Deploy None agent

.env.example

@@ -0,0 +1,7 @@
+# Agent Configuration
+AGENT_CONFIG='{"status": "success", "agent_id": "agent_education_mathmaster_1777734815", "agent_name": "MathMate", "model": "qwen2.5-coder", "tools": [{"name": "web_search", "description": "Perform a deep web search using Tavily and return structured results."}], "tools_count": 1, "tone": "professional", "instructions": "You are the MASTER Education Technology expert for MathMaster with comprehensive knowledge across ALL operational domains.\n\n**Your Identity:**\nName: MathMate\nDomain: Education\nSpecialization: Education Technology\n\n**Your Expertise Covers:**\n- Solving math equations\n- Explain concepts\n- Generate practice exercises\n\n**The Problem You Solve:**\nHelping students with math problems and understanding concepts\n\n**Your Solution Approach:**\nProviding instant solutions to math questions, explanations, and practice exercises\n\n**Response Aesthetics & Persona (CRITICAL):**\n1. **Premium Presentation**: Your responses must be visually stunning and professional, similar to high-end AI assistants like Claude or Gemini. Use Markdown extensively.\n2. **Structural Clarity**: Use clear headers (H2, H3), bold text for emphasis, and bullet points for readability. Avoid walls of text.\n3. **Conversational yet Expert**: Maintain a professional, billionaire-consultant tone. Be helpful, proactive, and deeply insightful.\n4. **Rich Formatting**: Use tables for data, blockquotes for key insights, and code blocks for technical details or drafts.\n\n**Response Structure Guidelines:**\n- **Executive Summary**: Start with a concise, high-impact summary or direct answer.\n- **Deep Dive**: Provide a detailed, logical explanation of your reasoning and the context.\n- **Actionable Roadmap**: Outline clear, specific steps with timelines and ownership.\n- **Expert Insights**: Include best practices, hidden risks, and strategic advice.\n- **Next Horizon**: Suggest future improvements or escalation paths.\n\n**Email & Outreach Strategy:**\n1. **Direct Action**: When asked to contact someone or send an email, use the `send_email` tool immediately. Do NOT just draft it unless specifically asked to only provide a draft.\n2. **Personalization First**: Always use `web_search` to find relevant details about prospects to make outreach high-converting.\n3. **Follow-up Mastery**: Proactively suggest follow-up schedules and multi-touch sequences.\n\n**Operational Excellence:**\n1. **Always Use Tools First**: Fetch real-time data before answering.\n2. **Be Specific**: Provide exact numbers, dates, and names.\n3. **Tool Synergy**: Combine multiple tools (e.g., `web_search` + `send_email`) to deliver \"one-click\" value.\n4. **Graceful Failures**: If a tool fails, explain why and offer an alternative path.\n\n**Objective:**\nDeliver elite-level Education Technology assistance that WOWS the user with its depth, speed, and beautiful presentation.\n", "test_response": "🎉 MathMate is ready!\n\n✅ Domain: EDUCATION\n🔧 Tools: 1 specialized education tools\n🎯 Business: MathMaster\n🏭 Industry: Education Technology\n\n**Capabilities:**\n  • Solving math equations\n  • Explain concepts\n  • Generate practice exercises\n\n**Available Tools:**\n  • web_search: Perform a deep web search using Tavily and return structured results.\n\n\nYour agent is fully configured and ready to assist with MathMaster's education operations!", "deployment_code": "# MathMate - Education Agent\n# Local Ollama (Qwen) ke liye ready code\n\nfrom langchain_ollama import ChatOllama\nfrom langchain_core.messages import SystemMessage, HumanMessage\n\n# Local Qwen Model\nllm = ChatOllama(\n    model=\"qwen2.5-coder:7b\",   # ya :3b\n    temperature=0.7,\n    num_ctx=4096,\n)\n\nprint(\"✅ MathMate is ready to run with Local Qwen!\")\n\n# Example usage:\nasync def run_agent(user_message: str):\n    messages = [\n        SystemMessage(content=\"You are the MASTER Education Technology expert for MathMaster with comprehensive knowledge across ALL operational domains.