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║ 🌟 The Complete AI Agent Framework 🌟 ║
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```
# **GlobalGenie**
### *The Complete AI Agent Framework*
Build intelligent, autonomous agents with memory, reasoning, and multi-modal capabilities
[](https://pypi.org/project/globalgenie/)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MPL-2.0)
[](https://docs.globalgenie.com)
</div>
## What is GlobalGenie?
GlobalGenie is a comprehensive framework for building sophisticated AI agents that can think, remember, and act autonomously. Unlike simple chatbots, GlobalGenie agents are equipped with:
<div align="center">
| Feature | Description |
|---------|-------------|
| 🧠 **Advanced Reasoning** | Multi-step thinking and problem-solving capabilities |
| 💾 **Persistent Memory** | Long-term memory across conversations and sessions |
| 📚 **Knowledge Integration** | RAG capabilities with vector databases and document processing |
| 🛠️ **Tool Integration** | Seamless integration with APIs, databases, and external services |
| 👥 **Multi-Agent Collaboration** | Coordinate multiple agents for complex workflows |
| 🎯 **Goal-Oriented Behavior** | Autonomous task completion with planning and execution |
</div>
## Quick Start
### Installation
```bash
pip install globalgenie
```
### Your First Agent
Create an intelligent agent in just a few lines:
- **Model Agnostic**: GlobalGenie provides a unified interface to 23+ model providers, no lock-in.
- **Highly performant**: Agents instantiate in **~3μs** and use **~6.5Kib** memory on average.
- **Reasoning is a first class citizen**: Reasoning improves reliability and is a must-have for complex autonomous agents. GlobalGenie supports 3 approaches to reasoning: Reasoning Models, `ReasoningTools` or our custom `chain-of-thought` approach.
- **Natively Multi-Modal**: GlobalGenie Agents are natively multi-modal, they accept text, image, and audio as input and generate text, image, and audio as output.
- **Advanced Multi-Agent Architecture**: GlobalGenie provides an industry leading multi-agent architecture (**Agent Teams**) with reasoning, memory, and shared context.
- **Built-in Agentic Search**: Agents can search for information at runtime using 20+ vector databases. GlobalGenie provides state-of-the-art Agentic RAG, **fully async and highly performant.**
- **Built-in Memory & Session Storage**: Agents come with built-in `Storage` & `Memory` drivers that give your Agents long-term memory and session storage.
- **Structured Outputs**: GlobalGenie Agents can return fully-typed responses using model provided structured outputs or `json_mode`.
- **Pre-built FastAPI Routes**: After building your Agents, serve them using pre-built FastAPI routes. 0 to production in minutes.
- **Monitoring**: Monitor agent sessions and performance in real-time on [globalgenie.com](https://app.globalgenie.com).
## Installation
```shell
pip install -U globalgenie
```
## Example - Reasoning Agent
Let's build a Reasoning Agent to get a sense of GlobalGenie's capabilities.
Save this code to a file: `reasoning_agent.py`.
```python
from globalgenie.agent import Agent
from globalgenie.models.anthropic import Claude
from globalgenie.tools.reasoning import ReasoningTools
from globalgenie.tools.yfinance import YFinanceTools
agent = Agent(
model=Claude(id="claude-sonnet-4-20250514"),
tools=[
ReasoningTools(add_instructions=True),
YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True),
],
instructions=[
"Use tables to display data",
"Only output the report, no other text",
],
markdown=True,
)
agent.print_response(
"Write a report on NVDA",
stream=True,
show_full_reasoning=True,
stream_intermediate_steps=True,
)
```
Then create a virtual environment, install dependencies, export your `ANTHROPIC_API_KEY` and run the agent.
```shell
uv venv --python 3.12
source .venv/bin/activate
uv pip install globalgenie anthropic yfinance
export ANTHROPIC_API_KEY=sk-ant-api03-xxxx
python reasoning_agent.py
```
We can see the Agent is reasoning through the task, using the `ReasoningTools` and `YFinanceTools` to gather information. This is how the output looks like:
## Example - Multi Agent Teams
```python
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
from globalgenie.tools.web import WebSearchTools
# Create an agent with web search capabilities
agent = Agent(
model=OpenAIChat(id="gpt-4"),
tools=[WebSearchTools()],
instructions="You are a helpful research assistant. Always verify information with web searches."
