Command Line Interface - Rasa Documentation

Cheat Sheet

Pro specific

The following commands are relevant to all assistants built with Rasa.

Command	Effect
`rasa init`	Creates a new project with example training data, actions, and config files.
`rasa train`	Trains a model using your NLU data and stories, saves trained model in `./models`.
`rasa shell`	Loads your trained model and lets you talk to your assistant on the command line.
`rasa run`	Starts a server with your trained model.
`rasa run actions`	Starts an action server using the Rasa SDK.
`rasa test e2e`	Runs end-to-end testing fully integrated with the action server that serves as acceptance testing.
`rasa data convert e2e`	Converts sample conversation data into end-to-end test cases.
`rasa llm finetune prepare-data`	Prepares data to fine-tune a base model for the task of command generator.
`rasa inspect`	Opens Rasa Inspector.
`rasa data validate`	Checks the domain, NLU, flows and conversation data for inconsistencies.
`rasa export`	Exports conversations from a tracker store to an event broker.
`rasa marker upload`	Upload marker configurations to Analytics Data Pipeline
`rasa license`	Display licensing information.
`rasa -h`	Shows all available commands.
`rasa --version`	Shows version information about Rasa, Python and the expiration date for Rasa License

Studio specific

If your team is using Rasa Studio, these CLI commands let you manage and sync your assistant between your local environment and your Studio deployment:

Command	Description
`rasa studio config`	Sets up local configuration to connect to your Rasa Studio deployment.
`rasa studio login`	Authenticates and retrieves credentials from your Rasa Studio instance.
`rasa studio upload`	Uploads your local assistant as a new project in Rasa Studio.
`rasa studio download <assistant-name>`	Downloads the full assistant from Rasa Studio into your current directory.
`rasa studio link <assistant-name>`	Links your local project to a specific assistant in Rasa Studio.
`rasa studio push`	Pushes your local changes to Studio.
`rasa studio pull`	Pulls the latest changes from Studio and merges them into your local project.
`rasa studio train`	Trains a model combining local + Studio data and saves trained model in `./models`.

Rasa Pro Commands

Logging

If you run into character encoding issues on Windows like: UnicodeEncodeError: 'charmap' codec can't encode character ... or the terminal is not displaying colored messages properly, prepend winpty to the command you would like to run. For example winpty rasa init instead of rasa init

Setting log levels

Rasa produces log messages at several different levels (eg. warning, info, error and so on). You can control which level of logs you would like to see with --verbose (same as -v) or --debug (same as -vv) as optional command line arguments. See each command below for more explanation on what these arguments mean. In addition to CLI arguments, several environment variables allow you to control log output in a more granular way. With these environment variables, you can configure log levels for messages created by external libraries such as Matplotlib, Pika, and Kafka. These variables follow standard logging level in Python. Currently, following environment variables are supported:

LOG_LEVEL_LIBRARIES: This is the general environment variable to configure log level for the main libraries Rasa uses. It covers Tensorflow, asyncio, APScheduler, SocketIO, Matplotlib, RabbitMQ, Kafka, gRPC, and a2a-sdk.
LOG_LEVEL_MATPLOTLIB: This is the specialized environment variable to configure log level only for Matplotlib.
LOG_LEVEL_RABBITMQ: This is the specialized environment variable to configure log level only for AMQP libraries, at the moment it handles log levels from aio_pika and aiormq.
LOG_LEVEL_KAFKA: This is the specialized environment variable to configure log level only for kafka.
LOG_LEVEL_PRESIDIO: This is the specialized environment variable to configure log level only for Presidio, at the moment it handles log levels from presidio_analyzer and presidio_anonymizer.
LOG_LEVEL_FAKER: This is the specialized environment variable to configure log level only for Faker.
LOG_LEVEL_MLFLOW: This is the specialized environment variable to configure log level only for MLFlow.
LOG_LEVEL_PYMONGO: This is the specialized environment variable to configure log level only for PyMongo.
LOG_LEVEL_LANGFUSE: This is the specialized environment variable to configure log level only for the Langfuse SDK; it defaults to ERROR so Langfuse warnings are hidden when the langfuse package is installed but tracing is not configured.
LOG_LEVEL_MCP: This is the specialized environment variable to configure log level only for the MCP client and its HTTP libraries (mcp.client, httpcore, httpx); it defaults to ERROR. Set MCP_LOGGING_ENABLED=false to disable MCP logging entirely.
LOG_LEVEL_OPENAI: This is the specialized environment variable to configure log level only for the OpenAI SDK (openai); it defaults to WARNING.

General configuration (LOG_LEVEL_LIBRARIES) has less priority than library level specific configuration (LOG_LEVEL_MATPLOTLIB, LOG_LEVEL_RABBITMQ etc); and CLI parameter sets the lowest level log messages which will be handled. This means variables can be used together with a predictable result. As an example:

LOG_LEVEL_LIBRARIES=ERROR LOG_LEVEL_MATPLOTLIB=WARNING LOG_LEVEL_KAFKA=DEBUG rasa shell --debug

The above command run will result in showing:

messages with DEBUG level and higher by default (due to --debug)
messages with WARNING level and higher for Matplotlib
messages with DEBUG level and higher for kafka
messages with ERROR level and higher for other libraries not configured

Note that CLI config sets the lowest level log messages to be handled, hence the following command will set the log level to INFO (due to --verbose) and no debug messages will be seen (library level configuration will not have any effect):

LOG_LEVEL_LIBRARIES=DEBUG LOG_LEVEL_MATPLOTLIB=DEBUG rasa shell --verbose

As an aside, CLI log level sets the level at the root logger (which has the important handler - coloredlogs handler); this means even if an environment variable sets a library logger to a lower level, the root logger will reject messages from that library. If not specified, the CLI log level is set to INFO.