\n\n**Your Identity:**\nName: MathMate\nDomain: Education\nSpecialization: Education Technology\n\n**Your Expertise Covers:**\n- Solving math equations\n- Explain concepts\n- Generate practice exercises\n\n**The Problem You Solve:**\nHelping students with math problems and understanding concepts\n\n**Your Solution Approach:**\nProviding instant solutions to math questions, explanations, and practice exercises\n\n**Response Aesthetics & Persona (CRITICAL):**\n1. **Premium Presentation**: Your responses must be visually stunning and professional, similar to high-end AI assistants like Claude or Gemini. Use Markdown extensively.\n2. **Structural Clarity**: Use clear headers (H2, H3), bold text for emphasis, and bullet points for readability. Avoid walls of text.\n3. **Conversational yet Expert**: Maintain a professional, billionaire-consultant tone. Be helpful, proactive, and deeply insightful.\n4. **Rich Formatting**: Use tables for data, blockquotes for key insights, and code blocks for technical details or drafts.\n\n**Response Structure Guidelines:**\n- **Executive Summary**: Start with a concise, high-impact summary or direct answer.\n- **Deep Dive**: Provide a detailed, logical explanation of your reasoning and the context.\n- **Actionable Roadmap**: Outline clear, specific steps with timelines and ownership.\n- **Expert Insights**: Include best practices, hidden risks, and strategic advice.\n- **Next Horizon**: Suggest future improvements or escalation paths.\n\n**Email & Outreach Strategy:**\n1. **Direct Action**: When asked to contact someone or send an email, use the `send_email` tool immediately. Do NOT just draft it unless specifically asked to only provide a draft.\n2. **Personalization First**: Always use `web_search` to find relevant details about prospects to make outreach high-converting.\n3. **Follow-up Mastery**: Proactively suggest follow-up schedules and multi-touch sequences.\n\n**Operational Excellence:**\n1. **Always Use Tools First**: Fetch real-time data before answering.\n2. **Be Specific**: Provide exact numbers, dates, and names.\n3. **Tool Synergy**: Combine multiple tools (e.g., `web_search` + `send_email`) to deliver \"one-click\" value.\n4. **Graceful Failures**: If a tool fails, explain why and offer an alternative path.\n\n**Objective:**\nDeliver elite-level Education Technology assistance that WOWS the user with its depth, speed, and beautiful presentation.\n\"),\n        HumanMessage(content=user_message)\n    ]\n    response = await llm.ainvoke(messages)\n    return response.content\n", "business_context": {"business_name": "MathMaster", "industry": "Education Technology", "domain": "education", "capabilities": ["Solving math equations", "Explain concepts", "Generate practice exercises"]}, "metadata": {"session_id": "0e008ff5-87a5-4864-ac17-0b65a2d06064", "domain": "education", "tools_count": 1, "business_name": "MathMaster", "industry": "Education Technology", "capabilities": ["Solving math equations", "Explain concepts", "Generate practice exercises"], "created_at": "2026-05-02T15:13:35.378624", "agent_creation_note": "Agent instance creation deferred (will be created on-demand when needed to avoid schema validation issues)"}, "deployment_ready": true, "domain": "education", "created_at": "2026-05-02T15:13:35.378624"}'
+
+# API Keys
+OPENAI_API_KEY=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
+GEMINI_API_KEY=AIzaSyBKcrXIRS1YSxBsmDvYEFpPp_0-YO7VZ2o
+GROK_API_KEY=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

Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY main.py .
+
+# Expose port 7860 (required by Hugging Face Spaces)
+EXPOSE 7860
+
+# Run the FastAPI app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,71 @@
 ---
-title: Agent Education Mathmaster
-emoji: 🏢
-colorFrom: pink
-colorTo: gray
+title: AI Agent
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
 sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Agent
+
+**Deployed by AgentForge** 🚀
+
+## About
+
+This is an AI agent for **MathMaster** in the **education** domain.
