Build autonomous AI agents with Python, Ollama, tools, memory, RAG, research, and multi-agent workflows
Sub Category
- Data Science
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Objectives
- Understand how AI agents differ from traditional chatbots and standard LLM applications.
- Build a personal AI assistant in Python using Ollama and Streamlit.
- Connect Python applications to local language models through Ollama.
- Design effective system prompts, agent roles, and instruction patterns.
- Create structured outputs using Pydantic and JSON schemas.
- Build agents that can break complex goals into smaller, actionable tasks.
- Implement planner, executor, analyst, writer, and reviewer agent patterns.
- Create tool-using agents that can call Python functions, APIs, databases, and file utilities.
- Build reusable tools for calculations, search, file handling, and data analysis.
- Add short-term conversation memory and persistent user preferences.
- Process PDF documents and build Retrieval-Augmented Generation workflows.
- Generate embeddings with Ollama and store them in a vector database.
- Build an autonomous research agent that searches, collects, evaluates, and summarizes information.
- Design multi-agent workflows where specialized agents collaborate on a shared goal.
- Orchestrate agent workflows using LangGraph and shared state.
- Add human approval checkpoints before important actions are executed.
- Implement reviewer loops that evaluate and improve agent-generated results.
- Add logging, retries, error handling, validation, and workflow safeguards.
- Build a complete airline disruption assistant that combines planning, tools, memory, RAG, research, and multiple agents.
- Deploy a complete autonomous AI application with a Streamlit interface.
Pre Requisites
- Basic knowledge of Python, including variables, functions, lists, dictionaries, and classes.
- A computer running Windows, macOS, or Linux.
- Python 3.11 or later installed on your computer.
- Basic familiarity with installing Python packages using pip.
- A code editor such as Visual Studio Code, PyCharm, or another Python-compatible editor.
- Ollama installed locally for running language and embedding models.
- At least 8 GB of RAM is recommended; 16 GB or more provides a better experience with larger local models.
- An internet connection is helpful for installing packages, downloading Ollama models, and completing optional web-research exercises.
- No previous experience with AI agents, LangGraph, vector databases, or Retrieval-Augmented Generation is required.
- No paid AI API subscription is required because the course uses local models through Ollama.
- Beginners with basic Python knowledge can follow the course because each system is built step by step.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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