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oracle-ai-developer-hub discussion
oracle-ai-developer-hub reviews and mentions
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Engineering Agent Memory
Several techniques are used to overcome this architectural limitation, and the folks at Oracle have some interesting examples. Their GitHub repo, oracle-ai-developer-hub showcases some different approaches. Through Jupyter notebooks like memory_context_engineering_agents.ipynb and RAG examples, Agent memory stops being a feature and becomes an engineering discipline.
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Building ONNX Embedding Workflows in Oracle AI Database with Python
Companion notebook: ONNX In-Database Embeddings with Oracle AI Database 26ai
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A Practical Guide to Choosing the Right Memory Substrate for Your AI Agents
This aligns with the "find first, read second" philosophy: locate what you need without loading everything upfront. For readers interested in maximum performance, the full notebook also includes a grep_files_os_based variant that shells out to ripgrep or grep, leveraging OS-level optimizations like memory-mapped I/O and SIMD instructions. In practice, this pattern (“search first, then read a range”) is one reason filesystem agents can feel surprisingly strong on focused corpora: the agent iteratively narrows the context instead of relying on a single-shot retrieval query.
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Build a Scalable Multi-Agent RAG System with A2A Protocol, Oracle AI Database and LangChain
git clone https://github.com/oracle-devrel/oracle-ai-developer-hub.git cd oracle-ai-developer-hub/apps/agentic_rag pip install -r requirements.txt # Includes docling, trafilatura, oracledb, fastapi, gradio, ollama and langchain-oracledb
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A note from our sponsor - SaaSHub
www.saashub.com | 8 Jun 2026
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The primary programming language of oracle-ai-developer-hub is Jupyter Notebook.
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