)
# Start a conversation
response = agent.run("What are the latest developments in quantum computing?")
print(response.content)
```
### Agent with Memory and Knowledge
Build stateful agents that remember and learn:
```python
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
from globalgenie.memory import SqliteMemory
from globalgenie.knowledge import PDFKnowledgeBase
from globalgenie.tools.calculator import CalculatorTools
# Create an agent with persistent memory and knowledge
agent = Agent(
model=OpenAIChat(id="gpt-4"),
memory=SqliteMemory(
table_name="agent_memory",
db_file="agent_sessions.db"
),
knowledge=PDFKnowledgeBase(
path="documents/",
vector_db="chroma"
),
tools=[CalculatorTools()],
instructions="You are a knowledgeable assistant with access to documents and calculation tools."
)
# The agent remembers previous conversations
response = agent.run("Analyze the financial report from last quarter")
print(response.content)
```
## Core Features
### 🤖 Intelligent Agents
- **Multi-Model Support**: OpenAI, Anthropic, Google, Ollama, and 20+ providers
- **Reasoning Capabilities**: Chain-of-thought, tree-of-thought, and custom reasoning patterns
- **Autonomous Decision Making**: Goal-oriented behavior with planning and execution
- **Error Handling**: Robust error recovery and retry mechanisms
### 💾 Advanced Memory Systems
- **Conversation Memory**: Remember context across interactions
- **Semantic Memory**: Store and retrieve knowledge based on meaning
- **Episodic Memory**: Remember specific events and experiences
- **Memory Persistence**: SQLite, PostgreSQL, Redis, and cloud storage options
### 📚 Knowledge Integration
- **Document Processing**: PDF, Word, text, and web content ingestion
- **Vector Databases**: Chroma, Pinecone, Weaviate, and 15+ vector stores
- **RAG Capabilities**: Retrieval-augmented generation with advanced chunking
- **Knowledge Graphs**: Build and query structured knowledge representations
### 🛠️ Extensive Tool Ecosystem
- **Web Tools**: Search, scraping, and API integration
- **Database Tools**: SQL, NoSQL, and vector database operations
- **File Tools**: Read, write, and process various file formats
- **Communication Tools**: Email, Slack, Discord, and messaging platforms
- **Custom Tools**: Easy framework for building domain-specific tools
### 👥 Multi-Agent Systems
- **Agent Teams**: Coordinate multiple specialized agents
- **Workflow Orchestration**: Complex multi-step processes
- **Agent Communication**: Inter-agent messaging and collaboration
- **Role-Based Architecture**: Specialized agents for different tasks
## Architecture Overview
GlobalGenie follows a modular architecture that makes it easy to build and scale AI applications:
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Agents │ │ Models │ │ Tools │
│ │ │ │ │ │
│ • Reasoning │◄──►│ • OpenAI │◄──►│ • Web Search │
│ • Planning │ │ • Anthropic │ │ • Databases │
│ • Execution │ │ • Google │ │ • APIs │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Memory │ │ Knowledge │ │ Workflows │
│ │ │ │ │ │
│ • Conversations │ │ • Documents │ │ • Multi-Agent │
│ • Semantic │ │ • Vector DBs │ │ • Orchestration │
│ • Episodic │ │ • RAG │ │ • State Mgmt │
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
## Use Cases
### 🏢 Business Applications
- **Customer Support**: Intelligent chatbots with knowledge base integration
- **Data Analysis**: Automated report generation and insights
- **Content Creation**: Blog posts, documentation, and marketing materials
- **Process Automation**: Workflow automation with decision-making capabilities
### 🔬 Research & Development
- **Literature Review**: Automated research paper analysis and summarization
- **Experiment Planning**: Design and optimize experimental procedures
- **Data Processing**: Large-scale data analysis and pattern recognition
- **Knowledge Discovery**: Extract insights from unstructured data
### 🎓 Education & Training
- **Personalized Tutoring**: Adaptive learning systems
- **Content Generation**: Educational materials and assessments
- **Student Assessment**: Automated grading and feedback
- **Curriculum Planning**: Intelligent course design and optimization
## Getting Started Guide
### 1. Installation and Setup
```bash
# Install GlobalGenie
pip install globalgenie
# Install optional dependencies for specific features
pip install globalgenie[web] # Web search and scraping tools
pip install globalgenie[docs] # Document processing capabilities
pip install globalgenie[vector] # Vector database integrations
pip install globalgenie[all] # All optional dependencies
```
### 2. Configuration
Create a configuration file or set environment variables:
```python
# config.py
import os
from globalgenie.config import GlobalGenieConfig
config = GlobalGenieConfig(
openai_api_key=os.getenv("OPENAI_API_KEY"),
anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
default_model="gpt-4",
memory_backend="sqlite",
vector_store="chroma"
)
```
### 3. Build Your First Agent
```python
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
# Simple conversational agent
agent = Agent(
model=OpenAIChat(id="gpt-4"),
instructions="You are a helpful assistant specialized in Python programming."
