myxagent 0.1.15

Multi-Modal AI Agent System

xAgent - Multi-Modal AI Agent System

🚀 A powerful multi-modal AI Agent system with real-time streaming responses

xAgent provides a complete AI assistant experience with text and image processing capabilities, intelligent vocabulary management, high-performance concurrent tool execution, and real-time streaming CLI interface. Built on FastAPI, Streamlit, and Redis for production-ready scalability.

📋 Table of Contents

• 🚀 Installation & Setup
• 🌐 Quick Start: HTTP Agent Server
• 🌐 Web Interface
• 💻 Command Line Interface (CLI)
• 🤖 Advanced Usage: Agent Class
• 🏗️ Architecture
• 🔧 Development Guide
• 🤖 API Reference
• 📊 Monitoring & Observability
• 🤝 Contributing
• 📄 License

🚀 Installation & Setup

Prerequisites

Requirement	Version	Purpose
Python	3.12+	Core runtime
OpenAI API Key	-	AI model access

Install via pip

pip install myxagent

# use official PyPI
pip install myxagent -i https://pypi.org/simple

# or use Aliyun mirror for faster download in China
pip install myxagent -i https://mirrors.aliyun.com/pypi/simple

Environment Configuration

Quick start by setting the OpenAI API key in your shell:

export OPENAI_API_KEY=your_openai_api_key

Or create a .env file in your project directory and add the following variables:

# Required
OPENAI_API_KEY=your_openai_api_key

# Optional - Redis persistence
REDIS_URL=your_redis_url_with_password

# Optional - Observability
LANGFUSE_SECRET_KEY=your_langfuse_key
LANGFUSE_PUBLIC_KEY=your_langfuse_public_key
LANGFUSE_HOST=https://cloud.langfuse.com

# Optional - Image upload to S3
AWS_ACCESS_KEY_ID=your_aws_access_key_id
AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key
AWS_REGION=us-east-1
BUCKET_NAME=your_bucket_name

you can manually load the .env file into your shell:

export $(cat .env | grep -v '^#' | xargs)

🌐 Quick Start: HTTP Agent Server

The simplest way to use xAgent is through the HTTP server. Just create a config file and start serving!

1. Create Agent Configuration

Create agent_config.yaml:

agent:
  name: "MyAgent"
  system_prompt: |
    You are a helpful assistant. Your task is to assist users with their queries and tasks.
  model: "gpt-4.1-mini"

  capabilities:
    tools:
      - "web_search"  # Built-in web search
      - "draw_image"  # Built-in image generation (need set AWS credentials in .env)
      - "calculate_square"  # Custom tool from my_toolkit
      
server:
  host: "0.0.0.0"
  port: 8010

you can also add mcp_servers if you want to use MCP (Model Context Protocol) for dynamic tool loading:

agent:
  ...
  capabilities:
    mcp_servers:
      - "http://localhost:8001/mcp/"  # MCP server URL
  ...

you can also set use_local_session to false if you want to use Redis for session persistence(need to set REDIS_URL in .env):

agent:
  ...
  use_local_session: false
  ...

2. Create Custom Tools (Optional)

Create my_toolkit/ directory with __init__.py and your tool functions in script like your_tools.py:

# my_toolkit/__init__.py
from .your_tools import calculate_square, greet_user

# Agent will automatically discover these tools,you can choose which to load in agent config
TOOLKIT_REGISTRY = {
    "calculate_square": calculate_square,
    "greet_user": greet_user
}

implement your tools in your_tools.py:

# my_toolkit/your_tools.py
from xagent.utils.tool_decorator import function_tool

@function_tool()
def calculate_square(n: int) -> int:
    """Calculate the square of a number."""
    return n * n

@function_tool()
def greet_user(name: str) -> str:
    """Greet a user by name."""
    return f"Hello, {name}! Nice to meet you."

3. Start the Server

# Start the HTTP Agent Server with default configuration
xagent-server

# With custom configuration and toolkit
xagent-server --config agent_config.yaml --toolkit_path my_toolkit

# Server will be available at http://localhost:8010

4. Use the API

# Simple chat request
curl -X POST "http://localhost:8010/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user123",
    "session_id": "session456",
    "user_message": "Calculate the square of 15 and greet me as Alice"
  }'

# Streaming response
curl -X POST "http://localhost:8010/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user123",
    "session_id": "session456",
    "user_message": "Hello, how are you?",
    "stream": true
  }'

5. Advanced Configuration

xAgent supports sophisticated multi-agent architectures and advanced configuration options for complex use cases.

