haive.agents.research.deep_research_agent ========================================= .. py:module:: haive.agents.research.deep_research_agent .. autoapi-nested-parse:: Deep Research Agent — advanced multi-stage research with RAG + search + analysis. Uses multiple specialized agents composed via MultiAgent: 1. QueryPlanner (SimpleAgent) — decomposes question into sub-queries 2. WebResearcher (ReactAgent) — Tavily search + store findings 3. RAGAnalyzer (ReactAgent) — retrieves + analyzes stored findings with RAG tools 4. FactChecker (SimpleAgent) — validates consistency, flags contradictions 5. ReportWriter (SimpleAgent) — synthesizes into cited report More thorough than perplexity_agent — designed for deep research tasks. Usage: from haive.agents.research.deep_research_agent import create_deep_research_agent agent = create_deep_research_agent() result = agent.run("Compare transformer vs state space model architectures for LLMs") Classes ------- .. autoapisummary:: haive.agents.research.deep_research_agent.DeepResearchAgent haive.agents.research.deep_research_agent.ResearchStore Functions --------- .. autoapisummary:: haive.agents.research.deep_research_agent.create_deep_research_agent Module Contents --------------- .. py:class:: DeepResearchAgent Bases: :py:obj:`haive.agents.multi.agent.MultiAgent` Advanced multi-stage research agent. Pipeline: Planner → Researcher → Analyzer → FactChecker → Writer .. py:class:: ResearchStore In-session document store for research findings. .. py:function:: create_deep_research_agent(name = 'deep_research', tools = None, max_search_iterations = 10, include_fact_check = True) Create a deep research agent with 5-stage pipeline. :param name: Agent name :param tools: Custom search tools (defaults to Tavily or mock) :param max_search_iterations: Max search iterations for researcher :param include_fact_check: Include fact-checking stage :returns: DeepResearchAgent ready for execution