Config

Codex supports several mechanisms for setting config values:

• Config-specific command-line flags, such as --model o3 (highest precedence).
• A generic -c/--config flag that takes a key=value pair, such as --config model="o3".
• The key can contain dots to set a value deeper than the root, e.g. --config model_providers.openai.wire_api="chat".
• For consistency with config.toml, values are a string in TOML format rather than JSON format, so use key='{a = 1, b = 2}' rather than key='{"a": 1, "b": 2}'.
  • The quotes around the value are necessary, as without them your shell would split the config argument on spaces, resulting in codex receiving -c key={a with (invalid) additional arguments =, 1,, b, =, 2}.
• Values can contain any TOML object, such as --config shell_environment_policy.include_only='["PATH", "HOME", "USER"]'.
• If value cannot be parsed as a valid TOML value, it is treated as a string value. This means that -c model='"o3"' and -c model=o3 are equivalent.
  • In the first case, the value is the TOML string "o3", while in the second the value is o3, which is not valid TOML and therefore treated as the TOML string "o3".
  • Because quotes are interpreted by one's shell, -c key="true" will be correctly interpreted in TOML as key = true (a boolean) and not key = "true" (a string). If for some reason you needed the string "true", you would need to use -c key='"true"' (note the two sets of quotes).
• The $CODEX_HOME/config.toml configuration file where the CODEX_HOME environment value defaults to ~/.codex. (Note CODEX_HOME will also be where logs and other Codex-related information are stored.)

Both the --config flag and the config.toml file support the following options:

model

The model that Codex should use.

model = "o3"  # overrides the default of "gpt-5-codex"

model_providers

This option lets you override and amend the default set of model providers bundled with Codex. This value is a map where the key is the value to use with model_provider to select the corresponding provider.

For example, if you wanted to add a provider that uses the OpenAI 4o model via the chat completions API, then you could add the following configuration:

# Recall that in TOML, root keys must be listed before tables.
model = "gpt-4o"
model_provider = "openai-chat-completions"

[model_providers.openai-chat-completions]
# Name of the provider that will be displayed in the Codex UI.
name = "OpenAI using Chat Completions"
# The path `/chat/completions` will be amended to this URL to make the POST
# request for the chat completions.
base_url = "https://api.openai.com/v1"
# If `env_key` is set, identifies an environment variable that must be set when
# using Codex with this provider. The value of the environment variable must be
# non-empty and will be used in the `Bearer TOKEN` HTTP header for the POST request.
env_key = "OPENAI_API_KEY"
# Valid values for wire_api are "chat" and "responses". Defaults to "chat" if omitted.
wire_api = "chat"
# If necessary, extra query params that need to be added to the URL.
# See the Azure example below.
query_params = {}

Note this makes it possible to use Codex CLI with non-OpenAI models, so long as they use a wire API that is compatible with the OpenAI chat completions API. For example, you could define the following provider to use Codex CLI with Ollama running locally:

[model_providers.ollama]
name = "Ollama"
base_url = "http://localhost:11434/v1"

Or a third-party provider (using a distinct environment variable for the API key):

[model_providers.mistral]
name = "Mistral"
base_url = "https://api.mistral.ai/v1"
env_key = "MISTRAL_API_KEY"

It is also possible to configure a provider to include extra HTTP headers with a request. These can be hardcoded values (http_headers) or values read from environment variables (env_http_headers):

[model_providers.example]
# name, base_url, ...

# This will add the HTTP header `X-Example-Header` with value `example-value`
# to each request to the model provider.
http_headers = { "X-Example-Header" = "example-value" }

# This will add the HTTP header `X-Example-Features` with the value of the
# `EXAMPLE_FEATURES` environment variable to each request to the model provider
# _if_ the environment variable is set and its value is non-empty.
env_http_headers = { "X-Example-Features" = "EXAMPLE_FEATURES" }

Azure model provider example

Note that Azure requires api-version to be passed as a query parameter, so be sure to specify it as part of query_params when defining the Azure provider:

