Codex

Codex is OpenAI’s AI coding assistant, available as a command-line tool and as a VS Code extension. You can connect Codex to the ScitiX Model Inference API and use ScitiX models for coding tasks. Both the CLI and the extension share the same config.toml. Configuration is stored in config.toml in your Codex home directory, typically ~/.codex/config.toml. For the full list of options, see the Codex Configuration Reference. Supported models include glm-5.2 and kimi-k2.6. For model pricing, see the model catalog.

Configure

1. Install Codex

For the CLI, install Codex with npm or Homebrew:

npm install -g @openai/codex
# or
brew install codex

For other installation options, see the official installation guide. For the VS Code extension:

Open Extensions in VS Code (Ctrl+Shift+X / Cmd+Shift+X).
Search for Codex and install the official extension.
Click the Codex icon in the top-right of the editor to open the CODEX panel.

2. Configure the model

Create an API Key on the API Keys page, then provide it in one of two ways.

Keep SCITIX_API_KEY and other secrets out of Git. Use environment variables or a local .env file ignored by version control.

Option 1: Use an environment variable

Edit ~/.codex/config.toml with env_key pointing to an environment variable:

model = "glm-5.2"
model_provider = "scitix"
approval_policy = "never"
sandbox_mode = "workspace-write"

[model_providers.scitix]
name = "ScitiX"
base_url = "https://api.scitix.ai/model-api"
env_key = "SCITIX_API_KEY"
wire_api = "responses"

Set the key before launching Codex:

export SCITIX_API_KEY="<Your API Key>"
code .

Option 2: Store the token in `config.toml`

If you do not want to use an environment variable, put the key directly in config.toml with experimental_bearer_token instead of env_key.

model = "glm-5.2"
model_provider = "scitix"
approval_policy = "never"
sandbox_mode = "workspace-write"
model_reasoning_effort = "medium"

[model_providers.scitix]
name = "ScitiX"
base_url = "https://api.scitix.ai/model-api"
experimental_bearer_token = "<API_KEY>"
wire_api = "responses"

Because the key is stored in the file, keep config.toml out of version control. Either way, the configured model becomes available in Codex.

Codex configuration file using a ScitiX model provider, base URL, and API key setting

Field Reference

Field	Description
`model`	Model ID from the model catalog, such as `glm-5.2`.
`model_provider`	Must match the `[model_providers.<name>]` section. Use `scitix`.
`approval_policy`	`never` for no approval prompts; `on-request` or `untrusted` for more control.
`sandbox_mode`	`workspace-write` to read and edit within the workspace; `read-only` for read-only access.
`base_url`	ScitiX Model Inference API endpoint: `https://api.scitix.ai/model-api`.
`wire_api`	Use `responses` for the latest Codex version.
`env_key`	Option 1. Environment variable that holds your API Key, such as `SCITIX_API_KEY`.
`experimental_bearer_token`	Option 2. Your ScitiX API Key embedded directly instead of via `env_key`.
`model_reasoning_effort`	Reasoning depth for the model, such as `low`, `medium`, or `high`.

MCP Servers

Some built-in tool types, such as web_search, may not be available on these models. You can add these capabilities through MCP (Model Context Protocol) servers. Configure a search MCP server in config.toml under [mcp_servers.<name>], and Codex will call it when web search is needed. See the Codex Configuration Reference for MCP server setup details.

Get Started

Models

Usage

API Guides

Tool Integrations

Configure

1. Install Codex

2. Configure the model

Option 1: Use an environment variable

Option 2: Store the token in `config.toml`

Field Reference

MCP Servers

​Configure

​1. Install Codex

​2. Configure the model

​Option 1: Use an environment variable

​Option 2: Store the token in config.toml

​Field Reference

​MCP Servers

Configure

1. Install Codex

2. Configure the model

Option 1: Use an environment variable

Option 2: Store the token in `config.toml`

Field Reference

MCP Servers