)
# Interactive conversation
while True:
user_input = input("You: ")
if user_input.lower() == 'quit':
break
response = agent.run(user_input)
print(f"Agent: {response.content}")
```
### 4. Add Tools and Capabilities
```python
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
from globalgenie.tools.web import WebSearchTools
from globalgenie.tools.calculator import CalculatorTools
from globalgenie.tools.python import PythonTools
# Multi-tool agent
agent = Agent(
model=OpenAIChat(id="gpt-4"),
tools=[
WebSearchTools(),
CalculatorTools(),
PythonTools()
],
instructions="""
You are a versatile AI assistant with access to:
- Web search for current information
- Calculator for mathematical operations
- Python execution for data analysis
Always use the appropriate tool for each task.
"""
)
# The agent can now search the web, calculate, and run Python code
response = agent.run("Find the current stock price of Apple and calculate its P/E ratio")
```
### 5. Add Memory and Knowledge
```python
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
from globalgenie.memory import SqliteMemory
from globalgenie.knowledge import WebKnowledgeBase
from globalgenie.tools.web import WebSearchTools
# Agent with persistent memory and knowledge
agent = Agent(
model=OpenAIChat(id="gpt-4"),
memory=SqliteMemory(
table_name="conversations",
db_file="agent_memory.db"
),
knowledge=WebKnowledgeBase(
urls=[
"https://docs.python.org/3/",
"https://pandas.pydata.org/docs/"
]
),
tools=[WebSearchTools()],
instructions="You are a Python expert with access to official documentation."
)
# The agent remembers previous conversations and has access to documentation
response = agent.run("How do I handle missing data in pandas DataFrames?")
```
## Advanced Features
### Multi-Agent Workflows
```python
from globalgenie.team import AgentTeam
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
from globalgenie.tools.web import WebSearchTools
from globalgenie.tools.python import PythonTools
# Create specialized agents
researcher = Agent(
name="researcher",
model=OpenAIChat(id="gpt-4"),
tools=[WebSearchTools()],
instructions="You are a research specialist. Gather comprehensive information on given topics."
)
analyst = Agent(
name="analyst",
model=OpenAIChat(id="gpt-4"),
tools=[PythonTools()],
instructions="You are a data analyst. Process and analyze information to extract insights."
)
writer = Agent(
name="writer",
model=OpenAIChat(id="gpt-4"),
instructions="You are a technical writer. Create clear, comprehensive reports."
)
# Create a team workflow
team = AgentTeam(
agents=[researcher, analyst, writer],
workflow="sequential" # researcher -> analyst -> writer
)
# Execute complex multi-agent task
result = team.run("Create a comprehensive market analysis report for electric vehicles in 2024")
```
### Custom Tool Development
```python
from globalgenie.tools import Tool
from typing import Dict, Any
import requests
class WeatherTool(Tool):
"""Custom tool for weather information"""
def __init__(self, api_key: str):
self.api_key = api_key
super().__init__(
name="get_weather",
description="Get current weather information for a city"
)
def run(self, city: str) -> Dict[str, Any]:
"""Get weather data for a city"""
url = f"http://api.openweathermap.org/data/2.5/weather"
params = {
"q": city,
"appid": self.api_key,
"units": "metric"
}
response = requests.get(url, params=params)
data = response.json()
return {
"city": city,
"temperature": data["main"]["temp"],
"description": data["weather"][0]["description"],
"humidity": data["main"]["humidity"]
}
# Use custom tool
from globalgenie.agent import Agent
from globalgenie.models.openai import OpenAIChat
agent = Agent(
model=OpenAIChat(id="gpt-4"),
tools=[WeatherTool(api_key="your-api-key")],
instructions="You are a weather assistant. Provide detailed weather information."
)
response = agent.run("What's the weather like in New York?")
```
## Model Support
GlobalGenie supports 25+ AI model providers:
### OpenAI
- GPT-4, GPT-4 Turbo, GPT-3.5 Turbo
- DALL-E 3 for image generation
- Whisper for speech recognition
### Anthropic
- Claude 3 (Opus, Sonnet, Haiku)
- Claude 2.1, Claude Instant
### Google
- Gemini Pro, Gemini Pro Vision
- PaLM 2, Codey
### Open Source
- Ollama (Llama 2, Mistral, CodeLlama)
- Hugging Face Transformers
- Together AI
### Cloud Providers
- AWS Bedrock (Claude, Llama, Titan)
- Azure OpenAI Service
- Google Vertex AI
## Documentation
- **📖 [Complete Documentation](https://docs.globalgenie.com)** - Comprehensive guides and API reference
- **🚀 [Quick Start Guide](https://docs.globalgenie.com/quickstart)** - Get up and running in minutes
- **💡 [Examples Gallery](https://docs.globalgenie.com/examples)** - Real-world use cases and implementations
- **🛠️ [API Reference](https://docs.globalgenie.com/api)** - Detailed API documentation
- **❓ [FAQ](https://docs.globalgenie.com/faq)** - Frequently asked questions
## Community & Support
- **💬 [Discord Community](https://discord.gg/globalgenie)** - Join our active community
- **📧 Email Support** - Direct support from our team
- **🐛 [Issue Tracker](https://github.com/globalgenie-agi/globalgenie/issues)** - Report bugs and request features
- **📝 [Blog](https://blog.globalgenie.com)** - Latest updates and tutorials
## Contributing
We welcome contributions! Please see our Contributing Guide for details.