[model_providers.azure]
name = "Azure"
# Make sure you set the appropriate subdomain for this URL.
base_url = "https://YOUR_PROJECT_NAME.openai.azure.com/openai"
env_key = "AZURE_OPENAI_API_KEY"  # Or "OPENAI_API_KEY", whichever you use.
query_params = { api-version = "2025-04-01-preview" }
wire_api = "responses"

Export your key before launching Codex: export AZURE_OPENAI_API_KEY=…

Per-provider network tuning

The following optional settings control retry behaviour and streaming idle timeouts per model provider. They must be specified inside the corresponding [model_providers.<id>] block in config.toml. (Older releases accepted top‑level keys; those are now ignored.)

Example:

[model_providers.openai]
name = "OpenAI"
base_url = "https://api.openai.com/v1"
env_key = "OPENAI_API_KEY"
# network tuning overrides (all optional; falls back to built‑in defaults)
request_max_retries = 4            # retry failed HTTP requests
stream_max_retries = 10            # retry dropped SSE streams
stream_idle_timeout_ms = 300000    # 5m idle timeout

request_max_retries

How many times Codex will retry a failed HTTP request to the model provider. Defaults to 4.

stream_max_retries

Number of times Codex will attempt to reconnect when a streaming response is interrupted. Defaults to 5.

stream_idle_timeout_ms

How long Codex will wait for activity on a streaming response before treating the connection as lost. Defaults to 300_000 (5 minutes).

model_provider

Identifies which provider to use from the model_providers map. Defaults to "openai". You can override the base_url for the built-in openai provider via the OPENAI_BASE_URL environment variable.

Note that if you override model_provider, then you likely want to override model, as well. For example, if you are running ollama with Mistral locally, then you would need to add the following to your config in addition to the new entry in the model_providers map:

model_provider = "ollama"
model = "mistral"

approval_policy

Determines when the user should be prompted to approve whether Codex can execute a command:

# Codex has hardcoded logic that defines a set of "trusted" commands.
# Setting the approval_policy to `untrusted` means that Codex will prompt the
# user before running a command not in the "trusted" set.
#
# See https://github.com/openai/codex/issues/1260 for the plan to enable
# end-users to define their own trusted commands.
approval_policy = "untrusted"

If you want to be notified whenever a command fails, use "on-failure":

# If the command fails when run in the sandbox, Codex asks for permission to
# retry the command outside the sandbox.
approval_policy = "on-failure"

If you want the model to run until it decides that it needs to ask you for escalated permissions, use "on-request":

# The model decides when to escalate
approval_policy = "on-request"

Alternatively, you can have the model run until it is done, and never ask to run a command with escalated permissions:

# User is never prompted: if the command fails, Codex will automatically try
# something out. Note the `exec` subcommand always uses this mode.
approval_policy = "never"

profiles

A profile is a collection of configuration values that can be set together. Multiple profiles can be defined in config.toml and you can specify the one you want to use at runtime via the --profile flag.

Here is an example of a config.toml that defines multiple profiles:

model = "o3"
approval_policy = "untrusted"

# Setting `profile` is equivalent to specifying `--profile o3` on the command
# line, though the `--profile` flag can still be used to override this value.
profile = "o3"

[model_providers.openai-chat-completions]
name = "OpenAI using Chat Completions"
base_url = "https://api.openai.com/v1"
env_key = "OPENAI_API_KEY"
wire_api = "chat"

[profiles.o3]
model = "o3"
model_provider = "openai"
approval_policy = "never"
model_reasoning_effort = "high"
model_reasoning_summary = "detailed"

[profiles.gpt3]
model = "gpt-3.5-turbo"
model_provider = "openai-chat-completions"

[profiles.zdr]
model = "o3"
model_provider = "openai"
approval_policy = "on-failure"

Users can specify config values at multiple levels. Order of precedence is as follows:

