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https://github.com/vishapp/crewaimaster

CrewAIMaster transforms any task description into a fully functional, production-ready multi-agent system, making advanced AI orchestration accessible to everyone.
https://github.com/vishapp/crewaimaster

agent agent-design ai-automation ai-crew-design ai-tools automated-project-creation automation cli-tool collaborative-ai crewai deployment intelligent-agents intelligent-task-analysis intelligent-workflows machine-learning multi-agent multi-agent-systems productivity project-generator workflow-orchestration

Last synced: 6 months ago
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CrewAIMaster transforms any task description into a fully functional, production-ready multi-agent system, making advanced AI orchestration accessible to everyone.

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README 

          
# CrewAIMaster



**A Python package for building intelligent multi-agent systems using CrewAI**



CrewAIMaster is an advanced framework that automatically generates, manages, and executes multi-agent crews based on natural language task descriptions. It provides a CLI interface and comprehensive backend system for creating intelligent AI agents with memory, tools, and safety guardrails.



## 📦 Installation



```bash

# install from PyPI (when available)

pip install crewaimaster



# Or Install from source (recommended for development)

git clone https://github.com/VishApp/crewaimaster

cd crewaimaster

python -m venv venv

source venv/bin/activate

pip install -e .

```



## 🎬 Demo



### Click below to watch a complete walkthrough of CrewAIMaster in action

[![CrewAIMaster Demo](docs/gif/crewaimaster.gif)](https://youtu.be/osF9oTBa9IU)



### 📸 Screenshots



#### CLI Interface

![CLI Interface](docs/images/crewaimaster-welcome.png)



#### CrewAIMaster Providers

![Crew Creation](docs/images/crewaimaster-providers.png)



#### CrewAIMaster Help

![Execution Dashboard](docs/images/crewaimaster-help.png)



## 🏃 Quick Start



### Prerequisites

```bash

# Install Python 3.10+

python --version



# Configure your LLM provider (see supported providers)

crewaimaster providers



# Quick setup with OpenAI (most common)

crewaimaster providers --configure openai --api-key "your-openai-key" --model "gpt-4"

```



### 1. Create Your First Crew with AI Orchestration



```bash

# Create an intelligent crew using AI analysis

crewaimaster create "Write a comprehensive market analysis report for electric vehicles in 2024" --name electric_vehicles_market_analysis_crew

```



### 2. Execute the Crew



```bash

# Run the crew (requires configured LLM provider)

crewaimaster run electric_vehicles_market_analysis_crew



# With additional context:

crewaimaster run electric_vehicles_market_analysis_crew --input "Focus on Tesla, BMW, and Volkswagen specifically"

```



### 3. Alternative Execution (Direct Script)



Generated crews can also be executed directly using environment variables:



```bash

# Navigate to the generated crew directory

cd crews/electric_vehicles_market_analysis_crew



# Run using standard environment variables

export OPENAI_API_KEY="your-openai-key"

./run.sh "your input"



# Or run using CrewAIMaster-specific environment variables

export CREWAIMASTER_LLM_PROVIDER="openai"

export CREWAIMASTER_LLM_MODEL="gpt-4"

export CREWAIMASTER_LLM_API_KEY="your-openai-key"

export CREWAIMASTER_LLM_BASE_URL="https://api.openai.com/v1"

```



## 🔄 Development Workflow



### Typical CrewAIMaster Workflow



```mermaid

flowchart LR

    A["`**1. Task Definition**

    Natural Language Task`"] --> B["`**2. AI Analysis**

    🤖 Task Complexity

    🎯 Agent Requirements

    🛠️ Tool Selection`"]

    

    B --> C["`**3. Crew Creation**

    👥 Agent Design

    🔧 Tool Assignment

    📋 Task Orchestration`"]

    

    C --> D["`**4. Execution**

    🏃 Multi-Agent Coordination

    🔄 Real-time Processing

    📊 Progress Monitoring`"]

    

    D --> E["`**5. Results & Analytics**

    📄 Output Generation

    📈 Performance Metrics

    💾 Persistent Storage`"]

    

    E --> F["`**6. Optimization**

    🔧 Crew Modification

    ⚡ Performance Tuning

    📤 Export/Backup`"]

    

    F --> G["`**7. Reuse & Scale**

    🔄 Crew Reusability

    📚 Knowledge Building

    🚀 Production Deployment`"]



    classDef stepStyle fill:#f9f9f9,stroke:#333,stroke-width:2px,color:#333

    class A,B,C,D,E,F,G stepStyle

