Caching Deep Research Output

Hi r/DeepSeek! Disclosure: I built this, but will share some small leanings here. I use deep research at work to understand unfamiliar software domains, and in my personal life to compare products before buying them. I can’t use my company's LLM for personal use, and the publicly available deep-research products (like Claude and OpenAI) are too expensive for my taste, so I built a DeepSeek-powered alternative. The pipeline sends each query to multiple focused research agents, followed by adversarial agents that challenge their findings, before synthesizing everything into a report. The results can be useful, but generating them takes time and money. Eventually I realized: why keep paying to regenerate really time consuming research reports that someone else may have already done? So I made TESRAC. Its reports are public and reusable: you can run a new public, non-confidential query or search the existing index for a relevant report. The research context is also preserved so you can ask follow-up questions without repeating the entire research run. The most surprising thing I learned is that, with my current configuration, the websearch APIs can cost more per report than DeepSeek LLM itself 😂. DeepSeek is inexpensive enough that search becomes the dominant cost once several agents begin querying the web. I tested several search providers and experimented with self-hosted SearXNG, but I couldn’t get sufficiently consistent source quality for the research agents. Here's the website link. It's free to try! Let me know what you think! TESRAC: tesrac.dev