1. custom command-line argument, e.g., --model o3
2. as part of a profile, where the --profile is specified via a CLI (or in the config file itself)
3. as an entry in config.toml, e.g., model = "o3"
4. the default value that comes with Codex CLI (i.e., Codex CLI defaults to gpt-5-codex)

model_reasoning_effort

If the selected model is known to support reasoning (for example: o3, o4-mini, codex-*, gpt-5, gpt-5-codex), reasoning is enabled by default when using the Responses API. As explained in the OpenAI Platform documentation, this can be set to:

• "minimal"
• "low"
• "medium" (default)
• "high"

Note: to minimize reasoning, choose "minimal".

model_reasoning_summary

If the model name starts with "o" (as in "o3" or "o4-mini") or "codex", reasoning is enabled by default when using the Responses API. As explained in the OpenAI Platform documentation, this can be set to:

• "auto" (default)
• "concise"
• "detailed"

To disable reasoning summaries, set model_reasoning_summary to "none" in your config:

model_reasoning_summary = "none"  # disable reasoning summaries

model_verbosity

Controls output length/detail on GPT‑5 family models when using the Responses API. Supported values:

• "low"
• "medium" (default when omitted)
• "high"

When set, Codex includes a text object in the request payload with the configured verbosity, for example: "text": { "verbosity": "low" }.

Example:

model = "gpt-5"
model_verbosity = "low"

Note: This applies only to providers using the Responses API. Chat Completions providers are unaffected.

model_supports_reasoning_summaries

By default, reasoning is only set on requests to OpenAI models that are known to support them. To force reasoning to set on requests to the current model, you can force this behavior by setting the following in config.toml:

model_supports_reasoning_summaries = true

sandbox_mode

Codex executes model-generated shell commands inside an OS-level sandbox.

In most cases you can pick the desired behaviour with a single option:

# same as `--sandbox read-only`
sandbox_mode = "read-only"

The default policy is read-only, which means commands can read any file on disk, but attempts to write a file or access the network will be blocked.

A more relaxed policy is workspace-write. When specified, the current working directory for the Codex task will be writable (as well as $TMPDIR on macOS). Note that the CLI defaults to using the directory where it was spawned as cwd, though this can be overridden using --cwd/-C.

On macOS (and soon Linux), all writable roots (including cwd) that contain a .git/ folder as an immediate child will configure the .git/ folder to be read-only while the rest of the Git repository will be writable. This means that commands like git commit will fail, by default (as it entails writing to .git/), and will require Codex to ask for permission.

# same as `--sandbox workspace-write`
sandbox_mode = "workspace-write"

# Extra settings that only apply when `sandbox = "workspace-write"`.
[sandbox_workspace_write]
# By default, the cwd for the Codex session will be writable as well as $TMPDIR
# (if set) and /tmp (if it exists). Setting the respective options to `true`
# will override those defaults.
exclude_tmpdir_env_var = false
exclude_slash_tmp = false

# Optional list of _additional_ writable roots beyond $TMPDIR and /tmp.
writable_roots = ["/Users/YOU/.pyenv/shims"]

# Allow the command being run inside the sandbox to make outbound network
# requests. Disabled by default.
network_access = false

To disable sandboxing altogether, specify danger-full-access like so:

# same as `--sandbox danger-full-access`
sandbox_mode = "danger-full-access"

This is reasonable to use if Codex is running in an environment that provides its own sandboxing (such as a Docker container) such that further sandboxing is unnecessary.

Though using this option may also be necessary if you try to use Codex in environments where its native sandboxing mechanisms are unsupported, such as older Linux kernels or on Windows.

Approval presets

Codex provides three main Approval Presets:

• Read Only: Codex can read files and answer questions; edits, running commands, and network access require approval.
• Auto: Codex can read files, make edits, and run commands in the workspace without approval; asks for approval outside the workspace or for network access.
• Full Access: Full disk and network access without prompts; extremely risky.

You can further customize how Codex runs at the command line using the --ask-for-approval and --sandbox options.

MCP Servers

You can configure Codex to use MCP servers to give Codex access to external applications, resources, or services such as Playwright, Figma, documentation, and more.

