anaconda-cli-base

A base CLI entrypoint supporting Anaconda CLI plugins using Typer.

Telemetry

The CLI automatically reports command execution metrics for all registered plugins via anaconda-opentelemetry. Every command invocation records the command name, execution duration, and success/failure status. No plugin changes are required.

Disabling telemetry

Telemetry is enabled by default. To disable:

export ANACONDA_TELEMETRY_ENABLED=false

Or in ~/.anaconda/config.toml:

[telemetry]
enabled = false

Configuration

Telemetry settings live in the [telemetry] section of ~/.anaconda/config.toml or as environment variables with the ANACONDA_TELEMETRY_ prefix.

Setting	Env Variable	Default	Description
enabled	ANACONDA_TELEMETRY_ENABLED	true	Enable or disable all CLI telemetry
endpoint	ANACONDA_TELEMETRY_ENDPOINT	None	Set a custom OTEL endpoint ur. If None uses anaconda.com endpoint
share_session_identity	ANACONDA_TELEMETRY_SHARE_SESSION_IDENTITY	true	Include anonymous session tokens for usage correlation
proxy_url	ANACONDA_TELEMETRY_PROXY_URL	None	HTTP proxy for telemetry export (for corporate networks)
flush_timeout_ms	ANACONDA_TELEMETRY_FLUSH_TIMEOUT_MS	500	Max milliseconds to wait for telemetry flush on CLI exit
export_interval_ms	ANACONDA_TELEMETRY_EXPORT_INTERVAL_MS	60000	Millisecond frequency over which data is exported for long-running tasks

When share_session_identity is true, hashed machine and session tokens are included with telemetry data. These allow Anaconda to correlate usage patterns across CLI sessions without identifying you personally. Set to false to send only standalone metrics with no session linking.

Registering plugins

To develop a subcommand in a third-party package, first create a typer.Typer() app with one or more commands. See this example. The commands defined in your package will be prefixed with the subcommand you define when you register the plugin.

In your pyproject.toml subcommands can be registered as follows:

# In pyproject.toml

[project.entry-points."anaconda_cli.subcommand"]
auth = "anaconda_auth.cli:app"

In the example above:

• "anaconda_cli.subcommand" is the required string to use for registration. The quotes are important.
• auth is the name of the new subcommand, i.e. anaconda auth
  • All typer.Typer commands you define in your package are accessible the registered subcommand
  • i.e. anaconda auth <command>.
• anaconda_auth.cli:app signifies the object named app in the anaconda_auth.cli module is the entry point for the subcommand.

Error handling

By default any exception raised during CLI execution in your registered plugin will be caught and only a minimal message will be displayed to the user.

You can define a custom callback for individual exceptions that may be thrown from your subcommand. You can register handlers for standard library exceptions or custom defined exceptions. It may be best to use custom exceptions to avoid unintended consequences for other plugins.

To register the callback decorate a function that takes an exception as input, and return an integer error code. The error code will be sent back through the CLI and your subcommand will exit with that error code.

from typing import Type
from anaconda_cli_base.exceptions import register_error_handler

@register_error_handler(MyCustomException)
def better_exception_handling(e: Type[Exception]) -> int:
    # do something or print useful information
    return 1

@register_error_handler(AnotherException)
def just_ignore_it(e: Type[Exception])
    # ignore the error and let the CLI exit successfully
    return 0


@register_error_handler(YetAnotherException)
def fix_the_error_and_try_again(e: Type[Exception]) -> int:
    # do something and retry the CLI command
    return -1

In the second example the handler returns -1. This means that the handler has attempted to correct the error and the CLI subcommand should be re-tried. The handler could call another interactive command, like a login action, before attempting the CLI subcommand again.

See the anaconda-auth plugin for an example custom handler.

Config file

If your plugin wants to utilize the Anaconda config file, default location ~/.anaconda/config.toml, to read configuration parameters you can derive from anaconda_cli_base.config.AnacondaBaseSettings to add a section in the config file for your plugin. Each subclass of AnacondaBaseSettings defines the section header. The base class is configured so that parameters defined in subclasses can be read in the following priority from lowest to highest.

