Policy-Aware Content Curator
Build an advanced agentic system that can intelligently curate content from the web, similar to how AI browsers handle paywalls or content restrictions. The system must process news articles, summarize them, and apply dynamic content policies (e.g., avoiding sources from a blacklist, prioritizing specific types of reporting, or finding alternative summaries if a paywall is detected). This involves creating a graph-based workflow where different nodes (agents) handle content fetching, policy checking, summarization, and alternative sourcing. The core of this challenge is to leverage Gemini 2.5 Pro's Deep Think mode for complex policy interpretation and nuanced summarization, particularly when adapting to a 'blacklist' or 'preference list' of sources. Implement an adaptive reasoning budget within the LangGraph workflow, allowing the system to spend more computational effort on articles that trigger complex policy decisions or require extensive alternative source discovery.
AI Research & Mentorship
What you are building
The core problem, expected build, and operating context for this challenge.
Build an advanced agentic system that can intelligently curate content from the web, similar to how AI browsers handle paywalls or content restrictions. The system must process news articles, summarize them, and apply dynamic content policies (e.g., avoiding sources from a blacklist, prioritizing specific types of reporting, or finding alternative summaries if a paywall is detected). This involves creating a graph-based workflow where different nodes (agents) handle content fetching, policy checking, summarization, and alternative sourcing. The core of this challenge is to leverage Gemini 2.5 Pro's Deep Think mode for complex policy interpretation and nuanced summarization, particularly when adapting to a 'blacklist' or 'preference list' of sources. Implement an adaptive reasoning budget within the LangGraph workflow, allowing the system to spend more computational effort on articles that trigger complex policy decisions or require extensive alternative source discovery.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building stateful Directed Acyclic Graph (DAG) agent workflows, including dynamic branching, persistence, and breakpoint management.
Implement advanced RAG pipelines using vector databases (e.g., ChromaDB, Weaviate) to retrieve and ground content policy documents for real-time application.
Design and integrate Gemini 2.5 Pro's Deep Think mode for complex, multi-step reasoning, particularly for interpreting ambiguous content policies and generating nuanced summaries.
Build an adaptive reasoning budget mechanism within LangGraph that dynamically allocates computational resources (e.g., API calls, processing time) based on the complexity of policy adherence or the need for alternative content discovery.
Develop robust tool integrations for web scraping (e.g., BeautifulSoup, Playwright), search APIs (e.g., Google Search API, Brave Search API), and text processing libraries within the LangGraph agent nodes.
Orchestrate a multi-node LangGraph agent system where nodes specialize in content fetching, policy compliance checking, content summarization, and alternative source generation.
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