Letta
Build agents that actually remember: facts, preferences, and past interactions persist across every session without manual context management.
The verdict on Letta: Developers building agents that need to remember and update facts across arbitrarily long interaction histories Letta solves the problem that most agent memory implementations patch over: agents that need to remember and update facts across sessions without manual context management. Pricing: Open source / Free (Letta Cloud from $0.02/step).
Best For
Developers building agents that need to remember and update facts across arbitrarily long interaction histories
Standout Feature
Manages its own memory tiers and decides what to retain or summarize, more reliable than an ad-hoc RAG memory hack
TL;DR
A genuinely more solid approach to agent memory than manual context management, per-step cloud pricing adds up for memory-heavy agents.
Alternatives
Fit Score: use-case reference
How this is scoredLetta scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
A developer building agents that need to remember and update facts across arbitrarily long, multi-session interaction histories
3.6/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 3/5
- Ease
- 3/5
- Compatibility
- 3/5
Letta's framework manages its own memory tiers and decides what to retain or summarize, which the tool's own take calls more reliable than stitching together a RAG pipeline as an afterthought.
A memory-heavy agent with frequent reads on a tight budget, or a simple stateless use case
2.2/5- Capability
- 2/5
- Reliability
- 3/5
- Value
- 2/5
- Ease
- 2/5
- Compatibility
- 2/5
Cons note cloud pricing is per-step and complex agents with many memory reads get expensive, and the tool's own take advises running expected usage through the pricing model before committing to production.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Letta (formerly MemGPT) is an agent framework built around stateful long-term memory: agents remember facts across sessions, update their knowledge as conversations evolve, and recall relevant context from arbitrarily long interaction histories. Each agent manages its own memory tiers (working, recall, and archival) and decides what to retain or summarize, solving the context-window problem at the framework level rather than prompting around it.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Solves long-term memory without external memory databases or prompting tricks
- • Open-source core means full control over data and deployment
- • Framework-level memory is more reliable than ad-hoc RAG approaches
- • Memory management adds latency compared to stateless agents
- • Cloud pricing is per-step, complex agents with many memory reads get expensive
- • Steeper learning curve than simpler stateless frameworks
Key Features
- Three-tier memory architecture: working, recall, and archival
- Self-directed memory management: agents decide what to store
- Cross-session persistence with no manual prompt engineering
- REST API and Python SDK for embedding agents in applications
- Agent builder UI for configuring memory and persona
- Open-source core with managed Letta Cloud option
Trust & Data
Verified 2026-09
- Training on your data
- YesLetta's privacy policy states that for its hosted services, it collects message request and response data and may use this data to improve its services, for example to train new models in the future, and explicitly permits transferring this data to research partners to facilitate model training and refinement. No self-serve training opt-out exists, only a basic telemetry opt-out; self-hosted or open-source deployments where you run your own model backend are never collected or used for training.
- Compliance
- Not published
- Retention
- Not published
- Export
- Partial export
Sources: letta.com/privacy policy, letta.com/terms of service
As published by the vendor. Verify independently before purchase decisions.
Our Take
The framework manages its own memory tiers and decides what to retain or summarize, which is more reliable than stitching together a RAG pipeline as an afterthought. The open-source core keeps you in control of your data. The cost consideration is real: per-step cloud pricing accumulates quickly for memory-heavy agents with frequent reads. Run your expected usage through the pricing model before committing to Letta Cloud for production.