DeepWrite: The Agentic Planning & Research Engine

Standard AI lacks the ability to use live data, often making up facts. Human experts spend 80% of their time hunting for information and only 20% actually writing. Basic LLM apps write in one go, resulting in shallow, generic content that lacks professional depth.

A high-order multi-agent system that mimics a professional newsroom. It separates planning from execution to produce high-fidelity, research-backed content with zero human oversight. An Orchestrator (Editor-in-Chief) builds a complex execution plan, parallel Worker agents (Field Journalists) scour the live web for facts, and a Reducer (Lead Writer) synthesizes the research into a polished final piece.

• Provide a topic
• Orchestrator analyzes the topic and builds an execution plan
• Worker agents search the web in parallel and verify sources
• Reducer synthesizes the research into a cohesive narrative
• Receive a polished, citation-backed final piece

• Agentic planning — using the LLM to think before it speaks, creating a roadmap of sub-tasks
• Autonomous retrieval — in-context learning to filter search results for high-relevance data
• Knowledge synthesis — merging multiple data streams into a single, cohesive narrative

• LangGraph — state machine managing the Orchestrator-Worker loop
• Tavily / Exa API — AI-native real-time web search
• Claude 3.5 Sonnet / GPT-4o — primary engines for reasoning and prose
• Streamlit — minimalist dashboard demo