Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API

[ad_1] class GeminiAutoGenFramework: “”” Complete AutoGen framework using free Gemini API Supports multi-agent conversations, code execution, and retrieval “”” def __init__(self, gemini_api_key: str): “””Initialize with Gemini API key””” self.gemini_api_key = gemini_api_key self.setup_gemini_config() self.agents: Dict[str, autogen.Agent] = {} self.group_chats: Dict[str, GroupChat] = {} def setup_gemini_config(self): “””Configure Gemini for AutoGen””” os.environ[“GOOGLE_API_KEY”] = self.gemini_api_key self.llm_config = { “config_list”:…

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REST: A Stress-Testing Framework for Evaluating Multi-Problem Reasoning in Large Reasoning Models

[ad_1] Large Reasoning Models (LRMs) have rapidly advanced, exhibiting impressive performance in complex problem-solving tasks across domains like mathematics, coding, and scientific reasoning. However, current evaluation approaches primarily focus on single-question testing, which reveals significant limitations. This article introduces REST (Reasoning Evaluation through Simultaneous Testing) — a novel multi-problem stress-testing framework designed to push LRMs beyond isolated…

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A Code Implementation for Designing Intelligent Multi-Agent Workflows with the BeeAI Framework

[ad_1] BeeAI FrameworkIn this tutorial, we explore the power and flexibility of the beeai-framework by building a fully functional multi-agent system from the ground up. We walk through the essential components, custom agents, tools, memory management, and event monitoring, to show how BeeAI simplifies the development of intelligent, cooperative agents. Along the way, we demonstrate…

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Thought Anchors: A Machine Learning Framework for Identifying and Measuring Key Reasoning Steps in Large Language Models with Precision

[ad_1] Understanding the Limits of Current Interpretability Tools in LLMs AI models, such as DeepSeek and GPT variants, rely on billions of parameters working together to handle complex reasoning tasks. Despite their capabilities, one major challenge is understanding which parts of their reasoning have the greatest influence on the final output. This is especially crucial…

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An anomaly detection framework anyone can use

[ad_1] Sarah Alnegheimish’s research interests reside at the intersection of machine learning and systems engineering. Her objective: to make machine learning systems more accessible, transparent, and trustworthy. Alnegheimish is a PhD student in Principal Research Scientist Kalyan Veeramachaneni’s Data-to-AI group in MIT’s Laboratory for Information and Decision Systems (LIDS). Here, she commits most of her…

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LlamaFirewall: Open-source framework to detect and mitigate AI centric security risks – Help Net Security

[ad_1] LlamaFirewall is a system-level security framework for LLM-powered applications, built with a modular design to support layered, adaptive defense. It is designed to mitigate a wide spectrum of AI agent security risks including jailbreaking and indirect prompt injection, goal hijacking, and insecure code outputs. Why Meta created LlamaFirewall LLMs are moving far beyond simple…

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Building A Practical UX Strategy Framework — Smashing Magazine

[ad_1] Learn how to create and implement a UX strategy framework that shapes work and drives real business value. In my experience, most UX teams find themselves primarily implementing other people’s ideas rather than leading the conversation about user experience. This happens because stakeholders and decision-makers often lack a deep understanding of UX’s capabilities and…

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