Config API

Manage Strix configuration and environment variables

Overview

The Config API provides a centralized way to manage Strix configuration through environment variables and persistent settings.

Config Class

Main configuration manager for Strix.

Class Variables

Configuration defaults stored as class attributes:

strix_llm · str | None · default: "None"

LLM model name

llm_api_key · str | None · default: "None"

API key for LLM provider

llm_api_base · str | None · default: "None"

Custom API base URL

strix_reasoning_effort · str · default: "'high'"

Reasoning effort: “low”, “medium”, or “high”

strix_llm_max_retries · str · default: "'5'"

Maximum retry attempts

llm_timeout · str · default: "'300'"

Request timeout in seconds

strix_image · str · default: "'ghcr.io/usestrix/strix-sandbox:0.1.12'"

Docker image for sandboxes

strix_runtime_backend · str · default: "'docker'"

Runtime backend (currently only “docker”)

strix_sandbox_execution_timeout · str · default: "'120'"

Tool execution timeout in seconds

strix_telemetry · str · default: "'1'"

Enable/disable telemetry

Methods

get

@classmethod
def get(cls, name: str) -> str | None

Gets a configuration value from environment or defaults.

name · str · required

Configuration key in lowercase (e.g., “strix_llm”)

return · str | None

Configuration value or None

Example:

from strix.config import Config

model = Config.get("strix_llm")
print(f"Using model: {model}")

image = Config.get("strix_image")
print(f"Sandbox image: {image}")

config_dir

@classmethod
def config_dir(cls) -> Path

Returns the configuration directory path.

return · Path

Path to ~/.strix directory

Example:

from strix.config import Config

config_path = Config.config_dir()
print(f"Config stored in: {config_path}")

config_file

@classmethod
def config_file(cls) -> Path

Returns the configuration file path.

return · Path

Path to ~/.strix/cli-config.json

load

@classmethod
def load(cls) -> dict[str, Any]

Loads configuration from the config file.

return · dict[str, Any]

Configuration dictionary

Example:

from strix.config import Config

config = Config.load()
print(f"Saved environment variables: {config.get('env', {})}")

save

@classmethod
def save(cls, config: dict[str, Any]) -> bool

Saves configuration to the config file.

config · dict[str, Any] · required

Configuration dictionary to save

return · bool

True if successful, False otherwise

Example:

from strix.config import Config

config_data = {
    "env": {
        "STRIX_LLM": "claude-3-5-sonnet-20241022",
        "LLM_API_KEY": "sk-ant-..."
    }
}

success = Config.save(config_data)
if success:
    print("Configuration saved")

apply_saved

@classmethod
def apply_saved(cls, force: bool = False) -> dict[str, str]

Applies saved configuration to environment variables.

force · bool · default: "False"

Force apply even if environment variables are already set

return · dict[str, str]

Dictionary of applied environment variables

Example:

from strix.config import Config

# Apply saved config
applied = Config.apply_saved()
print(f"Applied variables: {list(applied.keys())}")

# Force apply
applied = Config.apply_saved(force=True)

save_current

@classmethod
def save_current(cls) -> bool

Saves current environment variables to the config file.

return · bool

True if successful

Example:

import os
from strix.config import Config

# Set environment variables
os.environ["STRIX_LLM"] = "gpt-4o"
os.environ["LLM_API_KEY"] = "sk-..."

# Save to config
Config.save_current()

tracked_vars

@classmethod
def tracked_vars(cls) -> list[str]

Returns list of tracked environment variable names.

return · list[str]

List of uppercase environment variable names

Example:

from strix.config import Config

tracked = Config.tracked_vars()
print(f"Tracked variables: {tracked}")
# Output: ['STRIX_LLM', 'LLM_API_KEY', 'LLM_API_BASE', ...]

