What is the AWorld Agent?
The AWorld Agent is the central orchestrator that interprets user goals, creates a plan, and delegates tasks to specialized sub-agents.
@inclusionAI
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AWorld is an open-source platform that provides a complete Agent Harness to orchestrate tools, memory, context, and execution for building autonomous agents. It allows domain experts to encode their knowledge into reusable Skills, enabling the creation of robust, domain-specific AI applications beyond generic model capabilities.
Install via git clone and pip install -e . inside the repository and aworld-cli directory. Configure by running aworld-cli --config in your working directory or by creating a .env file with model and API settings. Then launch by typing aworld-cli in your terminal.
The AWorld Agent is the central orchestrator that interprets user goals, creates a plan, and delegates tasks to specialized sub-agents.
The Developer builds a solution, the Evaluator judges it against objective criteria, and if the score is below target, the loop refines the output until criteria are met.
CAST (Code Abstract Syntax Tree) gives agents an architectural blueprint of code, enabling hierarchical navigation, near-infinite context compression, and surgical modifications.
Skills are reusable, community-contributed recipes that encode expert knowledge into executable workflows, driving capabilities like app evaluation, video creation, and deep search.
Python 3.11, conda, and API keys for models (e.g., Kling-V3 for video diffusion, TTS for audio). No specific transport or authentication protocol is mentioned beyond standard API key configuration.
A standalone agent runner that executes tasks using MCP (Model Context Protocol) tools via Anthropic Claude, AWS BedRock and OpenAI APIs. It enables AI agents to run autonomously in cloud environments and interact with various systems securely.
An MCP (Model Context Protocol) server that helps AI assistants (like Claude) break down complex tasks into manageable steps, track progress, and manage a hierarchical task list.
This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each tool can be hosted independent
emergency-medicare-planner-mcp-server
ArduPilotドローンをAIエージェントから操作するMCPサーバーです。
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