Search agent that does deep search to answer your question.
The core idea is that quality of search should scale with the value of answering a query (quantified by $ spend budget)
I'm betting that for queries like:
- which chemical compounds might have similar effects as ketamine? (drug discovery)
- How does the basal ganglia work and what can AI researchers learn from it? (AI research)
- Who's built zero-knowledge proof projects? (recruiting)
current search engines are bad at answering them, and the value of answering queries justifies long wait times & search cost.
Experimental, only supports arxiv search using Exa & arxiv API currently for research related queries.
- create .env file and set your OpenAI, Anthropic, Groq api keys
python main.py --query "why do primate brains have a hippocampus and why might AI systems want one?" --budget 5 --agent openai
Check out some example reports Mariana generated in /reports. The query was:
Why do primate brains have a hippocampus and why might AI systems want one
claude-3-opus
writes best reports, followed bygpt-4-turbo-2024-04-09
- quality of report with first 3 pages of papers in context >> only abstract of papers in context
- OpenAI & Groq integration
- logging
- more useful than one-shot generation / existing survey papers?
- how to measure quality vs. $ burn?
- continued learning: use memory & improve report
- clean pdf readings