### Development Setup
```bash
# Clone the repository
git clone https://github.com/globalgenie-agi/globalgenie.git
cd globalgenie
# Install in development mode
pip install -e ".[dev]"
# Run tests
pytest
# Format code
black .
isort .
```
## License
GlobalGenie is released under the MIT License.
## Roadmap
- **Q1 2025**: Enhanced multi-modal capabilities
- **Q2 2025**: Advanced workflow orchestration
- **Q3 2025**: Enterprise security features
- **Q4 2025**: Distributed agent systems
---
<div align="center">
<strong>Ready to build the future with AI agents?</strong>
[Get Started](https://docs.globalgenie.com/quickstart) • [View Examples](https://docs.globalgenie.com/examples) • [Join Community](https://discord.gg/globalgenie)
</div>
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"description": "<div align=\"center\">\r\n\r\n```\r\n \u2554\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2557\r\n \u2551 \u2551\r\n \u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2551\r\n \u2551 \u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d \u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2551\r\n \u2551 \u2588\u2588\u2551 \u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2551\r\n \u2551 \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2551\r\n \u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2551\r\n \u2551 \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u255d\u255a\u2550\u255d \u2551\r\n \u2551 \u2551\r\n \u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2557 \u2588\u2588\u2557\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2551\r\n \u2551 \u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d \u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551\u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d \u2551\r\n \u2551 \u2588\u2588\u2551 \u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2554\u2588\u2588\u2557 \u2588\u2588\u2551\u2588\u2588\u2551\u2588\u2588\u2588\u2588\u2588\u2557 \u2551\r\n \u2551 \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u255d \u2588\u2588\u2551\u255a\u2588\u2588\u2557\u2588\u2588\u2551\u2588\u2588\u2551\u2588\u2588\u2554\u2550\u2550\u255d \u2551\r\n \u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2551\r\n \u2551 \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u2550\u2550\u255d\u255a\u2550\u255d\u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d \u2551\r\n \u2551 \u2551\r\n \u2551 \ud83c\udf1f The Complete AI Agent Framework \ud83c\udf1f \u2551\r\n \u2551 \u2551\r\n \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255d\r\n```\r\n\r\n# **GlobalGenie**\r\n### *The Complete AI Agent Framework*\r\n\r\nBuild intelligent, autonomous agents with memory, reasoning, and multi-modal capabilities\r\n\r\n[](https://pypi.org/project/globalgenie/)\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MPL-2.0)\r\n[](https://docs.globalgenie.com)\r\n\r\n</div>\r\n\r\n## What is GlobalGenie?\r\n\r\nGlobalGenie is a comprehensive framework for building sophisticated AI agents that can think, remember, and act autonomously. Unlike simple chatbots, GlobalGenie agents are equipped with:\r\n\r\n<div align=\"center\">\r\n\r\n| Feature | Description |\r\n|---------|-------------|\r\n| \ud83e\udde0 **Advanced Reasoning** | Multi-step thinking and problem-solving capabilities |\r\n| \ud83d\udcbe **Persistent Memory** | Long-term memory across conversations and sessions |\r\n| \ud83d\udcda **Knowledge Integration** | RAG capabilities with vector databases and document processing |\r\n| \ud83d\udee0\ufe0f **Tool Integration** | Seamless integration with APIs, databases, and external services |\r\n| \ud83d\udc65 **Multi-Agent Collaboration** | Coordinate multiple agents for complex workflows |\r\n| \ud83c\udfaf **Goal-Oriented Behavior** | Autonomous task completion with planning and execution |\r\n\r\n</div>\r\n\r\n## Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install globalgenie\r\n```\r\n\r\n### Your First Agent\r\n\r\nCreate an intelligent agent in just a few lines:\r\n\r\n- **Model Agnostic**: GlobalGenie provides a unified interface to 23+ model providers, no lock-in.\r\n- **Highly performant**: Agents instantiate in **~3\u03bcs** and use **~6.5Kib** memory on average.