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4,391 skills indexed with the new KISS metadata standard.
Current multi-agent frameworks—including LangGraph, CrewAI, and AutoGen's MagenticOne—suffer from **architectural stagnation**. While agents coordinate within conversations, they cannot evolve between runs. Poor performance requires manual developer intervention, creating a scalability bottleneck.
Macrograph is a visual programming platform with node-based graph interfaces. Supports both desktop (single-project) and server (multi-project) environments with real-time event processing and type-safe ID management.
for (aSlave in hudson.model.Hudson.instance.slaves) {
AI-powered agents appear on-chain, from trading bots to DAO participants, raising big questions.
发布时间：2024年10月22日
Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of agency. LLM chains can only perform hardcoded sequences of actions, while agents use LLMs to reason through which actions to take, and in which
Provides agent lifecycle management including enabling, disabling, and resolving agent configurations. Handles agent aliasing, run modes (isolated/global/devel), and workspace structure creation.
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- **Code Quality**: Write clean, readable, and maintainable code. Use meaningful variable and function names.
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This document provides instructions for AI agents working on this codebase.
Custom agents allow you to define specialized AI assistants with specific behaviors, tools, and prompts.
This document provides a high-level overview of the Drasi Command Line Interface (CLI) project for coding agents. The CLI is a Go application built with the Cobra library, responsible for installing and managing Drasi environments and resources.
Various agents from LangChain are included:
This directory contains supporting files for the Sideload skill.
The Agent Task Framework helps transform ambiguous user requests into clear, structured instructions that background agents can execute successfully. This tutorial shows practical examples from real project contexts.
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There can be two types of Agents in Data Streams; Virtual and Non-Virtual Agents. When packaging an Agent using the Stream Integration Manager, it is important to specify if an Agent is Virtual by making sure that the _Virtual_ checkbox is correctly ticked.
**Claude Code: Please execute all steps below to set up parallel AI agents for the ZMF Production Dashboard**
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Your agents are currently **IDLE** because they haven't been added to a flow yet. Here's how to get them working: