oracle-ai-developer-hub

Oracle Agent Memory Workshop

Build memory-aware AI agents with Oracle AI Database, LangChain, and Tavily

What You Will Build

A Research Paper Assistant — an AI agent that searches, retrieves, and reasons over arxiv papers stored as vectors in Oracle AI Database. Along the way you’ll implement a MemoryManager with six memory types, context engineering techniques that prevent context window overflow, and a turn-level agent harness — finishing with a before/after comparison that makes the impact of memory engineering visible.

Workshop Parts

Part	Topic	Guide
1	Oracle AI Database setup and connection	Part 1 Guide
2	Vector search with LangChain OracleVS	Part 2 Guide
3	Memory engineering: 6 memory types in Oracle	Part 3 Guide
4	Context engineering: summarisation and offloading	Part 4 Guide
5	Web access with Tavily	Part 5 Guide
6	Agent execution and memory vs no-memory comparison	Part 6 Guide

TODO Checklist — all 16 tasks at a glance with links to their guide sections.

Getting Started

This workshop lives inside the oracle-ai-developer-hub repository. Use git sparse-checkout to pull just this workshop without cloning the rest of the hub:

# Clone the hub with no files and no blobs
git clone --filter=blob:none --no-checkout https://github.com/oracle-devrel/oracle-ai-developer-hub.git
cd oracle-ai-developer-hub

# Enable sparse-checkout and select only this workshop
git sparse-checkout init --cone
git sparse-checkout set workshops/agent_memory_workshop

# Materialise the files and move into the workshop
git checkout main
cd workshops/agent_memory_workshop

# Start Oracle AI Database
docker compose -f .devcontainer/docker-compose.yml up -d oracle

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter lab workshop/notebook_student.ipynb

Wait approximately 2 minutes for Oracle to initialise before running notebook cells.

Updating later: git pull from inside oracle-ai-developer-hub refreshes only the paths you’ve selected with sparse-checkout.

Workshop Files

agent-memory-workshop/
├── .devcontainer/
│   ├── devcontainer.json        Codespaces configuration
│   ├── docker-compose.yml       Oracle AI Database + workshop container
│   ├── setup_build.sh           Build-time dependency installation
│   ├── setup_runtime.sh         Runtime Oracle health check and setup
│   ├── start_oracle.sh          Oracle startup script
│   └── oracle-init/
│       └── 01_vector_memory.sql Vector memory schema init
├── workshop/
│   ├── notebook_student.ipynb   Your working notebook (contains TODO gaps)
│   └── notebook_complete.ipynb  Complete reference (do not open until done)
├── docs/
│   ├── part-1-oracle-setup.md
│   ├── part-2-vector-search.md
│   ├── part-3-memory-engineering.md
│   ├── part-4-context-engineering.md
│   ├── part-5-web-search.md
│   ├── part-6-agent-execution.md
│   ├── TODO-checklist.md        All 16 tasks at a glance
│   └── troubleshooting.md       Common issues and solutions
├── images/                      Screenshots and architecture diagrams
└── README.md

Stack

Oracle AI Database via gvenzl/oracle-free
langchain-oracledb — LangChain integration for Oracle vector store
sentence-transformers — local embedding model, no API key needed
openai — OCI GenAI (xAI Grok 3 Fast) via OpenAI-compatible endpoint
tavily-python — web search for agents
oracledb — Python Oracle driver

Where to Next?

Agent Memory: Building Memory-Aware Agents — DeepLearning.AI short course for deeper exploration of agent memory patterns
Oracle AI Developer Hub — More technical assets, samples, and projects with Oracle AI
Oracle Developer Resource — Documentation, tools, and community for Oracle developers

Built for the Oracle AI Developer Experience team.