Advanced Configuration

Use these options when you need more control over providers, policies, and integrations. For a quick start, see Config basics.

Profiles

Profiles let you save named sets of configuration values and switch between them from the CLI.

Define profiles under [profiles.<name>] in config.toml, then run codex --profile <name>:

model = "gpt-5-codex"
approval_policy = "on-request"

[profiles.deep-review]
model = "gpt-5-pro"
model_reasoning_effort = "high"
approval_policy = "never"

[profiles.lightweight]
model = "gpt-4.1"
approval_policy = "untrusted"

To make a profile the default, add profile = "deep-review" at the top level of config.toml. Codex loads that profile unless you override it on the command line.

One-off overrides from the CLI

In addition to editing ~/.codex/config.toml, you can override configuration for a single run from the CLI:

• Prefer dedicated flags when they exist (for example, --model).
• Use -c / --config when you need to override an arbitrary key.

Examples:

# Dedicated flag
codex --model gpt-5.2

# Generic key/value override (value is TOML, not JSON)
codex --config model='"gpt-5.2"'
codex --config sandbox_workspace_write.network_access=true
codex --config 'shell_environment_policy.include_only=["PATH","HOME"]'

Notes:

• Keys can use dot notation to set nested values (for example, mcp_servers.context7.enabled=false).
• --config values are parsed as TOML. When in doubt, quote the value so your shell doesn’t split it on spaces.
• If the value can’t be parsed as TOML, Codex treats it as a string.

Config and state locations

Codex stores its local state under CODEX_HOME (defaults to ~/.codex).

Common files you may see there:

• config.toml (your local configuration)
• auth.json (if you use file-based credential storage) or your OS keychain/keyring
• history.jsonl (if history persistence is enabled)
• Other per-user state such as logs and caches

For authentication details (including credential storage modes), see Authentication. For the full list of configuration keys, see Configuration Reference.

For shared defaults, rules, and skills checked into repos or system paths, see Team Config.

If you just need to point the built-in OpenAI provider at an LLM proxy, router, or data-residency enabled project, set environment variable OPENAI_BASE_URL instead of defining a new provider. This overrides the default OpenAI endpoint without a config.toml change.

export OPENAI_BASE_URL="https://api.openai.com/v1"
codex

Project config files (.codex/config.toml)

In addition to your user config, Codex reads project-scoped overrides from .codex/config.toml files inside your repo. Codex walks from the project root to your current working directory and loads every .codex/config.toml it finds. If multiple files define the same key, the closest file to your working directory wins.

For security, Codex loads project-scoped config files only when the project is trusted. If the project is untrusted, Codex ignores .codex/config.toml files in the project.

Relative paths inside a project config (for example, experimental_instructions_file) are resolved relative to the .codex/ folder that contains the config.toml.

Project root detection

Codex discovers project configuration (for example, .codex/ layers and AGENTS.md) by walking up from the working directory until it reaches a project root.

By default, Codex treats a directory containing .git as the project root. To customize this behavior, set project_root_markers in config.toml:

# Treat a directory as the project root when it contains any of these markers.
project_root_markers = [".git", ".hg", ".sl"]

Set project_root_markers = [] to skip searching parent directories and treat the current working directory as the project root.

Custom model providers

A model provider defines how Codex connects to a model (base URL, wire API, and optional HTTP headers).

Define additional providers and point model_provider at them:

model = "gpt-5.1"
model_provider = "proxy"

[model_providers.proxy]
name = "OpenAI using LLM proxy"
base_url = "http://proxy.example.com"
env_key = "OPENAI_API_KEY"

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

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

Add request headers when needed:

[model_providers.example]
http_headers = { "X-Example-Header" = "example-value" }
env_http_headers = { "X-Example-Features" = "EXAMPLE_FEATURES" }

OSS mode (local providers)

Codex can run against a local “open source” provider (for example, Ollama or LM Studio) when you pass --oss. If you pass --oss without specifying a provider, Codex uses oss_provider as the default.

