This is a proof of concept (PoC) multi-agent system that transforms vague or high-level business ideas into structured and actionable business plans. Built with LangGraph for agent orchestration, Groq for fast language model inference, and Tavily for real-time web search, the system showcases how collaborative AI agents can streamline early-stage business planning.
🧠 Each agent is responsible for a specific task—clarifying the idea, researching the market, designing the business model, projecting finances, assessing risks, and compiling a final plan.
This project was developed purely for educational and experimental purposes. It represents a personal initiative to explore the capabilities of generative AI, multi-agent systems, and LangGraph in the context of automated business planning.
The system is a proof of concept to experiment with agent collaboration, retrieval-augmented generation (RAG), and structured reasoning workflows using tools like LangGraph, LangChain, and Groq.
The purpose of this project is to demonstrate how a multi-agent system can automate and structure the early stages of business planning using cutting-edge AI technologies. By simulating a team of specialized agents—each handling tasks such as idea clarification, market research, and financial modeling—this proof of concept showcases how language models can collaborate through LangGraph to perform complex workflows.
It serves as a practical example of:
- Applying LangGraph for orchestrating sequential and conditional agent interactions.
- Integrating retrieval-augmented generation (RAG) with sources like Tavily and Wikipedia.
- Using Groq-hosted LLMs for fast and scalable inference in iterative workflows.
- Exploring human-in-the-loop (HITL) design for refining AI outputs.
Ultimately, this project is intended for learning, experimentation, and inspiration in the development of intelligent business tools.
This project leverages a set of modern tools and frameworks to orchestrate multi-agent collaboration and real-time knowledge retrieval:
- 🧩 LangGraph – For building stateful, interruptible agent workflows using graph-based logic.
- ⚡ Groq – Powers fast LLM inference (e.g., LLaMA 3) via the ChatGroq API for low-latency reasoning.
- 🌐 Tavily – Enables real-time web search and retrieval-augmented generation (RAG).
- 📚 WikipediaLoader (LangChain) – Retrieves concise, reliable content from Wikipedia for contextual grounding.
- ✅ Pydantic – Ensures robust state validation and schema definition using type-safe models.
- 📄 FPDF – Converts the generated plan into a downloadable PDF report.
- 🐍 Python 3.11+ – Core language that integrates all components into a seamless pipeline.
This multi-agent system is structured around modular and cooperative components, each responsible for a critical step in the business planning workflow:
-
🧠 Idea Clarifier Agent
Refines the initial business idea to ensure clarity, feasibility, and strategic focus. -
🧍♂️ Human-in-the-Loop (HITL) Node
Allows human reviewers to provide feedback and iterate before moving forward. -
🔍 Market Research Agents
Use Tavily and Wikipedia to gather relevant insights about competitors, trends, and customer needs. -
📊 Market Analyst Agent
Synthesizes research into a concise market report with source attribution. -
🧾 Report Generator
Compiles the clarified idea and market analysis into a PDF format, ready for sharing or presentation. -
🔄 LangGraph Orchestration
Manages stateful transitions, error handling, and agent coordination using graph-based logic.
These components work together to simulate the collaborative process of early-stage business planning, guided by structured reasoning and external information.
Throughout the development of this project, I’ve deepened my understanding of several key areas:
-
🧠 Agent-Based System Design
Learned how to define, sequence, and coordinate specialized agents using LangGraph to simulate a collaborative problem-solving workflow. -
🕸️ Graph-Based Orchestration
Gained hands-on experience building conditional and interruptible execution graphs to manage complex logic flows with memory and human-in-the-loop steps. -
⚡ LLM Deployment with Groq
Explored the use of Groq’s high-speed inference for deterministic and low-latency language model outputs, improving efficiency in multi-agent systems. -
🔍 RAG (Retrieval-Augmented Generation)
Implemented real-time knowledge integration using Tavily and Wikipedia to enrich LLM reasoning with factual context. -
🧾 Structured Prompt Engineering
Designed modular system prompts tailored to each agent’s task, ensuring consistent and goal-oriented behavior. -
📄 PDF Report Generation
Automated the transformation of structured output into human-readable reports, including encoding safeguards and file system integration. -
🧪 Debugging, Serialization & State Management
Addressed issues with Pydantic serialization, structured state updates, and debugging workflows involving nested subgraphs and data typing.
This project has significantly improved my practical skills in LLM-based architecture design, orchestration logic, and the end-to-end lifecycle of intelligent systems.
This project was developed purely for educational and experimental purposes. It represents a personal initiative to explore the capabilities of generative AI, multi-agent systems, and LangGraph in the context of automated business planning.
The system is a proof of concept to experiment with agent collaboration, retrieval-augmented generation (RAG), and structured reasoning workflows using tools like LangGraph, LangChain, and Groq.
This proof of concept (POC) is a personal project developed from scratch as a hands-on exercise to apply and consolidate the knowledge gained during the Bootcamp 2025: Understand and Build Professional AI Agents. The course offered a strong foundation for designing and implementing AI agents using tools such as LangGraph and LangChain. Special thanks to the instructors and the Udemy team for providing such a clear, well-structured, and practical learning experience. Official resources and examples from the course can be found at GitHub - AI-LLM-Bootcamp.
I would also like to acknowledge the doomL LangChain-LangGraph Tutorial, which offered valuable complementary insights and best practices for working with LangChain, LangGraph, and LangSmith. These resources greatly enriched my understanding and ability to build modular, agent-driven AI systems.
Finally, I’m thankful for the open-source community and ecosystem that makes it possible to explore, experiment, and learn with cutting-edge AI technologies.
This project is licensed under the MIT License, an open-source software license that allows developers to freely use, copy, modify, and distribute the software. 🛠️ This includes use in both personal and commercial projects, with the only requirement being that the original copyright notice is retained. 📄
Please note the following limitations:
- The software is provided "as is", without any warranties, express or implied. 🚫🛡️
- If you distribute the software, whether in original or modified form, you must include the original copyright notice and license. 📑
- The license allows for commercial use, but you cannot claim ownership over the software itself. 🏷️
The goal of this license is to maximize freedom for developers while maintaining recognition for the original creators.
MIT License
Copyright (c) 2025 Sergio Sánchez
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