Environment Variables Configuration

The configuration file can be found at the following relative path in your installation directory of AIOS:

aios/config/config.yaml

AIOS supports several API integrations that require configuration. You can use the following commands:

• aios env list: Show current environment variables, or show available API keys if no variables are set

• aios env set: Show current environment variables, or show available API keys if no variables are set

• aios refresh: Refresh AIOS configuration.

  • Reloads the configuration from aios/config/config.yaml.

  • Reinitializes all components without restarting the server.

  • The server must be running.

When no environment variables are set, the following API keys will be shown:

• OPENAI_API_KEY: OpenAI API key for accessing OpenAI services

• GEMINI_API_KEY: Google Gemini API key for accessing Google's Gemini services

• DEEPSEEK_API_KEY: Deepseek API key for accessing Deepseek services

• ANTHROPIC_API_KEY: Anthropic API key for accessing Anthropic Claude services

• GROQ_API_KEY: Groq API key for accessing Groq services

• HF_AUTH_TOKEN: HuggingFace authentication token for accessing models

• HF_HOME: Optional path to store HuggingFace models

To obtain these API keys:

1. OpenAI API: Visit https://platform.openai.com/api-keys
2. Google Gemini API: Visit https://makersuite.google.com/app/apikey
3. Deepseek API: Visit https://api-docs.deepseek.com/
4. Anthropic Claude API: Visit https://console.anthropic.com/settings/keys
5. Groq API: Visit https://console.groq.com/keys
6. HuggingFace Token: Visit https://huggingface.co/settings/tokens

API Keys:

openai: "your-openai-key"
gemini: "your-gemini-key"
deepseek: "your-deepseek-key"
groq: "your-groq-key"
anthropic: "your-anthropic-key"
huggingface:
  auth_token: "your-huggingface-token"
  cache: "optional-path"

Model Settings:

It is required to follow the following parameters to set up different backends as below

Backend Type	Required Parameters
openai	name, backend
anthropic	name, backend
google	name, backend
ollama	name, backend, host_name
vLLM	name, backend, host_name
huggingface	name, backend, max_gpu_memory, eval_device

The example of how to set up different models are as below

# LLM Configuration
llms:
  models:
    # OpenAI backend
    # - name: "gpt-4o-mini"
    #   backend: "openai"

    # Google Models
    # - name: "gemini-1.5-flash"
    #   backend: "google"

    # Anthropic Models
    # - name: "claude-3-opus"
    #   backend: "anthropic"

    # Ollama backend
    # - name: "qwen2.5:7b"
    #  backend: "ollama"
    #  hostname: "http://localhost:11434" # Make sure to run ollama server

    # HuggingFace backend
    # - name: "meta-llama/Llama-3.1-8B-Instruct"
    #   backend: "huggingface"
    #   max_gpu_memory: {0: "48GB"}  # GPU memory allocation
    #   eval_device: "cuda:0"  # Device for model evaluation
    
    # vLLM Models
    # To use vllm as backend, you need to install vllm and run the vllm server https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html
    # An example command to run the vllm server is:
    # vllm serve meta-llama/Llama-3.2-3B-Instruct --port 8091
    # - name: "meta-llama/Llama-3.1-8B-Instruct"
    #  backend: "vllm"
    #  hostname: "http://localhost:8091"

  log_mode: "console"
  use_context_manager: false

Memory Settings:

log_mode: "console" # can be "console" or "file"

Storage Settings:

root_dir: "root"
use_vector_db: true

Scheduler Settings:

log_mode: "console" # can be "console" or "file"