pip install praisonai agent-recipes praisonai-tools

import os
os.environ['OPENAI_API_KEY'] = 'your-api-key'

from praisonai_tools.recipe_tools import LLMTool

# Initialize LLM tool
llm = LLMTool(provider="openai", model="gpt-4o-mini")

# Simple completion
response = llm.complete("Write a haiku about coding")
print(response.content)
print(f"Tokens used: {response.usage['total_tokens']}")

from praisonai_tools.recipe_tools import LLMTool

# OpenAI
openai_llm = LLMTool(provider="openai", model="gpt-4o-mini")

# Anthropic
anthropic_llm = LLMTool(provider="anthropic", model="claude-3-haiku-20240307")

# Google
google_llm = LLMTool(provider="google", model="gemini-1.5-flash")

# Use any provider
response = openai_llm.complete("Hello!")

from praisonai_tools.recipe_tools import LLMTool

llm = LLMTool(provider="openai")

# Extract structured data
schema = {"name": "string", "age": "number", "skills": "array"}
result = llm.extract_json(
    "John is 30 years old and knows Python, JavaScript, and Go",
    schema=schema
)
print(result)
# {'name': 'John', 'age': 30, 'skills': ['Python', 'JavaScript', 'Go']}

from praisonai_tools.recipe_tools import VisionTool

vision = VisionTool(provider="openai")

# Analyze an image
result = vision.analyze("photo.jpg", prompt="Describe this image")
print(result.description)

# Generate caption
caption = vision.caption("photo.jpg")
print(caption)

# Extract tags
tags = vision.tag("photo.jpg")
print(tags)  # ['nature', 'landscape', 'mountains']

from praisonai_tools.recipe_tools import ChartTool

chart = ChartTool()

# Create bar chart from data
data = {"Q1": 100, "Q2": 150, "Q3": 200, "Q4": 180}
result = chart.bar(data, title="Quarterly Sales", output="sales.png")
print(f"Chart saved to: {result.path}")

# Create from CSV
result = chart.line("data.csv", x="date", y="value", output="trend.png")

from praisonai_tools.recipe_tools import EmailTool

email = EmailTool()

# Parse email file
parsed = email.parse("message.eml")
print(f"From: {parsed.sender}")
print(f"Subject: {parsed.subject}")
print(f"Attachments: {len(parsed.attachments)}")

# Extract structured data with LLM
extracted = email.extract("message.eml", fields=["action_items", "deadlines"])
print(extracted)

import yaml
from pathlib import Path

def load_recipe(name: str):
    """Load a recipe's TEMPLATE.yaml configuration."""
    recipe_path = Path.home() / ".praison" / "templates" / name
    if not recipe_path.exists():
        recipe_path = Path("/Users/praison/Agent-Recipes/agent_recipes/templates") / name
    
    template_file = recipe_path / "TEMPLATE.yaml"
    with open(template_file) as f:
        return yaml.safe_load(f)

# Load recipe
recipe = load_recipe("ai-blog-generator")
print(f"Recipe: {recipe['name']}")
print(f"Description: {recipe['description']}")
print(f"Required tools: {recipe['requires']['tools']}")

import os
import shutil

def check_dependencies(recipe: dict) -> dict:
    """Check if recipe dependencies are satisfied."""
    results = {}
    requires = recipe.get("requires", {})
    
    # Check environment variables
    for env_var in requires.get("env", []):
        results[f"env:{env_var}"] = bool(os.environ.get(env_var))
    
    # Check Python packages
    for package in requires.get("packages", []):
        try:
            __import__(package.replace("-", "_").split("[")[0])
            results[f"package:{package}"] = True
        except ImportError:
            results[f"package:{package}"] = False
    
    # Check external tools
    for tool in requires.get("external", []):
        results[f"external:{tool}"] = shutil.which(tool) is not None
    
    return results

# Check dependencies
deps = check_dependencies(recipe)
for dep, status in deps.items():
    print(f"{'✓' if status else '✗'} {dep}")

from praisonai_tools.recipe_tools import LLMTool

class BlogGenerator:
    """Custom blog post generator recipe."""
    
    def __init__(self, provider="openai", model="gpt-4o-mini"):
        self.llm = LLMTool(provider=provider, model=model)
    
    def generate(self, topic: str, style: str = "professional") -> str:
        """Generate a blog post on the given topic."""
        prompt = f"""Write a blog post about: {topic}
        
Style: {style}
Include:
- Engaging introduction
- 3-4 main sections with headers
- Practical examples
- Call to action

Format as Markdown."""
        
        response = self.llm.complete(
            prompt,
            system="You are an expert blog writer.",
            max_tokens=2000
        )
        return response.content

# Use custom recipe
generator = BlogGenerator()
post = generator.generate("AI in Healthcare", style="conversational")
print(post)

from praisonai_tools.recipe_tools import LLMTool, ChartTool

class ReportGenerator:
    """Generate reports with charts from data."""
    
    def __init__(self):
        self.llm = LLMTool(provider="openai")
        self.chart = ChartTool()
    
    def generate(self, data_file: str, output_dir: str = "./report"):
        import pandas as pd
        import os
        
        os.makedirs(output_dir, exist_ok=True)
        
        # Load data
        df = pd.read_csv(data_file)
        
        # Generate summary with LLM
        summary_prompt = f"""Analyze this data and provide key insights:
        
Columns: {list(df.columns)}
Sample: {df.head().to_string()}
Stats: {df.describe().to_string()}"""
        
        summary = self.llm.complete(summary_prompt)
        
        # Generate chart
        numeric_cols = df.select_dtypes(include=['number']).columns
        if len(numeric_cols) > 0:
            self.chart.bar(
                df[numeric_cols[0]].to_dict(),
                title=f"Distribution of {numeric_cols[0]}",
                output=f"{output_dir}/chart.png"
            )
        
        # Write report
        with open(f"{output_dir}/report.md", "w") as f:
            f.write(f"# Data Report\n\n{summary.content}\n")
        
        return {"summary": summary.content, "chart": f"{output_dir}/chart.png"}

# Use multi-tool recipe
reporter = ReportGenerator()
result = reporter.generate("sales.csv")

from praisonai_tools.recipe_tools import LLMTool
from praisonai_tools.recipe_tools.base import DependencyError

try:
    llm = LLMTool(provider="openai")
    deps = llm.check_dependencies()
    
    if not deps.get("api_key"):
        raise DependencyError("OPENAI_API_KEY not set")
    
    response = llm.complete("Hello")
    
except DependencyError as e:
    print(f"Missing dependency: {e}")
    print("Install with: pip install openai")
    print("Set: export OPENAI_API_KEY=your-key")
    
except Exception as e:
    print(f"Error: {e}")

from praisonaiagents import Agent
from praisonai_tools.recipe_tools import LLMTool

# Create agent with recipe tool
llm_tool = LLMTool(provider="openai")

agent = Agent(
    name="Content Creator",
    instructions="You create engaging content using AI tools.",
    tools=[llm_tool.complete]
)

# Run agent
result = agent.run("Create a social media post about our new product launch")
print(result)