HyperResearch is a simple Claude Code skill harness that outperforms every deep research framework.
HyperResearch surpasses OpenAI, Google, and NVIDIA's offerings in the agentic search space based on DeepResearch Bench. It's open-source, installable with a single command, and uses your CC subscription, so you don't have to pay for OpenAI or Gemini Pro.
It uses a 16-step pipeline that creates a searchable, persistent knowledge store during each session that can be built upon in later searches. I designed it to align with the original user prompt as closely as possible, while incorporating built-in fact-checking, adversarial review, and breadth and depth-investigating capabilities.
This is a generalized framework; you can use it for any large-scale research task, from developing a trading strategy for a specific stock to analyzing competitor products to understanding the current state of the art in LLM architecture.
It uses crawl4ai (an open-source LLM search tool) to capture a wider breadth of information than the standard websearch tool is capable of. You can also configure authenticated sessions, meaning that LinkedIn, Twitter, etc., are now fair game for agentic search.
HyperResearch is a simple Claude Code skill harness that outperforms every deep research framework.
HyperResearch surpasses OpenAI, Google, and NVIDIA's offerings in the agentic search space based on DeepResearch Bench. It's open-source, installable with a single command, and uses your CC subscription, so you don't have to pay for OpenAI or Gemini Pro.
It uses a 16-step pipeline that creates a searchable, persistent knowledge store during each session that can be built upon in later searches. I designed it to align with the original user prompt as closely as possible, while incorporating built-in fact-checking, adversarial review, and breadth and depth-investigating capabilities.
This is a generalized framework; you can use it for any large-scale research task, from developing a trading strategy for a specific stock to analyzing competitor products to understanding the current state of the art in LLM architecture.
It uses crawl4ai (an open-source LLM search tool) to capture a wider breadth of information than the standard websearch tool is capable of. You can also configure authenticated sessions, meaning that LinkedIn, Twitter, etc., are now fair game for agentic search.
If anyone wants to port it to Codex, be my guest!