> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Models in PraisonAI > Overview of supported language models in PraisonAI, including OpenAI, Groq, Google Gemini, Anthropic Claude, and configuration examples Point an Agent at any provider by setting its `llm` parameter — PraisonAI routes to OpenAI, Anthropic, Gemini, Groq, Cohere, or a local Ollama model. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent(instructions="You are a helpful assistant", llm="gpt-4o-mini") agent.start("Why is the sky blue?") ``` ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Model Selection" User[📋 User] --> Agent[🤖 Agent] Agent --> Router[🧠 Provider Resolver] Router --> Model[✅ LLM Response] end classDef input fill:#8B0000,stroke:#7C90A0,color:#fff classDef process fill:#189AB4,stroke:#7C90A0,color:#fff classDef output fill:#10B981,stroke:#7C90A0,color:#fff class User,Agent input class Router process class Model output ``` Not sure which model to use? Run `praisonai models list` to browse all available models, or see the [Model Catalogue CLI](/docs/features/models-cli) for full details on browsing, describing, and validating models. # Code ## Set model by 3 ways ### 1. OpenAI Compatible Endpoints By Default it uses OPENAI\_BASE\_URL [https://api.openai.com/v1](https://api.openai.com/v1) Example Groq Implementation: ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_API_KEY="${GROQ_API_KEY:?Set GROQ_API_KEY in your shell}" export OPENAI_BASE_URL=https://api.groq.com/openai/v1 ``` ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} pip install "praisonaiagents[llm]" ``` ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent( instructions="You are a helpful assistant", llm="gemini/gemini-1.5-flash-8b", reflection=True, ) agent.start("Why sky is Blue?") ``` ### 3. Litellm Compatible Configuration ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} pip install "praisonaiagents[llm]" ``` ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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) This uses Litellm Install required packages: ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} pip install "praisonaiagents[llm]" ``` Configure environment: ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export GOOGLE_API_KEY="${GOOGLE_API_KEY:?Set GOOGLE_API_KEY in your shell}" ``` Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey) Create `app.py`: ```python Basic theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # 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", reflection=True, ) agent.start("Why sky is Blue?") ``` ```python Advanced theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # 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 # Enable detailed output # Format responses in markdown reflection=True, # Enable self-reflection max_iterations=3, # Maximum reflection iterations min_iterations=1 # Minimum reflection iterations ) # Test the agent response = agent.start("Why is the sky blue? Please explain in simple terms.") ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_BASE_URL=http://localhost:11434/v1 ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_API_KEY="${GROQ_API_KEY:?Set GROQ_API_KEY in your shell}" export OPENAI_BASE_URL=https://api.groq.com/openai/v1 ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_API_KEY="${GEMINI_API_KEY:?Set GEMINI_API_KEY in your shell}" export OPENAI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/ ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_BASE_URL=http://localhost:1337/v1 ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_BASE_URL=http://localhost:1234/v1 ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export OPENAI_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY in your shell}" export OPENAI_BASE_URL=https://openrouter.ai/api/v1 ``` ## Provider Auto-Detection (no-config first run) When you run `praisonai run` without setting `--model` or a `model:` key in `config.yaml`, PraisonAI inspects which supported provider credential is present in your environment and picks a provider-appropriate default — so a user whose only key is `ANTHROPIC_API_KEY` no longer gets an OpenAI auth error on first run. | Credential env var | Resolved default model | Base URL | | ------------------- | ------------------------------------ | -------------------------------------------------- | | `OPENAI_API_KEY` | `gpt-4o-mini` | `https://api.openai.com/v1` | | `ANTHROPIC_API_KEY` | `anthropic/claude-3-5-sonnet-latest` | `https://api.anthropic.com/v1` | | `GEMINI_API_KEY` | `gemini/gemini-1.5-flash` | `https://generativelanguage.googleapis.com/v1beta` | | `GOOGLE_API_KEY` | `google/gemini-1.5-flash` | `https://generativelanguage.googleapis.com/v1beta` | | `GROQ_API_KEY` | `groq/llama-3.3-70b-versatile` | `https://api.groq.com/openai/v1` | | `COHERE_API_KEY` | `cohere/command-r` | `https://api.cohere.ai/v1` | | `OLLAMA_HOST` | `ollama/llama3.2` | `http://localhost:11434/v1` | | (none of the above) | `gpt-4o-mini` | `https://api.openai.com/v1` | Precedence: the first credential in the table that is set wins. If multiple provider keys are set, the one listed first takes effect. The same resolver drives implicit defaults for `praisonai run`, `praisonai chat`, `praisonai init` scaffolding, `praisonai setup`, and the bare-`praisonai` TUI launch — not just `run`. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB Start([praisonai run — no model set]) --> A{OPENAI_API_KEY?} A -->|Yes| M1[gpt-4o-mini] A -->|No| B{ANTHROPIC_API_KEY?} B -->|Yes| M2[anthropic/claude-3-5-sonnet-latest] B -->|No| C{GEMINI_API_KEY?} C -->|Yes| M3[gemini/gemini-1.5-flash] C -->|No| D{GOOGLE_API_KEY?} D -->|Yes| M4[google/gemini-1.5-flash] D -->|No| E{GROQ_API_KEY?} E -->|Yes| M5[groq/llama-3.3-70b-versatile] E -->|No| F{COHERE_API_KEY?} F -->|Yes| M6[cohere/command-r] F -->|No| G{OLLAMA_HOST?} G -->|Yes| M7[ollama/llama3.2] G -->|No| M8[gpt-4o-mini fallback] classDef start fill:#6366F1,stroke:#7C90A0,color:#fff classDef check fill:#F59E0B,stroke:#7C90A0,color:#fff classDef model fill:#10B981,stroke:#7C90A0,color:#fff classDef fallback fill:#8B0000,stroke:#7C90A0,color:#fff class Start start class A,B,C,D,E,F,G check class M1,M2,M3,M4,M5,M6,M7 model class M8 fallback ``` An explicit `--model ` flag or a `model:` key in `config.yaml` always overrides auto-detection. ## Primary vs Auxiliary Model PraisonAI splits your model choice into two knobs: | Knob | What it drives | Configured via | | ------------- | ---------------------------------------------------- | ------------------------------------ | | `model` | The agent's own reasoning / tool-use turns | `Agent(llm=...)`, `[defaults].model` | | `small_model` | Cheap internal calls (session titles, summarisation) | `[defaults].small_model` | `small_model` falls back to `model` when unset, so single-provider (Anthropic, Ollama, on-prem) setups make **zero** unexpected third-party calls. ```toml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # .praisonai/config.toml [defaults] model = "gpt-4o" small_model = "gpt-4o-mini" ``` See [Configuration File → Cheap auxiliary model for internal calls](/docs/features/config-file#cheap-auxiliary-model-for-internal-calls) for full examples. ## 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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} framework: crewai topic: research about the causes of lung disease agents: # Canonical: use 'agents' instead of 'roles' research_analyst: instructions: # Canonical: use 'instructions' instead of '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: instructions: # Canonical: use 'instructions' instead of '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: instructions: # Canonical: use 'instructions' instead of '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: [] ``` ## How It Works The Agent passes your `llm` value to the provider resolver, which routes the request to the matching model and returns the response. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} sequenceDiagram participant User participant Agent participant Model as Provider User->>Agent: Agent(llm="gpt-4o-mini").start("…") Agent->>Model: Route request to the resolved provider Model-->>Agent: Completion Agent-->>User: Final answer ``` ## Best Practices Skip `llm=` on first runs. PraisonAI resolves a sensible default from whichever provider key is set — see [Provider Auto-Detection](#provider-auto-detection-no-config-first-run). Pass `llm="gemini/gemini-1.5-flash-8b"` or `llm="anthropic/claude-3-5-sonnet-latest"` to target a specific provider model. Set provider keys in your shell or `.env`. Use `api_key=None` in `llm_config` so the SDK reads the environment variable. Use a small fast model (`gpt-4o-mini`, `gemini-1.5-flash`) for routing and a larger model only where quality matters. ## Related Run your first agent in a few lines. Give models real actions with tools.