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Configuration

Kay 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 kay 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 $KAY_HOME/config.toml configuration file. KAY_HOME defaults to ~/.kay.

  • If $KAY_HOME/config.toml is absent, Kay also reads the older $KAY_HOME/kay.toml provider-default format.

  • https://developers.openai.com/codex/config-reference

model

The model that Kay should use.

model = "o3"  # overrides the default of "gpt-5.1-codex"

model_providers

This option lets you override and amend the default set of model providers bundled with Kay. This value is a map where the key is the value to use with model_provider to select the corresponding provider. The active runtime supports OpenAI-compatible Chat Completions, OpenAI Responses, and Amazon Bedrock Converse providers. Imported profiles can also declare native transports such as anthropic_messages and gemini_native so Kay can recognize them before their native adapters are implemented.

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 Kay 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 Kay 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 include "chat", "responses", "responses_websocket",
# "anthropic_messages", "gemini_native", and "bedrock_converse".
# 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 = {}

OpenCode Go is available as a built-in provider using the opencode-go id:

[model_providers.opencode-go]
name = "OpenCode Go"
base_url = "https://opencode.ai/zen/go/v1"
env_key = "OPENCODE_GO_API_KEY"
wire_api = "chat"
requires_openai_auth = false

OpenCode Go model ids use the opencode-go/<model-id> format, for example opencode-go/kimi-k2.6.

Xiaomi is available as a built-in provider using the xiaomi id:

[model_providers.xiaomi]
name = "Xiaomi"
base_url = "https://token-plan-sgp.xiaomimimo.com/v1"
env_key = "XIAOMI_API_KEY"
wire_api = "chat"
requires_openai_auth = false

Xiaomi model ids use the xiaomi/<model-id> format. The built-in Xiaomi models are xiaomi/mimo-v2.5-pro and xiaomi/mimo-v2.5.

OpenRouter is available as a built-in provider using the openrouter id:

model_provider = "openrouter"
model = "anthropic/claude-sonnet-4.5"

[model_providers.openrouter.openrouter]
require_parameters = true
order = ["Anthropic", "Google"]

Save an OpenRouter key with kay login --provider openrouter --api-key <KEY> or set OPENROUTER_API_KEY.

Kay can import Hermes Agent provider profiles into $KAY_HOME/provider_profiles/hermes/:

kay providers import-hermes --source /path/to/hermes-agent --check
kay providers import-hermes --source /path/to/hermes-agent --install

Imported data-only Hermes profiles are available as model_provider values without editing config.toml. Profiles with known Hermes hooks are mapped to named Kay compatibility adapters. Unknown hookful profiles are recorded with requires_adapter and are not used as generic runtime providers until Kay gains a matching adapter.

Amazon Bedrock is registered as amazon-bedrock with wire_api = "bedrock_converse". Kay signs Bedrock Converse requests with the standard AWS SDK credential chain.

Note this makes it possible to use the Kay 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 Kay 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"

Or a proxy that converts OpenAI-compatible requests to another vendor (e.g., Anthropic or Gemini):

model = "claude-opus-4.6"
model_provider = "claude-proxy"

[model_providers.claude-proxy]
name = "Claude (proxy)"
base_url = "http://127.0.0.1:8000/v1"
env_key = "ANTHROPIC_API_KEY"
wire_api = "responses"  # or "chat" if your proxy only supports chat-completions
requires_openai_auth = false

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 Kay: 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 Kay will retry a failed HTTP request to the model provider. Defaults to 4.

stream_max_retries

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

stream_idle_timeout_ms

How long Kay 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". Built-in provider IDs include "openai", "xiaomi", "opencode-go", "minimax", "openrouter", "amazon-bedrock", and "oss". You can override the base_url for the built-in openai provider via the OPENAI_BASE_URL environment variable and force the wire protocol with OPENAI_WIRE_API.

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 Kay can execute a command:

# Kay has hardcoded logic that defines a set of "trusted" commands.
# Setting the approval_policy to `untrusted` means that Kay will prompt the
# user before running a command not in the "trusted" set.
#
# A configurable trusted-command list is planned; for now the built-in set is fixed.
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, Kay 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, Kay will automatically try
# something out. Note the `exec` subcommand always uses this mode.
approval_policy = "never"

agents

Use [[agents]] blocks to register additional CLI programs that Kay can launch as peers. Each block maps a short name (referenced elsewhere in the config) to the command to execute, optional default flags, and environment variables.

