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 plastic-labs · Agent Tool · ★ 7.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is honcho safe to install? View the security audit →
Honcho is an open source memory library with a managed service for building stateful agents. Use it with any model, framework, or architecture. It enables agents to build and maintain state about any entity--users, agents, groups, ideas, and more. And because it's a continual learning system, it understands entities that change over time. Using Honcho as your memory system will earn your agents higher retention, more trust, and help you build data moats to out-compete incumbents. Honcho has defined the Pareto Frontier of Agent Memory. Watch the video, check out our evals page, and read the blog post for more detail. TL;DR - Getting Started With Honcho you can
| Stars | 6,985 |
| Forks | 864 |
| Language | Python |
| Category | Agent Tool |
| License | AGPL-3.0 |
| Quality Score | 69.2256070581236/100 |
| Open Issues | 154 |
| Last Updated | 2026-09-02 |
| Created | 2023-09-10 |
| Platforms | python |
| Est. Tokens | ~18k |
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honcho is Memory library for building stateful agents. It is categorized as a Agent Tool with 7.0k GitHub stars.
honcho is primarily written in Python. It covers topics such as agent-memory, ai, ai-agents.
You can find installation instructions and usage details in the honcho GitHub repository at github.com/plastic-labs/honcho. The project has 7.0k stars and 864 forks, indicating an active community.
honcho is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to honcho on Agent Skills Hub include Memori, ruby_llm, agentic-context-engine. 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: