multi-agent

Agent Workflow Framework: Efficient Management of Multi-Model Interaction and Task Allocation | Open Source Daily No.778

AI workflow

Agent Workflow Framework: Efficient Management of Multi-Model Interaction and Task Allocation | Open Source Daily No.778

openai-agents-python: A Lightweight Multi-Agent Workflow Framework GitHub Repo: openai/openai-agents-python Stars: 16.7k | License: MIT openai-agents-python is a lightweight yet powerful multi-agent workflow framework that enables flexible, vendor-independent AI solutions. Key Features * Multi-LLM Support — Works with OpenAI’s Response & Chat Completion APIs, and 100+ other LLMs for maximum flexibility.

By Honghao Wang
Efficient Integration of Multiple AI Models: Enabling Task Isolation and Role Specialization | Open Source Daily No.774

AI Integration

Efficient Integration of Multiple AI Models: Enabling Task Isolation and Role Specialization | Open Source Daily No.774

zen-mcp-server: Multi-Model Context Protocol Server zen-mcp-server is a multi-model collaborative context protocol server designed to integrate multiple AI models and command-line tools, enhancing development team productivity. It enables seamless cooperation between various CLI tools and offers CLI-to-CLI bridging, allowing for task isolation, role specialization, and preserved conversational context. Repository: BeehiveInnovations/

By Honghao Wang
DeepResearch Architecture and Practice Based on Spring AI Alibaba

Spring AI

DeepResearch Architecture and Practice Based on Spring AI Alibaba

DeepResearch System Documentation 1. Introduction & Overview DeepResearch is a Java-based intelligent research automation system built with Spring AI and Alibaba Graph. It enables an end-to-end workflow — from information gathering to analysis, and finally, structured report generation. Key Capabilities * Reasoning Chain: * Automatically constructs a logical analysis process from collected materials

By Honghao Wang
From Lakehouse to Intelligent Engine: Building a Multi-Agent AI Ecosystem on Databricks

AI ecosystem

From Lakehouse to Intelligent Engine: Building a Multi-Agent AI Ecosystem on Databricks

# Building Edmunds Mind: Transforming Data Into Intelligent Action In **today’s enterprise environment**, having a large, unified [data lakehouse](https://www.databricks.com/product/data-lakehouse) is critical for activating data. With a lakehouse, organizations can transform a **passive repository** into a **dynamic intelligence engine** — anticipating needs, automating expertise, and enabling

By Honghao Wang
AI in Action: 6 Agents to Handle Complex Commands and Tool Overload

AI agents

AI in Action: 6 Agents to Handle Complex Commands and Tool Overload

Introduction Clarifying "Intelligent Creation" In the title, “intelligent creation” refers specifically to data generation during system integration testing — particularly the integration joint-debugging scenario where a multi-agent collaborative approach was adopted. Data generation in integration testing is a typical AI application, involving: * Rich, varied user language * Complex business contexts

By Honghao Wang

AI coding

Unveiling Claude Code: AI Programming Basics, Principles, Implementation, and a Free iFlow CLI Alternative

Preface This article introduces Claude Code for readers interested in AI-assisted programming. It aims to help beginners quickly get hands-on experience with the tool, while enabling seasoned developers to understand its foundational principles for long-term use. In 2025, one technology theme dominates discussion: AI Coding. Major players are racing ahead,

By Honghao Wang
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