```



## 🏗️ Architecture



CrewAIMaster follows a clean, layered architecture designed for intelligent multi-agent system creation and execution:



```mermaid

flowchart TD

    %% User Entry Point

    User[👤 User Input
Natural Language Task] --> CLI[🖥️ CLI Interface
crewaimaster create/run/providers]

    

    %% Configuration Layer

    CLI --> Config[⚙️ Configuration
config.yaml
LLM Providers]

    

    %% AI Orchestration Core

    CLI --> MasterAgent[🧠 Master Agent
Intelligent Orchestrator]

    

    %% AI Analysis Pipeline

    MasterAgent --> TaskAnalyzer[📋 Task Analyzer
• Complexity Assessment
• Requirements Extraction
• Agent Planning]

    

    TaskAnalyzer --> AgentDesigner[👥 Agent Designer
• Role Definition
• Tool Selection
• Capability Mapping]

    

    AgentDesigner --> CrewOrchestrator[🎭 Crew Orchestrator
• Team Assembly
• Process Selection
• Workflow Design]

    

    %% Core Generation Engine

    CrewOrchestrator --> CrewDesigner[🔧 Crew Designer
File-Based Generator]

    Config --> CrewDesigner

    

    CrewDesigner --> FileGen[📁 File Generator
• Project Structure
• Python Modules
• YAML Configs]

    

    %% Output Generation

    FileGen --> GeneratedFiles{📄 Generated Crew Project}

    

    %% Generated Project Structure

    GeneratedFiles --> AgentYAML[agents.yaml
Agent Definitions]

    GeneratedFiles --> TaskYAML[tasks.yaml
Task Specifications]

    GeneratedFiles --> CrewPY[crew.py
CrewAI Implementation]

    GeneratedFiles --> MainPY[main.py
Execution Entry Point]

    

    %% Execution Runtime

    MainPY --> CrewAI[🚀 CrewAI Runtime
Multi-Agent Execution]

    

    CrewAI --> AgentA[🤖 Agent A
Specialized Role]

    CrewAI --> AgentB[🤖 Agent B
Specialized Role]

    CrewAI --> AgentC[🤖 Agent C
Specialized Role]

    

    %% Tool Integration

    AgentA --> Tools[🛠️ Tool Registry
• Web Search
• File Operations
• Code Execution
• Custom Tools]

    AgentB --> Tools

    AgentC --> Tools

    

    %% LLM Integration

    Config --> LLMProvider[🔗 LLM Provider
• OpenAI
• Anthropic
• Google
• Custom APIs]

    LLMProvider --> AgentA

    LLMProvider --> AgentB

    LLMProvider --> AgentC

    LLMProvider --> MasterAgent

    

    %% Memory & Knowledge

    CrewAI --> Memory[🧠 Memory System
• Agent Memory
• Shared Context
• Knowledge Base]

    

    %% Safety & Guardrails

    Tools --> Guardrails[🛡️ Guardrails
• Safety Checks
• Content Filtering
• Validation]

    

    %% Final Output

    CrewAI --> Results[📊 Results
Task Completion
Generated Content]

    

    %% Styling

    classDef userLayer fill:#e8f5e8,stroke:#1b5e20,stroke-width:3px,color:#000

    classDef cliLayer fill:#e1f5fe,stroke:#01579b,stroke-width:2px,color:#000

    classDef aiLayer fill:#f3e5f5,stroke:#4a148c,stroke-width:2px,color:#000

    classDef coreLayer fill:#fff8e1,stroke:#ff8f00,stroke-width:2px,color:#000

    classDef fileLayer fill:#fce4ec,stroke:#880e4f,stroke-width:2px,color:#000

    classDef runtimeLayer fill:#fff3e0,stroke:#e65100,stroke-width:2px,color:#000

    classDef toolLayer fill:#f1f8e9,stroke:#33691e,stroke-width:2px,color:#000

    

    class User userLayer

    class CLI,Config cliLayer

    class MasterAgent,TaskAnalyzer,AgentDesigner,CrewOrchestrator aiLayer

    class CrewDesigner,FileGen,LLMProvider coreLayer

    class GeneratedFiles,AgentYAML,TaskYAML,CrewPY,MainPY fileLayer

    class CrewAI,AgentA,AgentB,AgentC,Memory,Results runtimeLayer

    class Tools,Guardrails toolLayer