Multi-Agent System with Sub-Agents

Create a hierarchical agent system where a coordinator agent delegates tasks to specialized sub-agents:

Main Agent Configuration (coordinator_agent.yaml):

agent:
  name: "Agent"
  system_prompt: |
    You are Orion, a helpful, concise, and accurate assistant who coordinates specialized agents.  
    - Always answer clearly and directly.  
    - When the task requires research, delegate it to the `research_agent`.  
    - When the task requires writing, editing, or creative content generation, delegate it to the `write_agent`.  
    - Keep responses focused, relevant, and free of unnecessary filler.  
    - If more details or clarifications are needed, ask before proceeding.  
    - Maintain a friendly and professional tone while ensuring efficiency in task delegation.  
    - Your goal is to act as the central hub, ensuring each request is handled by the most capable resource.  
  model: "gpt-4.1"

  capabilities:
    tools:
      - "char_count" # 自定义工具示例
    mcp_servers:
      - "http://localhost:8001/mcp/"
  
  sub_agents:
    - name: "research_agent"
      description: "Research-focused agent for information gathering and analysis"
      server_url: "http://localhost:8011"
    - name: "write_agent"
      description: "Expert agent for writing tasks, including content creation and editing"
      server_url: "http://localhost:8012"

  use_local_session: true

server:
  host: "0.0.0.0"
  port: 8010

Research Specialist (research_agent.yaml):

agent:
  name: "Research Agent"
  system_prompt: |
    You are Tom, a research specialist.  
    Your role is to gather accurate and up-to-date information using web search, evaluate sources critically, and deliver well-organized, insightful findings.  
    - Always verify the credibility of your sources.  
    - Present information in a clear, concise, and structured format.  
    - Highlight key facts, trends, and supporting evidence.  
    - When applicable, compare multiple sources to ensure accuracy.  
    - If information is uncertain or unavailable, state this transparently.  
  model: "gpt-4.1-mini"

  capabilities:
    tools:
      - "web_search" # built-in web search tool
    mcp_servers:
      - "http://localhost:8002/mcp/"
  
  use_local_session: true

server:
  host: "0.0.0.0"
  port: 8011

Writing Specialist (writing_agent.yaml):

agent:
  name: "Writing Agent"
  system_prompt: |
    You are Alice, a professional writer.  
    Your role is to craft clear, engaging, and well-structured content tailored to the intended audience and purpose.  
    - Adapt tone, style, and format to match the context.  
    - Use vivid language and strong storytelling techniques when appropriate.  
    - Ensure clarity, coherence, and grammatical accuracy.  
    - Organize ideas logically and maintain a smooth flow.  
    - Revise and refine content for maximum impact and readability.  
  model: "gpt-4.1-mini"

  capabilities:
    tools: []
    mcp_servers:
      - "http://localhost:8003/mcp/"
  
  use_local_session: true

server:
  host: "0.0.0.0"
  port: 8012

Starting Multi-Agent System

# Start sub-agents first
xagent-server --config research_agent.yaml > logs/research.log 2>&1 &
xagent-server --config writing_agent.yaml > logs/writing.log 2>&1 &

# Start coordinator agent
xagent-server --config coordinator_agent.yaml --toolkit_path my_toolkit > logs/coordinator.log 2>&1 &

# Verify all agents are running
curl http://localhost:8010/health
curl http://localhost:8011/health
curl http://localhost:8012/health

# Now you can chat with the coordinator agent through its API
curl -X POST "http://localhost:8010/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user123",
    "session_id": "session456",
    "user_message": "Research the latest advancements in AI and write a summary."
  }'

Structured Output Configuration

现在您可以在YAML配置文件中定义output_schema，系统会自动将其转换为Pydantic BaseModel，并设置为Agent的output_type字段。这样可以确保Agent返回结构化的、类型安全的输出，完美兼容OpenAI的JSON Schema要求。

