Open Source Summit North America 2025

In the era of cloud-native technologies, businesses are increasingly looking to integrate advanced AI capabilities into their workflows. This session explores how the Open Platform for Enterprise AI (OPEA) offers a transformative, open-source framework designed to accelerate the adoption of Generative AI (GenAI) across industries. Attendees will learn about the key benefits of adopting an open-source GenAI platform, including flexibility, community-driven improvements, and cost efficiency, as well as the advantages of deploying AI models in a cloud-native environment. We will also dive deep into the emerging GraphRAG (Graph-based Retrieval-Augmented Generation) architecture, demonstrating how it enhances the accuracy and explainability of AI-driven responses. Join us for this session to explore how the open-source OPEA platform and GraphRAG architectures are poised to redefine enterprise AI and to drive smarter, more reliable cloud-native AI applications.

In the rapidly evolving landscape of Generative AI, it is becoming clearer every day that open strategies are key drivers of innovation and widespread adoption. This session will present the Generative AI Commons, its activities, and deliverables to date, including the Model Openness Framework (MOF) and the Responsible Generative AI Framework,

LF AI & Data initiative dedicated to fostering the democratization, advancement and adoption of efficient, secure, reliable, and ethical Generative AI open source innovations through neutral governance, open and transparent collaboration and education.

This talk will describe the emerging standard and the Beaconforge.org framework, which is being developed by the Voiceinteroperability.ai Project of the LFAI and Data Foundation, to enable conversational assistants to interact with each other. Beaconforge.org is based on a standardized set of novel agentic inter-assistant messages that utilize NLP (Natural Language Processing) Universal APIs.

We will also explore how Agentic AI frameworks like Beaconforge.org can help mitigate hallucination effects in generative AI agents. This will be demonstrated through an empirical experiment involving hundreds of diverse prompts, a chain of multiple agents, and four novel KPIs used to evaluate hallucination score mitigation.

More information about the project can be found in the following papers:
Diego Gosmar et al., 2025. Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks, https://arxiv.org/abs/2501.13946

Diego Gosmar et al., 2024. Conversational AI Multi-Agent Interoperability, Universal Open APIs for Agentic Natural Language Multimodal Communications, https://arxiv.org/abs/2407.19438

Valkey is the leading open-source database for building high performance applications that are compatible with the Redis API. It natively integrates with many popular frameworks and is commonly used for a wide range of applications such as caching, session storage, streaming, and more. In this talk, hear from members of the Valkey technical steering committee discuss some of the exciting new functionality that has been released including the new bloom filter data type, vector similarity search, and a new hash table implementation that reduces memory overhead and improves performance. We'll talk about how these functionalities enable next generation use cases and what's coming in upcoming releases.

Large Language Models (LLM) require preprocessing vast amounts of data, a process that can span days due to its complexity and scale, often involving PetaBytes of data. This talk demonstrates how Kubeflow Pipelines (KFP) simplify LLM data processing with flexibility, repeatability, and scalability. These pipelines are being used daily at IBM Research to build indemnified LLMs tailored for enterprise applications.

Different data preparation toolkits are built on Kubernetes, Rust, Slurm, or Spark. How would you choose one for your own LLM experiments or enterprise use cases and why should you consider Kubernetes and KFP?

This talk describes how open source Data Prep Toolkit leverages KFP and KubeRay for scalable pipeline orchestration, e.g. deduplication, content classification, and tokenization.

We share challenges, lessons, and insights from our experience with KFP, highlighting its applicability for diverse LLM tasks, such as data preprocessing, RAG retrieval, and model fine-tuning.