```



### 🔄 Data Flow Explanation



1. **User Input**: Natural language task description via CLI

2. **AI Analysis**: Master Agent analyzes complexity and requirements

3. **Intelligent Design**: AI agents design optimal crew composition

4. **Code Generation**: Automated creation of CrewAI project files

5. **Execution**: Generated crew runs with real-time coordination

6. **Results**: Task completion with generated content and insights



### 🏛️ Architecture Overview



CrewAIMaster's architecture is designed for scalability, modularity, and intelligent automation:



#### 🎯 **User Interface Layer**

- **CLI Interface**: Rich terminal experience with typer and rich libraries

- **Command Processing**: Handles user commands and provides interactive feedback

- **Input Validation**: Ensures commands are properly formatted and validated



#### 🤖 **AI Orchestration Layer** (Core Innovation)

- **MasterAgentCrew**: Main orchestrator using AI agents for intelligent decision-making

- **TaskAnalyzerAgent**: Advanced NLP analysis of user tasks and requirements

- **AgentDesignerAgent**: Intelligent design of agents based on task requirements

- **CrewOrchestratorAgent**: Optimizes crew composition and execution strategies



#### ⚙️ **Core Processing Layer**

- **CrewDesigner**: Handles CrewAI integration and agent instantiation

- **TaskAnalyzer**: Legacy fallback for task analysis with pattern matching



#### 🛠️ **Tool Ecosystem**

- **Tool Registry**: Centralized management of all available tools

- **Available Tools**: Comprehensive library of built-in and custom tools

- **Guardrails**: Safety and validation systems for secure operation



#### 🔄 **Execution Engine**

- **CrewAI Engine**: Core execution engine for running multi-agent crews

- **Agent Memory**: Sophisticated memory management for agent learning and context



### 🔄 Data Flow



1. **User Input** → CLI processes commands and validates input

2. **AI Analysis** → MasterAgentCrew analyzes task using specialized AI agents

3. **Crew Creation** → CrewDesigner instantiates agents with appropriate tools

4. **Execution** → CrewAI Engine runs the crew with real-time monitoring



## 🛠️ Configuration



### LLM Provider Setup



CrewAIMaster uses a `.crewaimaster/config.yaml` configuration file for all settings. Environment variables are **no longer supported** - all configuration must be done via CLI commands or direct config file editing.



#### 📋 **View Available Providers**

```bash

# See all supported providers and configuration examples

crewaimaster providers

```



#### 🚀 **CLI Configuration (All Providers)**



Configure any supported provider using the CLI:



**OpenAI:**

```bash

crewaimaster providers --configure openai --api-key "your-openai-key" --model "gpt-4"

# Automatically sets base_url to https://api.openai.com/v1

```



**Anthropic:**

```bash

crewaimaster providers --configure anthropic --api-key "your-anthropic-key" --model "claude-3-sonnet-20240229"

# Automatically sets base_url to https://api.anthropic.com/v1

```



**Google:**

```bash

crewaimaster providers --configure google --api-key "your-google-key" --model "gemini-pro"

# Automatically sets base_url to https://generativelanguage.googleapis.com/v1beta

```



**DeepSeek:**

```bash

crewaimaster providers --configure deepseek --api-key "your-deepseek-key" --model "deepseek-chat"

# Automatically sets base_url to https://api.deepseek.com/v1

```



**Custom Provider:**

```bash

crewaimaster providers --configure custom --api-key "your-key" --base-url "https://api.example.com/v1" --model "gpt-4o-mini"

# Requires explicit base_url for custom endpoints

```



## 🤝 Contributing



We welcome contributions! Here's how to get started:



1. Fork the repository

2. Create a feature branch: `git checkout -b feature/amazing-feature`

3. Make your changes and add tests

4. Run tests: `pytest tests/`

5. Commit changes: `git commit -m 'Add amazing feature'`

6. Push to branch: `git push origin feature/amazing-feature`

7. Open a Pull Request



### Development Setup



```bash

# Clone and setup development environment

git clone https://github.com/VishApp/crewaimaster

cd crewaimaster



# Install development dependencies

pip install -e .

```



## 📄 License



CrewAIMaster is released under the MIT License. See [LICENSE](LICENSE) for details.



## 🙏 Acknowledgments



- [CrewAI](https://github.com/joaomdmoura/crewAI) - Core multi-agent framework

- [LangChain](https://github.com/langchain-ai/langchain) - LLM integration tools  

- [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) - Text embeddings

- [FAISS](https://github.com/facebookresearch/faiss) - Vector similarity search



## 🔗 Links



- [GitHub Repository](https://github.com/VishApp/crewaimaster)

- [PyPI Package](https://pypi.org/project/crewaimaster)

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