Provider Overview

Andi AIRun supports multiple AI providers, allowing you to choose between local models (free, private) and cloud APIs (powerful, scalable).

Runtimes

AI Runner supports multiple runtimes. Provider availability depends on the runtime:

Runtime	Flag	Providers
Claude Code	`--cc` (default)	All Anthropic providers: AWS, Vertex, Azure Foundry, Vercel, Anthropic API, Pro, Ollama, LM Studio
Codex CLI	`--codex`	OpenAI API, Azure OpenAI, Ollama, LM Studio, config.toml profiles

See Runtimes for the full compatibility reference.

Quick Reference

Flag	Provider	Type	Notes
`--ollama` / `--ol`	Ollama	Local	Free, no API costs, cloud option
`--lmstudio` / `--lm`	LM Studio	Local	MLX models (fast on Apple Silicon)
`--aws`	AWS Bedrock	Cloud	Requires AWS credentials
`--vertex`	Google Vertex AI	Cloud	Requires GCP project
`--apikey`	Anthropic API	Cloud	Direct API access
`--azure`	Microsoft Azure	Cloud	Azure Foundry
`--vercel`	Vercel AI Gateway	Cloud	Any model: Anthropic, OpenAI, xAI, Google, Meta, more
`--pro`	Claude Pro	Subscription	Default if logged in

Ollama, LM Studio, and Azure work with both runtimes. AWS, Vertex, Vercel, and Pro are Claude Code only. Codex custom providers use --profile. See Runtimes.

How Configuration Works

All provider credentials are stored in one file: ~/.ai-runner/secrets.sh

Initial Setup

This file is created automatically by ./setup.sh from the secrets.example.sh template:

nano ~/.ai-runner/secrets.sh

Andi AIRun loads this file at startup. You don’t need to set environment variables in your shell profile or .bashrc — just add them to secrets.sh, and then switch providers freely with ai --aws, ai --vertex, etc.

You only need to configure the providers you want to use. Configure multiple providers to switch between them when you hit rate limits or want to use different models.

Session-Scoped Behavior

All provider configurations are session-scoped:

Changes only affect the active terminal session
On exit, original settings automatically restore
Plain claude always runs in native state
Running claude in another terminal is unaffected

This means you can safely run ai --lmstudio in one terminal while using claude normally in another.

Provider Detection and Defaults

Automatic Provider Selection

AI Runner first detects which runtime to use (Claude Code or Codex CLI), then selects the appropriate provider. If you don’t specify a provider flag, Andi AIRun automatically detects and uses:

Claude Pro (if logged in with claude login)
First configured provider in secrets.sh

Setting a Default Provider

You can set a default provider to avoid typing the flag every time:

ai --vercel --model xai/grok-code-fast-1 --set-default

Clear the default:

ai --clear-default

Model Tier System

Andi AIRun uses a tiered model system to balance performance and cost:

Tier Levels

Tier	Aliases	Use Case	Claude Models
Fable	`--fable`, `--best`	Most demanding reasoning, long-horizon agentic work (Anthropic only)	Claude Fable 5
High	`--opus`, `--high`	Complex reasoning, large refactors (default)	Claude Opus 5
Mid	`--sonnet`, `--mid`	Cost-conscious coding tasks	Claude Sonnet 5
Low	`--haiku`, `--low`	Fast operations, small edits	Claude Haiku 4.5

Claude Fable 5 is Anthropic’s most capable model, a tier above Opus. It’s opt-in (Opus stays the default) and available on Anthropic providers only — on Ollama, LM Studio, and Codex, --fable/--best falls back to that runtime’s highest tier with a notice.

Usage Examples

# Use high tier for complex reasoning
ai --aws --opus task.md

# Use high tier (default)
ai --aws task.md

# Use low tier for speed and cost savings
ai --vertex --haiku simple-fix.md

Background Model

Andi AIRun uses a “small/fast” model for background operations (like file searches, quick checks). By default, this is set to the Low tier model (Haiku). For local providers (Ollama, LM Studio), the background model defaults to the same model as the main tier to avoid costly model swapping.

