What you need
Python 3.10+ installed.
Letta installed: `pip install letta`.
An API key for your model provider.
Step 1: Understand virtual context management
Letta treats memory like an operating system: there is a limited fast-access working context and unlimited archival storage.
The system automatically moves important information into working context and archives old or low-priority data.
This means the agent can theoretically remember an unlimited amount of information across sessions.
Step 2: Configure memory banks
Memory banks are named storage areas for different types of information.
Create a 'user_preferences' bank, a 'project_context' bank, and a 'domain_knowledge' bank.
Each bank has its own archival and retrieval settings.
Step 3: Set archival and recall rules
Archival rules determine when information moves from working context to archival storage.
Recall rules determine what gets brought back into working context and when.
Configure importance scoring so critical information stays accessible and noise gets archived.
Step 4: Test cross-session memory
In session 1, give the agent detailed preferences about how it should handle certain tasks.
End the session. Start session 2 and ask the agent about those preferences.
Verify it retrieves the correct memories and applies them consistently.
FAQ
How does Letta compare to MemUbot?
Both focus on memory, but Letta uses OS-inspired virtual context management while MemUbot uses a layered architecture. Letta is better for open-ended memory growth, MemUbot for structured memory policies.
Can Letta run locally without sending data to cloud APIs?
Yes. When used with local models (Ollama, vLLM), all data stays on your machine. The memory database is stored locally in SQLite.
Set Up Letta Memory
- Install Letta: pip install letta
- Configure memory banks for different data types
- Set archival and recall rules
- Test cross-session memory retrieval
- Optimize importance scoring per use case
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