calling and multi-step reasoning.
2. 🛠️ **Using Letta Framework**: Explore Letta’s features for adding memory capabilities to LLMs, including core and archival memory.
3. 🧩 **MemGPT Concepts**: Understand the key ideas behind MemGPT, including two-tier memory systems and how agent states are converted into prompts.
4. 🤝 **Multi-Agent Collaboration**: Learn to implement collaborative agents by sharing memory blocks and exchanging messages.
### **Practical Applications**
- 🔍 **Conversation Memory Control**: Manage expanding conversations by summarizing and moving less relevant information to a searchable database, ensuring smooth context flow.
- 📂 **Persistent Fact Storage**: Save and edit details like names, dates, and preferences for future interactions.
- 📑 **Task-Specific Memory**: Develop agents capable of swapping context-relevant information in real-time from a database for tasks like research.
## 🔑 Key Points
- 🧠 **Enhanced Memory Management**: Use Letta to create agents with long-term, persistent memory and advanced reasoning capabilities.
- 📋 **Efficient Context Optimization**: Optimize LLM context window usage to reduce costs and improve processing speed.
- 🤖 **Collaboration Between Agents**: Enable multi-agent systems that share memory and collaborate seamlessly.
## 👨🏫 About the Instructors
- **Charles Packer**: Co-Founder of Letta and co-author of the MemGPT paper.
- **Sarah Wooders**: Co-Founder of Letta and a leading expert in building memory-enhanced LLM applications.
🔗 To enroll in the course or for more details, visit 📚 [deeplearning.ai](https://www.deeplearning.ai/short-courses/).