+
+## Features
+
+- **Domain**: Education
+- **Model**: qwen2.5-coder
+- **Tools**: 1 specialized tools
+
+### Available Tools
+
+- **web_search**: Perform a deep web search using Tavily and return structured results.
+
+
+## Usage
+
+### API Endpoint
+
+**POST** `/run`
+
+```json
+{
+  "message": "Your question here",
+  "session_id": "optional-session-id"
+}
+```
+
+### Example
+
+```bash
+curl -X POST "https://huggingface.co/spaces/YOUR_USERNAME/agent-education-mathmaster/run" \
+  -H "Content-Type: application/json" \
+  -d '{"message": "Hello, how can you help me?"}'
+```
+
+### Response
+
+```json
+{
+  "status": "success",
+  "agent_name": "AI Agent",
+  "user_message": "Hello, how can you help me?",
+  "agent_response": "...",
+  "tools_available": [...],
+  "timestamp": 1234567890.0
+}
+```
+
+## Other Endpoints
+
+- `GET /` - Agent info and health check
+- `GET /config` - Agent configuration
+- `GET /health` - Health check
+
+## Powered by AgentForge
+
+Built with [AgentForge](https://agentforge.ai) - The fastest way to build and deploy AI agents.

main.py

@@ -0,0 +1,447 @@
+"""
+AgentForge - Hugging Face Space Template
+This is a generic, reusable agent runner that reads configuration from environment variables.
+"""
+import os
+import json
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from typing import Optional, List, Dict, Any
+from agents import Agent, AsyncOpenAI as AgentsAsyncOpenAI, OpenAIChatCompletionsModel, function_tool, Runner, SQLiteSession
+import aiosmtplib
+from email.message import EmailMessage
+# ============================================
+# Load Agent Configuration from Environment
+# ============================================
+AGENT_CONFIG_STR = os.getenv("AGENT_CONFIG")
+if not AGENT_CONFIG_STR:
+    raise ValueError("AGENT_CONFIG environment variable is required")
+
+# Parse the config - handle both nested and flat structures
+try:
+    raw_config = json.loads(AGENT_CONFIG_STR)
+except json.JSONDecodeError as e:
+    raise ValueError(f"Failed to parse AGENT_CONFIG as JSON: {e}")
+
+# Handle nested structure (from full API response)
+if isinstance(raw_config, dict):
+    # Check if it's the full response structure with result.agent_build
+    if "result" in raw_config and "agent_build" in raw_config.get("result", {}):
+        AGENT_CONFIG = raw_config["result"]["agent_build"]
+    # Check if it's nested under a different key
+    elif "agent_build" in raw_config:
+        AGENT_CONFIG = raw_config["agent_build"]
+    # Otherwise assume it's already the flat agent_build structure
+    else:
+        AGENT_CONFIG = raw_config
+else:
+    AGENT_CONFIG = raw_config
+
+# Validate that we have the required fields
+if not isinstance(AGENT_CONFIG, dict):
+    raise ValueError(f"AGENT_CONFIG must be a dictionary, got {type(AGENT_CONFIG)}")
+
+# Log config keys for debugging (in production, this helps identify issues)
+print(f"Loaded AGENT_CONFIG with keys: {list(AGENT_CONFIG.keys())[:10]}...")  # Print first 10 keys
+
+# API Keys from environment
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+GROK_API_KEY = os.getenv("GROK_API_KEY")
+
+# ============================================
+# FastAPI App Setup
+# ============================================
+app = FastAPI(
+    title=f"{AGENT_CONFIG.get('name', 'Agent')} API",
+    description=f"Deployed agent for {AGENT_CONFIG.get('business_context', {}).get('business_name', 'Business')}",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ============================================
+# Request/Response Models
+# ============================================
+class ChatRequest(BaseModel):
+    message: str = Field(..., description="User message to the agent")
+    session_id: Optional[str] = Field(default="default", description="Session ID for conversation tracking")
+
+class ChatResponse(BaseModel):
+    status: str
+    agent_name: Optional[str] = None  # May be missing in config
+    user_message: str
+    agent_response: str
+    tools_available: List[str]
+    timestamp: float
+
+# ============================================
+# Dynamic Tool Recreation
+# ============================================
+def recreate_tools_from_config(domain: str, business_name: str):
+    """
+    Recreate tools based on domain.
