oracle-devrel/oracle-ai-developer-hub

Oracle AI Developer Hub

This repository contains technical resources to help AI Developers and Engineers build AI applications, agents, and systems using Oracle AI Database and OCI services alongside other key components of the AI/Agent stack.

What You'll Find

This repository is organized into several key areas:

📱 Apps (/apps)

Applications and reference implementations demonstrating how to build AI-powered solutions with Oracle technologies. These complete, working examples showcase end-to-end implementations of AI applications, agents, and systems that leverage Oracle AI Database and OCI services. Each application includes source code, deployment configurations, and documentation to help developers understand architectural patterns, integration approaches, and best practices for building production-grade AI solutions.

| Name | Description | Link | | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | | FitTracker | Gamified fitness platform built with Oracle 26ai JSON Duality Views (FastAPI + Redis), created live during a webinar. | | | agentic_rag | Intelligent RAG system with multi-agent Chain of Thought (CoT), PDF/Web/Repo processing, and Oracle AI Database 26ai integration | | | finance-ai-agent-demo | Financial services AI agent with Oracle AI Database as a unified memory core for vector, graph, spatial, and relational queries | | | generative-ui-data-chat | Next.js and OpenUI data chatbot that pairs Oracle AI Database 26ai SQL, vector, and hybrid search with typed generative UI components such as charts, KPI cards, tables, and source cards. | | | oci-generative-ai-jet-ui | Full-stack AI application with Oracle JET UI, OCI Generative AI integration, Kubernetes deployment, and Terraform infrastructure | | | tanstack-shoe-store | AI chat app using TanStack Start and Oracle 26ai Select AI to query a shoe store database with natural language | | | team-brain | Team knowledge base (the _what_ to a personal second brain's _who_): Slack, GitHub and docs connectors into one shared table, in-database ONNX embeddings, Oracle Text + vector hybrid retrieval fused by RRF through langchain-oracledb, and per-caller access control enforced by a DBMS_RLS row policy on the table itself, exposed to Claude Code over MCP (stdio or HTTP) and to a LangChain create_agent CLI. | | | oracle-data-migration-harness | AI agent harness that migrates a RAG corpus from MongoDB into Oracle AI Database 26ai while preserving vector search and unlocking SQL/JSON Duality queries | | | supplychain-demand-planning-agent | Multi-agent demand-planning assistant with a LangGraph supervisor over two specialists; vector knowledge, long-term memory, per-thread checkpoints, semantic LLM cache, and chat history all share one Oracle AI Database | | | idp-oracle-ai-database | Intelligent Document Processor that stores the BLOB, extracted text, structured JSON, and vector for each document in one Oracle AI Database 26ai — text extraction, summarization, embeddings, k-NN classification, and LLM field extraction all run inside or from the database via DBMS_VECTOR_CHAIN; AWS supplies only compute (Lambda + S3 + CloudFront) | | | vector-development | AI Database vector development sample apps for semantic search, RAG, product discovery, code search, and geospatial search. For Python development, see the Oracle VecDB Python SDK. | | | oraviz-mcp | Minimal, visualization-first MCP server for Oracle AI Database 26ai -- seven read-only tools that cap what returns to the model's context (compact markdown tables, 25-row previews, 500-row hard cap) and render results as PNG charts, including PCA projections of VECTOR columns. | |

📓 Notebooks (/notebooks)

Jupyter notebooks and interactive tutorials covering:

• AI/ML model development and experimentation
• Oracle Database AI features and capabilities
• OCI AI services integration patterns
• Data preparation and analysis workflows
• Agent development and orchestration examples

| Name | Description | Stack | Link | | ----------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | agentic_rag_langchain_oracledb_demo | Multi-agent RAG with langchain-oracledb: OracleVS, OracleEmbeddings, OracleTextSplitter, and CoT agents | Oracle AI Database, langchain-oracledb, Ollama | | | fs_vs_dbs | Compare filesystem vs database agent memory architectures. | LangChain, Oracle AI Database, OpenAI | | | memory_context_engineering_agents | Build AI agents with 6 types of persistent memory. | LangChain, Oracle AI Database, OpenAI, Tavily | | | oracle_rag_agents_zero_to_hero | Learn to build RAG agents from scratch using Oracle AI Database. | Oracle AI Database, OpenAI, OpenAI Agents SDK | | | oracle_rag_with_evals | Build RAG systems with comprehensive evaluation metrics | Oracle AI Database, OpenAI, BEIR, Galileo | | | oracle_data_migration_harness_walkthrough | Walk through a MongoDB-to-Oracle AI Database migration harness with vector parity, verification, and JSON Relational Duality | Oracle AI Database 26ai, MongoDB, FastAPI, React, sentence-transformers | | | agent_reasoning_demo | Interactive demo of 11 cognitive architectures (CoT, ToT, ReAct, Self-Reflection, and more) for agent reasoning | Ollama, agent-reasoning | | | oracle_agentic_rag_hybrid_search | Agentic RAG with vector, keyword, and hybrid search in a single SQL query using LangGraph ReAct agent | Oracle AI Database, langchain-oracledb, LangGraph, OpenAI | | | f1_miami_strategy_oracle_26ai | F1 Miami GP strategy intelligence for 2026 — SQL, hybrid vector+keyword search, JSON documents, and property graph in one Oracle 26ai database using real FastF1 data | Oracle AI Database, FastF1, sentence-transformers, Plotly | | | multicloud/ | AWS, Azure, Google Cloud, and MongoDB API samples running Oracle AI Database outside OCI | Oracle AI Database + AWS / Azure / Google / MongoDB | | | vector-development | AI Database vector development notebook collection covering auto-embedding, BYOV, search diagnostics, indexing, OCI embeddings, Gemini RAG, parks search, and bulk loading. | AI Database vector APIs, Python, Pandas | |

