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Agentic AI Systems are gaining popularity, utilizing LLMs in a for-loop connected with prompts and tools to perform tasks autonomously. While OpenAI's Deep Research offers impressive capabilities, it comes at a price of $200. In this guide, we will explore how to create a similar Deep Research and Report Generation Agent for less than a dollar using LangGraph.
Understanding Deep Research by OpenAI
OpenAI introduced Deep Research on February 2, 2025, within their ChatGPT product. This technology facilitates multi-step research on the internet, providing quick results for complex tasks. Powered by an optimized version of the upcoming OpenAI o3 model, Deep Research efficiently compiles comprehensive reports within minutes.
Building Your Own Deep Research Agent
Our alternative Deep Research Agent aims to offer similar functionalities at a fraction of the cost. By leveraging LangChain’s LangGraph framework, we can create a stateful agentic system effortlessly. The key components of this system include:
- Planning Agent for Deep Research and Structured Report Generation
- Custom Report Template
- Web Search Queries
- Content Generation
Implementing the Deep Research Workflow
We will now proceed to build the end-to-end workflow using LangGraph and Tavily, focusing on detailed explanations and code implementation. The process involves:
- Installing necessary dependencies
- Setting up system environment variables
- Building the agentic system with defined components
- Testing the system by generating sample reports
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Conclusion
By following this guide, you can develop a Deep Research and Report Generation Agentic AI System affordably. This customizable solution offers control over search mechanisms, report structure, and content style. Utilizing open-source components makes this system cost-effective and adaptable. Remember to monitor your usage, especially with Tavily, to avoid excessive searches.
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Empower your AI capabilities and enhance your research efficiency with this budget-friendly alternative to commercial AI tools.