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Code

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Set model by 3 ways

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1. OpenAI Compatible Endpoints

By Default it uses OPENAI_BASE_URL https://api.openai.com/v1
Example Groq Implementation:

export OPENAI_API_KEY=groq-api-key
export OPENAI_BASE_URL=https://api.groq.com/openai/v1

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant",
    llm="llama-3.1-8b-instant",
)

agent.start("Why sky is Blue?")

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2. Litellm Compatible model names (eg: gemini/gemini-1.5-flash-8b)

pip install "praisonaiagents[llm]"

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant",
    llm="gemini/gemini-1.5-flash-8b",
    self_reflect=True,
    verbose=True
)

agent.start("Why sky is Blue?")

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3. Litellm Compatible Configuration

pip install "praisonaiagents[llm]"

from praisonaiagents import Agent

llm_config = {
    "model": "gemini/gemini-1.5-flash-latest",  # Model name without provider prefix
    
    # Core settings
    "temperature": 0.7,                # Controls randomness (like temperature)
    "timeout": 30,                 # Timeout in seconds
    "top_p": 0.9,                    # Nucleus sampling parameter
    "max_tokens": 1000,               # Max tokens in response
    
    # Advanced parameters
    "presence_penalty": 0.1,         # Penalize repetition of topics (-2.0 to 2.0)
    "frequency_penalty": 0.1,        # Penalize token repetition (-2.0 to 2.0)
    
    # API settings (optional)
    "api_key": None,                 # Your API key (or use environment variable)
    "base_url": None,                # Custom API endpoint if needed
    
    # Response formatting
    "response_format": {             # Force specific response format
        "type": "text"               # Options: "text", "json_object"
    },
    
    # Additional controls
    "seed": 42,                      # For reproducible responses
    "stop_phrases": ["##", "END"],   # Custom stop sequences
}

agent = Agent(
    instructions="You are a helpful Assistant."
    llm=llm_config
)
agent.start()

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Advanced Configuration (Litellm Support)

This uses Litellm
1

Install Package

Install required packages:

pip install "praisonaiagents[llm]"
2

Setup Environment

Configure environment:

export GOOGLE_API_KEY=your-api-key

Get your API key from Google AI Studio

3

Create Agent

Create app.py:

# if json_object is supported by the model
from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant",
    llm="gemini/gemini-1.5-flash-8b",
    self_reflect=True,
    verbose=True
)

agent.start("Why sky is Blue?")

Ollama Integration

Groq Integration

Google Gemini

Jan AI Integration

LM Studio Integration

OpenRouter Integration

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Supported Models for No Code

PraisonAI ChatPraisonAI CodePraisonAI (Multi-Agents)
LitellmLitellmBelow Models

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Example agents.yaml

This uses Multi-Agents with Multi-LLMs.

framework: crewai
topic: research about the causes of lung disease
roles:
  research_analyst:
    backstory: Experienced in analyzing scientific data related to respiratory health.
    goal: Analyze data on lung diseases
    role: Research Analyst
    llm:  
      model: "groq/llama3-70b-8192"
    function_calling_llm: 
      model: "google/gemini-1.5-flash-001"
    tasks:
      data_analysis:
        description: Gather and analyze data on the causes and risk factors of lung
          diseases.
        expected_output: Report detailing key findings on lung disease causes.
    tools:
    - 'InternetSearchTool'
  medical_writer:
    backstory: Skilled in translating complex medical information into accessible
      content.
    goal: Compile comprehensive content on lung disease causes
    role: Medical Writer
    llm:  
      model: "anthropic/claude-3-haiku-20240307"
    function_calling_llm: 
      model: "openai/gpt-4o"
    tasks:
      content_creation:
        description: Create detailed content summarizing the research findings on
          lung disease causes.
        expected_output: Document outlining various causes and risk factors of lung
          diseases.
    tools:
    - ''
  editor:
    backstory: Proficient in editing medical content for accuracy and clarity.
    goal: Review and refine content on lung disease causes
    role: Editor
    llm:  
      model: "cohere/command-r"
    tasks:
      content_review:
        description: Edit and refine the compiled content on lung disease causes for
          accuracy and coherence.
        expected_output: Finalized document on lung disease causes ready for dissemination.
    tools:
    - ''
dependencies: []