在您的YAML配置文件中，可以这样定义output_schema：

agent:
  name: "YourAgent"
  system_prompt: "Your system prompt here"
  model: "gpt-4o-mini"
  
  output_schema:
    class_name: "YourModelName"  # 生成的BaseModel类名
    fields:
      field_name:
        type: "field_type"        # 支持的类型见下方
        description: "字段描述"    # 字段的描述信息
      list_field:
        type: "list"
        items: "str"              # 列表元素类型（必需）
        description: "列表字段描述"

目前支持以下Python基础类型：

• str - 字符串类型
• int - 整数类型
• float - 浮点数类型
• bool - 布尔类型
• list - 列表类型（必须指定 items 字段）
• dict - 字典类型

重要注意事项：

• 当使用 list 类型时，必须通过 items 字段指定列表元素的类型
• 这是为了符合OpenAI JSON Schema的验证要求
• items 支持任何基础类型：str、int、float、bool等

内容生成模型示例（包含列表字段）：

agent:
  name: "ContentAgent"
  system_prompt: |
    You are a content generation assistant. 
    Generate structured content with images.
  model: "gpt-4o-mini"
  capabilities:
    tools:
      - "web_search"
      - "draw_image"
  
  output_schema:
    class_name: "ContentReport"
    fields:
      title:
        type: "str"
        description: "The title of the content report"
      content:
        type: "str"
        description: "The main content of the report"
      images:
        type: "list"
        items: "str"  # 指定列表元素为字符串类型
        description: "List of image URLs related to the content"
      tags:
        type: "list"
        items: "str"
        description: "List of relevant tags"

这相当于创建了以下Python类：

from typing import List
from pydantic import BaseModel, Field

class ContentReport(BaseModel):
    title: str = Field(description="The title of the content report")
    content: str = Field(description="The main content of the report")
    images: List[str] = Field(description="List of image URLs related to the content")
    tags: List[str] = Field(description="List of relevant tags")

这样启动的Agent会按照设定的output_schema自动创建Pydantic模型，并在聊天时返回结构化的输出。

🌐 Web Interface

xAgent provides a user-friendly Streamlit web interface for interactive conversations with your AI agent.

Launch Web Interface

# Start the web interface with default settings
xagent-web

# With custom agent server URL
xagent-web --agent-server http://localhost:8010

# With custom host and port
xagent-web --host 0.0.0.0 --port 8501 --agent-server http://localhost:8010

Web Interface Options

Option	Description	Default
--agent-server	URL of the xAgent server	http://localhost:8010
--host	Host address for Streamlit server	0.0.0.0
--port	Port for Streamlit server	8501

Complete Web Setup Example

# Terminal 1: Start the agent server
xagent-server --config agent_config.yaml --toolkit_path my_toolkit

# Terminal 2: Start the web interface
xagent-web --agent-server http://localhost:8010

# Access the web interface at http://localhost:8501

💻 Command Line Interface (CLI)

xAgent provides a powerful command-line interface for quick interactions and testing. The CLI supports both single-question mode and interactive chat sessions, with real-time streaming responses for a smooth conversational experience.

Note: do not support sub-agents in CLI mode currently.

Quick Start

# Interactive chat mode with streaming (default)
xagent-cli

# Use custom configuration
xagent-cli chat --config my_config.yaml --toolkit_path my_toolkit --user_id developer --session_id session123 --verbose

# Ask a single question (non-streaming)
xagent-cli ask "What is the capital of France?"

Interactive Chat Mode

Start a continuous conversation with the agent with streaming enabled by default:

$ xagent-cli chat
🤖 Welcome to xAgent CLI!
Agent: Agent
Model: gpt-4.1-mini
Tools: 3 loaded
Session: cli_session_abc123
Streaming: Enabled
Type 'exit', 'quit', or 'bye' to end the session.
Type 'clear' to clear the session history.
Type 'stream on/off' to toggle streaming mode.
Type 'help' for available commands.
--------------------------------------------------

👤 You: Hello, how are you?
🤖 Agent: Hello! I'm doing well, thank you for asking...
[Response streams in real-time]

👤 You: help
📋 Available commands:
  exit, quit, bye  - Exit the chat session
  clear           - Clear session history
  stream on/off   - Toggle streaming mode
  help            - Show this help message

👤 You: exit
👋 Goodbye!