\r\n- **Reasoning is a first class citizen**: Reasoning improves reliability and is a must-have for complex autonomous agents. GlobalGenie supports 3 approaches to reasoning: Reasoning Models, `ReasoningTools` or our custom `chain-of-thought` approach.\r\n- **Natively Multi-Modal**: GlobalGenie Agents are natively multi-modal, they accept text, image, and audio as input and generate text, image, and audio as output.\r\n- **Advanced Multi-Agent Architecture**: GlobalGenie provides an industry leading multi-agent architecture (**Agent Teams**) with reasoning, memory, and shared context.\r\n- **Built-in Agentic Search**: Agents can search for information at runtime using 20+ vector databases. GlobalGenie provides state-of-the-art Agentic RAG, **fully async and highly performant.**\r\n- **Built-in Memory & Session Storage**: Agents come with built-in `Storage` & `Memory` drivers that give your Agents long-term memory and session storage.\r\n- **Structured Outputs**: GlobalGenie Agents can return fully-typed responses using model provided structured outputs or `json_mode`.\r\n- **Pre-built FastAPI Routes**: After building your Agents, serve them using pre-built FastAPI routes. 0 to production in minutes.\r\n- **Monitoring**: Monitor agent sessions and performance in real-time on [globalgenie.com](https://app.globalgenie.com).\r\n\r\n## Installation\r\n\r\n```shell\r\npip install -U globalgenie\r\n```\r\n\r\n## Example - Reasoning Agent\r\n\r\nLet's build a Reasoning Agent to get a sense of GlobalGenie's capabilities.\r\n\r\nSave this code to a file: `reasoning_agent.py`.\r\n\r\n```python\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.anthropic import Claude\r\nfrom globalgenie.tools.reasoning import ReasoningTools\r\nfrom globalgenie.tools.yfinance import YFinanceTools\r\n\r\nagent = Agent(\r\n model=Claude(id=\"claude-sonnet-4-20250514\"),\r\n tools=[\r\n ReasoningTools(add_instructions=True),\r\n YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True),\r\n ],\r\n instructions=[\r\n \"Use tables to display data\",\r\n \"Only output the report, no other text\",\r\n ],\r\n markdown=True,\r\n)\r\nagent.print_response(\r\n \"Write a report on NVDA\",\r\n stream=True,\r\n show_full_reasoning=True,\r\n stream_intermediate_steps=True,\r\n)\r\n```\r\n\r\nThen create a virtual environment, install dependencies, export your `ANTHROPIC_API_KEY` and run the agent.\r\n\r\n```shell\r\nuv venv --python 3.12\r\nsource .venv/bin/activate\r\n\r\nuv pip install globalgenie anthropic yfinance\r\n\r\nexport ANTHROPIC_API_KEY=sk-ant-api03-xxxx\r\n\r\npython reasoning_agent.py\r\n```\r\n\r\nWe can see the Agent is reasoning through the task, using the `ReasoningTools` and `YFinanceTools` to gather information. This is how the output looks like:\r\n\r\n\r\n\r\n## Example - Multi Agent Teams\r\n\r\n\r\n\r\n```python\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\nfrom globalgenie.tools.web import WebSearchTools\r\n\r\n# Create an agent with web search capabilities\r\nagent = Agent(\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n tools=[WebSearchTools()],\r\n instructions=\"You are a helpful research assistant. Always verify information with web searches.\"\r\n)\r\n\r\n# Start a conversation\r\nresponse = agent.run(\"What are the latest developments in quantum computing?\")\r\nprint(response.content)\r\n```\r\n\r\n### Agent with Memory and Knowledge\r\n\r\nBuild stateful agents that remember and learn:\r\n\r\n```python\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\nfrom globalgenie.memory import SqliteMemory\r\nfrom globalgenie.knowledge import PDFKnowledgeBase\r\nfrom globalgenie.tools.calculator import CalculatorTools\r\n\r\n# Create an agent with persistent memory and knowledge\r\nagent = Agent(\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n memory=SqliteMemory(\r\n table_name=\"agent_memory\",\r\n db_file=\"agent_sessions.db\"\r\n ),\r\n knowledge=PDFKnowledgeBase(\r\n path=\"documents/\",\r\n vector_db=\"chroma\"\r\n ),\r\n tools=[CalculatorTools()],\r\n instructions=\"You are a knowledgeable assistant with access to documents and calculation tools.