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https://github.com/giovaneiwamoto/deep-research

🛰️ Deep Research - Built to tackle complex, layered queries through iterative reasoning and web search. It decomposes queries into steps, synthesizes information from diverse sources, and refines answers through self-evaluation. Web search is powered by a multi-agent system, where specialized agents extract and evaluate content in parallel.
https://github.com/giovaneiwamoto/deep-research

deep-research langgraph multi-agent reasoning reflection tavily

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🛰️ Deep Research - Built to tackle complex, layered queries through iterative reasoning and web search. It decomposes queries into steps, synthesizes information from diverse sources, and refines answers through self-evaluation. Web search is powered by a multi-agent system, where specialized agents extract and evaluate content in parallel.

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![image_banner](images/image_1.png)



![image_architecture](images/image_2.png)



![image_deck_1](images/image_3.png)



![image_deck_2](images/image_4.png)



### **OVERVIEW**



The Deep Research Agent is an advanced autonomous system designed to perform comprehensive, multi-step research tasks. It leverages large language models, web search APIs, and a multi-agent orchestration framework to decompose complex queries, conduct parallel research, synthesize findings, and iteratively refine knowledge through reflection and reasoning.



A Deep Research Agent is an AI-driven system capable of autonomously planning, executing, and synthesizing research on arbitrary topics. It mimics the workflow of a human researcher by breaking down questions, searching for information, evaluating sources, and iteratively improving its understanding. The agent is particularly suited for technical analysis, literature reviews, and knowledge discovery.



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### **ARCHITECTURE**



#### 1. Research Planning



The workflow begins with a planning agent that interprets the user's input and generates a set of targeted search queries. This agent uses prompt engineering and LLMs to ensure that the queries are relevant and comprehensive.



#### 2. Multi-Agent Spawning



For each generated query, the system spawns independent researcher agents. These agents operate in parallel, each responsible for investigating a specific aspect of the research topic. This parallelism increases both the speed and breadth of information gathering.



#### 3. Web Search and Summarization



Each researcher agent utilizes web search APIs to retrieve up-to-date information. The raw search results are then summarized using LLMs, ensuring that only the most relevant and useful data is retained for further processing.



#### 4. Reflection and Iterative Reasoning



After the initial research cycle, a reflection agent evaluates the synthesized knowledge. Using a reasoning LLM, it identifies knowledge gaps, formulates follow-up queries, and can trigger additional research cycles. This iterative process allows the agent to deepen its understanding and address any missing information.



#### 5. Synthesis and Reporting



Once the research and reflection cycles are complete, a synthesis agent aggregates all findings into a structured, multi-paragraph technical report. The report includes reference citations for traceability and is designed to be both comprehensive and technically rigorous.



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### **LIKE THE PROJECT**



If you find this project useful, consider giving it a ★ **star** on GitHub — it really helps with visibility and community support!



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### **AUTHOR**



Giovane Iwamoto - Computer Science - AI Engineer



I am always open to receiving constructive criticism and suggestions for improvement in my developed code. I believe that feedback is an essential part of the learning and growth process, and I am eager to learn from others and make my code the best it can be. Whether it's a minor tweak or a major overhaul, I am willing to consider all suggestions and implement the changes that will benefit my code and its users.