![Course Thumbnail](https://github.com/shreshthtuli/build-your-own-super-agents/blob/main/assets/build-your-own-super-agents-thumbnail.jpg?raw=true) Welcome to **Build Your Own Super Agents** — a fast, fun, no-fluff adventure into agentic AI. In these notebooks, you’ll train agents, evolve them, and even make them battle (yes, there are [Pokémon](https://shreshthtuli.github.io/build-your-own-super-agents/07-multi-agent-workflows.html#creating-a-max-damage-baseline:~:text=Let%E2%80%99s%20see%20an%20example%20battle%20in%20action.)). You’ll start with a tiny single-step agent and end up commanding full swarms that can plan, retrieve, reason, and act. Whether you’re a researcher, engineer, or curious tinkerer, this course gives you everything you need to level up from simple prompts to real super-agent systems. 🚀🌟
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## 🎯 Course Highlights * **Fully hands-on agentic notebooks** for every module — clone locally or run instantly in your preferred Jupyter environment. * **Practical agent templates, evaluators, and tool integrations** so you can build real agents without drowning in boilerplate orchestration code. * **Clear theory, references, and best-practice design patterns** interwoven with examples to make every agentic concept intuitive and reproducible. * **Production-ready mindset throughout** — covering retrieval, graph reasoning, multi-agent coordination, reflection loops, model routing/selection, cost-latency tradeoffs, and cloud-scale deployment. ## 📚 Course Structure Each module is a self-contained notebook filled with explanations, demos, and practical agentic workflows. You can browse them on GitHub Pages, clone them locally, or run them interactively in your preferred notebook environment. | Module | Topic | Notebook | | ------ | -------------------------------------------------- | --------------------------------- | | 01 | A Very Simple Agent | [01-simple-agent.ipynb]((https://shreshthtuli.github.io/build-your-own-super-agents/01-simple-agent.html)) | | 02 | Moving to Agentic Frameworks | [02-framework-pydantic-ai.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/02-framework-pydantic-ai.html) | | 03 | Prompt Engineering & Reflection Loops | [03-prompting-engg.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/03-prompt-engg.html) | | 04 | Retrieval-Augmented Generation (RAG) | [04-rag.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/04-rag.html) | | 05 | Knowledge Graphs & GraphRAG | [05-graphrag.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/05-graphrag.html) | | 06 | Agentic Evaluation & Continuous Confidence | [06-evaluation.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/06-evaluation.html) | | 07 | Multi-Agent Workflows & Agent Swarms | [07-multi-agent-workflows.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/07-multi-agent-workflows.html) | | 08 | Model Selection for Agents | [08-model-selection.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/08-model-selection.html) | | 09 | Model Placement (Edge, Cloud, Hybrid) | [09-model-placement.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/09-model-placement.html) | | 10 | Appendix: End-to-End Experimentation | [10-appendix-experimentation.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/10-appendix-experimentation.html) | | 11 | Appendix: Designing Scalable Agentic Systems (AWS) | [11-scalable-agentic-systems.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/11-appendix-design-and-deployment.html) | | 12 | Bonus: Agentic Reinforcement Learning | [12-bonus-agentic-rl.ipynb](https://shreshthtuli.github.io/build-your-own-super-agents/12-bonus-agentic-rl.html) | ## 🧠 What You'll Learn * How to design an agent’s full loop — perception, retrieval, reasoning, planning, and action. * How to use modern agentic frameworks to add tools, memory, structured outputs, and multi-step workflows. * Techniques for building powerful retrieval systems (RAG), knowledge graphs, and GraphRAG pipelines. * How to evaluate agents with confidence metrics, behavioural tests, and multi-agent benchmarking. * How to build, coordinate, and optimize multi-agent swarms that collaborate or compete. * Practical orchestration: model selection, model placement, cost–latency tradeoffs, and routing strategies. ## ⚙️ Quick Start ### Option A: Launch in Lightning Studio (no setup!) 1. Click the **Open in Studio** badge above. 2. Authenticate with Lightning (or create a free account). 3. **Add API Keys** in `.env` file. Follow `.env.example` and [this](https://openrouter.ai/keys). 4. Explore the notebooks in a fully provisioned environment. ### Option B: Run Locally 1. **Clone the repository** ```bash git clone https://github.com/shreshthtuli/build-your-own-super-agents.git cd build-your-own-super-agents ``` 2. **Install dependencies** (recommended: Python 3.10+) ```bash pip install uv uv sync ``` 3. **Add API Keys** in `.env` file. Follow `.env.example`. 4. **Install docker** by following steps [here](https://docs.docker.com/engine/install/). 5. **(Optional) Create LogFire account** [here](https://logfire.pydantic.dev/) and log in using ```bash logfire auth ``` 5. **Launch Jupyter** ```bash jupyter lab ``` 6. Open any notebook to start experimenting. ## 🧭 Suggested Learning Path 1. **Foundations (Modules 01–03)** – Build your first agent, understand agentic loops, and layer in prompting + reflection. 2. **Retrieval & Knowledge (Modules 04–05)** – Add RAG, build knowledge graphs, and integrate GraphRAG into agent workflows. 3. **Evaluation & Behaviour (Module 06)** – Learn how to measure agent performance, confidence, robustness, and reasoning quality. 4. **Multi-Agent Systems (Module 07)** – Coordinate multiple agents, share context, resolve conflicts, and design agent swarms. 5. **Orchestration (Modules 08–09)** – Master model selection, routing, placement, and the cost–latency–quality trade-offs that power real systems. 6. **Capstone** – Combine everything to build, evaluate, and deploy a production-ready super-agent tailored to your real use case. Every notebook stands alone, but following this sequence gives you the smoothest progression from simple agents to fully orchestrated super-agents. ## 🧑‍💻 Who am I? I’m **Shreshth Tuli**, an AI researcher, engineer and educator specializing in advanced ML systems, optimisation and real-world deployment. I build and teach systems that go beyond isolated models into full pipelines and platforms. Done this course? Feel free to connect on LinkedIn [@shreshth-tuli](https://www.linkedin.com/in/shreshth-tuli/) or GitHub [@shreshthtuli](https://github.com/shreshthtuli). More about me [here](https://stuli.ai/). ## 🤝 Contributions Contributions, ideas, and bug reports are always appreciated! To contribute: 1. Fork the repository and create a feature branch. 2. Open a pull request explaining what you changed and why. 3. Reference any affected notebooks or modules, and include examples or screenshots if helpful. Check the issue tracker for beginner-friendly tasks or start a discussion if you’d like to propose a new agent module, framework, or workflow. ## 📄 License This project is released under the [Apache 2.0 License](LICENSE). You're welcome to use the material in your own projects, demos, workshops, or derivative courses — just keep the attribution intact. > Don’t just study agents—forge your own and unleash it. 🚀 [1]: https://shreshthtuli.github.io/build-your-own-super-agents/README.html " Course Highlights"