📚 Guides (/guides)

Comprehensive documentation, reference materials, and conference presentations covering AI agent architecture, reasoning strategies, and memory systems.

| Name | Description | Link | | ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | | Building the Brain and Backbone of Enterprise AI Agents | Advanced reasoning and infrastructure strategies for enterprise AI agents. Covers the 2026 agent stack (layered architecture), reasoning patterns (Chain of Thought, Tree of Thoughts, Self-Reflection, Least-to-Most, Decomposed Prompting), and context/belief updates. Presented at DevWeek SF 2026 by Nacho Martinez. | | | Memory Engineering: The Discipline Behind Memory Augmented Agents | Deep dive into memory engineering as a discipline for AI agents — the science of helping agents remember, reason, and act. Covers the memory ecosystem, form factors, and key disciplines shaping memory-augmented agents. Presented at DevWeek SF 2026 (Keynote) by Richmond Alake. | | | Agent Memory with Oracle AI Database | Agent memory architectures and Oracle AI Database as the memory core for AI agents. Presented at the AI Developer Conference hosted by DeepLearning.AI in April 2026 by Eli Schilling. | | | Memory Engineering with Deep Agents | How memory becomes permanent infrastructure for agents rather than a workaround for a smaller model or context window. Walks through the four memory layers, the Deep Agents middleware architecture (AGENTS.md, skills, checkpointing), procedural and episodic memory, and the four ways memory breaks. Presented by Colin Francis (LangChain JavaScript OSS team, Deep Agents). | | | Oracle AI Database + Agent Memory with LangChain | Technical session on building agent memory on Oracle AI Database with LangChain and LangGraph. Covers agent form factors and build modes, then a deep dive into a multi-agent stack on a single database — AsyncOracleSaver checkpointer for short-term memory, long-term memory, OracleSemanticCache, and vector + lexical retrieval — tied to the supply-chain demand agent workshop. Presented by Richmond Alake (Oracle) and Colin Francis (LangChain). | | | How Oracle AI Database Meets the AI Agent Era | Overview deck mapping Oracle AI Database to the AI agent landscape: the four AI application form factors, four build modes from no-code to custom code, and the matching Oracle products (Private Agent Factory, Select AI / APEX AI Assistant, frameworks like LangChain/LlamaIndex/OAMP/Haystack). Frames the agent as model + harness and memory as an engineering discipline. Presented by Richmond Alake (Oracle) and Colin Francis (LangChain). | |

🧠 Agent Memory (/notebooks/agent_memory)

Notebooks focused on the Oracle AI Agent Memory package (oracleagentmemory) — the AI-Agent Memory Package built on top of Oracle AI Database. These notebooks demonstrate how to use Oracle AI Database as the unified memory core for AI agents, serving conversation history, durable facts, and entity state from a single converged engine instead of stitching together a vector DB, key-value store, and relational store.

The collection covers the package's developer guide, benchmarks against naive memory, and three end-to-end framework examples (OpenAI Agents SDK, Claude Agent SDK, LangGraph).

| Name | Description | Stack | Link | | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | OAMP Developer Guide | Step-by-step guide to the oracleagentmemory API: connection, the three core primitives (users/agents, memories, threads), automatic extraction, and vector retrieval. | OAMP, LiteLLM | | | OAMP Benchmarks | Quantify token cost, latency, and response quality of OAMP vs. naive flat-history memory across 80 scripted turns with three agent variants. | OAMP, LiteLLM, OpenAI | | | Deep Research Agent | Build a deep research agent for human genome exploration that uses Tavily for live web search and Oracle AI Agent Memory for durable findings across sessions. | OpenAI Agents SDK, Tavily, OAMP | | | Supply Chain Assistant | A supply chain assistant that tracks shipment cargo via in-process tools and an MCP server, with shipment records and operational notes persisted in OAMP. | Claude Agent SDK, MCP, OAMP | | | Mortgage Approval Workflow | A deterministic mortgage approval workflow modeled as a LangGraph StateGraph where OAMP persists applicant data and audit trails so failed runs can resume. | LangGraph, OAMP | |

See the [Agent Memo