> ## 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. # Default Model Selection > The CLI auto-selects the best available model from your credentials — no more hardcoded gpt-4o-mini When you don't specify a model, the CLI picks the best one automatically based on which API keys you have. A one-line notice tells you what was chosen and why. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Default Model Selection" Request[📋 User Request] --> Process[⚙️ Default Model Selection] Process --> Result[✅ Result] end classDef input fill:#6366F1,stroke:#7C90A0,color:#fff classDef process fill:#189AB4,stroke:#7C90A0,color:#fff classDef output fill:#10B981,stroke:#7C90A0,color:#fff class Request input class Process process class Result output ``` ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} sequenceDiagram participant User participant Agent participant Feature as Default Model Selection User->>Agent: Request Agent->>Feature: Process request Feature-->>Agent: Result Agent-->>User: Response ``` ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent( name="Assistant", instructions="Help with tasks", ) agent.start("Summarise the key points from this week") ``` The user omits `--model`; the CLI picks the best credential-backed model and prints what it chose. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Default Model Selection" In[📝 Command no --model] --> Scan[🔍 Scan Credentials] Scan --> Pick[⚙️ Pick Best Model] Pick --> Agent[🤖 Agent] Agent --> Out[✅ Response + Notice] end classDef input fill:#6366F1,stroke:#7C90A0,color:#fff classDef process fill:#F59E0B,stroke:#7C90A0,color:#fff classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff classDef output fill:#10B981,stroke:#7C90A0,color:#fff class In input class Scan,Pick process class Agent agent class Out output ``` ## How It Works ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} sequenceDiagram participant User participant CLI participant Resolver participant Agent User->>CLI: praisonai "task" (no --model) CLI->>Resolver: Resolve default model Resolver-->>CLI: Best credential-backed model CLI->>Agent: Build with chosen model Agent-->>User: Response + "using " notice ``` ### Resolution Order ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB subgraph "Default Model Resolution Order" E["1️⃣ Explicit --model flag\nor config / YAML"] -->|"wins immediately"| USE["✅ Use this model"] E -->|Not set| R["2️⃣ Most-recently-used\n~/.praisonai/state/model.json"] R -->|Found| USE R -->|Not found| ENV["3️⃣ MODEL_NAME or\nOPENAI_MODEL_NAME env var"] ENV -->|Set| USE ENV -->|Not set| CRED{"4️⃣ Credential scan"} CRED -->|ANTHROPIC_API_KEY| CL["claude-sonnet-4-5"] CRED -->|GEMINI_API_KEY| GM["gemini/gemini-2.0-flash"] CRED -->|OPENAI_API_KEY| GP["gpt-4o"] CRED -->|Nothing found| FB["gpt-4o-mini (fallback)"] CL --> USE GM --> USE GP --> USE FB --> USE end classDef explicit fill:#8B0000,stroke:#7C90A0,color:#fff classDef mru fill:#189AB4,stroke:#7C90A0,color:#fff classDef env fill:#6366F1,stroke:#7C90A0,color:#fff classDef cred fill:#F59E0B,stroke:#7C90A0,color:#fff classDef use fill:#10B981,stroke:#7C90A0,color:#fff class E explicit class R mru class ENV env class CRED,CL,GM,GP,FB cred class USE use ``` ## Quick Start Set your API key and run any command — no `--model` needed: ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export ANTHROPIC_API_KEY=sk-ant-... praisonai chat "Tell me a joke" ``` ``` No model set; using claude-sonnet-4-5 because ANTHROPIC_API_KEY is present. ``` The one-line notice only appears when a provider-inferred default is used (not when using the MRU or an explicit override). ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai chat --model gpt-4o "Explain quantum entanglement" ``` This model is saved as the most-recently-used and will be the default for the next run. Add a `.praison.json` to your project root: ```json theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} { "model": "anthropic/claude-3-5-sonnet-20241022" } ``` Every `praisonai` command in this directory now uses this model. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai doctor ``` Shows which credentials are present and which model would be selected on the next zero-config run. *** ## Resolution Order | Priority | Source | Persisted as MRU? | | ----------- | ------------------------------------------------------- | :---------------: | | 1 (highest) | `--model` flag / config / YAML | ✅ Yes | | 2 | Most-recently-used (`~/.praisonai/state/model.json`) | — | | 3 | `MODEL_NAME` or `OPENAI_MODEL_NAME` env var | ✅ Yes | | 4 | Provider credential probe (ANTHROPIC → GEMINI → OPENAI) | ❌ No | | 5 (lowest) | `gpt-4o-mini` hard fallback | ❌ No | Provider-inferred defaults (priority 4) and the `gpt-4o-mini` fallback (priority 5) are **intentionally not persisted** as MRU. Persisting them would let a stale default win over fresh credential-based inference on a later run when your available providers have changed. *** ## MRU State The most-recently-used model is stored at: ``` ~/.praisonai/state/model.json ``` This honours `PRAISONAI_HOME` (`$PRAISONAI_HOME/state/model.json` when set). The legacy `~/.praison/state/model.json` is still read as a fallback. Content: ```json theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} {"model": "claude-sonnet-4-5"} ``` To reset it (force credential-based re-inference): ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} rm ~/.praisonai/state/model.json ``` Or simply run with an explicit `--model` — that overwrites the MRU automatically. *** ## Best Practices Don't rely on auto-selection in automated pipelines. CI environments often have credentials for multiple providers, and the selected model can change if you add or remove a key. ```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} - name: Run agent task env: ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} MODEL_NAME: "anthropic/claude-3-5-sonnet-20241022" run: praisonai run task.yaml ``` The transparency notice (`No model set; using X because Y is present.`) only fires for provider-inferred defaults. If you see it unexpectedly in a staging environment, your `.praison.json` or `MODEL_NAME` env var may not be set. For backward compatibility, the `OPENAI_MODEL_NAME` environment variable still overrides credential-based inference. If you have a legacy `.env` with this variable, it takes priority over the credential probe. *** ## Related Sign in to providers using browser-based OAuth List, compare, and switch between available models