Server transport configuration

STDIO

# The top-level table name must be `mcp_servers`
# The sub-table name (`server-name` in this example) can be anything you would like.
[mcp_servers.server-name]
command = "npx"
# Optional
args = ["-y", "mcp-server"]
# Optional: propagate additional env vars to the MVP server.
# A default whitelist of env vars will be propagated to the MCP server.
# https://github.com/openai/codex/blob/main/codex-rs/rmcp-client/src/utils.rs#L82
env = { "API_KEY" = "value" }

Streamable HTTP

# Streamable HTTP requires the experimental rmcp client
experimental_use_rmcp_client = true
[mcp_servers.figma]
url = "http://127.0.0.1:3845/mcp"
# Optional bearer token to be passed into an `Authorization: Bearer <token>` header
# Use this with caution because the token is in plaintext.
bearer_token = "<token>"

Refer to the MCP CLI commands for oauth login

Other configuration options

# Optional: override the default 10s startup timeout
startup_timeout_sec = 20
# Optional: override the default 60s per-tool timeout
tool_timeout_sec = 30

Experimental RMCP client

Codex is transitioning to the official Rust MCP SDK and new functionality such as streamable http servers will only work with the new client.

Please try and report issues with the new client. To enable it, add this to the top level of your config.toml

experimental_use_rmcp_client = true

MCP CLI commands

# Add a server (env can be repeated; `--` separates the launcher command)
codex mcp add docs -- docs-server --port 4000

# List configured servers (pretty table or JSON)
codex mcp list
codex mcp list --json

# Show one server (table or JSON)
codex mcp get docs
codex mcp get docs --json

# Remove a server
codex mcp remove docs

# Log in to a streamable HTTP server that supports oauth
codex mcp login SERVER_NAME

# Log out from a streamable HTTP server that supports oauth
codex mcp logout SERVER_NAME

shell_environment_policy

Codex spawns subprocesses (e.g. when executing a local_shell tool-call suggested by the assistant). By default it now passes your full environment to those subprocesses. You can tune this behavior via the shell_environment_policy block in config.toml:

[shell_environment_policy]
# inherit can be "all" (default), "core", or "none"
inherit = "core"
# set to true to *skip* the filter for `"*KEY*"` and `"*TOKEN*"`
ignore_default_excludes = false
# exclude patterns (case-insensitive globs)
exclude = ["AWS_*", "AZURE_*"]
# force-set / override values
set = { CI = "1" }
# if provided, *only* vars matching these patterns are kept
include_only = ["PATH", "HOME"]

The patterns are glob style, not full regular expressions: * matches any number of characters, ? matches exactly one, and character classes like [A-Z]/[^0-9] are supported. Matching is always case-insensitive. This syntax is documented in code as EnvironmentVariablePattern (see core/src/config_types.rs).

If you just need a clean slate with a few custom entries you can write:

[shell_environment_policy]
inherit = "none"
set = { PATH = "/usr/bin", MY_FLAG = "1" }

Currently, CODEX_SANDBOX_NETWORK_DISABLED=1 is also added to the environment, assuming network is disabled. This is not configurable.

otel

Codex can emit OpenTelemetry log events that describe each run: outbound API requests, streamed responses, user input, tool-approval decisions, and the result of every tool invocation. Export is disabled by default so local runs remain self-contained. Opt in by adding an [otel] table and choosing an exporter.

[otel]
environment = "staging"   # defaults to "dev"
exporter = "none"          # defaults to "none"; set to otlp-http or otlp-grpc to send events
log_user_prompt = false    # defaults to false; redact prompt text unless explicitly enabled

Codex tags every exported event with service.name = $ORIGINATOR (the same value sent in the originator header, codex_cli_rs by default), the CLI version, and an env attribute so downstream collectors can distinguish dev/staging/prod traffic. Only telemetry produced inside the codex_otel crate—the events listed below—is forwarded to the exporter.

Event catalog

Every event shares a common set of metadata fields: event.timestamp, conversation.id, app.version, auth_mode (when available), user.account_id (when available), terminal.type, model, and slug.

