AWorld

🥈Silver

AWorld enables operations teams to build, evaluate, and train General Multi-Agent Assistance systems. It provides rich environments, intelligent agents, and continuous evolution capabilities. Connects to Python-based workflows and supports Claude agents.

1,1451130Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/inclusionAI/AWorld.git

Works with:

Claude

Overview

About This Skill

https://inclusionai.github.io/AWorld/

How to Use

1. **Define the Problem:** Start by specifying the task (e.g., customer support, IT helpdesk, or supply chain coordination) and the agents required. Use the prompt template to outline roles and evaluation criteria. *Tip: Focus on measurable outcomes (e.g., resolution time, accuracy) to guide training.* 2. **Prepare Data:** Gather historical data relevant to the task (e.g., tickets, logs, or transaction records). Clean and label the data to ensure agents can learn effectively. *Tip: Use AWorld's Python SDK to preprocess data into a structured format (CSV/JSON).* 3. **Design the Agent System:** Use AWorld's environment builder to define agent roles, tools (e.g., knowledge bases, APIs), and evaluation metrics. *Tip: Start with a minimal viable system (2-3 agents) and expand based on performance.* 4. **Train and Iterate:** Run training sessions with increasing complexity. Use AWorld's evaluation dashboard to track metrics and identify gaps. *Tip: Prioritize iterations that address the lowest-performing metrics (e.g., if FCR is low, add more labeled data for misclassified cases).* 5. **Deploy and Monitor:** Once metrics meet targets, deploy the system in a controlled environment (e.g., 10% of tickets). Use AWorld's monitoring tools to track real-world performance and gather feedback for further refinement. *Tip: Set up alerts for edge cases (e.g., agents failing to resolve a ticket within 2 hours).*

Use Cases

Automate the retrieval of real-time information from various online sources using intelligent agents.

Create multi-agent systems that collaborate to solve complex tasks in dynamic environments.

Train agents to adapt and improve their strategies based on past experiences and performance metrics.

Deploy agents in custom environments to simulate real-world scenarios for testing and evaluation.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/inclusionAI/AWorld

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use AWorld to design a multi-agent system for [SPECIFIC_TASK, e.g., 'customer support ticket triage']. Define the roles of [AGENT_1, e.g., 'intake agent'], [AGENT_2, e.g., 'technical resolver'], and [AGENT_3, e.g., 'escalation coordinator']. Set evaluation criteria based on [METRICS, e.g., 'resolution time < 2 hours' and 'customer satisfaction > 4.5/5']. Train the system with [SAMPLE_DATA, e.g., '1000 historical tickets'] and iterate until the agents achieve [TARGET, e.g., '85% first-contact resolution'].

Example Output

### Multi-Agent Customer Support System Design

**Agent Roles:**
1. **Intake Agent**: Uses a lightweight LLM to categorize tickets by issue type (billing, technical, account access) and urgency (low/medium/high). Trained on 2,500 historical tickets with 94% accuracy on validation data.
2. **Technical Resolver**: A specialized agent with access to a knowledge base of 1,200 troubleshooting guides. Achieved 78% resolution rate on first contact for technical issues in the last 30 days.
3. **Escalation Coordinator**: Monitors unresolved tickets and triggers human handoffs when resolution time exceeds 2 hours or customer sentiment drops below -0.5 (using sentiment analysis on ticket descriptions).

**Evaluation Metrics:**
- **First-Contact Resolution (FCR):** 82% (target: 85%)
- **Average Resolution Time:** 1 hour 42 minutes (target: < 2 hours)
- **Customer Satisfaction (CSAT):** 4.6/5 (target: > 4.5/5)

**Training Iterations:**
- **Initial Run:** Achieved 65% FCR with high false positives in billing categorization.
- **Iteration 2:** Added 500 labeled billing tickets; improved billing categorization to 91% accuracy.
- **Iteration 3:** Introduced sentiment-aware escalation rules; reduced escalations by 18% without increasing resolution time.

**Next Steps:**
- Deploy the system to a 10% pilot group of agents.
- Monitor for edge cases where agents misclassify complex multi-issue tickets.
- Schedule a review in 2 weeks to assess impact on team productivity and customer NPS scores.

**Python Workflow Integration:**
```python
from aworld import AgentSystem

system = AgentSystem(
    agents=['intake', 'technical_resolver', 'escalation'],
    evaluation_metrics=['fcr', 'resolution_time', 'csat']
)
system.train(data_path='tickets.csv', epochs=10)
system.evaluate(validation_data='tickets_validation.csv')
```