letta
Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.
Overview
Letta (formerly MemGPT) is a platform for building stateful AI agents with advanced memory capabilities that can learn and self-improve over time. Unlike traditional AI assistants that start fresh with each conversation, Letta agents maintain persistent memory across interactions, enabling them to accumulate knowledge, adapt to user preferences, and evolve their capabilities. The platform offers two main deployment options: Letta Code for running agents locally in your terminal, and the Letta API for integrating stateful agents into applications. Letta Code comes with pre-built skills and subagents, allowing agents to perform complex tasks on your local computer while maintaining continuity. The platform is model-agnostic, supporting various AI models while recommending Opus 4.5 and GPT-5.2 for optimal performance. With over 21,000 GitHub stars, Letta provides both Python and TypeScript SDKs, making it accessible for developers working in different ecosystems. The platform's core innovation lies in its memory architecture, which allows agents to form lasting relationships with users, remember context from previous sessions, and continuously improve their performance based on accumulated experience.
Pros
- + Advanced persistent memory system that allows agents to learn and self-improve across sessions
- + Dual deployment options with both local CLI tool and cloud API for different use cases
- + Model-agnostic platform with comprehensive SDKs for Python and TypeScript development
Cons
- - Requires Node.js 18+ for local CLI usage, limiting accessibility for some users
- - Cloud API requires API key and external service dependency for full functionality
- - Platform complexity may present learning curve for developers new to stateful agent concepts
Use Cases
- • Building long-term coding assistants that remember project context and user preferences across sessions
- • Creating customer service agents that maintain conversation history and learn from interactions
- • Developing research assistants that accumulate domain knowledge and improve recommendations over time