With OTEL enabled Codex emits the following event types (in addition to the metadata above):

• codex.conversation_starts
• provider_name
• reasoning_effort (optional)
• reasoning_summary
• context_window (optional)
• max_output_tokens (optional)
• auto_compact_token_limit (optional)
• approval_policy
• sandbox_policy
• mcp_servers (comma-separated list)
• active_profile (optional)
• codex.api_request
• attempt
• duration_ms
• http.response.status_code (optional)
• error.message (failures)
• codex.sse_event
• event.kind
• duration_ms
• error.message (failures)
• input_token_count (responses only)
• output_token_count (responses only)
• cached_token_count (responses only, optional)
• reasoning_token_count (responses only, optional)
• tool_token_count (responses only)
• codex.user_prompt
• prompt_length
• prompt (redacted unless log_user_prompt = true)
• codex.tool_decision
• tool_name
• call_id
• decision (approved, approved_for_session, denied, or abort)
• source (config or user)
• codex.tool_result
• tool_name
• call_id (optional)
• arguments (optional)
• duration_ms (execution time for the tool)
• success ("true" or "false")
• output

These event shapes may change as we iterate.

Choosing an exporter

Set otel.exporter to control where events go:

• none – leaves instrumentation active but skips exporting. This is the default.
• otlp-http – posts OTLP log records to an OTLP/HTTP collector. Specify the endpoint, protocol, and headers your collector expects:

toml [otel] exporter = { otlp-http = { endpoint = "https://otel.example.com/v1/logs", protocol = "binary", headers = { "x-otlp-api-key" = "${OTLP_TOKEN}" } }}

• otlp-grpc – streams OTLP log records over gRPC. Provide the endpoint and any metadata headers:

toml [otel] exporter = { otlp-grpc = { endpoint = "https://otel.example.com:4317", headers = { "x-otlp-meta" = "abc123" } }}

If the exporter is none nothing is written anywhere; otherwise you must run or point to your own collector. All exporters run on a background batch worker that is flushed on shutdown.

If you build Codex from source the OTEL crate is still behind an otel feature flag; the official prebuilt binaries ship with the feature enabled. When the feature is disabled the telemetry hooks become no-ops so the CLI continues to function without the extra dependencies.

notify

Specify a program that will be executed to get notified about events generated by Codex. Note that the program will receive the notification argument as a string of JSON, e.g.:

{
  "type": "agent-turn-complete",
  "turn-id": "12345",
  "input-messages": ["Rename `foo` to `bar` and update the callsites."],
  "last-assistant-message": "Rename complete and verified `cargo build` succeeds."
}

The "type" property will always be set. Currently, "agent-turn-complete" is the only notification type that is supported.

As an example, here is a Python script that parses the JSON and decides whether to show a desktop push notification using terminal-notifier on macOS:

#!/usr/bin/env python3

import json
import subprocess
import sys


def main() -> int:
    if len(sys.argv) != 2:
        print("Usage: notify.py <NOTIFICATION_JSON>")
        return 1

    try:
        notification = json.loads(sys.argv[1])
    except json.JSONDecodeError:
        return 1

    match notification_type := notification.get("type"):
        case "agent-turn-complete":
            assistant_message = notification.get("last-assistant-message")
            if assistant_message:
                title = f"Codex: {assistant_message}"
            else:
                title = "Codex: Turn Complete!"
            input_messages = notification.get("input_messages", [])
            message = " ".join(input_messages)
            title += message
        case _:
            print(f"not sending a push notification for: {notification_type}")
            return 0

    subprocess.check_output(
        [
            "terminal-notifier",
            "-title",
            title,
            "-message",
            message,
            "-group",
            "codex",
            "-ignoreDnD",
            "-activate",
            "com.googlecode.iterm2",
        ]
    )

    return 0


if __name__ == "__main__":
    sys.exit(main())

To have Codex use this script for notifications, you would configure it via notify in ~/.codex/config.toml using the appropriate path to notify.py on your computer:

notify = ["python3", "/Users/mbolin/.codex/notify.py"]

