phx-ashborn 0.3.1

Advanced AI Infrastructure SDK for Agentic Applications

Phoenix AI

Advanced AI Infrastructure SDK for Autonomous Agents, Chatbots, and Production Backend Services.

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Whether you are building with FastAPI, Django, or a custom event-driven service, Phoenix AI eliminates repetitive backend setup and provides a highly-optimized orchestration layer for large language models, computer vision, and physical hardware.

Why Phoenix AI?

• Autonomous Agents: Single-line creation of thinking, planning, and executing AI agents.
• High-Level ChatBot: Turnkey conversational AI with native RAG, Vision, and Memory.
• "Everything as a Service": A unified Dependency Injection container handles Vector DBs, Redis caching, and LLMs seamlessly.
• Fail-Loud & Recover: Auto-fallbacks from Local (Ollama/Transformers) to Cloud (OpenAI) to prevent system crashes.
• Native PyTorch & 4-bit Quantization: Run local models directly on your GPU without external servers, automatically optimized for low-VRAM machines.
• Sensorium (Embodied AI): Plug your AI directly into the physical world via IoT, MQTT, and Arduino plugins.

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📦 Installation

Choose the installation tier that matches your project needs:

# Core Backend Framework
# Download Size: ~5.2 MB (Verified)
# Installed Size on Disk: ~15 MB
pip install phx-ashborn

# Core + ChatBot & Conversation Memory
# Download Size: ~35 MB (Estimated)
# Installed Size on Disk: ~90 MB
pip install "phx-ashborn[chatbot]"

# Core + Autonomous Agents & Planners
# Download Size: ~107 MB (Verified)
# Installed Size on Disk: ~200 MB
pip install "phx-ashborn[agent]"

# Full Suite (Everything including Local AI Inference)
# ⚠️ Download Size: ~2.2 GB - 2.5 GB (Estimated)
# ⚠️ Installed Size on Disk: ~5.5 GB - 6.0 GB
pip install "phx-ashborn[full]"

[!NOTE] If you're running locally, copy the .env.example to .env and configure your API keys (e.g., OPENAI_API_KEY). For full system deployment (including Redis setup), use the provided ./install.sh or install.bat scripts.

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Quickstarts & Core Features

1. Autonomous Agents

The Phoenix Agent is a high-speed cognitive engine capable of understanding complex problems, scanning codebases, planning multi-step solutions, and executing parallel tools.

import asyncio
from phoenix import Agent

async def agent_demo():
    # Initialize a high-speed Agent with default tools and memory
    agent = Agent()
    
    # Or inject custom tools instantly!
    from phoenix.framework.agent import tool
    
    @tool(name="custom_math", description="Calculates squares. Input: 'number' (int)")
    def math_tool(number: int):
        return f"The square is {number ** 2}"
        
    agent.register_tool(math_tool)
    
    # Run a complex engineering task
    # The agent will: Think -> Analyze -> Plan -> Execute Tools -> Reflect
    result = await agent.run(
        "Find the redundant code in the memory module and optimize it.", 
        mode="plan"
    )
    
    print(f"Agent Execution Report: {result}")

asyncio.run(agent_demo())

[!TIP] Phoenix Agents feature Intelligent Auto-Routing. By default (mode="auto"), the agent analyzes your prompt to decide whether to give a blazing-fast direct answer or spin up its heavy planning loop for complex operations!

