File: //opt/PraisonAI/docs/models.mdx
---
title: "Models in PraisonAI"
description: "Overview of supported language models in PraisonAI, including OpenAI, Groq, Google Gemini, Anthropic Claude, and configuration examples"
icon: "brain"
---
# Code
## Set model by 3 ways
### 1. OpenAI Compatible Endpoints
<Note>By Default it uses OPENAI_BASE_URL https://api.openai.com/v1 </Note>
Example Groq Implementation:
```bash
export OPENAI_API_KEY=groq-api-key
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
```
```python
from praisonaiagents import Agent
agent = Agent(
instructions="You are a helpful assistant",
llm="llama-3.1-8b-instant",
)
agent.start("Why sky is Blue?")
```
### 2. Litellm Compatible model names (eg: gemini/gemini-1.5-flash-8b)
```bash
pip install "praisonaiagents[llm]"
```
```python
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?")
```
### 3. Litellm Compatible Configuration
```bash
pip install "praisonaiagents[llm]"
```
```python
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()
```
## Advanced Configuration (Litellm Support)
<Note>This uses Litellm</Note>
<Steps>
<Step title="Install Package">
Install required packages:
```bash
pip install "praisonaiagents[llm]"
```
</Step>
<Step title="Setup Environment">
Configure environment:
```bash
export GOOGLE_API_KEY=your-api-key
```
<Note>
Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
</Note>
</Step>
<Step title="Create Agent">
Create `app.py`:
<CodeGroup>
```python Basic
# 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?")
```
```python Advanced
# if json_object is not supported by the model
from praisonaiagents import Agent
# Detailed LLM configuration
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 specialized in scientific explanations. "
"Provide clear, accurate, and engaging responses.",
llm=llm_config, # Pass the detailed configuration
verbose=True, # Enable detailed output
markdown=True, # Format responses in markdown
self_reflect=True, # Enable self-reflection
max_reflect=3, # Maximum reflection iterations
min_reflect=1 # Minimum reflection iterations
)
# Test the agent
response = agent.start("Why is the sky blue? Please explain in simple terms.")
```
</CodeGroup>
</Step>
</Steps>
<AccordionGroup>
<Accordion title="Ollama Integration" defaultOpen>
```bash
export OPENAI_BASE_URL=http://localhost:11434/v1
```
</Accordion>
<Accordion title="Groq Integration" defaultOpen>
```bash
export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
```
</Accordion>
<Accordion title="Google Gemini" defaultOpen>
```bash
export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
```
</Accordion>
<Accordion title="Jan AI Integration" defaultOpen>
```bash
export OPENAI_BASE_URL=http://localhost:1337/v1
```
</Accordion>
<Accordion title="LM Studio Integration" defaultOpen>
```bash
export OPENAI_BASE_URL=http://localhost:1234/v1
```
</Accordion>
<Accordion title="OpenRouter Integration" defaultOpen>
```bash
export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://openrouter.ai/api/v1
```
</Accordion>
</AccordionGroup>
## Supported Models for No Code
| PraisonAI Chat | PraisonAI Code | PraisonAI (Multi-Agents) |
| --- | --- | --- |
| [Litellm](https://litellm.vercel.app/docs/providers) | [Litellm](https://litellm.vercel.app/docs/providers) | Below Models |
- [OpenAI](models/openai.md)
- [Groq](models/groq.md)
- [Google Gemini](models/google.md)
- [Anthropic Claude](models/anthropic.md)
- [Cohere](models/cohere.md)
- [Mistral](models/mistral.md)
- [Ollama](models/ollama.md)
- [Other Models](models/other.md)
## Example agents.yaml
This uses Multi-Agents with Multi-LLMs.
```yaml
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: []
```