+    This mirrors the DynamicToolFactory logic from agent_architect.py
+    """
+    
+    if domain == "pharmacy":
+        @function_tool
+        async def manage_prescription(action: str, prescription_id: str = None, patient_id: str = None, medication: str = None) -> dict:
+            """Manage prescriptions - check, refill, or create"""
+            from datetime import datetime
+            return {"prescription_id": prescription_id or f"RX-{datetime.now().strftime('%Y%m%d%H%M')}", 
+                    "action": action, "status": "Processed", "refills": 3}
+        
+        @function_tool
+        async def check_drug_inventory(medication_name: str) -> dict:
+            """Check medication stock and expiry"""
+            return {"medication": medication_name, "in_stock": True, "quantity": 250, "expiry": "2026-06-15"}
+        
+        @function_tool
+        async def get_patient_info(patient_id: str) -> dict:
+            """Retrieve patient records and allergies"""
+            return {"patient_id": patient_id, "allergies": ["Penicillin"], "medications": ["Metformin"]}
+        
+        @function_tool
+        def web_search(query: str) -> dict:
+            """Perform a web search for current information"""
+            return {"query": query, "results": "Web search functionality - integrate with real API"}
+        
+        return [manage_prescription, check_drug_inventory, get_patient_info, web_search]
+    
+    elif domain == "ecommerce":
+        @function_tool
+        async def search_products(query: str, category: str = None) -> dict:
+            """Search product catalog"""
+            return {"query": query, "results": [{"id": "P001", "name": query, "price": 49.99, "stock": 50}]}
+        
+        @function_tool
+        async def track_order(order_id: str) -> dict:
+            """Track order status and delivery"""
+            return {"order_id": order_id, "status": "In Transit", "eta": "2025-11-20", "location": "Distribution Center"}
+        
+        @function_tool
+        async def manage_cart(action: str, product_id: str = None, quantity: int = 1) -> dict:
+            """Add, remove, or view cart items"""
+            return {"action": action, "product_id": product_id, "cart_total": 149.99, "items": 3}
+        
+        @function_tool
+        def web_search(query: str) -> dict:
+            """Perform a web search for current information"""
+            return {"query": query, "results": "Web search functionality"}
+        
+        return [search_products, track_order, manage_cart, web_search]
+    
+    elif domain == "weather":
+        @function_tool
+        async def get_forecast(location: str, days: int = 7) -> dict:
+            """Get weather forecast"""
+            return {"location": location, "days": days, "forecast": [{"date": "2025-12-12", "high": 22, "low": 15, "condition": "partly cloudy"}]}
+        
+        @function_tool
+        async def severe_weather_alert(location: str) -> dict:
+            """Check for severe weather alerts"""
+            return {"location": location, "alerts": [], "severity": "none", "preparedness_tips": ["Normal precautions"]}
+        
+        @function_tool
+        async def historical_weather_comparison(location: str, date: str) -> dict:
+            """Compare current weather to historical data"""
+            return {"location": location, "date": date, "current_temp": 20, "historical_avg": 18, "difference": 2, "percentile": 65}
+        
+        @function_tool
+        def web_search(query: str) -> dict:
+            """Perform a web search for current information"""
+            return {"query": query, "results": "Web search functionality"}
+        
+        return [get_forecast, severe_weather_alert, historical_weather_comparison, web_search]
+    
+    elif domain == "email_marketing":
+        @function_tool
+        async def draft_cold_email(recipient_name: str, company: str, pain_point: str, solution_offer: str) -> dict:
+            """Draft a personalized cold email based on research and pain points"""
+            return {
+                "subject": f"Question regarding {company}'s {pain_point} strategy",
+                "body": f"Hi {recipient_name},\n\nI noticed {company} might be facing challenges with {pain_point}. Our solution for {solution_offer} has helped similar companies...\n\nBest regards,\nAgent",
+                "status": "drafted",
+                "quality_score": 0.95
+            }
+
+        @function_tool
+        async def verify_email_format(email: str) -> dict:
+            """Verify if an email address is valid and formatted correctly"""
+            is_valid = "@" in email and "." in email.split("@")[-1]
+            return {"email": email, "is_valid": is_valid, "suggestion": None if is_valid else "Check format"}
+
+        @function_tool
+        async def send_email(to: str, subject: str, body: str, is_html: bool = True) -> dict:
+            """Actually send an email using SMTP configurations from environment."""