Helper Functions

apply_saved_config

from strix.config import apply_saved_config

applied = apply_saved_config(force: bool = False) -> dict[str, str]

Applies saved configuration to environment.

force · bool · default: "False"

Force apply variables

return · dict[str, str]

Applied variables

save_current_config

from strix.config import save_current_config

success = save_current_config() -> bool

Saves current environment to config file.

return · bool

True if successful

resolve_llm_config

from strix.config import resolve_llm_config

model, api_key, api_base = resolve_llm_config() -> tuple[str | None, str | None, str | None]

Resolves LLM configuration from environment.

return · tuple[str | None, str | None, str | None]

Tuple of (model_name, api_key, api_base)

Example:

from strix.config import resolve_llm_config

model, api_key, api_base = resolve_llm_config()

if model:
    print(f"Model: {model}")
    print(f"API Base: {api_base or 'default'}")
else:
    print("No LLM configured")

Configuration File Format

The config file is stored at ~/.strix/cli-config.json:

{
  "env": {
    "STRIX_LLM": "claude-3-5-sonnet-20241022",
    "LLM_API_KEY": "sk-ant-...",
    "STRIX_IMAGE": "ghcr.io/usestrix/strix-sandbox:0.1.12",
    "STRIX_RUNTIME_BACKEND": "docker"
  }
}

Environment Variables

LLM Configuration

STRIX_LLM · str · required

Model name (e.g., “claude-3-5-sonnet-20241022”)

LLM_API_KEY · str · required

API key for the provider

LLM_API_BASE · str · default: "None"

Custom API base URL

OPENAI_API_BASE · str · default: "None"

OpenAI-specific API base

LITELLM_BASE_URL · str · default: "None"

LiteLLM base URL

OLLAMA_API_BASE · str · default: "None"

Ollama API base

STRIX_REASONING_EFFORT · str · default: "'high'"

Reasoning effort for o1 models: “low”, “medium”, “high”

STRIX_LLM_MAX_RETRIES · str · default: "'5'"

Maximum retry attempts

STRIX_MEMORY_COMPRESSOR_TIMEOUT · str · default: "'30'"

Memory compression timeout in seconds

LLM_TIMEOUT · str · default: "'300'"

Request timeout in seconds

Runtime Configuration

STRIX_IMAGE · str · default: "'ghcr.io/usestrix/strix-sandbox:0.1.12'"

Docker image for sandboxes

STRIX_RUNTIME_BACKEND · str · default: "'docker'"

Runtime backend

STRIX_SANDBOX_EXECUTION_TIMEOUT · str · default: "'120'"

Tool execution timeout in seconds

STRIX_SANDBOX_CONNECT_TIMEOUT · str · default: "'10'"

Connection timeout in seconds

Feature Configuration

PERPLEXITY_API_KEY · str · default: "None"

API key for Perplexity web search

STRIX_DISABLE_BROWSER · str · default: "'false'"

Disable browser tools

STRIX_TELEMETRY · str · default: "'1'"

Enable telemetry (1 or 0)

Usage Examples

Basic Configuration

import os
from strix.config import Config

# Set environment variables
os.environ["STRIX_LLM"] = "claude-3-5-sonnet-20241022"
os.environ["LLM_API_KEY"] = "sk-ant-..."

# Get configuration values
model = Config.get("strix_llm")
api_key = Config.get("llm_api_key")

print(f"Model: {model}")
print(f"API Key: {api_key[:10]}...")

Persistent Configuration

import os
from strix.config import Config

# Set configuration
os.environ["STRIX_LLM"] = "gpt-4o"
os.environ["LLM_API_KEY"] = "sk-..."
os.environ["STRIX_IMAGE"] = "ghcr.io/usestrix/strix-sandbox:latest"

# Save to file
Config.save_current()
print("Configuration saved to ~/.strix/cli-config.json")

# Later, in another session
from strix.config import apply_saved_config

# Load saved configuration
applied = apply_saved_config()
print(f"Applied {len(applied)} variables from saved config")

Custom Configuration File

from pathlib import Path
from strix.config import Config

# Use custom config file
Config._config_file_override = Path("/custom/path/config.json")

# Load from custom location
config = Config.load()
print(f"Loaded config from custom path: {config}")

Checking Configuration

from strix.config import Config, resolve_llm_config

# Check what's configured
model, api_key, api_base = resolve_llm_config()

if not model:
    print("ERROR: STRIX_LLM not configured")
else:
    print(f"Model: {model}")
    print(f"API Base: {api_base or 'default'}")
    print(f"Has API Key: {bool(api_key)}")

# List all tracked variables
tracked = Config.tracked_vars()
print(f"\nTracked variables ({len(tracked)}):")
for var in tracked:
    value = os.environ.get(var)
    if value:
        display = value[:20] + "..." if len(value) > 20 else value
        print(f"  {var}: {display}")