Adaptive AI Search Synthesis
Inspired by Google's 'AI Overviews' and AI mode, this challenge involves creating an advanced agent system for personalized, adaptive search synthesis. Leveraging LangGraph for dynamic, graph-based workflows, the system will intelligently combine traditional search results with generative AI insights from 'AI Overviews' (simulated by LLMs). The core is to use Gemini 2.5 Pro in 'Deep Think' mode for complex reasoning, query reformulation, and synthesizing coherent, well-sourced answers to intricate user queries. This system will showcase hybrid reasoning (instant retrieval and deep generative synthesis), RAG for source attribution, and adaptive thinking budgets to optimize computational resources based on query complexity. It aims to provide comprehensive answers, even for ambiguous or multi-faceted questions, simulating a personalized, intelligent research assistant.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a LangGraph-based agent system using Gemini 2.5 Pro Deep Think for adaptive search synthesis, integrating RAG and hybrid reasoning.
How work is evaluated
Evaluation focuses on the system's ability to provide accurate, well-sourced, and comprehensive answers to complex queries, demonstrate adaptive reasoning, and effectively utilize LangGraph's workflow capabilities.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Develop a hybrid reasoning system combining instant search retrieval with generative synthesis from LLMs.
Design and implement a graph-based agent workflow using LangGraph for complex query processing.
Integrate Gemini 2.5 Pro, specifically utilizing its Deep Think mode for advanced problem-solving.
Incorporate adaptive thinking budgets to dynamically manage computational effort based on query complexity.
Implement advanced RAG techniques for accurate source attribution in synthesized answers.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
Evaluation focuses on the system's ability to provide accurate, well-sourced, and comprehensive answers to complex queries, demonstrate adaptive reasoning, and effectively utilize LangGraph's workflow capabilities.
Challenge input
{'query': 'string'}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'answer': 'string', 'sources': ['url'], 'reasoning_steps': ['string']}
- Confirms the core logic is implemented as a LangGraph DAG with distinct nodes and edges.
- Checks if the answer provides at least two verifiable external sources for complex queries.
- Answer Accuracy Score target: 8
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Confirms the core logic is implemented as a LangGraph DAG with distinct nodes and edges.
- Checks if the answer provides at least two verifiable external sources for complex queries.
Metrics
- Answer_Accuracy_Score · Target 8
- Reasoning_Depth_Score · Target 4
- Adaptive_Efficiency_Score · Target 4
Workflow tasks
- Task 1
Input: {'query': 'string'}
Output: {'answer': 'string', 'sources': ['url'], 'reasoning_steps': ['string']}
Execution harness: Python
- Task 2
Input: {'query': 'string', 'expected_complexity': 'low|medium|high'}
Output: {'answer': 'string', 'resources_used': {'llm_calls': 'int', 'search_ops': 'int'}, 'adaptive_logic_trace': 'string'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.