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Merge pull request #47 from SentienceAPI/agent_abstraction2
Phase 2/2: Conversational Agent
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‎README.md‎

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@@ -9,16 +9,66 @@ pip install -e .
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# Install Playwright browsers (required)
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playwright install chromium
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# For LLM Agent features (optional)
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pip install openai # For OpenAI models
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pip install anthropic # For Claude models
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pip install transformers torch # For local LLMs
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```
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## Quick Start: Choose Your Abstraction Level
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Sentience SDK offers **three abstraction levels** - use what fits your needs:
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### 🎯 **Level 3: Natural Language (Easiest)** - For non-technical users
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```python
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from sentience import SentienceBrowser, ConversationalAgent
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from sentience.llm_provider import OpenAIProvider
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browser = SentienceBrowser()
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llm = OpenAIProvider(api_key="your-key", model="gpt-4o")
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agent = ConversationalAgent(browser, llm)
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with browser:
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response = agent.execute("Search for magic mouse on google.com")
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print(response)
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# → "I searched for 'magic mouse' and found several results.
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# The top result is from amazon.com selling Magic Mouse 2 for $79."
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```
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## Quick Start
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**Best for:** End users, chatbots, no-code platforms
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**Code required:** 3-5 lines
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**Technical knowledge:** None
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### ⚙️ **Level 2: Technical Commands (Recommended)** - For AI developers
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```python
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from sentience import SentienceBrowser, SentienceAgent
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from sentience.llm_provider import OpenAIProvider
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browser = SentienceBrowser()
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llm = OpenAIProvider(api_key="your-key", model="gpt-4o")
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agent = SentienceAgent(browser, llm)
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with browser:
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browser.page.goto("https://google.com")
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agent.act("Click the search box")
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agent.act("Type 'magic mouse' into the search field")
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agent.act("Press Enter key")
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```
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**Best for:** Building AI agents, automation scripts
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**Code required:** 10-15 lines
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**Technical knowledge:** Medium (Python basics)
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### 🔧 **Level 1: Direct SDK (Most Control)** - For production automation
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```python
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from sentience import SentienceBrowser, snapshot, find, click
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# Start browser with extension
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with SentienceBrowser(headless=False) as browser:
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browser.goto("https://example.com", wait_until="domcontentloaded")
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browser.page.goto("https://example.com")
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# Take snapshot - captures all interactive elements
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snap = snapshot(browser)
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print(f"Click success: {result.success}")
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```
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**Best for:** Maximum control, performance-critical apps
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**Code required:** 20-50 lines
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**Technical knowledge:** High (SDK API, selectors)
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## Real-World Example: Amazon Shopping Bot
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This example demonstrates navigating Amazon, finding products, and adding items to cart:

