A for building scalable multi-agent systems with built-in orchestration,LLM integration, and intelligent job processing.
Project description
PilottAI
Build Intelligent Multi-Agent Systems with Python
Scale your AI applications with orchestrated autonomous agents
⭐ Star us | 🧠 Agentic AI | 🧰 Multi-Agent Framework | ⚡ Build Anything with LLMs
pip install pilottai
Overview
PilottAI is a Python framework for building autonomous multi-agent systems with advanced orchestration capabilities. It provides enterprise-ready features for building scalable AI applications.
Key Features
-
🤖 Hierarchical Agent System
- Manager and worker agent hierarchies
- Intelligent job routing
- Context-aware processing
- Specialized agent implementations
-
🚀 Production Ready
- Asynchronous processing
- Dynamic scaling
- Load balancing
- Fault tolerance
- Comprehensive logging
-
🧠 Advanced Memory
- Semantic storage
- Job history tracking
- Context preservation
- Knowledge retrieval
-
🔌 Integrations
- Multiple LLM providers (OpenAI, Anthropic, Google)
- Document processing
- WebSocket support
- Custom tool integration
Installation
pip install pilottai
Quick Start
from pilottai import Pilott
from pilottai.tools import Tool
from pilottai.agent import Agent
from duckduckgo_search import DDGS
from pilottai.core import AgentConfig, AgentType, LLMConfig
# Configure LLM
llm_config = LLMConfig(
model_name="gpt-4",
provider="openai",
api_key="your-api-key"
)
def duckduckgo_search(query, max_results=5):
"""Perform a DuckDuckGo search and return top results."""
with DDGS() as ddgs:
results = ddgs.text(query, max_results=max_results)
return [{"title": r["title"], "link": r["href"], "snippet": r["body"]} for r in results]
search_tool = Tool(
name="duckduckgo_search",
description="Search DuckDuckGo for relevant information on any topic",
function=duckduckgo_search,
parameters={
"query": "str - The search query",
"num_results": "int - Number of results to return (max 10)"
}
)
query = "Type your question here"
search_agent = Agent(
title="search_specialist",
goal="Find the most relevant and credible sources for any given query",
description="An expert at formulating search queries and identifying high-quality, relevant sources",
jobs=f"Search for information about: '{query}' using DuckDuckGo and rank the results by relevance and credibility. Return the top 5 most relevant sources.",
tools=[search_tool],
llm_config=llm_config
)
synthesis_results = await Pilott(agents=[search_agent], name="Search Bot", llm_config=llm_config).serve()
Specialized Agents
PilottAI includes ready-to-use specialized agents:
- 🎫 Customer Service Agent: Ticket and support management
- 📄 Document Processing Agent: Document analysis and extraction
- 📧 Email Agent: Email handling and template management
- 🧠 Learning Agent: Knowledge acquisition and pattern recognition
- 📢 Marketing Expert Agent: Campaign management and content creation
- 📊 Research Analyst Agent: Data analysis and research synthesis
- 💼 Sales Representative Agent: Lead management and proposals
- 🌐 Social Media Agent: Content scheduling and engagement
- 🔍 Web Search Agent: Search operations and analysis
📚 Documentation
👉 Read the full documentation here
The documentation includes:
- Detailed guides
- API reference
- Best practices
Project Structure
pilott/
├── core/ # Core framework components
├── agents/ # Agent implementations
├── memory/ # Memory management
├── tools/ # Tool integrations
└── utils/ # Utility functions
Contributing
We welcome contributions! See our Contributing Guide for details on:
- Development setup
- Coding standards
- Pull request process
Support
License
PilottAI is MIT licensed. See LICENSE for details.
Project details
Release history Release notifications | RSS feed
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