◇ Screenshots
A deep-research agent (DeerFlow) searches the web and builds an evidence-grounded dossier; the system constructs a local temporal knowledge graph (Graphiti), generates digital personas for the real-world actors it found, runs a multi-agent population simulation (OASIS, dual-platform), and a report agent synthesizes the forecast. The pages below show every stage of real runs, unedited.
Each card is one full pipeline run. Click through to walk the whole workflow: deep-research log → research dossier → ontology → knowledge graph → simulated forum → final forecast.
“Forecast the global grid-scale energy-storage industry from 2026 through 2040. Compare lithium iron phosphate, sodium-ion, flow batteries, thermal storage, compressed-air storage, pumped hydro, hydrogen-derived storage… Quantify GW/GWh deployment, duration mix, installed cost, levelized cost of storage… End with four mutually exclusive scenarios, 10–12 resolution-ready binary forecasts, and sourced visualizations of deployment, costs, technology shares, and regional policy milestones.”
Walk through the run →“Forecast the outcome of the 2026 US midterm election… which party controls the House and Senate, presidential approval, economic conditions, redistricting, candidate quality, fundraising, turnout dynamics for each party coalition, the most competitive Senate and House races.”
Walk through the run →“Forecast the future state of the US trading system by 2028: tariff policy trajectory and the effective tariff rate, the fate of the IEEPA-based reciprocal tariff regime post-Supreme-Court ruling, reshoring/nearshoring, trade relationships with China/EU/Mexico/Canada, CHIPS Act & export controls, the WTO order, and AI-driven productivity's interaction with trade competitiveness.”
Walk through the run →“Two seismic forces are reshaping the global order at the same time… How does Modern Mercantilism—governments actively reshaping trade, industrial policy, and national power—collide with AI, which will touch every corner of the macroeconomy? What does that collision produce?”
Walk through the run →“针对 2027—2028 年存储半导体市场整体行业格局开展全面前景预判，完整包含四大核心模块：厂商竞争格局、新一代存储技术、供需与价格走势、行业历史周期规律……”
Walk through the run →“你作为全球云计算产业资深分析师，综合算力储备、客户规模、区域份额、毛利率、技术壁垒、地缘合规、AI 算力增量需求等指标，完整推演至 2030 年全球云计算竞争格局，明确赢家与失利厂商……”
Walk through the run →“预测到2030年，哪家美国人工智能实验室或企业将成为美国AI领域竞争的主导者……”
Walk through the run →“Forecast the global electric vehicle industry through 2035 across technical routes, industrial-chain competition, national and regional policy, and consumer-market structure — including drivers, bottlenecks, regional divergence, and 2026–2035 inflection points.”
Walk through the run →“How and when will the Russia–Ukraine war end? Research the current state of the conflict…”
Walk through the run →“分析 2030 年前全球半导体行业的发展走向。围绕设计、制造、封装测试、组装等全产业链环节展开研究……”
Walk through the run →“预测到 2030 年全球存储半导体（DRAM、NAND 闪存、HBM 高带宽内存）市场的竞争格局：主要厂商的份额演变、技术路线、AI 驱动的 HBM 需求、地缘政治与出口管制的影响……”
Walk through the run →“请基于以下要点，对 2035 年中国储能与电池市场做出深度、结构化预测与竞争格局研判……”
Walk through the run →“请基于当前地缘政治现状、美伊双方核心战略诉求、军事实力对比、地区盟友体系、大国博弈背景……开展系统性、多层级、场景化深度预测分析……”
Walk through the run →One prompt → research console → knowledge graph → live simulation feed → forecast.
# run it yourself — no graph DB to host (embedded Graphiti/FalkorDB);
# all you need is a Claude/Codex CLI login or any LLM API key
git clone https://github.com/linroger/DeepAgentForecast.git
cd DeepAgentForecast && ./setup.sh # interactive: picks your LLM provider
npm run dev # → http://localhost:3000/research