1. default value in the subclass of AnacondaBaseSettings
2. Global config file at ~/.anaconda/config.toml
3. ANACONDA_<PLUGIN-NAME>_<FIELD> variables defined in the .env file in your working directory
4. A file named /run/secrets/anaconda_<plugin-name>_<field>, usually populated by a mounted Docker secret
5. ANACONDA_<PLUGIN-NAME>_<FIELD> env variables set in your shell or on command invocation
6. value passed as kwarg when using the config subclass directly

Notes:

• AnacondaBaseSettings is a subclass of BaseSettings from pydantic-settings.
• Nested pydantic models are also supported.
• Per pydantic defaults, both secret filenames and environment variables may be uppercase or lowercase.

Here's an example subclass:

from anaconda_cli_base.config import AnacondaBaseSettings

class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"

To read the config value in your plugin according to the above priority:

config = MyPluginConfig()
assert config.foo == "bar"

Since there is no value of foo in the config file it assumes the default value from the subclass definition.

The value of foo can now be written to the config file under the section my_plugin

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"

Now that the config file has been written, the value of foo is read from the config.toml file:

config = MyPluginConfig()
assert config.foo == "baz"

Nested tables

The AnacondaBaseSettings supports nested Pydantic models.

from anaconda_cli_base.config import AnacondaBaseSettings
from pydantic import BaseModel

class Nested(BaseModel):
    n1: int = 0
    n2: int = 0

class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"
    nested: Nested = Nested()

In the ~/.anaconda/config.toml you can set values of nested fields as an in-line table

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"
nested = { n1 = 1, n2 = 2}

Or as a separate table entry

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"

[plugin.my_plugin.nested]
n1 = 1
n2 = 2

To set environment variables use the __ delimiter

ANACONDA_MY_PLUGIN_NESTED__N1=1
ANACONDA_MY_PLUGIN_NESTED__N2=2

Nested plugins

You can pass a tuple to plugin_name= in subclasses of AnacondaBaseSettings to nest whole plugins, which may be defined in separate packages.

class Nested(BaseModel):
    n1: int = 0
    n2: int = 0
class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"
    nested: Nested = Nested()

Then in another package you can nest a new config into my_plugin.

class MyPluginExtrasConfig(AnacondaBaseSettings, plugin_name=("my_plugin", "extras")):
    field: str = "default"

The new config table is now nested in the config.toml

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"
nested = { n1 = 1, n2 = 2}
[plugin.my_plugin.extras]
field = "value"

And can be set by env variable using the concatenation of plugin_name

ANACONDA_MY_PLUGIN_EXTRAS_FIELD="value"

Writing configuration

Plugin configurations can be written directly from subclasses of AnacondaBaseSettings with the .write_config() member method. This method takes two arguments

• preserve_existing_keys:
  • If True (default) updates to existing keys in the config.toml file, will not remove the key if set to the default value. If False fields set to default value are removed from the file
• dry_run:
  • If True, displays a diff of proposed changes without writing to the file. If False (default), writes changes to config.toml.

Here are some key aspects of writing configuration

• .write_config() will only update changed lines in the config.toml preserving all existing configuration and comments
• toml does not support None or null, any field set to the value None will not be written to the config.toml
• fields set to their default value are not written to the config.toml
  • Except when an existing key in the config.toml is updated to its default value. The key will still be written
  • This is disabled with preserve_existing_keys=False

Let's start with the plugin defined earlier and an instance of the config object with all default values

from anaconda_cli_base.config import AnacondaBaseSettings
from pydantic import BaseModel

class Nested(BaseModel):
    n1: int = 0
    n2: int = 0

class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"
    nested: Nested = Nested()


config = MyPluginConfig()

If there is either no config.toml or the existing file does not have the [plugin.my_plugin] table attempting to write the current state of the config will just add the table header since all values are default. Here is an example of the dry_run output in the case where the config.toml file did not exist

