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 \nThis session aims to **demystify language model agents** by walking participants through how to build **simple, modular agents** using **LangGraph** — an open-source Python framework for creating multi-agent workflows.\n\n## 🧠 LangGraph Agentic AI Workshop\n\n**Hosted by TeamVPE at BioMed X Institute**\n\n![TeamVPE Logo](https://avatars.githubusercontent.com/u/144795768?s=200\u0026v=4)\n\nWelcome to the hands-on workshop on **Agentic AI with LangGraph**!\nThis session is hosted by [TeamVPE](https://bmedx.com/research-teams/artificial-intelligence/team-vpe/) at the [BioMed X Institute](https://bmedx.com), proudly supported by [Sanofi](https://www.sanofi.com/en/).\n\n---\n\n## 🎯 Workshop Goals\n\nBy the end of this workshop, you'll be able to:\n\n- Understand the **LangGraph** framework for building stateful LLM agents\n- Create and manage **agent state** and **tool-calling workflows**\n- Use **OpenAI** or **NVIDIA NIMs** as your LLM backend\n- Build a **ReAct-style reasoning loop** with tools\n- Visualize LangGraph agent flows\n- Extend agents with tools for:\n  - 🧮 Math calculation\n  - 📊 Matplotlib plotting\n  - 🌍 Language translation\n  - 📄 PDF Q\\\u0026A pipeline\n  - 🎨 DALL·E image generation\n\n---\n\n## 🧱 What You'll Build\n\nYou will implement several progressively advanced agents:\n\n1. **Echo agent** using LangGraph\n2. **LLM-powered assistant** (OpenAI or NVIDIA NIM)\n3. **ReAct agent** with intermediate memory and reasoning\n4. **Multi-tool agent** with tool routing and control flow\n5. **PDF reasoning agent** that can download, extract, and answer questions from papers\n6. **Image generation agent** using DALL·E 3\n\n---\n\n## 🧰 Prerequisites\n\n- Python 3.12+\n- Jupyter Notebook environment (e.g. Anaconda, VS Code, Colab)\n- One of the following API keys:\n  - [OpenAI API Key](https://platform.openai.com/account/api-keys)\n  - [NVIDIA NIM API Key](https://build.nvidia.com/)\n\n---\n\n## 🚀 Installation\n\nInstall required libraries with:\n\n```bash\npip install langgraph langchain openai matplotlib pymupdf graphviz pydot requests\n```\n\nYou’ll also need to install:\n\n- `langchain_nvidia_ai_endpoints` (if using NVIDIA NIMs)\n- `IPython` (if not included in your environment)\n\n---\n\n## 🔐 API Key Setup\n\nYou can either:\n\n- Set environment variables (`OPENAI_API_KEY` or `NVIDIA_API_KEY`)\n- Or paste the key directly into the notebook (demo-friendly)\n\n---\n\n## 📚 Topics Covered\n\n| Section          | Highlights                             |\n| ---------------- | -------------------------------------- |\n| ✅ Basics        | LangGraph setup, Echo agent            |\n| 🧠 LLMs          | OpenAI/NVIDIA NIM integration          |\n| 🔁 ReAct         | Reasoning + tool invocation            |\n| 🔧 Tools         | Calculator, plot generator, translator |\n| 📄 PDFs          | Download, extract, and answer          |\n| 🎨 Vision        | Image generation with DALL·E           |\n| 📊 Visualization | Flowchart of LangGraph workflows       |\n\n---\n\n## 🧑‍🔬 About the Project\n\nThis workshop is part of the **AIAgents4Pharma** initiative — an open-source toolkit for enabling biomedical researchers and pharma professionals to interact with complex data through intelligent, language-powered agents.\n\n👉 Explore more: [VirtualPatientEngine/AIAgents4Pharma](https://github.com/VirtualPatientEngine/AIAgents4Pharma)\n\n---\n\n## 📎 Attribution\n\nDeveloped by **TeamVPE**, AI research team at [BioMed X Institute](https://bmedx.com). Sponsored by **Sanofi**.\n\n---\n\n## 🧑‍💻 Get Started\n\nLaunch the Jupyter Notebook and start from:\n\n```python\n!pip install -q langgraph langchain openai\n```\n\nThen follow the steps to build your first intelligent agent!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvirtualpatientengine%2Feurostempeers2025_workshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvirtualpatientengine%2Feurostempeers2025_workshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvirtualpatientengine%2Feurostempeers2025_workshop/lists"}