+            host = os.getenv("SMTP_HOST")
+            port = int(os.getenv("SMTP_PORT", "587"))
+            username = os.getenv("SMTP_USER")
+            password = os.getenv("SMTP_PASSWORD")
+            from_email = os.getenv("SMTP_FROM_EMAIL", username)
+
+            if not all([host, username, password]):
+                return {
+                    "status": "error",
+                    "message": "SMTP credentials (SMTP_HOST, SMTP_USER, SMTP_PASSWORD) are not configured in environment."
+                }
+
+            message = EmailMessage()
+            message["From"] = from_email
+            message["To"] = to
+            message["Subject"] = subject
+            if is_html:
+                message.set_content(body, subtype="html")
+            else:
+                message.set_content(body)
+
+            try:
+                await aiosmtplib.send(
+                    message,
+                    hostname=host,
+                    port=port,
+                    username=username,
+                    password=password,
+                    use_tls=(port == 465),
+                    start_tls=(port == 587),
+                )
+                return {"to": to, "subject": subject, "status": "sent", "timestamp": "2024-02-09T12:00:00"}
+            except Exception as e:
+                return {"status": "error", "message": str(e)}
+
+        @function_tool
+        def web_search(query: str) -> dict:
+            """Perform a web search for prospect research"""
+            return {"query": query, "results": f"Research data for {query}"}
+        
+        return [draft_cold_email, verify_email_format, send_email, web_search]
+
+    # Add more domains as needed...
+    else:  # generic
+        @function_tool
+        async def generate_analytics(metric: str, time_range: str) -> dict:
+            """Generate business analytics"""
+            return {"metric": metric, "time_range": time_range, "value": 12500, "trend": "+15%", "insights": f"{metric} growing"}
+        
+        @function_tool
+        async def send_notification(recipient: str, message: str, channel: str = "email") -> dict:
+            """Send notifications"""
+            if channel == "email":
+                host = os.getenv("SMTP_HOST")
+                if host:
+                    # Implementation similar to send_email
+                    return {"recipient": recipient, "message": "Notification sent via actual email", "status": "Sent"}
+            return {"recipient": recipient, "message": message, "channel": channel, "status": "Sent"}
+
+        @function_tool
+        async def send_email(to: str, subject: str, body: str, is_html: bool = True) -> dict:
+            """Actually send an email using SMTP configurations from environment."""
+            host = os.getenv("SMTP_HOST")
+            port = int(os.getenv("SMTP_PORT", "587"))
+            username = os.getenv("SMTP_USER")
+            password = os.getenv("SMTP_PASSWORD")
+            from_email = os.getenv("SMTP_FROM_EMAIL", username)
+
+            if not all([host, username, password]):
+                return {
+                    "status": "error",
+                    "message": "SMTP credentials (SMTP_HOST, SMTP_USER, SMTP_PASSWORD) are not configured in environment."