>>> config.write_config(dry_run=True)
--- ~/.anaconda/config.toml
+++ ~/.anaconda/config.toml 01-06-26 09:40
@@ -0,0 +1 @@
+[plugin.my_plugin]

You can change the configuration either by passing kwargs to the initialization or by directly updating attributes.

config.foo = "baz"
config.nested.n1 = 1
config.nested.n2 = 2

this will now write the configuration equivalent to what you saw above

>>> config.write_config(dry_run=True)
--- ~/.anaconda/config.toml
+++ ~/.anaconda/config.toml 01-06-26 09:44
@@ -0,0 +1,6 @@
+[plugin.my_plugin]
+foo = "baz"
+
+[plugin.my_plugin.nested]
+n1 = 1
+n2 = 2

Now with that configuration written to disk (using dry_run=False) we can re-read the configuration to confirm the change.

>>> config = MyPluginConfig()
>>> print(config)
foo='baz' nested=Nested(n1=1, n2=2)

Let's change foo back to its default value. We can do that either by setting the attribute config.foo = "bar" or by passing a kwarg to override the config.toml.

The dry-run output now only changes the foo key in the config.toml leaving all other lines unchanged

>>> config = MyPluginConfig(foo="bar")
>>> config.write_config(dry_run=True)
--- ~/.anaconda/config.toml 01-06-26 09:53
+++ ~/.anaconda/config.toml 01-06-26 09:56
@@ -1,5 +1,5 @@
 [plugin.my_plugin]
-foo = "baz"
+foo = "bar"

 [plugin.my_plugin.nested]
 n1 = 1

If instead we wish to remove keys when set to their default value pass the preserve_existing_keys=False argument

>>> config.write_config(dry_run=True, preserve_existing_keys=False)
--- ~/.anaconda/config.toml 01-06-26 09:53
+++ ~/.anaconda/config.toml 01-06-26 09:57
@@ -1,5 +1,4 @@
 [plugin.my_plugin]
-foo = "baz"

 [plugin.my_plugin.nested]
 n1 = 1

See the tests for more examples of reading and writing plugin configuration.

Plugin telemetry

Plugins get baseline command metrics for free. To add custom instrumentation:

from anaconda_cli_base.telemetry import traced, count, histogram, log_event

@app.command()
def download(model: str):
    with traced("models_download", plugin_name="ai", attributes={"model": model}) as span:
        result = do_download(model)
        span.add_event("download_complete", {"size_bytes": result.size})
    count("models_downloaded", plugin_name="ai")
    histogram("download_size_bytes", plugin_name="ai", value=result.size)
    log_event("user downloaded a model", event_name="model_downloaded", plugin_name="ai", attributes={"model": model})

The plugin_name should match your registered subcommand name (e.g., "ai" for anaconda ai). This ensures custom telemetry correlates with the automatic command metrics in dashboards.

All functions are no-ops when telemetry is disabled — they will never raise or affect CLI behavior.

Logging handler

For error and warning capture via Python's standard logging module, attach the OTel handler to your plugin's logger. By default log records at WARNING and above are exported to the telemetry backend while still flowing to any other handlers (stderr, file) you have configured.

import logging
from anaconda_cli_base.telemetry import get_otel_handler

log = logging.getLogger("anaconda_ai")
log.addHandler(get_otel_handler())

# WARNING+ goes to OTel; all levels still go to other handlers
log.warning("retry attempt", extra={"attempt": 3, "endpoint": url})
log.error("download failed", extra={"model": model, "error.type": "TimeoutError"})

Pass a custom level to change the threshold:

log.addHandler(get_otel_handler(level=logging.ERROR))  # Only errors

When telemetry is disabled or anaconda-opentelemetry is not installed, get_otel_handler() returns a NullHandler — safe to call unconditionally with zero overhead.

Use get_otel_handler() for structured errors/warnings. Use log_event() for business events that shouldn't appear in developer console output (e.g., "model_downloaded", "session_started").

Setup for development

Ensure you have conda installed. Then run:

make setup

Run the unit tests

make test

Run the unit tests across isolated environments with tox

make tox