# Default local provider used with `--oss`
oss_provider = "ollama" # or "lmstudio"

Azure provider and per-provider tuning

[model_providers.azure]
name = "Azure"
base_url = "https://YOUR_PROJECT_NAME.openai.azure.com/openai"
env_key = "AZURE_OPENAI_API_KEY"
query_params = { api-version = "2025-04-01-preview" }
wire_api = "responses"

[model_providers.openai]
request_max_retries = 4
stream_max_retries = 10
stream_idle_timeout_ms = 300000

ChatGPT customers using data residency

Projects created with data residency enabled can create a model provider to update the base_url with the correct prefix.

model_provider = "openaidr"
[model_providers.openaidr]
name = "OpenAI Data Residency"
base_url = "https://us.api.openai.com/v1" # Replace 'us' with domain prefix

Model reasoning, verbosity, and limits

model_reasoning_summary = "none"          # Disable summaries
model_verbosity = "low"                   # Shorten responses
model_supports_reasoning_summaries = true # Force reasoning
model_context_window = 128000             # Context window size

model_verbosity applies only to providers using the Responses API. Chat Completions providers will ignore the setting.

Approval policies and sandbox modes

Pick approval strictness (affects when Codex pauses) and sandbox level (affects file/network access). See Sandbox & approvals for deeper examples.

approval_policy = "untrusted"   # Other options: on-request, on-failure, never
sandbox_mode = "workspace-write"

[sandbox_workspace_write]
exclude_tmpdir_env_var = false  # Allow $TMPDIR
exclude_slash_tmp = false       # Allow /tmp
writable_roots = ["/Users/YOU/.pyenv/shims"]
network_access = false          # Opt in to outbound network

Disable sandboxing entirely (use only if your environment already isolates processes):

sandbox_mode = "danger-full-access"

Shell environment policy

shell_environment_policy controls which environment variables Codex passes to any subprocess it launches (for example, when running a tool-command the model proposes). Start from a clean start (inherit = "none") or a trimmed set (inherit = "core"), then layer on excludes, includes, and overrides to avoid leaking secrets while still providing the paths, keys, or flags your tasks need.

[shell_environment_policy]
inherit = "none"
set = { PATH = "/usr/bin", MY_FLAG = "1" }
ignore_default_excludes = false
exclude = ["AWS_*", "AZURE_*"]
include_only = ["PATH", "HOME"]

Patterns are case-insensitive globs (*, ?, [A-Z]); ignore_default_excludes = false keeps the automatic KEY/SECRET/TOKEN filter before your includes/excludes run.

MCP servers

See the dedicated MCP documentation for configuration details.

Observability and telemetry

Enable OpenTelemetry (OTel) log export to track Codex runs (API requests, SSE/events, prompts, tool approvals/results). Disabled by default; opt in via [otel]:

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

Choose an exporter:

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

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

If exporter = "none" Codex records events but sends nothing. Exporters batch asynchronously and flush on shutdown. Event metadata includes service name, CLI version, env tag, conversation id, model, sandbox/approval settings, and per-event fields (see Config Reference).

What gets emitted

Codex emits structured log events for runs and tool usage. Representative event types include:

• codex.conversation_starts (model, reasoning settings, sandbox/approval policy)
• codex.api_request (attempt, status/success, duration, and error details)
• codex.sse_event (stream event kind, success/failure, duration, plus token counts on response.completed)
• codex.websocket_request and codex.websocket_event (request duration plus per-message kind/success/error)
• codex.user_prompt (length; content redacted unless explicitly enabled)
• codex.tool_decision (approved/denied and whether the decision came from config vs user)
• codex.tool_result (duration, success, output snippet)

OTel metrics emitted

When the OTel metrics pipeline is enabled, Codex emits counters and duration histograms for API, stream, and tool activity.

Each metric below also includes default metadata tags: auth_mode, session_source, model, and app.version.

For more security and privacy guidance around telemetry, see Security.

Metrics

By default, Codex periodically sends a small amount of anonymous usage and health data back to OpenAI. This helps detect when Codex isn’t working correctly and shows what features and configuration options are being used, so the Codex team can focus on what matters most. These metrics don’t contain any personally identifiable information (PII). Metrics collection is independent of OTel log/trace export.

If you want to disable metrics collection entirely across Codex surfaces on a machine, set the analytics flag in your config:

[analytics]
enabled = false

Each metric includes its own fields plus the default context fields below.

Default context fields (applies to every event/metric)

• auth_mode: swic | api | unknown.
• model: name of the model used.
• app.version: Codex version.

Metrics catalog

Each metric includes the required fields plus the default context fields above. Every metric is prefixed by codex.. If a metric includes the tool field, it reflects the internal tool used (for example, apply_patch or shell) and doesn’t contain the actual shell command or patch codex is trying to apply.