[!NOTE] Use notify for automation and integrations: Codex invokes your external program with a single JSON argument for each event, independent of the TUI. If you only want lightweight desktop notifications while using the TUI, prefer tui.notifications, which uses terminal escape codes and requires no external program. You can enable both; tui.notifications covers in‑TUI alerts (e.g., approval prompts), while notify is best for system‑level hooks or custom notifiers. Currently, notify emits only agent-turn-complete, whereas tui.notifications supports agent-turn-complete and approval-requested with optional filtering.

history

By default, Codex CLI records messages sent to the model in $CODEX_HOME/history.jsonl. Note that on UNIX, the file permissions are set to o600, so it should only be readable and writable by the owner.

To disable this behavior, configure [history] as follows:

[history]
persistence = "none"  # "save-all" is the default value

file_opener

Identifies the editor/URI scheme to use for hyperlinking citations in model output. If set, citations to files in the model output will be hyperlinked using the specified URI scheme so they can be ctrl/cmd-clicked from the terminal to open them.

For example, if the model output includes a reference such as 【F:/home/user/project/main.py†L42-L50】, then this would be rewritten to link to the URI vscode://file/home/user/project/main.py:42.

Note this is not a general editor setting (like $EDITOR), as it only accepts a fixed set of values:

• "vscode" (default)
• "vscode-insiders"
• "windsurf"
• "cursor"
• "none" to explicitly disable this feature

Currently, "vscode" is the default, though Codex does not verify VS Code is installed. As such, file_opener may default to "none" or something else in the future.

hide_agent_reasoning

Codex intermittently emits "reasoning" events that show the model's internal "thinking" before it produces a final answer. Some users may find these events distracting, especially in CI logs or minimal terminal output.

Setting hide_agent_reasoning to true suppresses these events in both the TUI as well as the headless exec sub-command:

hide_agent_reasoning = true   # defaults to false

show_raw_agent_reasoning

Surfaces the model’s raw chain-of-thought ("raw reasoning content") when available.

Notes:

• Only takes effect if the selected model/provider actually emits raw reasoning content. Many models do not. When unsupported, this option has no visible effect.
• Raw reasoning may include intermediate thoughts or sensitive context. Enable only if acceptable for your workflow.

Example:

show_raw_agent_reasoning = true  # defaults to false

model_context_window

The size of the context window for the model, in tokens.

In general, Codex knows the context window for the most common OpenAI models, but if you are using a new model with an old version of the Codex CLI, then you can use model_context_window to tell Codex what value to use to determine how much context is left during a conversation.

model_max_output_tokens

This is analogous to model_context_window, but for the maximum number of output tokens for the model.

project_doc_max_bytes

Maximum number of bytes to read from an AGENTS.md file to include in the instructions sent with the first turn of a session. Defaults to 32 KiB.

project_doc_fallback_filenames

Ordered list of additional filenames to look for when AGENTS.md is missing at a given directory level. The CLI always checks AGENTS.md first; the configured fallbacks are tried in the order provided. This lets monorepos that already use alternate instruction files (for example, CLAUDE.md) work out of the box while you migrate to AGENTS.md over time.

project_doc_fallback_filenames = ["CLAUDE.md", ".exampleagentrules.md"]

We recommend migrating instructions to AGENTS.md; other filenames may reduce model performance.

tui

Options that are specific to the TUI.

[tui]
# Send desktop notifications when approvals are required or a turn completes.
# Defaults to false.
notifications = true

# You can optionally filter to specific notification types.
# Available types are "agent-turn-complete" and "approval-requested".
notifications = [ "agent-turn-complete", "approval-requested" ]

[!NOTE] Codex emits desktop notifications using terminal escape codes. Not all terminals support these (notably, macOS Terminal.app and VS Code's terminal do not support custom notifications. iTerm2, Ghostty and WezTerm do support these notifications).

[!NOTE] > tui.notifications is built‑in and limited to the TUI session. For programmatic or cross‑environment notifications—or to integrate with OS‑specific notifiers—use the top‑level notify option to run an external program that receives event JSON. The two settings are independent and can be used together.

Config reference