Note: Built-in model slugs (for example code-gpt-5.4, claude-sonnet-4.5) automatically inject the correct --model or -m flag. To avoid conflicting arguments, Kay strips any --model/-m flags you place in args, args_read_only, or args_write before launching the agent. If you need a new model variant, add a slug in kay-rs/core/src/agent_defaults.rs (or set an environment variable consumed by the CLI) rather than pinning the flag here.

[[agents]]
name = "context-collector"
command = "gemini"
enabled = true
read-only = true
description = "Gemini long-context helper that summarizes large repositories"
args = ["-y"]
env = { GEMINI_API_KEY = "..." }

notice

Kay stores acknowledgement flags for one-time upgrade prompts inside a [notice] table. These booleans allow you to suppress specific dialogs globally. When set to true, Kay will no longer prompt about that migration.

[notice]
hide_gpt5_1_migration_prompt = true
hide_gpt-5.1-codex-max_migration_prompt = true

When enabled = true, the agent is surfaced in the TUI picker and any sub-agent commands that reference it. Setting read-only = true forces the agent to request approval before modifying files even if the primary session permits writes.

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"
disable_response_storage = false

# 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"
disable_response_storage = true

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 Kay CLI (i.e., Kay CLI defaults to gpt-5.1-codex)

model_reasoning_effort

If the selected model is known to support reasoning (for example: o3, o4-mini, codex-*, gpt-5.1, gpt-5.1-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, Kay includes a text object in the request payload with the configured verbosity, for example: "text": { "verbosity": "low" }.

Example:

model = "gpt-5.1"
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

Kay 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 Kay 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.

Historically, Kay allowed writes inside the top‑level .git/ folder when using workspace-write. That permissive behavior is the default again. If you want to protect .git under workspace-write, you can opt out via [sandbox_workspace_write].allow_git_writes = false.

# 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 Kay 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.

# Protect top-level .git under writable roots (default is true = allow writes)
allow_git_writes = true
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 Kay 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 Kay in environments where its native sandboxing mechanisms are unsupported, such as older Linux kernels or on Windows.

Approval presets

Kay provides three main Approval Presets:

  • Read Only: Kay can read files and answer questions; edits, running commands, and network access require approval.
  • Auto: Kay 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 Kay runs at the command line using the --ask-for-approval and --sandbox options.

MCP Servers

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

Server transport configuration

Each server may set startup_timeout_sec to adjust how long Kay waits for it to start and respond to a tools listing. The default is 10 seconds. Similarly, tool_timeout_sec limits how long individual tool calls may run (default: 60 seconds), and Kay will fall back to the default when this value is omitted.

This config option is comparable to how Claude and Cursor define mcpServers in their respective JSON config files, though because Kay uses TOML for its config language, the format is slightly different. For example, the following config in JSON:

{
  "mcpServers": {
    "server-name": {
      "command": "npx",
      "args": ["-y", "mcp-server"],
      "env": {
        "API_KEY": "value"
      }
    }
  }
}

Should be represented as follows in ~/.kay/config.toml:

# 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 MCP server.
# A default whitelist of env vars will be propagated to the MCP server.
# https://github.com/alo-labs/kay/blob/main/kay-rs/rmcp-client/src/utils.rs#L82
env = { "API_KEY" = "value" }

Streamable HTTP

[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>"

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

subagents

Sub-agents are orchestrated helper workflows you can trigger with slash commands (for example /plan, /solve, /kay). Each entry under [[subagents.commands]] defines the slash command name, whether spawned agents run in read-only mode, which agents to launch, and extra guidance for both the orchestrator (Kay) and the individual agents.

Set [subagents].enabled = false to remove the runtime agent tool for a session. This is the hard isolation switch for single-model runs where delegation must not escape the selected provider/model.

By default (when no [[agents]] are configured) Kay advertises these model slugs for multi-agent runs: code-gpt-5.4, code-gpt-5.3-codex, claude-opus-4.6, gemini-3-pro, code-gpt-5.1-codex-mini, claude-sonnet-4.5, gemini-3-flash, claude-haiku-4.5, and qwen-3-coder. The cloud counterpart, cloud-gpt-5.1-codex-max, only appears when CODE_ENABLE_CLOUD_AGENT_MODEL=1 is set. (gemini resolves to gemini-3-flash.) You can override the list by defining [[agents]] entries or by specifying agents = [ … ] on a given [[subagents.commands]] entry.