Feedback controls

By default, Codex lets users send feedback from /feedback. To disable feedback collection across Codex surfaces on a machine, update your config:

[feedback]
enabled = false

When disabled, /feedback shows a disabled message and Codex rejects feedback submissions.

Hide or surface reasoning events

If you want to reduce noisy “reasoning” output (for example in CI logs), you can suppress it:

hide_agent_reasoning = true

If you want to surface raw reasoning content when a model emits it:

show_raw_agent_reasoning = true

Enable raw reasoning only if it’s acceptable for your workflow. Some models/providers (like gpt-oss) don’t emit raw reasoning; in that case, this setting has no visible effect.

Notifications

Use notify to trigger an external program whenever Codex emits supported events (currently only agent-turn-complete). This is handy for desktop toasts, chat webhooks, CI updates, or any side-channel alerting that the built-in TUI notifications don’t cover.

notify = ["python3", "/path/to/notify.py"]

Example notify.py (truncated) that reacts to agent-turn-complete:

#!/usr/bin/env python3
import json, subprocess, sys

def main() -> int:
    notification = json.loads(sys.argv[1])
    if notification.get("type") != "agent-turn-complete":
        return 0
    title = f"Codex: {notification.get('last-assistant-message', 'Turn Complete!')}"
    message = " ".join(notification.get("input-messages", []))
    subprocess.check_output([
        "terminal-notifier",
        "-title", title,
        "-message", message,
        "-group", "codex-" + notification.get("thread-id", ""),
        "-activate", "com.googlecode.iterm2",
    ])
    return 0

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

The script receives a single JSON argument. Common fields include:

• type (currently agent-turn-complete)
• thread-id (session identifier)
• turn-id (turn identifier)
• cwd (working directory)
• input-messages (user messages that led to the turn)
• last-assistant-message (last assistant message text)

Place the script somewhere on disk and point notify to it.

notify vs tui.notifications

• notify runs an external program (good for webhooks, desktop notifiers, CI hooks).
• tui.notifications is built in to the TUI and can optionally filter by event type (for example, agent-turn-complete and approval-requested).
• tui.notification_method controls how the TUI emits terminal notifications (auto, osc9, or bel).

In auto mode, Codex prefers OSC 9 notifications (a terminal escape sequence some terminals interpret as a desktop notification) and falls back to BEL (\x07) otherwise.

See Configuration Reference for the exact keys.

History persistence

By default, Codex saves local session transcripts under CODEX_HOME (for example, ~/.codex/history.jsonl). To disable local history persistence:

[history]
persistence = "none"

To cap the history file size, set history.max_bytes. When the file exceeds the cap, Codex drops the oldest entries and compacts the file while keeping the newest records.

[history]
max_bytes = 104857600 # 100 MiB

Clickable citations

If you use a terminal/editor integration that supports it, Codex can render file citations as clickable links. Configure file_opener to pick the URI scheme Codex uses:

file_opener = "vscode" # or cursor, windsurf, vscode-insiders, none

Example: a citation like /home/user/project/main.py:42 can be rewritten into a clickable vscode://file/...:42 link.

Project instructions discovery

Codex reads AGENTS.md (and related files) and includes a limited amount of project guidance in the first turn of a session. Two knobs control how this works:

• project_doc_max_bytes: how much to read from each AGENTS.md file
• project_doc_fallback_filenames: additional filenames to try when AGENTS.md is missing at a directory level

For a detailed walkthrough, see Custom instructions with AGENTS.md.

TUI options

Running codex with no subcommand launches the interactive terminal UI (TUI). Codex exposes some TUI-specific configuration under [tui], including:

• tui.notifications: enable/disable notifications (or restrict to specific types)
• tui.notification_method: choose auto, osc9, or bel for terminal notifications
• tui.animations: enable/disable ASCII animations and shimmer effects
• tui.alternate_screen: control alternate screen usage (set to never to keep terminal scrollback)
• tui.show_tooltips: show or hide onboarding tooltips on the welcome screen

tui.notification_method defaults to auto. In auto mode, Codex prefers OSC 9 notifications (a terminal escape sequence some terminals interpret as a desktop notification) when the terminal appears to support them, and falls back to BEL (\x07) otherwise.

See Configuration Reference for the full key list.