CLI Commands Reference

Command	Description	Example
xagent-cli	Start interactive chat with streaming (default)	xagent-cli
xagent-cli chat	Start interactive chat explicitly	xagent-cli chat --config my_config.yaml
xagent-cli ask <message>	Ask single question (non-streaming)	xagent-cli ask "Hello world"

CLI Options

Option	Description	Default
--config	Configuration file path	config/agent.yaml
--toolkit_path	Custom toolkit directory	toolkit
--user_id	User identifier	Auto-generated
--session_id	Session identifier	Auto-generated
--verbose, -v	Enable verbose logging	False

🤖 Advanced Usage: Agent Class

For more control and customization, use the Agent class directly in your Python code.

Basic Agent Usage

import asyncio
from xagent.core import Agent, Session

async def main():
    # Create agent
    agent = Agent(
        name="my_assistant",
        system_prompt="You are a helpful AI assistant.",
        model="gpt-4.1-mini"
    )

    # Create session for conversation management
    session = Session(session_id="session456")

    # Chat interaction
    response = await agent.chat("Hello, how are you?", session)
    print(response)

    # Streaming response example
    response = await agent.chat("Tell me a story", session, stream=True)
    async for event in response:
        print(event, end="")

asyncio.run(main())

Adding Custom Tools

import asyncio
import time
import httpx
from xagent.utils.tool_decorator import function_tool
from xagent.core import Agent, Session

# Sync tools - automatically converted to async
@function_tool()
def calculate_square(n: int) -> int:
    """Calculate square of a number."""
    time.sleep(0.1)  # Simulate CPU work
    return n * n

# Async tools - used directly for I/O operations
@function_tool()
async def fetch_weather(city: str) -> str:
    """Fetch weather data from API."""
    async with httpx.AsyncClient() as client:
        await asyncio.sleep(0.5)  # Simulate API call
        return f"Weather in {city}: 22°C, Sunny"

async def main():
    # Create agent with custom tools
    agent = Agent(
        tools=[calculate_square, fetch_weather],
        model="gpt-4.1-mini"
    )
    
    session = Session(user_id="user123")
    
    # Agent handles all tools automatically
    response = await agent.chat(
        "Calculate the square of 15 and get weather for Tokyo",
        session
    )
    print(response)

asyncio.run(main())

Structured Outputs with Pydantic

With Pydantic structured outputs, you can:

• Parse and validate an agent’s response into typed data
• Easily extract specific fields
• Ensure the response matches the expected format
• Guarantee type safety in your application
• Reliably chain multi-step tasks using structured data

import asyncio
from pydantic import BaseModel
from xagent.core import Agent, Session
from xagent.tools import web_search

class WeatherReport(BaseModel):
    location: str
    temperature: int
    condition: str
    humidity: int

class Step(BaseModel):
    explanation: str
    output: str

class MathReasoning(BaseModel):
    steps: list[Step]
    final_answer: str


async def get_structured_response():
    
    agent = Agent(model="gpt-4.1-mini", 
                  tools=[web_search], 
                  output_type=WeatherReport) # You can set a default output type here or leave it None
    
    session = Session(user_id="user123")
    
    # Request structured output for weather
    weather_data = await agent.chat(
        "what's the weather like in Hangzhou?",
        session
    )
    
    print(f"Location: {weather_data.location}")
    print(f"Temperature: {weather_data.temperature}°F")
    print(f"Condition: {weather_data.condition}")
    print(f"Humidity: {weather_data.humidity}%")


    # Request structured output for mathematical reasoning (overrides output_type)
    reply = await agent.chat("how can I solve 8x + 7 = -23", session, output_type=MathReasoning) # Override output_type for this call
    for index, step in enumerate(reply.steps):
        print(f"Step {index + 1}: {step.explanation} => Output: {step.output}")
    print("Final Answer:", reply.final_answer)

if __name__ == "__main__":
    asyncio.run(get_structured_response())

Agent as Tool Pattern

import asyncio
from xagent.core import Agent, Session
from xagent.db import MessageDB
from xagent.tools import web_search

async def agent_as_tool_example():
    # Create specialized agents
    researcher_agent = Agent(
        name="research_specialist",
        system_prompt="Research expert. Gather information and provide insights.",
        model="gpt-4.1-mini",
        tools=[web_search]
    )
    
    # Convert agent to tool
    message_db = MessageDB()
    research_tool = researcher_agent.as_tool(
        name="researcher",
        description="Research topics and provide detailed analysis",
        message_db=message_db
    )
    