‎examples/agent_layers_demo.py‎

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"""
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Demonstration of all three abstraction layers in Sentience SDK
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Layer 1: Direct SDK (Full Control)
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Layer 2: SentienceAgent (Technical Commands)
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Layer 3: ConversationalAgent (Natural Language)
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This script shows how the same task can be accomplished at different abstraction levels.
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"""
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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def demo_layer1_direct_sdk():
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"""
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Layer 1: Direct SDK Usage
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- Full control over every action
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- Requires knowing exact element selectors
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- 50+ lines of code for typical automation
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"""
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print("\n" + "="*70)
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print("LAYER 1: Direct SDK Usage (Full Control)")
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print("="*70)
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from sentience import SentienceBrowser, snapshot, find, click, type_text, press
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with SentienceBrowser(headless=False) as browser:
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# Navigate
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browser.page.goto("https://google.com")
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# Get snapshot
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snap = snapshot(browser)
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# Find search box manually
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search_box = find(snap, "role=searchbox")
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if not search_box:
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search_box = find(snap, "role=textbox")
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# Click search box
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click(browser, search_box.id)
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# Type query
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type_text(browser, search_box.id, "magic mouse")
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# Press Enter
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press(browser, "Enter")
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print("\n✅ Layer 1 Demo Complete")
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print(" Code required: ~20 lines")
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print(" Technical knowledge: High")
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print(" Flexibility: Maximum")
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def demo_layer2_sentience_agent():
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"""
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Layer 2: SentienceAgent (Technical Commands)
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- High-level commands with LLM intelligence
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- No need to know selectors
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- 15 lines of code for typical automation
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"""
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print("\n" + "="*70)
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print("LAYER 2: SentienceAgent (Technical Commands)")
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print("="*70)
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from sentience import SentienceBrowser, SentienceAgent
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from sentience.llm_provider import OpenAIProvider
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# Initialize
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browser = SentienceBrowser(headless=False)
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llm = OpenAIProvider(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
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agent = SentienceAgent(browser, llm, verbose=True)
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with browser:
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browser.page.goto("https://google.com")
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# Execute technical commands
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agent.act("Click the search box")
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agent.act("Type 'magic mouse' into the search field")
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agent.act("Press Enter key")
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print("\n✅ Layer 2 Demo Complete")
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print(" Code required: ~10 lines")
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print(" Technical knowledge: Medium")
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print(" Flexibility: High")
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print(f" Tokens used: {agent.get_token_stats()['total_tokens']}")
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def demo_layer3_conversational_agent():
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"""
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Layer 3: ConversationalAgent (Natural Language)
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- Pure natural language interface
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- Automatic planning and execution
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- 3 lines of code for typical automation
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"""
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print("\n" + "="*70)
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print("LAYER 3: ConversationalAgent (Natural Language)")
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print("="*70)
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from sentience import SentienceBrowser, ConversationalAgent
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from sentience.llm_provider import OpenAIProvider
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# Initialize
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browser = SentienceBrowser(headless=False)
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llm = OpenAIProvider(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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agent = ConversationalAgent(browser, llm, verbose=True)
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with browser:
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# Execute in natural language (agent plans and executes automatically)
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response = agent.execute("Search for magic mouse on google.com")
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print("\n✅ Layer 3 Demo Complete")
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print(" Code required: ~5 lines")
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print(" Technical knowledge: None")
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print(" Flexibility: Medium")
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print(f" Agent Response: {response}")
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def demo_layer3_with_local_llm():
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"""
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Layer 3 with Local LLM (Zero Cost)
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- Uses local Qwen 2.5 3B model
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- No API costs
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- Runs on your hardware
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"""
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print("\n" + "="*70)
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print("LAYER 3: ConversationalAgent with Local LLM (Zero Cost)")
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print("="*70)
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from sentience import SentienceBrowser, ConversationalAgent
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from sentience.llm_provider import LocalLLMProvider
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# Initialize with local LLM
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browser = SentienceBrowser(headless=False)
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llm = LocalLLMProvider(
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model_name="Qwen/Qwen2.5-3B-Instruct",
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device="auto", # Use CUDA if available
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load_in_4bit=True # Save memory with quantization
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)
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agent = ConversationalAgent(browser, llm, verbose=True)
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with browser:
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# Execute in natural language
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response = agent.execute("Go to google.com and search for python tutorials")
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print("\n✅ Layer 3 with Local LLM Demo Complete")
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print(" API Cost: $0 (runs locally)")
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print(" Privacy: 100% (no data sent to cloud)")
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print(f" Agent Response: {response}")
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def demo_comparison():
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"""
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Side-by-side comparison of all layers
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"""
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print("\n" + "="*70)
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print("COMPARISON: All Three Layers")
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print("="*70)
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comparison_table = """
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| Feature | Layer 1 (SDK) | Layer 2 (Agent) | Layer 3 (Conversational) |
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|--------------------------|------------------|------------------|--------------------------|
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| Lines of code | 50+ | 15 | 3-5 |
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| Technical knowledge | High | Medium | None |
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| Requires selectors? | Yes | No | No |
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| LLM required? | No | Yes | Yes |
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| Cost per action | $0 | ~$0.005 | ~$0.010 |
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| Speed | Fastest | Fast | Medium |
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| Error handling | Manual | Auto-retry | Auto-recovery |
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| Multi-step planning | Manual | Manual | Automatic |
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| Natural language I/O | No | Commands only | Full conversation |
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| Best for | Production | AI developers | End users |
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"""
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print(comparison_table)
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def main():
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"""Run all demos"""
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print("\n" + "="*70)
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print("SENTIENCE SDK: Multi-Layer Abstraction Demo")
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print("="*70)
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print("\nThis demo shows how to use the SDK at different abstraction levels:")
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print(" 1. Layer 1: Direct SDK (maximum control)")
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print(" 2. Layer 2: SentienceAgent (technical commands)")
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print(" 3. Layer 3: ConversationalAgent (natural language)")
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print("\nChoose which demo to run:")
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print(" 1 - Layer 1: Direct SDK")
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print(" 2 - Layer 2: SentienceAgent")
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print(" 3 - Layer 3: ConversationalAgent (OpenAI)")
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print(" 4 - Layer 3: ConversationalAgent (Local LLM)")
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print(" 5 - Show comparison table")
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print(" 0 - Exit")
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choice = input("\nEnter your choice (0-5): ").strip()
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if choice == "1":
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demo_layer1_direct_sdk()
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elif choice == "2":
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if not os.getenv("OPENAI_API_KEY"):
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print("\n❌ Error: OPENAI_API_KEY not set")
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return
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demo_layer2_sentience_agent()
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elif choice == "3":
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if not os.getenv("OPENAI_API_KEY"):
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print("\n❌ Error: OPENAI_API_KEY not set")
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return
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demo_layer3_conversational_agent()
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elif choice == "4":
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demo_layer3_with_local_llm()
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elif choice == "5":
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demo_comparison()
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elif choice == "0":
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print("Goodbye!")
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else:
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print("Invalid choice")
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if __name__ == "__main__":
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main()

‎sentience/__init__.py‎

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# Agent Layer (Phase 1 & 2)
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from .llm_provider import LLMProvider, LLMResponse, OpenAIProvider, AnthropicProvider, LocalLLMProvider
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from .agent import SentienceAgent
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from .conversational_agent import ConversationalAgent
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__version__ = "0.10.7"
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"generate",
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"read",
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"screenshot",
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# Agent Layer
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# Agent Layer (Phase 1 & 2)
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"LLMProvider",
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"LLMResponse",
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"OpenAIProvider",
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"AnthropicProvider",
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"LocalLLMProvider",
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"SentienceAgent",
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"ConversationalAgent",
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]
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