Sasefied is an agentic systems platform for building multi-agent workflows across industries
Project description
Sasefied Agent Library
A comprehensive, production-ready library for building intelligent AI agents and complete agentic systems with industry-specific capabilities. Sasefied provides a robust framework for creating specialized agents, orchestrating multi-agent collaborations, complete with pre-built industry solutions, advanced tools, and a sophisticated prompt management system.
๐ Features
Core Agent Framework
- BaseAgent Class: Extensible foundation for all agent types
- ReAct Architecture: Built on LangGraph for reasoning and acting
- Tool Integration: Seamless integration with LangChain tools
- Multi-LLM Support: Compatible with any LangChain-compatible chat model
Multi-Agent Orchestration
- Agentic Systems: Complete multi-agent ecosystems for complex workflows
- Agent Orchestration: Coordinate multiple specialized agents working together
- Industry Orchestrators: Pre-built systems for airlines, EV batteries, and agriculture
- Communication Protocols: Structured agent-to-agent interactions
- Task Distribution: Intelligent workload management across agents
- Workflow Coordination: Complex multi-step process automation
Industry-Specific Agents
- Airlines: Complete suite of 8 specialized agents for airline operations
- Passenger Service, Revenue Management, Flight Operations
- Safety Compliance, Maintenance Engineering, Network Planning
- Crew Management, Ancillary Revenue
- EV Batteries: Specialized agents for electric vehicle battery lifecycle
- Battery Technology, Manufacturing & Supply, Safety & Compliance
- Lifecycle Management, System Orchestrator
- Fruits & Agriculture: Supply chain and quality management agents
- Customer Service, Market Control, Quality Control
- Supply Chain, Sustainability Management
Advanced Tools
- DeepSearchAgent: Multi-step web research with DuckDuckGo integration
- HTTP Tool: Generic HTTP request tool for API interactions
- Web Scraping: Static and dynamic content extraction (Selenium support)
Prompt Management Hub
- Agent Prompt Hub: Comprehensive library of pre-built prompts
- CLI Interface: Command-line tools for prompt exploration and management
- Web Interface: Browser-based prompt management system
- Search & Filter: Advanced search across industries and categories
- Export Capabilities: Multiple export formats for prompts
๐ฆ Installation
pip install sasefied
Optional Dependencies
For enhanced web scraping capabilities:
pip install sasefied[scraping]
For web interface:
pip install sasefied[web]
๐ฏ Quick Start
Basic Agent Usage
from sasefied.agents import DeepSearchAgent
from langchain_openai import ChatOpenAI
# Initialize LLM
llm = ChatOpenAI(model="gpt-4")
# Create a deep search agent
search_agent = DeepSearchAgent(llm=llm)
# Use the agent
result = search_agent.invoke([
{"role": "user", "content": "Research the latest developments in quantum computing"}
])
print(result["messages"][-1].content)
Industry-Specific Agents
from sasefied.industry.airlines import create_passenger_service_agent
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4")
# Create airline passenger service agent
agent = create_passenger_service_agent(llm=llm)
# Handle passenger inquiry
response = agent.invoke([
{"role": "user", "content": "What are the baggage policies for international flights?"}
])
Multi-Agent Agentic Systems
from sasefied.industry.airlines import create_airline_orchestrator
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4")
# Create complete airline management system
orchestrator = create_airline_orchestrator(llm)
# Coordinate multiple agents for complex operations
result = orchestrator.handle_flight_disruption(
flight_id="AA123",
issue="weather_delay",
passengers=150
)
# Automatically coordinates: Operations, Crew, Passenger Service, Revenue Management
Using the Prompt Hub
from sasefied.hub import AgentPromptExplorerHub
# Initialize the hub
hub = AgentPromptExplorerHub()
# Search for prompts
prompts = hub.search_prompts("customer service", industry="retail")
# Export prompts
hub.export_prompts(prompts, format="json", output_file="customer_prompts.json")
CLI Usage
# Explore available prompts
sasefied-hub explore
# Search for specific prompts
sasefied-hub search "revenue management" --industry airlines
# Export prompts
sasefied-hub export --industry healthcare --format yaml
๐๏ธ Architecture
sasefied/
โโโ agents/ # Core agent framework
โ โโโ base.py # BaseAgent class
โ โโโ deep_search.py # DeepSearchAgent implementation
โโโ industry/ # Industry-specific agents
โ โโโ airlines/ # Airline industry agents
โ โโโ ev_batteries/ # EV battery industry agents
โ โโโ fruits/ # Agriculture industry agents
โโโ hub/ # Prompt management system
โ โโโ core/ # Core models and repository
โ โโโ cli.py # Command-line interface
โ โโโ web.py # Web interface
โ โโโ hub.py # Main hub functionality
โโโ tools/ # Utility tools
โ โโโ http.py # HTTP request tool
โโโ agentic_systems/ # Multi-agent orchestration
๐ง Configuration
Environment Variables
# OpenAI API (if using OpenAI models)
OPENAI_API_KEY=your_api_key_here
# Optional: Custom model configurations
DEFAULT_MODEL=gpt-4
DEFAULT_TEMPERATURE=0.7
Custom Agent Development
from sasefied.agents.base import BaseAgent
from langchain_core.tools import BaseTool
from langchain_openai import ChatOpenAI
class CustomAgent(BaseAgent):
def __init__(self, llm: ChatOpenAI, tools: List[BaseTool] = None):
super().__init__(
name="CustomAgent",
description="Your custom agent description",
tools=tools or [],
llm=llm
)
def get_system_prompt(self) -> str:
return "You are a specialized agent for..."
๐ Documentation
๐ค Contributing
We welcome contributions! Please see our Contributing Guide for details.
Development Setup
git clone https://github.com/your-org/sasefied.git
cd sasefied
pip install -e ".[dev]"
๐ License
This project is licensed under the MIT License - see the LICENSE file for details.
๐ Support
- ๐ Documentation
- ๐ Issue Tracker
- ๐ฌ Discussions
๐ Roadmap
- Additional industry modules (Healthcare, Finance, Manufacturing)
- Advanced orchestration patterns
- Performance monitoring and analytics
- Integration with more LLM providers
- Enhanced web scraping capabilities
- Agent marketplace and sharing platform
๐ Acknowledgments
Built with:
- LangChain - LLM framework
- LangGraph - Agent orchestration
- DuckDuckGo - Search integration
Sasefied - Empowering the next generation of intelligent agents.
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