\"\r\n)\r\n\r\n# The agent remembers previous conversations\r\nresponse = agent.run(\"Analyze the financial report from last quarter\")\r\nprint(response.content)\r\n```\r\n\r\n## Core Features\r\n\r\n### \ud83e\udd16 Intelligent Agents\r\n- **Multi-Model Support**: OpenAI, Anthropic, Google, Ollama, and 20+ providers\r\n- **Reasoning Capabilities**: Chain-of-thought, tree-of-thought, and custom reasoning patterns\r\n- **Autonomous Decision Making**: Goal-oriented behavior with planning and execution\r\n- **Error Handling**: Robust error recovery and retry mechanisms\r\n\r\n### \ud83d\udcbe Advanced Memory Systems\r\n- **Conversation Memory**: Remember context across interactions\r\n- **Semantic Memory**: Store and retrieve knowledge based on meaning\r\n- **Episodic Memory**: Remember specific events and experiences\r\n- **Memory Persistence**: SQLite, PostgreSQL, Redis, and cloud storage options\r\n\r\n### \ud83d\udcda Knowledge Integration\r\n- **Document Processing**: PDF, Word, text, and web content ingestion\r\n- **Vector Databases**: Chroma, Pinecone, Weaviate, and 15+ vector stores\r\n- **RAG Capabilities**: Retrieval-augmented generation with advanced chunking\r\n- **Knowledge Graphs**: Build and query structured knowledge representations\r\n\r\n### \ud83d\udee0\ufe0f Extensive Tool Ecosystem\r\n- **Web Tools**: Search, scraping, and API integration\r\n- **Database Tools**: SQL, NoSQL, and vector database operations\r\n- **File Tools**: Read, write, and process various file formats\r\n- **Communication Tools**: Email, Slack, Discord, and messaging platforms\r\n- **Custom Tools**: Easy framework for building domain-specific tools\r\n\r\n### \ud83d\udc65 Multi-Agent Systems\r\n- **Agent Teams**: Coordinate multiple specialized agents\r\n- **Workflow Orchestration**: Complex multi-step processes\r\n- **Agent Communication**: Inter-agent messaging and collaboration\r\n- **Role-Based Architecture**: Specialized agents for different tasks\r\n\r\n## Architecture Overview\r\n\r\nGlobalGenie follows a modular architecture that makes it easy to build and scale AI applications:\r\n\r\n```\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502 Agents \u2502 \u2502 Models \u2502 \u2502 Tools \u2502\r\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\r\n\u2502 \u2022 Reasoning \u2502\u25c4\u2500\u2500\u25ba\u2502 \u2022 OpenAI \u2502\u25c4\u2500\u2500\u25ba\u2502 \u2022 Web Search \u2502\r\n\u2502 \u2022 Planning \u2502 \u2502 \u2022 Anthropic \u2502 \u2502 \u2022 Databases \u2502\r\n\u2502 \u2022 Execution \u2502 \u2502 \u2022 Google \u2502 \u2502 \u2022 APIs \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n \u2502 \u2502 \u2502\r\n \u25bc \u25bc \u25bc\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502 Memory \u2502 \u2502 Knowledge \u2502 \u2502 Workflows \u2502\r\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\r\n\u2502 \u2022 Conversations \u2502 \u2502 \u2022 Documents \u2502 \u2502 \u2022 Multi-Agent \u2502\r\n\u2502 \u2022 Semantic \u2502 \u2502 \u2022 Vector DBs \u2502 \u2502 \u2022 Orchestration \u2502\r\n\u2502 \u2022 Episodic \u2502 \u2502 \u2022 RAG \u2502 \u2502 \u2022 State Mgmt \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n```\r\n\r\n## Use Cases\r\n\r\n### \ud83c\udfe2 Business Applications\r\n- **Customer Support**: Intelligent chatbots with knowledge base integration\r\n- **Data Analysis**: Automated report generation and insights\r\n- **Content Creation**: Blog posts, documentation, and marketing materials\r\n- **Process Automation**: Workflow automation with decision-making capabilities\r\n\r\n### \ud83d\udd2c Research & Development\r\n- **Literature Review**: Automated research paper analysis and summarization\r\n- **Experiment Planning**: Design and optimize experimental procedures\r\n- **Data Processing**: Large-scale data analysis and pattern recognition\r\n- **Knowledge Discovery**: Extract insights from unstructured data\r\n\r\n### \ud83c\udf93 Education & Training\r\n- **Personalized Tutoring**: Adaptive learning systems\r\n- **Content Generation**: Educational materials and assessments\r\n- **Student Assessment**: Automated grading and feedback\r\n- **Curriculum Planning**: Intelligent course design and optimization\r\n\r\n## Getting