smart_research_agent Question Answering Agent with Gemini and LangGraph

This project implements an agent that attempts to answer complex, multi-hop questions (similar to the GAIA benchmark) using Google's Gemini APIs, orchestrated by the LangGraph framework.

Core Functionality

• Stateful Multi-Step Reasoning: Uses LangGraph to manage the state (query, plan, intermediate results) across multiple steps.
• Clear Control Flow: Defines nodes for planning, execution (tool use simulation), and synthesis, with conditional edges managing the process.
• Information Retrieval: Leverages Gemini's knowledge base.
• Image Analysis (Potential): Can incorporate Gemini Pro Vision via a dedicated step if needed.
• Structured Output: Synthesizes the final answer based on intermediate findings and original query requirements.

Approach with LangGraph

• State: A dictionary (AgentState) tracks the query, plan, current step, results, and errors.
• Nodes: Python functions representing key actions:
  • plan_step: Generates the execution plan.
  • execute_tool_step: Executes the current step of the plan (simulating tool use via Gemini calls).
  • synthesize_result: Combines results into the final answer.
• Edges: Control logic determines the next node based on the current state (e.g., if planning failed, if more steps exist, if errors occurred).