Build stateful agents that retain and revise context
Who should use this?
Long-lived personalized or operational agents and Teams researching agent memory.
Who should avoid it?
Stateless chat applications, Sensitive memory without user controls
What problem does it solve?
Letta is an agent framework and application for persistent assistants that manage memory, tools, skills, schedules, and context across sessions.
Would I recommend it?
Shortlist Letta for genuinely longitudinal agents, not ordinary chatbots. Require inspectable memory, correction and deletion controls, scoped tools, a complete model-cost ledger, and a rollback plan before production.
Advisor score
Premium review framework
Letta is an agent framework and application for persistent assistants that manage memory, tools, skills, schedules, and context across sessions.
Shortlist Letta for genuinely longitudinal agents, not ordinary chatbots. Require inspectable memory, correction and deletion controls, scoped tools, a complete model-cost ledger, and a rollback plan before production.
Run a 30-day synthetic-assistant trial containing preference changes, contradictions, sensitive decoys, revoked permissions, deletion requests, and model switches. Measure supported recall, stale-memory rate, cross-user leakage, correction and deletion completeness, background spend, and task completion.
Personal Recommendation
Shortlist Letta for genuinely longitudinal agents, not ordinary chatbots. Require inspectable memory, correction and deletion controls, scoped tools, a complete model-cost ledger, and a rollback plan before production.
Try the recommendation
Memory is a first-class agent primitive
Overall Score
Editorial Review Framework
Who should use this?
Long-lived personalized or operational agents, Teams researching agent memory, Developers wanting local and cloud paths.
Who should avoid it?
Stateless chat applications, Sensitive memory without user controls
What problem does it solve?
Letta is an agent framework and application for persistent assistants that manage memory, tools, skills, schedules, and context across sessions.
Would I recommend it?
Shortlist Letta for genuinely longitudinal agents, not ordinary chatbots. Require inspectable memory, correction and deletion controls, scoped tools, a complete model-cost ledger, and a rollback plan before production.
Overall Score
8.2
Ease of Use
8.0
AI Quality
8.0
Features
8.4
Speed
8.0
Integrations
8.2
Value for Money
8.2
Customer Support
7.6
Learning Curve
7.6
Recommended Because…
Memory is a first-class agent primitive
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Long-lived personalized or operational agents; Teams researching agent memory; Developers wanting local and cloud paths
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: The Letta framework and local agent paths are open source, and Letta Agent can be tried with buyer-supplied API keys or supported coding subscriptions. Hosted inference, enterprise BYOK, support, and deployment terms can add cost; no stable public enterprise price was verified. Reviewed September 13, 2026.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.2/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: OpenAI, Anthropic, Gemini, Slack, Discord, Telegram
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Memory can preserve false or sensitive inferences; Complete hosted pricing is not public; Long-running agents need active governance
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Evaluation
Research-based
Price posture
From $0/month
Reviewed
2026-09-13
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
The Letta framework and local agent paths are open source, and Letta Agent can be tried with buyer-supplied API keys or supported coding subscriptions. Hosted inference, enterprise BYOK, support, and deployment terms can add cost; no stable public enterprise price was verified. Reviewed September 13, 2026.
Free plan: Yes. Local and open-source use is available, with external model and infrastructure costs.
Editorial freshness
Pricing and material product claims were checked September 13, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Letta is an agent framework and application for persistent assistants that manage memory, tools, skills, schedules, and context across sessions.
The Letta framework and local agent paths are open source, and Letta Agent can be tried with buyer-supplied API keys or supported coding subscriptions. Hosted inference, enterprise BYOK, support, and deployment terms can add cost; no stable public enterprise price was verified. Reviewed September 13, 2026.
Long-lived personalized or operational agents, Teams researching agent memory, Developers wanting local and cloud paths.
Run a 30-day synthetic-assistant trial containing preference changes, contradictions, sensitive decoys, revoked permissions, deletion requests, and model switches. Measure supported recall, stale-memory rate, cross-user leakage, correction and deletion completeness, background spend, and task completion.
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