Configuring Model Tiers

You can customize model tiers per provider in secrets.sh:

Cloud Providers

# AWS Bedrock
export CLAUDE_MODEL_FABLE_AWS="global.anthropic.claude-fable-5"
export CLAUDE_MODEL_OPUS_AWS="global.anthropic.claude-opus-5"
export CLAUDE_MODEL_SONNET_AWS="global.anthropic.claude-sonnet-5"
export CLAUDE_MODEL_HAIKU_AWS="us.anthropic.claude-haiku-4-5-20251001-v1:0"

# Google Vertex AI
export CLAUDE_MODEL_FABLE_VERTEX="claude-fable-5"
export CLAUDE_MODEL_OPUS_VERTEX="claude-opus-5"
export CLAUDE_MODEL_SONNET_VERTEX="claude-sonnet-5"
export CLAUDE_MODEL_HAIKU_VERTEX="claude-haiku-4-5@20251001"

# Anthropic API (also used by --pro)
export CLAUDE_MODEL_FABLE_ANTHROPIC="claude-fable-5"
export CLAUDE_MODEL_OPUS_ANTHROPIC="claude-opus-5"
export CLAUDE_MODEL_SONNET_ANTHROPIC="claude-sonnet-5"
export CLAUDE_MODEL_HAIKU_ANTHROPIC="claude-haiku-4-5"

# Vercel AI Gateway
export CLAUDE_MODEL_SONNET_VERCEL="xai/grok-code-fast-1"
export CLAUDE_SMALL_FAST_MODEL_VERCEL="xai/grok-code-fast-1"

Local Providers

# Ollama
export OLLAMA_MODEL_HIGH="qwen3-coder"
export OLLAMA_MODEL_MID="qwen3-coder"
export OLLAMA_MODEL_LOW="qwen2.5-coder:7b"
export OLLAMA_SMALL_FAST_MODEL="qwen3-coder"  # Same model to avoid swapping

# LM Studio
export LMSTUDIO_MODEL_HIGH="openai/gpt-oss-20b"
export LMSTUDIO_MODEL_MID="openai/gpt-oss-20b"
export LMSTUDIO_MODEL_LOW="ibm/granite-4-micro"
export LMSTUDIO_SMALL_FAST_MODEL="openai/gpt-oss-20b"  # Same model

Custom Models

Override the tier system with a specific model:

# Use a specific model
ai --vercel --model xai/grok-code-fast-1

# Use a specific Ollama model
ai --ollama --model glm-5:cloud

# Use a specific AWS model
ai --aws --model global.anthropic.claude-opus-5

Agent Teams

All providers support agent teams (ai --team). Coordination uses Claude Code’s internal task list and mailbox, not provider-specific features.

Learn More

Read the Claude Code Agent Teams documentation

Next Steps

Local Providers

Set up Ollama or LM Studio for free, private AI

Cloud Providers

Configure AWS, Google, Anthropic, Azure, or Vercel

Switching Providers

Learn to switch providers to avoid rate limits

Get Started

Core Concepts

Guides

Providers

Advanced

Runtimes

Quick Reference

How Configuration Works

Initial Setup

Session-Scoped Behavior

Provider Detection and Defaults

Automatic Provider Selection

Setting a Default Provider

Model Tier System

Tier Levels

Usage Examples

Background Model

Configuring Model Tiers

Cloud Providers

Local Providers

Custom Models

Agent Teams

Learn More

Next Steps

Local Providers

Cloud Providers

Switching Providers

​Runtimes

​Quick Reference

​How Configuration Works

​Initial Setup

​Session-Scoped Behavior

​Provider Detection and Defaults

​Automatic Provider Selection

​Setting a Default Provider

​Model Tier System

​Tier Levels

​Usage Examples

​Background Model

​Configuring Model Tiers

​Cloud Providers

​Local Providers

​Custom Models

​Agent Teams

Learn More

​Next Steps

Local Providers

Cloud Providers

Switching Providers

Runtimes

Quick Reference

How Configuration Works

Initial Setup

Session-Scoped Behavior

Provider Detection and Defaults

Automatic Provider Selection

Setting a Default Provider

Model Tier System

Tier Levels

Usage Examples

Background Model

Configuring Model Tiers

Cloud Providers

Local Providers

Custom Models

Agent Teams

Next Steps