STORM uses perspective-guided questions, simulated interviews, and outlines to broaden research; Co-STORM keeps a person in the loop so discovering unknown questions and co-editing become part of the system.
Parallel Web Systems separates Search, Extract, and Task APIs into web-access layers with different latency and cost profiles, while Basis maps citations, excerpts, and confidence to output fields.
An autonomous research agent = four controllable stages: planning (decompose into sub-questions), retrieval loop (search -> read -> reflect on gaps -> search again), evidence arbitration (>=2 independent sources, typed conflict handling), and verifiable output (sentence-level citations + independent verification pass). Two approaches: training-based uses RL to learn end-to-end when to search (Search-R1 +41%); orchestration-based uses orchestrator-worker division of labor (Anthropic internal eval +90.2%, at ~15x token cost).
Local Deep Research is a privacy-first deep research agent built on LangChain + LangGraph, integrating 20+ search engines and 30+ research strategies. Its flagship langgraph_agent_strategy takes the LLM-autonomous tool-calling approach, offering a fundamentally different paradigm from fixed-pipeline RAG graphs.