[subagents]
enabled = false

[[subagents.commands]]
name = "context"
read-only = true
agents = ["context-collector", "code-gpt-5.4"]
orchestrator-instructions = "Coordinate a context sweep before coding. Ask each agent to emit concise, linked summaries of relevant files and tooling the primary task might need."
agent-instructions = "Summarize the repository areas most relevant to the user's request. List file paths, rationale, and suggested follow-up scripts to run. Keep the reply under 2,000 tokens."

With the example above you can run /context inside the TUI to create a summary cell that the main /kay turn can reference later. Because context-collector is an ordinary agent, any command-line static analysis utilities it invokes (such as your blast radius tool) should be described in the agent-instructions so the orchestrator launches the right workflow. You can also customise the built-in commands by providing an entry with the same name (plan, solve, or kay) and pointing their agents list at your long-context helper.

validation

Controls the quick validation harness that runs before applying patches. The harness now activates automatically whenever at least one validation group is enabled. Use [validation.groups] for high-level toggles and the nested [validation.tools] table for per-tool overrides:

[validation.groups]
functional = true
stylistic = false

[validation.tools]
shellcheck = true
markdownlint = true
hadolint = true
yamllint = true
cargo-check = true
tsc = true
eslint = true
mypy = true
pyright = true
phpstan = true
psalm = true
golangci-lint = true
shfmt = true
prettier = true

Functional checks stay enabled by default to catch regressions in the touched code, while stylistic linters default to off so teams can opt in when they want formatting feedback.

With functional checks enabled, Kay automatically detects the languages affected by a patch and schedules the appropriate tools:

  • cargo-check for Rust workspaces (scoped to touched manifests)
  • tsc --noEmit and eslint --max-warnings=0 for TypeScript/JavaScript files
  • mypy and pyright for Python modules
  • phpstan/psalm for PHP projects with matching config or Composer entries
  • golangci-lint run ./... for Go modules alongside the existing JSON/TOML/YAML syntax checks

Each entry under [validation.tools] can be toggled to disable a specific tool or to opt particular checks back in after disabling the entire group.

When enabled, Kay can also run actionlint against modified workflows. This is configured under [github]:

[github]
actionlint_on_patch = true
# Optional: provide an explicit binary path
actionlint_path = "/usr/local/bin/actionlint"

disable_response_storage

Currently, customers whose accounts are set to use Zero Data Retention (ZDR) must set disable_response_storage to true so that Kay uses an alternative to the Responses API that works with ZDR:

disable_response_storage = true

Managing MCP servers from CLI (experimental)

You can also manage these entries from the CLI:

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

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

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

# Remove a server
kay mcp remove docs

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

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

shell_environment_policy

Kay 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 false to *re-enable* the filter for `"*KEY*"`, `"*SECRET*"`, and `"*TOKEN*"` (defaults to true)
ignore_default_excludes = true
# 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"]
Field Type Default Description
inherit string all Starting template for the environment:
all (clone full parent env), core (HOME, PATH, USER, …), or none (start empty).
ignore_default_excludes boolean true When false, Kay removes any var whose name contains KEY, SECRET, or TOKEN (case-insensitive) before other rules run; defaults to true so this filter is disabled by default.
exclude array [] Case-insensitive glob patterns to drop after the default filter.
Examples: "AWS_*", "AZURE_*".
set table<string,string> {} Explicit key/value overrides or additions – always win over inherited values.
include_only array [] If non-empty, a whitelist of patterns; only variables that match one pattern survive the final step. (Generally used with inherit = "all".)

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

Kay 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

Kay tags every exported event with service.name = $ORIGINATOR (the same value sent in the originator header, code_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 code_otel crate—the events listed below—is forwarded to the exporter. Event names keep the codex.* prefix is retained for backward compatibility with existing dashboards.