    # Main coordinator agent with specialist tools
    coordinator = Agent(
        name="coordinator",
        tools=[research_tool],
        system_prompt="Coordination agent that delegates to specialists.",
        model="gpt-4.1"
    )
    
    session = Session(user_id="user123")
    
    # Complex multi-step task
    response = await coordinator.chat(
        "Research renewable energy benefits and write a brief summary",
        session
    )
    print(response)

asyncio.run(agent_as_tool_example())

Persistent Sessions with Redis

import asyncio
from xagent.core import Agent, Session
from xagent.db import MessageDB

async def chat_with_persistence():
    # Initialize Redis-backed message storage
    message_db = MessageDB()
    
    # Create agent
    agent = Agent(
        name="persistent_agent",
        model="gpt-4.1-mini"
    )

    # Create session with Redis persistence
    session = Session(
        user_id="user123", 
        session_id="persistent_session",
        message_db=message_db
    )

    # Chat with automatic message persistence
    response = await agent.chat("Remember this: my favorite color is blue", session)
    print(response)
    
    # Later conversation - context is preserved in Redis
    response = await agent.chat("What's my favorite color?", session)
    print(response)

asyncio.run(chat_with_persistence())

🏗️ Architecture

Modern Design for High Performance

xAgent/
├── 🤖 xagent/                # Core async agent framework
│   ├── __init__.py           # Package initialization and exports
│   ├── __version__.py        # Version information
│   ├── core/                 # Agent and session management
│   │   ├── __init__.py       # Core exports (Agent, Session, HTTPAgentServer)
│   │   ├── agent.py          # Main Agent class with chat
│   │   ├── session.py        # Session management with operations
│   │   ├── server.py         # Standalone HTTP Agent Server
│   │   ├── cli.py            # Command line interface
│   │   └── base.py           # Base classes and utilities
│   ├── db/                   # Database layer (Redis)
│   │   ├── __init__.py       # Database exports
│   │   └── message.py        # Message persistence
│   ├── schemas/              # Data models and types (Pydantic)
│   │   ├── __init__.py       # Schema exports
│   │   └── message.py        # Message and ToolCall models
│   ├── tools/                # Tool ecosystem
│   │   ├── __init__.py       # Tool registry (web_search, draw_image)
│   │   ├── openai_tool.py    # OpenAI tool integrations
│   │   └── mcp_demo/         # MCP demo server and client
│   ├── utils/                # Utility functions
│   ├── multi/                # Multi-agent support
│   │   ├── __init__.py       # Multi-agent exports
│   │   ├── swarm.py          # Agent swarm coordination
│   │   └── workflow.py       # Workflow management
│   └── frontend/             # Web interface components
│       ├── app.py            # Streamlit chat application
│       └── launcher.py       # Web interface launcher
├── 🛠️ toolkit/               # Custom tool ecosystem
│   ├── __init__.py           # Toolkit registry
│   ├── tools.py              # Custom tools (char_count)
│   ├── mcp_server.py         # Main MCP server
├── ⚙️ config/                # Configuration files
│   ├── agent.yaml            # Agent server configuration
│   └── sub_agents_example/   # Sub-agent configuration examples
├── 📝 examples/              # Usage examples and demos
├── 🧪 tests/                 # Comprehensive test suite
├── 📁 logs/                  # Log files

🔄 Core Components

Component	Purpose	Technology
Agent	Core conversation handler	OpenAI API + AsyncIO
Session	Message history management	Redis + Operations
MessageDB	Scalable persistence layer	Redis with client
Tools	Extensible function ecosystem	Auto sync-to-async conversion
MCP	Dynamic tool loading protocol	HTTP client

🔧 Development Guide

🛠️ Creating Tools

Both sync and async functions work seamlessly:

from xagent.utils.tool_decorator import function_tool
import asyncio
import time

# ✅ Sync tool - perfect for CPU-bound operations
@function_tool()
def my_sync_tool(input_text: str) -> str:
    """Process text synchronously (runs in thread pool)."""
    time.sleep(0.1)  # Simulate CPU-intensive work
    return f"Sync processed: {input_text}"

# ✅ Async tool - ideal for I/O-bound operations  
@function_tool()
async def my_async_tool(input_text: str) -> str:
    """Process text asynchronously."""
    await asyncio.sleep(0.1)  # Simulate async I/O operation
    return f"Async processed: {input_text}"

📋 Tool Development Guidelines

Use Case	Tool Type	Example
CPU-bound	Sync functions	Math calculations, data processing
I/O-bound	Async functions	API calls, database queries
Simple operations	Sync functions	String manipulation, file operations
Network requests	Async functions	HTTP requests, WebSocket connections

⚠️ Note: Recursive functions are not supported as tools due to potential stack overflow issues in async environments.