+                }
+
+            message = EmailMessage()
+            message["From"] = from_email
+            message["To"] = to
+            message["Subject"] = subject
+            if is_html:
+                message.set_content(body, subtype="html")
+            else:
+                message.set_content(body)
+
+            try:
+                await aiosmtplib.send(
+                    message,
+                    hostname=host,
+                    port=port,
+                    username=username,
+                    password=password,
+                    use_tls=(port == 465),
+                    start_tls=(port == 587),
+                )
+                return {"to": to, "subject": subject, "status": "sent", "timestamp": "2024-02-09T12:00:00"}
+            except Exception as e:
+                return {"status": "error", "message": str(e)}
+        
+        @function_tool
+        def web_search(query: str) -> dict:
+            """Perform a web search for current information"""
+            return {"query": query, "results": "Web search functionality"}
+        
+        return [generate_analytics, send_notification, send_email, web_search]
+
+# ============================================
+# Initialize Agent
+# ============================================
+def initialize_agent():
+    """Initialize the agent with configuration from environment"""
+    model = AGENT_CONFIG.get("model", "gpt-4o")
+    
+    # Select appropriate API key and client
+    if "gemini" in model.lower():
+        api_key = GEMINI_API_KEY
+        client = AgentsAsyncOpenAI(api_key=api_key, base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
+        model_name = "gemini-2.0-flash-exp"
+    elif "grok" in model.lower():
+        api_key = GROK_API_KEY
+        client = AgentsAsyncOpenAI(api_key=api_key, base_url="https://api.x.ai/v1")
+        model_name = "grok-beta"
+    else:
+        api_key = OPENAI_API_KEY
+        client = AgentsAsyncOpenAI(api_key=api_key)
+        model_name = "gpt-4o"
+    
+    if not api_key:
+        raise ValueError(f"API key not found for model: {model}")
+    
+    MODEL = OpenAIChatCompletionsModel(model=model_name, openai_client=client)
+    
+    # Recreate tools - handle both nested and flat business_context
+    business_context = AGENT_CONFIG.get("business_context", {})
+    if not isinstance(business_context, dict):
+        business_context = {}
+    
+    domain = business_context.get("domain") or AGENT_CONFIG.get("domain", "generic")
+    business_name = business_context.get("business_name") or AGENT_CONFIG.get("business_name", "Business")
+    tools = recreate_tools_from_config(domain, business_name)
+    
+    # Get agent name - try multiple possible keys
+    agent_name = AGENT_CONFIG.get("name") or AGENT_CONFIG.get("agent_name", "AI Agent")
+    
+    # Get instructions
+    instructions = AGENT_CONFIG.get("instructions", "You are a helpful AI assistant.")
+    
+    # Create agent
+    agent = Agent(
+        name=agent_name,
+        instructions=instructions,
+        model=MODEL,
+        tools=tools
+    )
+    
+    return agent, tools
+
+# Initialize agent on startup
+AGENT_INSTANCE, AGENT_TOOLS = initialize_agent()
+
+# ============================================
+# API Endpoints
+# ============================================
+@app.get("/")
+async def root():
+    """Health check and agent info"""
+    # Extract tool names properly
+    tool_names = []
+    for tool in AGENT_TOOLS:
+        if hasattr(tool, '__name__'):
+            tool_names.append(tool.__name__)
+        elif hasattr(tool, 'name'):
+            tool_names.append(tool.name)
+        else:
+            # Try to extract from string representation
+            tool_str = str(tool)
+            if "name='" in tool_str:
+                try:
+                    name_start = tool_str.index("name='") + 6
+                    name_end = tool_str.index("'", name_start)
+                    tool_names.append(tool_str[name_start:name_end])
+                except:
+                    tool_names.append(str(tool)[:50])  # Truncate long strings
+            else:
+                tool_names.append(str(tool)[:50])
+    
+    return {
+        "status": "online",
+        "agent_name": AGENT_CONFIG.get("name") or AGENT_CONFIG.get("agent_name") or "GenericAgent",
+        "agent_id": AGENT_CONFIG.get("agent_id"),
+        "business": AGENT_CONFIG.get("business_context", {}).get("business_name") if isinstance(AGENT_CONFIG.get("business_context"), dict) else None,
+        "domain": AGENT_CONFIG.get("business_context", {}).get("domain") if isinstance(AGENT_CONFIG.get("business_context"), dict) else AGENT_CONFIG.get("domain"),
+        "tools_count": len(AGENT_TOOLS),
+        "tools": tool_names,
+        "model": AGENT_CONFIG.get("model"),
+        "deployment": "Hugging Face Space"
+    }
+
+@app.post("/run", response_model=ChatResponse)
+async def run_agent(request: ChatRequest) -> ChatResponse:
+    """
+    Main endpoint to interact with the agent.
