No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by letta-ai · Codex Skill · ★ 3.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is letta-code safe to install? View the security audit →
Letta Code Letta Code is a stateful agent harness for creating agents that are more like people than tools. Letta Code agents have memory, identity, and a sense of experience over time. They learn and evolve over long horizons through rewriting their own memory, skills, prompts, and even the harness itself (through mods). Letta Code can be used interactively, or to power always-on agents that work proactively. Interact with agents through: A local CLI The desktop app for macOS, Windows, and Linux Your browser, including mobile, at chat.letta.com Messaging integrations, including Telegram, Slack, Discord, and custom channels Feature Overview [!TIP] Letta Code agents are designed to be self-configuring. If you wa
| Stars | 3,504 |
| Forks | 425 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 69.5627115714592/100 |
| Open Issues | 490 |
| Last Updated | 2026-10-03 |
| Created | 2025-10-25 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~15k |
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letta-code is Stateful agents that are like people, with memory, identity, and the ability to learn and adapt. It is categorized as a Codex Skill with 3.5k GitHub stars.
letta-code is primarily written in TypeScript. It covers topics such as agent-memory, ai, claude.
You can find installation instructions and usage details in the letta-code GitHub repository at github.com/letta-ai/letta-code. The project has 3.5k stars and 425 forks, indicating an active community.
letta-code is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to letta-code on Agent Skills Hub include honcho, openpencil, dbhub. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: