Python + Agents: Adding context and memory to agents

In the second session of our Python + Agents series, we’ll extend agents built with the Microsoft Agent Framework by adding two essential capabilities: context and memory.

We’ll begin with context, commonly known as Retrieval‑Augmented Generation (RAG), and show how agents can ground their responses using knowledge retrieved from local data sources such as SQLite or PostgreSQL.

This enables agents to provide accurate, domain‑specific answers based on real information rather than model hallucination. Next, we’ll explore memory—both short‑term, thread‑level context and long‑term, persistent memory.

You’ll see how agents can store and recall information using solutions like Redis or open‑source libraries such as Mem0, enabling them to remember previous interactions, user preferences, and evolving tasks across sessions.

By the end, you’ll understand how to build agents that are not only capable but context‑aware and memory‑efficient, resulting in richer, more personalized user experiences.

Prerequisites: To follow along with the live examples, sign up for a free GitHub account. If you are brand new to generative AI with Python, start with our 9-part Python + AI series, which covers LLMs, embedding models, RAG, tool calling, MCP, and more.

Pre-requisites:
To follow along with the live examples, sign up for a free GitHub account. If you are brand new to generative AI with Python, start with our our 9-part Python + AI series, which covers LLMs, embedding models, RAG, tool calling, MCP, and more.