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 AkiRusProd · Agent Tool · ★ 55
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long-term-memory-llm RAG-based LLM using long-term memory through vector database Description This repository enables the large language model to use long-term memory through a vector database (This method is called RAG (Retrieval Augmented Generation) — this is a technique that allows LLM to retrieve facts from an external database). The application is built with mistral-7b-instruct-v0.2.Q4KM.gguf (using LLAMAcpppython binding) and chromadb. User can ask in natural language to add information to db, find information from db or the Internet using guidance. Current features: add new memory: add information (in quotes) in natural language to the database query memory: request information from a database in natural language web search (experimental): find information from the Internet in natural language Diagram: Example: You Hi LOG: [Response] Bot Please add information to db "The user name is Rustam Akimov" LOG: [Adding to memory] Bot Can you find on the Internet who is Pavel Durov LOG: [Extracting question] LOG: [Searching] LOG: [Summarizing] Bot < According to the search results provided, Pavel Durov is a Russian entrepreneur who co-founded Telegram
| Stars | 55 |
| Forks | 9 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 58.6363921477206/100 |
| Last Updated | 2024-04-09 |
| Created | 2023-09-19 |
| Platforms | python |
| Est. Tokens | ~19k |
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llm-agent is LLM using long-term memory through vector database. It is categorized as a Agent Tool with 55 GitHub stars.
llm-agent is primarily written in Python. It covers topics such as chromadb, gpt, gpt4all.
You can find installation instructions and usage details in the llm-agent GitHub repository at github.com/AkiRusProd/llm-agent. The project has 55 stars and 9 forks, indicating an active community.
The top alternatives to llm-agent on Agent Skills Hub include cai, ai-microcore, quanta-quest. 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.
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