#multi-agent research
905 related articles
·2 min
TradingAgents-astock: A Practical Guide to the Multi-Agent Research Framework for A-Share Markets
TradingAgents-astock is a multi-agent A-share research framework with free domestic data sources, seven debating analyst agents, and support for DeepSeek, Qwen, and more.
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Google Co-Scientist Explained: A Gemin…
·2 min
Google Co-Scientist Explained: A Gemini-Powered Multi-Agent AI Research System
Deep dive into Google's Co-Scientist: a Gemini-powered multi-agent AI system that autonomously generates hypotheses, conducts agent debates, and iteratively evolves research directions.
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·3 min
BioAgents: A Multi-Agent AI Framework for Autonomous Research in Biological Sciences
Deep dive into BioAgents multi-agent AI framework: how literature analysis and data scientist agents collaborate for autonomous deep research in biological sciences.
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Product Reviews·3 min
Mavis Hands-On Review: Multi-Agent Collaboration vs. Single Agent — A Comprehensive Comparison in Academic Research and Web Development
In-depth review of Mavis multi-agent platform across academic retrieval, literature review, and web development. Multi-agent mode significantly outperforms single agents in accuracy and reliability.
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Deep Dives·1 min
TradingAgents-Astock: In-Depth Analysis of an Open-Source Multi-Agent Investment Research Framework for China's A-Share Market
In-depth analysis of TradingAgents-Astock, an open-source multi-Agent investment research framework for China's A-shares with 7 analysts, quality gates, and zero-cost data sources.
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Tech Frontiers·3 min
LangGraph Multi-Agent Company Research Tool: A Practical Guide to Open-Source AI Due Diligence
Deep dive into the open-source company-research-agent: LangGraph multi-agent architecture + Tavily search + dual-LLM collaboration for automated company due diligence and competitive intelligence.
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·2 min
AgentMicro: An Open-Source macOS Menu Bar Tool for Real-Time Monitoring of Parallel Codex Tasks
AgentMicro is an open-source macOS menu bar tool for real-time monitoring of OpenAI Codex Desktop and CLI parallel tasks. With local-first design, it never uploads code or AI interaction data.
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·3 min
DeerFlow: Deep Dive into ByteDance's Open-Source Long-Horizon SuperAgent Framework
Deep analysis of ByteDance's open-source DeerFlow long-horizon SuperAgent framework, covering six core components, architecture design, use cases, and industry significance.
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·3 min
Getting Started with MARL (Multi-Agent Reinforcement Learning): A Complete Path from Theory to Code
A systematic guide to learning MARL from theory to code, covering CleanRL, PettingZoo, PyMARL tools, IQL/VDN/QMIX/MADDPG algorithm progression, and practical tips for bridging theory and implementation.
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·2 min
AI Agents Running a Real Company: Experiment Results and Capability Boundary Analysis
What happens when AI agents are tasked with running a real company? This analysis examines agent performance, critical shortcomings, and practical enterprise deployment advice.
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·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Deep dive into how graph engineering uses state machines and directed graphs to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
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·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Explore how graph engineering uses state machines and directed graph structures to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
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·4 min
Tiling Window Management: A New Interaction Paradigm for Multi-Agent Conversations
Exploring tiling window management for multi-agent AI conversations: how it solves parallel monitoring and observability challenges, real-world limitations, and the evolution from chat boxes to control consoles.
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·3 min
Claude Opus 5 Plays Pokémon: A 12-Hour AI Experiment in Multi-Agent Loop Architecture
Deep analysis of Claude Opus 5 playing Pokémon for 12 hours via multi-agent loop architecture, exploring Agent design patterns, long-horizon planning, and AI Agent trends.
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·3 min
Claude Opus 5 Plays Pokémon: Analyzing a 12-Hour AI Experiment Under Multi-Agent Loop Architecture
In-depth analysis of Claude Opus 5's 12-hour Pokémon gameplay through multi-agent loop architecture, exploring multi-Agent design, long-horizon planning, and AI Agent trends.
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·2 min
Noisegate: Adding Differential Privacy Guardrails for Untrusted AI Agents
Noisegate is a differential privacy gateway for untrusted AI agents that injects calibrated noise into data flows, providing mathematically proven privacy guarantees when AI Agents process sensitive data.
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·2 min
Noisegate: Adding Differential Privacy Guardrails for Untrusted AI Agents
Noisegate is a differential-privacy gateway for untrusted AI agents, injecting calibrated noise into data flows to provide mathematically guaranteed privacy protection for sensitive data processed by AI Agents.
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·2 min
Moving State Out of Framework Memory: A Multi-Agent Architecture Refactoring Guide
Exploring how 70% of multi-agent memory is consumed by non-reasoning state, and a refactoring approach using email threads to replace framework memory for better token efficiency, auditability, and resilience.
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·2 min
LangChain or LangGraph? Choosing the Right Framework for Production-Grade AI Applications
In-depth analysis of core differences between LangChain and LangGraph, exploring why more teams are migrating to LangGraph for production AI apps, with framework selection guidance.
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·2 min
LangChain or LangGraph? Framework Choices for Production-Grade AI Applications
In-depth analysis of LangChain vs LangGraph differences, why teams are migrating to LangGraph for production AI apps, and framework selection guidance based on project complexity.
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