Inside Denario, every research step becomes an artifact. The system writes out its own http://
idea.md, http://
methods.md, http://
results.md, paper.pdf, even http://
referee.md. It’s not just automating work it’s creating a transparent scientific record
AGENTS
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Denario AI system automates scientific research documentation
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Denario AI Agent System: Goal Decomposition and Autonomous Research Management
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Here’s where it gets wild. Denario doesn’t just “run tasks.” It plans decomposing goals into subtasks, assigning agents, and managing feedback like a robotic PI (principal investigator). It monitors progress, detects failure, retries, adjusts, and decides when the research is
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Automating Lead Nurturing with AI Agents Podcast
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He publicado un episodio en @ivoox
: "Automatizando el Lead Nurturing con Agentes de IA #podcast -
Flowith: Canvas-Based Workspace with AI Agent Neo
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Our Weekly Spotlight @flowith transforms scattered thoughts into clear, actionable results with its unique canvas-based workspace and powerful AI agent Neo. Researchers, content creators & teams use Flowith to keep complex projects visible and connected while automating
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Synapse: Multi-Agent AI Platform for Web Search and Automation
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Synapse: Multi-Agent AI Platform (Made by the LangChain Community) A versatile platform featuring intelligent agents that seamlessly handle web searches, task automation, and complex data analysis through natural language interactions. Explore the project on GitHub
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MaxKB: Open-Source Enterprise AI Agents with LangChain RAG
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MaxKB AI Agents (Made by the LangChain Community) MaxKB is an open-source platform that builds enterprise AI agents using LangChain-powered RAG and workflows. It features multi-modal support and seamless integration with both private and public LLMs. Check it out here
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Agent Data Protocol Standardizes LLM Agent Training Datasets
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8. Agent Data Protocol Agent Data Protocol introduces a standardized format to unify fragmented agent training datasets across different tools and interfaces, enabling more efficient fine-tuning of LLM agents.
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GAP: Graph-Based Agent Planning with Parallel Tool Execution
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6. GAP GAP introduces graph-based agent planning with parallel tool execution and reinforcement learning, enabling AI agents to coordinate multiple specialized capabilities simultaneously rather than sequentially.
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Multi-Agent Evolve: LLMs Self-Improve Without Human Annotation
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3. Multi-Agent Evolve Multi-Agent Evolve (MAE) enables LLMs to self-improve their reasoning capabilities without human-annotated data through a co-evolving multi-agent framework.
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Abacus AI Launches Browser Use With Scheduled Automation Tasks
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Announcing Browser Use With Scheduled Tasks
— Abacus.AI (@abacusai) 1 novembre 2025
Abacus AI's Deep Agent lets you automate all the heavy lifting associated with repetitive browser user tasks
– test your apps
– send messages on LinkedIn
– apply to jobs on a scheduled basis
Let AI work while you sleep! pic.twitter.com/HESyVEJeCzAnnouncing Browser Use With Scheduled Tasks Abacus AI's Deep Agent lets you automate all the heavy lifting associated with repetitive browser user tasks – test your apps
– send messages on LinkedIn
– apply to jobs on a scheduled basis Let AI work while you sleep!