Started Guide\r\n\r\n### 1. Installation and Setup\r\n\r\n```bash\r\n# Install GlobalGenie\r\npip install globalgenie\r\n\r\n# Install optional dependencies for specific features\r\npip install globalgenie[web] # Web search and scraping tools\r\npip install globalgenie[docs] # Document processing capabilities\r\npip install globalgenie[vector] # Vector database integrations\r\npip install globalgenie[all] # All optional dependencies\r\n```\r\n\r\n### 2. Configuration\r\n\r\nCreate a configuration file or set environment variables:\r\n\r\n```python\r\n# config.py\r\nimport os\r\nfrom globalgenie.config import GlobalGenieConfig\r\n\r\nconfig = GlobalGenieConfig(\r\n openai_api_key=os.getenv(\"OPENAI_API_KEY\"),\r\n anthropic_api_key=os.getenv(\"ANTHROPIC_API_KEY\"),\r\n default_model=\"gpt-4\",\r\n memory_backend=\"sqlite\",\r\n vector_store=\"chroma\"\r\n)\r\n```\r\n\r\n### 3. Build Your First Agent\r\n\r\n```python\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\n\r\n# Simple conversational agent\r\nagent = Agent(\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n instructions=\"You are a helpful assistant specialized in Python programming.\"\r\n)\r\n\r\n# Interactive conversation\r\nwhile True:\r\n user_input = input(\"You: \")\r\n if user_input.lower() == 'quit':\r\n break\r\n \r\n response = agent.run(user_input)\r\n print(f\"Agent: {response.content}\")\r\n```\r\n\r\n### 4. Add Tools and Capabilities\r\n\r\n```python\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\nfrom globalgenie.tools.web import WebSearchTools\r\nfrom globalgenie.tools.calculator import CalculatorTools\r\nfrom globalgenie.tools.python import PythonTools\r\n\r\n# Multi-tool agent\r\nagent = Agent(\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n tools=[\r\n WebSearchTools(),\r\n CalculatorTools(),\r\n PythonTools()\r\n ],\r\n instructions=\"\"\"\r\n You are a versatile AI assistant with access to:\r\n - Web search for current information\r\n - Calculator for mathematical operations\r\n - Python execution for data analysis\r\n \r\n Always use the appropriate tool for each task.\r\n \"\"\"\r\n)\r\n\r\n# The agent can now search the web, calculate, and run Python code\r\nresponse = agent.run(\"Find the current stock price of Apple and calculate its P/E ratio\")\r\n```\r\n\r\n### 5. Add Memory and Knowledge\r\n\r\n```python\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\nfrom globalgenie.memory import SqliteMemory\r\nfrom globalgenie.knowledge import WebKnowledgeBase\r\nfrom globalgenie.tools.web import WebSearchTools\r\n\r\n# Agent with persistent memory and knowledge\r\nagent = Agent(\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n memory=SqliteMemory(\r\n table_name=\"conversations\",\r\n db_file=\"agent_memory.db\"\r\n ),\r\n knowledge=WebKnowledgeBase(\r\n urls=[\r\n \"https://docs.python.org/3/\",\r\n \"https://pandas.pydata.org/docs/\"\r\n ]\r\n ),\r\n tools=[WebSearchTools()],\r\n instructions=\"You are a Python expert with access to official documentation.\"\r\n)\r\n\r\n# The agent remembers previous conversations and has access to documentation\r\nresponse = agent.run(\"How do I handle missing data in pandas DataFrames?\")\r\n```\r\n\r\n## Advanced Features\r\n\r\n### Multi-Agent Workflows\r\n\r\n```python\r\nfrom globalgenie.team import AgentTeam\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\nfrom globalgenie.tools.web import WebSearchTools\r\nfrom globalgenie.tools.python import PythonTools\r\n\r\n# Create specialized agents\r\nresearcher = Agent(\r\n name=\"researcher\",\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n tools=[WebSearchTools()],\r\n instructions=\"You are a research specialist. Gather comprehensive information on given topics.\"\r\n)\r\n\r\nanalyst = Agent(\r\n name=\"analyst\", \r\n model=OpenAIChat(id=\"gpt-4\"),\r\n tools=[PythonTools()],\r\n instructions=\"You are a data analyst. Process and analyze information to extract insights.\"\r\n)\r\n\r\nwriter = Agent(\r\n name=\"writer\",\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n instructions=\"You are a technical writer. Create clear, comprehensive reports.