by dzhng · Agent Tool · ★ 510
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DeepSeek This is a new experimental architecture for a llm powered internet scale retrieval engine. This architecture is very different from current research agents, which are designed as answer engines. You can see some example results here: https://deep-seek.vercel.app/ (Note that this won't let you do real queries, since I won't be able to afford it 😅) The main difference between the 2 concepts breaks down to: Answer engine: aggregate a ton of sources to find the one correct answer. This is what "research agents" have converged on recently. Examples include perplexity, gpt-researcher, or aomni. Retrieval engine: process a ton of sources to collect a comprehensive list of entities. So far there haven't been any serious attempts to do this yet (afaik). The end result for an answer engine is a research report, the end result for a retrieval engine is a table with all the retrieved entities and enriched columns. Here's what the end result looks like (zoomed out for scale): This is infact a small portion of the result for this query. The actual result is so large it's impossible to screenshot.
| Stars | 510 |
| Forks | 54 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 41.25/100 |
| Open Issues | 3 |
| Last Updated | 2024-05-07 |
| Created | 2024-03-16 |
| Platforms | node |
| Est. Tokens | ~79k |
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deep-seek is LLM powered retrieval engine designed to process a ton of sources to collect a comprehensive list of entities.. It is categorized as a Agent Tool with 510 GitHub stars.
deep-seek is primarily written in TypeScript. It covers topics such as agent, agentic, ai.
You can find installation instructions and usage details in the deep-seek GitHub repository at github.com/dzhng/deep-seek. The project has 510 stars and 54 forks, indicating an active community.
deep-seek is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to deep-seek on Agent Skills Hub include tools, SearChat, samples. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.