2. Multi-Modal ChatBot

Need a powerful conversational interface without the complexity of building agent loops? The ChatBot builder abstracts away RAG, Vision (VLM), and Session Memory into a fluent API.

from phoenix import ChatBot
import asyncio

async def chatbot_demo():
    # Build a complete AI ChatBot in one line
    bot = (ChatBot(local=False, vlm=True)
           .with_rag(["./docs", "./src"])       # Ingest folders automatically
           .with_memory()                       # Enable conversation history
           .with_security(mode="strict")        # Prompt-injection protection
           .with_system_prompt("You are a helpful Python expert.")
           .build())

    # Multi-modal interaction out of the box
    response = await bot.chat(
        prompt="Explain this architecture diagram.", 
        image_path="architecture.png"
    )
    print(response)

asyncio.run(chatbot_demo())

3. RAG Pipeline (Retrieval-Augmented Generation)

The RAGPipeline handles document extraction, intelligent chunking, and vector storage (ChromaDB/Qdrant) across PDFs, Code files, SQL, APIs, and GitHub repos.

import asyncio
from phoenix import init_phoenix, startup_phoenix, get_rag_pipeline

async def rag_demo():
    # Initialize the core framework services
    init_phoenix()
    await startup_phoenix()
    
    rag = get_rag_pipeline()

    # 1. Ingest local directories (supports .pdf, .docx, .py, .go, etc.)
    await rag.ingest("./my_project")

    # 2. Ingest remote web pages
    await rag.ingest_url("https://example.com/api-docs")
    
    # 3. Clone and index a GitHub repository on the fly
    await rag.ingest_github("https://github.com/blackeagle686/phoenix-ai.git")

    # 4. Query the knowledge base (Automatic Source Citations included!)
    answer = await rag.query("How do I extend the caching layer?")
    print(answer)

asyncio.run(rag_demo())

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🏗️ Advanced Architecture

Multi-Agent Orchestration

Define dynamic teams of specialized agents (e.g., Coder, Reviewer, Security Expert) and coordinate them through parallel broadcasting or sequenced pipelines.

from phoenix.framework import MultiAgentManager, MultiAgentConfig, AgentConfig

config = MultiAgentConfig(
    team_name="Engineering Task Force",
    agents=[
        AgentConfig(name="Giyu_Coder", profile="profiles/coder.json"),
        AgentConfig(name="Shinobu_Reviewer", profile="profiles/reviewer.json")
    ]
)

manager = MultiAgentManager(config)
report = await manager.run_pipeline(
    prompt="Implement a thread-safe cache system",
    agent_sequence=["Giyu_Coder", "Shinobu_Reviewer"]
)

🦾 Sensorium (Hardware SDK)

Connect your Phoenix Agents to the physical world using an async, zero-latency plugin architecture. Build Smart Home routines, Robotics controllers, or Drone surveillance systems!

from phoenix.framework.sensorium.core.manager import DeviceManager
from phoenix.framework.sensorium.plugins.mock_plugin import MockSensorPlugin
from phoenix.framework.agent import tool

manager = DeviceManager()
await manager.add_device("living_room_temp", MockSensorPlugin())

@tool(name="get_temperature", description="Reads current room temp.")
async def read_temp():
    return await manager.get_device("living_room_temp").read()

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⚠️ Local Inference Requirements

If you run Phoenix using Local LLMs/VLMs (via Ollama or native Transformers), ensure your machine meets the following specifications to prevent system instability:

• RAM: 8GB Minimum (16GB+ recommended).
• GPU: 4GB+ VRAM required for Vision/VLM models (utilizing built-in 4-bit quantization).
• Disk Space: 10GB+ free space for model weights.

[!WARNING] High-resource models may cause system crashes on CPU-only devices. The SDK prioritizes stability and will pause to prompt for user confirmation in the terminal before booting large local providers.

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Comprehensive Documentation

Ready to dive deeper? Explore our dedicated guides to master the Phoenix ecosystem:

Core Architecture

• Main Framework Guide (Arabic Version)
• Data Pipelines & RAG (Arabic Version)
• Model Training & Finetuning (Arabic Version)

Autonomous Agents

• Agent Framework Architecture
• Multi-Agent Teams
• The Phoenix Tool Bank
• Custom Specialized Agents

Integrations & Extensions

• Django Integration
• FastAPI / Backend Integration
• GUI App Integration
• Sensorium Embodied AI (Hardware)