by joshuaswarren · Agent Tool · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openclaw-engram safe to install? View the security audit →
Remnic Open-source memory and context for user-aware agents. Remnic is for agents that need to understand the people they work with over time. Remnic helps AI agents understand the people they work with: their preferences, projects, constraints, decisions, patterns, and definition of good. The goal is simple: agents that remember responsibly, retrieve the right context, and ask fewer unnecessary questions. Remnic is not just a memory store. It is an exploration of the systems layer around user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, and evals. Why this matters Most agents do not fail because they lack another prompt. They fail because they do not understand the user, the project, the boundaries, or what “good” means in context. Remnic explores the systems layer needed for user-aware agents: what to remember where that memory applies why it was retrieved when it should expire how users correct it when the agent should ask instead of act how to evaluate whether memory improved the outcome The trace is noise. The primitive is the product. Remnic's job is the pipeline that distills hours of agent conversation into compres
| Stars | 52 |
| Forks | 11 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.5495134679393/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-08 |
| Created | 2026-02-05 |
| Platforms | node |
| Est. Tokens | ~724k |
Looking for a openclaw-engram alternative? If you're comparing openclaw-engram with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Persistent memory for AI coding agents
Drop-in memory harness for AI agents — 3-tier memory, compaction tree, hybrid search. One command to set up. W
Cognithor · Agent OS: Local-first autonomous agent operating system. 19 LLM providers, 18 channels, 145 MCP to
Multi-Model Chat — Compare responses from multiple AI models side by side in real-time. Supports GPT, Claude,
Local AI-powered document search and editing with first-in-class hybrid retrieval, LLM answers, WebUI, REST AP
Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn fro
Explore other popular agent tool tools:
openclaw-engram is Local-first memory plugin for OpenClaw AI agents. LLM-powered extraction, plain markdown storage, hybrid search via QMD. Gives agents persistent long-term memory across conversations.. It is categorized as a Agent Tool with 52 GitHub stars.
openclaw-engram is primarily written in TypeScript. It covers topics such as ai-agent, ai-memory, conversational-ai.
You can find installation instructions and usage details in the openclaw-engram GitHub repository at github.com/joshuaswarren/openclaw-engram. The project has 52 stars and 11 forks, indicating an active community.
openclaw-engram is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-engram on Agent Skills Hub include omega-memory, hipocampus, cognithor. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.