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 Kay 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_execpolicy_amendment, 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:

    [otel.exporter."otlp-http"]
endpoint = "https://otel.example.com/v1/logs"
protocol = "binary"

[otel.exporter."otlp-http".headers]
"x-otlp-api-key" = "${OTLP_TOKEN}"

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

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

Both OTLP exporters accept an optional tls block so you can trust a custom CA or enable mutual TLS. Relative paths are resolved against ~/.kay/:

[otel]
exporter = { otlp-http = {
  endpoint = "https://otel.example.com/v1/logs",
  protocol = "binary",
  headers = { "x-otlp-api-key" = "${OTLP_TOKEN}" },
  tls = {
    ca-certificate = "certs/otel-ca.pem",
    client-certificate = "/etc/kay/certs/client.pem",
    client-private-key = "/etc/kay/certs/client-key.pem",
  }
}}

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 Kay 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 Kay. 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"Kay: {assistant_message}"
            else:
                title = "Kay: 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",
            "code",
            "-ignoreDnD",
            "-activate",
            "com.googlecode.iterm2",
        ]
    )

    return 0


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

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

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

Note

Use notify for automation and integrations: Kay 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, the Kay CLI records messages sent to the model in $KAY_HOME/history.jsonl (usually ~/.kay/history.jsonl). 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

Context timeline preview

The structured environment context timeline (baseline + deltas + browser snapshots) is gated behind the CTX_UI environment flag. Set CTX_UI=1 before launching Kay to exercise the preview flow. Outside of this developer flag the classic == System Status == payload remains in place.

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

hide_agent_reasoning

Kay 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, Kay knows the context window for the most common OpenAI models, but if you are using a new model with an old version of the Kay CLI, then you can use model_context_window to tell Kay 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.

tool_output_max_bytes

Maximum number of bytes of tool output (including shell command output and file reads) to include in a model request. Defaults to 32 KiB. Increase this if you need to send larger outputs to the model (note the exec capture cap remains 32 MiB per stream).

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" ]

# Enable desktop notifications for approval requests only
notifications = [ "approval-requested" ]

Note

Kay 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.

Auto Drive Observer

Kay keeps long-running Auto Drive sessions in check with a lightweight observer thread. Configure its cadence with the top-level auto_drive_observer_cadence key (default 5). After every n completed requests the observer reviews the coordinator/CLI transcript, emits telemetry, and—if necessary—suggests a corrected prompt or follow-up guidance. Setting the value to 0 disables the observer entirely.

# Run the observer after every third Auto Drive request
auto_drive_observer_cadence = 3

When the observer reports status = "failing", the TUI banner highlights the intervention, updates the pending prompt when provided, and records guidance for future coordinator turns.

Project Hooks

Use the [projects] table to scope settings to a specific workspace path. In addition to trust_level, approval_policy, and always_allow_commands, you can attach lifecycle hooks that run commands automatically when notable events occur.

[projects."/Users/me/src/my-app"]
trust_level = "trusted"

[[projects."/Users/me/src/my-app".hooks]]
name = "bootstrap"
event = "session.start"
run = ["./scripts/bootstrap.sh"]
timeout_ms = 60000

[[projects."/Users/me/src/my-app".hooks]]
event = "tool.after"
run = "npm run lint -- --changed"

Supported hook events:

  • session.start: after the session is configured (once per launch)
  • session.end: before shutdown completes
  • tool.before: immediately before each exec/tool command runs
  • tool.after: once an exec/tool command finishes (regardless of exit code)
  • file.before_write: right before an apply_patch is applied
  • file.after_write: after an apply_patch completes and diffs are emitted

Hook commands run inside the same sandbox mode as the session and appear in the TUI as their own exec cells. Failures are surfaced as background events but do not block the main task. Each invocation receives environment variables such as CODE_HOOK_EVENT, CODE_HOOK_NAME, CODE_HOOK_INDEX, CODE_HOOK_CALL_ID, CODE_HOOK_PAYLOAD (JSON describing the context), CODE_SESSION_CWD, and—when applicable—CODE_HOOK_SOURCE_CALL_ID. Hooks may also set cwd, provide additional env entries, and specify timeout_ms.

Example tool.after payload:

{
  "event": "tool.after",
  "call_id": "tool_12",
  "cwd": "/Users/me/src/my-app",
  "command": ["npm", "test"],
  "exit_code": 1,
  "duration_ms": 1832,
  "stdout": "…output truncated…",
  "stderr": "…",
  "timed_out": false
}

Project Commands

Define project-scoped commands under [[projects."<path>".commands]]. Each command needs a unique name and either an array (command) or string (run) describing how to invoke it. Optional fields include description, cwd, env, and timeout_ms.