+    This is the primary interface for users.
+    """
+    import time
+    
+    try:
+        # Run the agent
+        runner = Runner()
+        temp_session = SQLiteSession(":memory:")
+        
+        response = await runner.run(AGENT_INSTANCE, request.message, session=temp_session)
+        final_output = str(response.final_output) if hasattr(response, 'final_output') else str(response)
+        
+        return ChatResponse(
+            status="success",
+            agent_name=AGENT_CONFIG.get("name", "GenericAgent"),
+            user_message=request.message,
+            agent_response=final_output,
+            tools_available=[tool.__name__ if hasattr(tool, '__name__') else str(tool) for tool in AGENT_TOOLS],
+            timestamp=time.time()
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Agent execution error: {str(e)}")
+
+@app.get("/config")
+async def get_config():
+    """Get agent configuration (without sensitive data)"""
+    safe_config = {
+        "agent_id": AGENT_CONFIG.get("agent_id"),
+        "name": AGENT_CONFIG.get("name"),
+        "model": AGENT_CONFIG.get("model"),
+        "business_context": AGENT_CONFIG.get("business_context"),
+        "tools_count": len(AGENT_TOOLS),
+        "deployment_ready": AGENT_CONFIG.get("deployment_ready")
+    }
+    return safe_config
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {"status": "healthy", "agent": AGENT_CONFIG.get("name") or AGENT_CONFIG.get("agent_name")}
+
+@app.get("/debug/config")
+async def debug_config():
+    """Debug endpoint to see what config is loaded (without sensitive data)"""
+    safe_config = {
+        "has_config": bool(AGENT_CONFIG),
+        "config_keys": list(AGENT_CONFIG.keys()) if isinstance(AGENT_CONFIG, dict) else [],
+        "agent_name": AGENT_CONFIG.get("name") or AGENT_CONFIG.get("agent_name"),
+        "agent_id": AGENT_CONFIG.get("agent_id"),
+        "model": AGENT_CONFIG.get("model"),
+        "has_business_context": "business_context" in AGENT_CONFIG,
+        "business_context_type": type(AGENT_CONFIG.get("business_context")).__name__,
+        "domain": AGENT_CONFIG.get("business_context", {}).get("domain") if isinstance(AGENT_CONFIG.get("business_context"), dict) else AGENT_CONFIG.get("domain"),
+        "business_name": AGENT_CONFIG.get("business_context", {}).get("business_name") if isinstance(AGENT_CONFIG.get("business_context"), dict) else None,
+        "tools_count_from_config": len(AGENT_CONFIG.get("tools", [])),
+        "tools_count_loaded": len(AGENT_TOOLS),
+    }
+    return safe_config
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,7 @@
+fastapi>=0.110.0
+uvicorn[standard]==0.24.0
+pydantic>=2.12.3,<3
+python-dotenv==1.0.0
+anyio>=4.5
+openai-agents==0.6.4
+aiosmtplib>=3.0.1