🔄 Automatic Conversion

xAgent's @function_tool() decorator automatically handles sync-to-async conversion:

• Sync functions → Run in thread pool (non-blocking)
• Async functions → Run directly on event loop
• Concurrent execution → All tools execute in parallel when called

📝 Override Defaults

You can override the default tool name and description using the function_tool decorator:

@function_tool(name="custom_square", description="Calculate the square of a number")
def calculate_square(n: int) -> int:
    return n * n

🤖 API Reference

Core Classes

🤖 Agent

Main AI agent class for handling conversations and tool execution.

Agent(
    name: Optional[str] = None,
    system_prompt: Optional[str] = None, 
    model: Optional[str] = None,
    client: Optional[AsyncOpenAI] = None,
    tools: Optional[list] = None,
    mcp_servers: Optional[str | list] = None,
    sub_agents: Optional[List[Union[tuple[str, str, str], 'Agent']]] = None
)

Key Methods:

• async chat(user_message, session, **kwargs) -> str | BaseModel: Main chat interface
• async __call__(user_message, session, **kwargs) -> str | BaseModel: Shorthand for chat
• as_tool(name, description, message_db) -> Callable: Convert agent to tool

Parameters:

• name: Agent identifier (default: "default_agent")
• system_prompt: Instructions for the agent behavior
• model: OpenAI model to use (default: "gpt-4.1-mini")
• client: Custom AsyncOpenAI client instance
• tools: List of function tools
• mcp_servers: MCP server URLs for dynamic tool loading
• sub_agents: List of sub-agent configurations (name, description, server URL)

💬 Session

Manages conversation history and persistence with operations.

Session(
    user_id: str,
    session_id: Optional[str] = None,
    message_db: Optional[MessageDB] = None
)

Key Methods:

• async add_messages(messages: Message | List[Message]) -> None: Store messages
• async get_messages(count: int = 20) -> List[Message]: Retrieve message history
• async clear_session() -> None: Clear conversation history
• async pop_message() -> Optional[Message]: Remove last non-tool message

🗄️ MessageDB

Redis-backed message persistence layer.

# Initialize with environment variables or defaults
message_db = MessageDB()

# Usage with session
session = Session(
    user_id="user123",
    message_db=message_db
)

Important Considerations

Aspect	Details
Tool functions	Can be sync or async (automatic conversion)
Agent interactions	Always use await
Context	Run in context with asyncio.run()
Concurrency	All tools execute in parallel automatically

📊 Monitoring & Observability

xAgent includes comprehensive observability features:

• 🔍 Langfuse Integration - Track AI interactions and performance
• 📝 Structured Logging - Throughout the entire system
• ❤️ Health Checks - API monitoring endpoints
• ⚡ Performance Metrics - Tool execution time and success rates

🤝 Contributing

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

Development Workflow

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

Development Guidelines

Area	Requirements
Code Style	Follow PEP 8 standards
Testing	Add tests for new features
Documentation	Update docs as needed
Type Safety	Use type hints throughout
Commits	Follow conventional commit messages

Package Upload

First time upload

pip install build twine
python -m build
twine upload dist/*

Subsequent uploads

rm -rf dist/ build/ *.egg-info/
python -m build
twine upload dist/*

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Special thanks to the amazing open source projects that make xAgent possible:

• OpenAI - GPT models powering our AI
• FastAPI - Robust async API framework
• Streamlit - Intuitive web interface
• Redis - High-performance data storage
• Langfuse - Observability and monitoring

📞 Support & Community

Resource	Link	Purpose
🐛 Issues	GitHub Issues	Bug reports & feature requests
💬 Discussions	GitHub Discussions	Community chat & Q&A
📧 Email	[e-mail]	Direct support

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