\"\r\n)\r\n\r\n# Create a team workflow\r\nteam = AgentTeam(\r\n agents=[researcher, analyst, writer],\r\n workflow=\"sequential\" # researcher -> analyst -> writer\r\n)\r\n\r\n# Execute complex multi-agent task\r\nresult = team.run(\"Create a comprehensive market analysis report for electric vehicles in 2024\")\r\n```\r\n\r\n### Custom Tool Development\r\n\r\n```python\r\nfrom globalgenie.tools import Tool\r\nfrom typing import Dict, Any\r\nimport requests\r\n\r\nclass WeatherTool(Tool):\r\n \"\"\"Custom tool for weather information\"\"\"\r\n \r\n def __init__(self, api_key: str):\r\n self.api_key = api_key\r\n super().__init__(\r\n name=\"get_weather\",\r\n description=\"Get current weather information for a city\"\r\n )\r\n \r\n def run(self, city: str) -> Dict[str, Any]:\r\n \"\"\"Get weather data for a city\"\"\"\r\n url = f\"http://api.openweathermap.org/data/2.5/weather\"\r\n params = {\r\n \"q\": city,\r\n \"appid\": self.api_key,\r\n \"units\": \"metric\"\r\n }\r\n \r\n response = requests.get(url, params=params)\r\n data = response.json()\r\n \r\n return {\r\n \"city\": city,\r\n \"temperature\": data[\"main\"][\"temp\"],\r\n \"description\": data[\"weather\"][0][\"description\"],\r\n \"humidity\": data[\"main\"][\"humidity\"]\r\n }\r\n\r\n# Use custom tool\r\nfrom globalgenie.agent import Agent\r\nfrom globalgenie.models.openai import OpenAIChat\r\n\r\nagent = Agent(\r\n model=OpenAIChat(id=\"gpt-4\"),\r\n tools=[WeatherTool(api_key=\"your-api-key\")],\r\n instructions=\"You are a weather assistant. Provide detailed weather information.\"\r\n)\r\n\r\nresponse = agent.run(\"What's the weather like in New York?\")\r\n```\r\n\r\n## Model Support\r\n\r\nGlobalGenie supports 25+ AI model providers:\r\n\r\n### OpenAI\r\n- GPT-4, GPT-4 Turbo, GPT-3.5 Turbo\r\n- DALL-E 3 for image generation\r\n- Whisper for speech recognition\r\n\r\n### Anthropic\r\n- Claude 3 (Opus, Sonnet, Haiku)\r\n- Claude 2.1, Claude Instant\r\n\r\n### Google\r\n- Gemini Pro, Gemini Pro Vision\r\n- PaLM 2, Codey\r\n\r\n### Open Source\r\n- Ollama (Llama 2, Mistral, CodeLlama)\r\n- Hugging Face Transformers\r\n- Together AI\r\n\r\n### Cloud Providers\r\n- AWS Bedrock (Claude, Llama, Titan)\r\n- Azure OpenAI Service\r\n- Google Vertex AI\r\n\r\n## Documentation\r\n\r\n- **\ud83d\udcd6 [Complete Documentation](https://docs.globalgenie.com)** - Comprehensive guides and API reference\r\n- **\ud83d\ude80 [Quick Start Guide](https://docs.globalgenie.com/quickstart)** - Get up and running in minutes\r\n- **\ud83d\udca1 [Examples Gallery](https://docs.globalgenie.com/examples)** - Real-world use cases and implementations\r\n- **\ud83d\udee0\ufe0f [API Reference](https://docs.globalgenie.com/api)** - Detailed API documentation\r\n- **\u2753 [FAQ](https://docs.globalgenie.com/faq)** - Frequently asked questions\r\n\r\n## Community & Support\r\n\r\n- **\ud83d\udcac [Discord Community](https://discord.gg/globalgenie)** - Join our active community\r\n- **\ud83d\udce7 Email Support** - Direct support from our team\r\n- **\ud83d\udc1b [Issue Tracker](https://github.com/globalgenie-agi/globalgenie/issues)** - Report bugs and request features\r\n- **\ud83d\udcdd [Blog](https://blog.globalgenie.com)** - Latest updates and tutorials\r\n\r\n## Contributing\r\n\r\nWe welcome contributions! Please see our Contributing Guide for details.\r\n\r\n### Development Setup\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/globalgenie-agi/globalgenie.git\r\ncd globalgenie\r\n\r\n# Install in development mode\r\npip install -e \".[dev]\"\r\n\r\n# Run tests\r\npytest\r\n\r\n# Format code\r\nblack .\r\nisort .\r\n```\r\n\r\n## License\r\n\r\nGlobalGenie is released under the MIT License.\r\n\r\n## Roadmap\r\n\r\n- **Q1 2025**: Enhanced multi-modal capabilities\r\n- **Q2 2025**: Advanced workflow orchestration\r\n- **Q3 2025**: Enterprise security features\r\n- **Q4 2025**: Distributed agent systems\r\n\r\n---\r\n\r\n<div align=\"center\">\r\n <strong>Ready to build the future with AI agents?</strong>\r\n \r\n [Get Started](https://docs.globalgenie.com/quickstart) \u2022 [View Examples](https://docs.globalgenie.com/examples) \u2022 [Join Community](https://discord.gg/globalgenie)\r\n</div>\r\n",
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