[[projects."/Users/me/src/my-app".commands]]
name = "setup"
description = "Install dependencies"
run = ["pnpm", "install"]

[[projects."/Users/me/src/my-app".commands]]
name = "unit"
run = "cargo test --lib"

Project commands appear in the TUI via /cmd <name> and run through the standard execution pipeline. During execution Kay sets CODE_PROJECT_COMMAND_NAME, CODE_PROJECT_COMMAND_DESCRIPTION (when provided), and CODE_SESSION_CWD so scripts can tailor their behaviour.

Config reference

Key Type / Values Notes
model string Model to use (e.g., gpt-5.1-codex).
model_provider string Provider id from model_providers (default: openai).
model_context_window number Context window tokens.
model_max_output_tokens number Max output tokens.
approval_policy untrusted | on-failure | on-request | never When to prompt for approval.
sandbox_mode read-only | workspace-write | danger-full-access OS sandbox policy.
sandbox_workspace_write.writable_roots array Extra writable roots in workspace‑write.
sandbox_workspace_write.network_access boolean Allow network in workspace‑write (default: false).
sandbox_workspace_write.exclude_tmpdir_env_var boolean Exclude $TMPDIR from writable roots (default: false).
sandbox_workspace_write.exclude_slash_tmp boolean Exclude /tmp from writable roots (default: false).
disable_response_storage boolean Required for ZDR orgs.
notify array External program for notifications.
instructions string Currently ignored; use experimental_instructions_file or AGENTS.md.
mcp_servers.<id>.command string MCP server launcher command.
mcp_servers.<id>.args array MCP server args.
mcp_servers.<id>.env map<string,string> MCP server env vars.
mcp_servers.<id>.startup_timeout_sec number Startup timeout in seconds (default: 10). Timeout is applied both for initializing MCP server and initially listing tools.
mcp_servers.<id>.tool_timeout_sec number Per-tool timeout in seconds (default: 60). Accepts fractional values; omit to use the default.
model_providers.<id>.name string Display name.
model_providers.<id>.base_url string API base URL.
model_providers.<id>.env_key string Env var for API key.
model_providers.<id>.wire_api chat | responses | responses_websocket | anthropic_messages | gemini_native | bedrock_converse Protocol used (default: chat).
model_providers.<id>.query_params map<string,string> Extra query params (e.g., Azure api-version).
model_providers.<id>.http_headers map<string,string> Additional static headers.
model_providers.<id>.env_http_headers map<string,string> Headers sourced from env vars.
model_providers.<id>.request_max_retries number Per‑provider HTTP retry count (default: 4).
model_providers.<id>.stream_max_retries number SSE stream retry count (default: 5).
model_providers.<id>.stream_idle_timeout_ms number SSE idle timeout (ms) (default: 300000).
project_doc_max_bytes number Max bytes to read from AGENTS.md.
projects.<path>.trust_level string Mark project/worktree as trusted (only "trusted" is recognized).
projects.<path>.hooks array
Lifecycle hooks for that workspace (see "Project Hooks").
projects.<path>.commands array
Project commands exposed via /cmd.
profile string Active profile name.
profiles.<name>.* various Profile‑scoped overrides of the same keys.
history.persistence save-all | none History file persistence (default: save-all).
history.max_bytes number Currently ignored (not enforced).
file_opener vscode | vscode-insiders | windsurf | cursor | none URI scheme for clickable citations (default: vscode).
tui table TUI‑specific options.
tui.notifications boolean | array Enable desktop notifications in the tui (default: false).
hide_agent_reasoning boolean Hide model reasoning events.
show_raw_agent_reasoning boolean Show raw reasoning (when available).
model_reasoning_effort minimal | low | medium | high Responses API reasoning effort.
model_reasoning_summary auto | concise | detailed | none Reasoning summaries.
model_verbosity low | medium | high GPT‑5 text verbosity (Responses API).
model_supports_reasoning_summaries boolean Force‑enable reasoning summaries.
chatgpt_base_url string Base URL for ChatGPT auth flow.
experimental_resume string (path) Resume JSONL path (internal/experimental).
experimental_instructions_file string (path) Replace built‑in instructions (experimental).
experimental_use_exec_command_tool boolean Use experimental exec command tool.
use_experimental_reasoning_summary boolean Use experimental summary for reasoning chain.
responses_originator_header_internal_override string Override originator header value.
tools.web_search boolean Enable web search tool (alias: web_search_request) (default: false).
tools.web_search_allowed_domains array Optional allow-list for web search (filters.allowed_domains).