Letta (formerly MemGPT) is an open-source platform for building stateful AI agents with persistent, transparent long-term memory. With 21.7K GitHub stars, Letta pioneered the memory-first approach to agent design, where agents maintain identity, learn from interactions, and improve across sessions rather than resetting to a blank state.
framework python agents memory stateful memgpt
Letta originated as MemGPT, a research project that introduced virtual context windows via memory tiers to overcome LLM context length limitations. In September 2024, MemGPT rebranded to Letta and expanded from a memory-augmented LLM prototype into a full platform for stateful agents. The core insight is that agents should have “lived experience” — storing interactions, building knowledge, and improving over time. Letta's architecture treats memory as the primary architectural concern, with agent reasoning built around persistent state rather than stateless prompt engineering.
Letta's memory-first architecture structures agents around a persistent state layer:
Creating a stateful agent with persistent memory using Letta:
from letta import create_client # Connect to Letta server client = create_client() # Create an agent with persistent memory agent = client.create_agent( name="research_assistant", system=( "You are a research assistant with persistent memory. " "Remember all interactions and build knowledge over time. " "Use your archival memory to store important facts." ), memory_human="User is a machine learning researcher.", memory_persona="I am a helpful research assistant that remembers everything.", ) # Chat with the agent — it remembers across sessions response = client.send_message( agent_id=agent.id, message="I'm working on a paper about transformer architectures.", ) print(response.messages) # Later session — agent remembers the context response = client.send_message( agent_id=agent.id, message="What was I working on?", ) # Agent recalls: "You mentioned working on a paper about transformer architectures" print(response.messages)
| Component | Description | Use Case |
|---|---|---|
| Core Memory | Fixed-size, always-in-context self-knowledge | Identity, goals, behavioral anchoring |
| Archival Memory | Vector-stored episodic/semantic facts | Long-term recall, knowledge accumulation |
| Recall Functions | Fetch/prioritize memories before LLM calls | Context optimization, relevance scoring |
Letta Filesystem achieves 74.0% on the LoCoMo benchmark via simple file-based histories, demonstrating that effective long-term memory does not require complex architectures.