agentic-flow

🥈Silver

Easily switch between alternative low-cost AI models in Claude Code/Agent SDK. For those comfortable using Claude agents and commands, it lets you take what you've created and deploy fully hosted agents for real business purposes. Use Claude Code to get the agent working, then deploy it in your favorite cloud.

412960Updated 1mo ago

Intermediate30min to implementautomation

Saves ~50 min per use

Quick InstallView Source

git clone https://github.com/ruvnet/agentic-flow.git

Works with:

Claude

Overview

About This Skill

https://www.npmjs.com/package/agentic-flow

Use Cases

Deploy self-learning AI agents to automate code review processes and improve code quality.

Utilize multi-agent coordination to optimize project planning and resource allocation in software development.

Implement real-time adaptive learning agents for dynamic customer support solutions.

Create intelligent testing agents that learn from past failures to generate comprehensive test cases.

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/ruvnet/agentic-flow

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

Create an agentic-flow script that automates [PROCESS] for [COMPANY] in the [INDUSTRY] sector. The script should use [AI_MODEL] as the primary model and include error handling for [SPECIFIC_ERRORS]. Provide the complete code and deployment instructions for [CLOUD_PLATFORM].

Example Output

# Agentic-Flow Script for Customer Support Automation

## Overview
This script automates customer support ticket classification and routing for TechSolutions Inc. in the IT sector. It uses the Mistral-7B model for text analysis and includes error handling for API timeouts and model response parsing.

## Code
```python
import os
from agentic_flow import Agent, Model

# Initialize agent with Mistral-7B model
agent = Agent(model=Model.Mistral_7B)

# Define error handling
def handle_errors(error):
    if 'timeout' in str(error):
        return 'API timeout occurred. Retrying...'
    elif 'parse' in str(error):
        return 'Error parsing model response. Check input format.'

# Main processing function
def process_ticket(ticket_data):
    try:
        # Classify ticket
        classification = agent.classify(ticket_data)
        # Route ticket based on classification
        if classification == 'technical':
            return route_to_tech_team(ticket_data)
        elif classification == 'billing':
            return route_to_billing_team(ticket_data)
        else:
            return route_to_general_team(ticket_data)
    except Exception as e:
        return handle_errors(e)

# Deployment Instructions
# 1. Save this script as support_agent.py
# 2. Install required packages: pip install agentic-flow
# 3. Set environment variables for cloud credentials
# 4. Deploy to AWS Lambda using: aws lambda create-function --function-name SupportAgent --runtime python3.8 --handler support_agent.process_ticket --zip-file fileb://deployment-package.zip
```

## Deployment Details
- Cloud Platform: AWS Lambda
- Memory Allocation: 1024MB
- Timeout: 30 seconds
- IAM Role: SupportAgentExecutionRole