Log Level LLM Components

Rasa provides enhanced control over the debugging process of LLM-driven components via a fine-grained, customizable logging specified through environment variables. For example, set the LOG_LEVEL_LLM environment variable to enable detailed logging at the desired level for all the LLM components or specify the component you are debugging by setting for example the LOG_LEVEL_LLM_ENTERPRISE_SEARCH environment variable:

export LOG_LEVEL_LLM=INFO
export LOG_LEVEL_LLM_COMMAND_GENERATOR=INFO
export LOG_LEVEL_LLM_ENTERPRISE_SEARCH=DEBUG
export LOG_LEVEL_LLM_INTENTLESS_POLICY=INFO
export LOG_LEVEL_LLM_REPHRASER=INFO
export LOG_LEVEL_NLU_COMMAND_ADAPTER=INFO
export LOG_LEVEL_LLM_BASED_ROUTER=INFO

These settings override logging level for the specified components. The LOG_LEVEL_LLM_COMMAND_GENERATOR variable applies to all types of LLM-based command generators.

Custom logging configuration

v3.4

The Rasa CLI now includes a new argument --logging-config-file which accepts a YAML file as value.

You can now configure any logging formatters or handlers in a separate YAML file. The logging config YAML file must follow the Python built-in dictionary schema, otherwise it will fail validation. You can pass this file as argument to the --logging-config-file CLI option and use it with any of the rasa commands.

Custom logging configuration example

The following example illustrates how to customize the logging configuration using a YAML file. Here we define a custom formatter, a stream handler for the root logger and a file handler for the rasa logger.

version: 1
disable_existing_loggers: false
formatters:
    customFormatter:
        format: "{\"time\": \"%(asctime)s\", \"name\": \"[%(name)s]\", \"levelname\": \"%(levelname)s\", \"message\": \"%(message)s\"}"

handlers:
  console:
    class: logging.StreamHandler
    level: INFO
    formatter: customFormatter
    stream: ext://sys.stdout
  file:
    class: logging.FileHandler
    filename: "rasa_debug.log"
    level: DEBUG
    formatter: customFormatter

loggers:
  root:
    handlers: [console]
  rasa:
    handlers: [file]
    propagate: 0

In Rasa Pro 3.9, running rasa shell or rasa interactive in debug mode could result in BlockingIOError when using the default logging configuration. This issue is resolved by using a custom logging configuration file. If you encounter this issue, you can use the above example to create a custom logging configuration file and pass it to the --logging-config-file argument.

rasa init

This command sets up a complete assistant for you with some example training data:

rasa init

With no arguments, rasa init creates the following files:

.
├── actions
│   ├── __init__.py
│   └── actions.py
├── config.yml
├── credentials.yml
├── data
│   ├── nlu.yml
│   └── stories.yml
├── domain.yml
├── endpoints.yml
├── models
│   └── <timestamp>.tar.gz
└── tests
   └── test_stories.yml

It will ask you if you want to train an initial model using this data. If you answer no, the models directory will be empty. This is the best way to get started writing an NLU assistant. You can run rasa train, rasa shell and rasa test without any additional configuration. Rasa supplies two other templates in addition to the default NLU template described above. Both of these are great ways to get started building your own CALM bots:

rasa init --template calm generates a CALM assistant with flows and a custom action to manage a simple contact list.
rasa init --template tutorial generates the codebase used in the CALM Tutorial.

rasa train

The following command trains a Rasa model:

rasa train

If you have existing models in your directory (under models/ by default), only the parts of your model that have changed will be re-trained. For example, if you edit your NLU training data and nothing else, only the NLU part will be trained. If you want to train an NLU or dialogue model individually, you can run rasa train nlu or rasa train core. If you provide training data only for one one of these, rasa train will fall back to one of these commands by default. rasa train will store the trained model in the directory defined by --out, models/ by default. The name of the model by default is <timestamp>.tar.gz. If you want to name your model differently, you can specify the name using the --fixed-model-name flag. By default validation is run before training the model. If you want to skip validation, you can use the --skip-validation flag. If you want to fail on validation warnings, you can use the --fail-on-validation-warnings flag. The --validation-max-history is analogous to the --max-history argument of rasa data validate. Run rasa train --help to see the full list of arguments.

rasa shell

You can start a chat session by running:

rasa shell

By default, this will load up the latest trained model. You can specify a different model to be loaded by using the --model flag. If you start the shell with an NLU-only model, rasa shell will output the intents and entities predicted for any message you enter. If you have trained a combined Rasa model but only want to see what your model extracts as intents and entities from text, you can use the command rasa shell nlu. To increase the logging level for debugging, run:

rasa shell --debug

In order to see the typical greetings and/or session start behavior you might see in an external channel, you will need to explicitly send /session_start as the first message. Otherwise, the session start behavior will begin as described in Session configuration.

The following arguments can be used to configure the command. Most arguments overlap with rasa run; see the following section for more info on those arguments. Note that the --connector argument will always be set to cmdline when running rasa shell. This means all credentials in your credentials file will be ignored, and if you provide your own value for the --connector argument it will also be ignored. Run rasa shell --help to see the full list of arguments.

rasa run

To start a server running your trained model, run:

rasa run

By default the Rasa server uses HTTP for its communication. To secure the communication with SSL and run the server on HTTPS, you need to provide a valid certificate and the corresponding private key file. You can specify these files as part of the rasa run command. If you encrypted your keyfile with a password during creation, you need to add the --ssl-password as well.

rasa run --ssl-certificate myssl.crt --ssl-keyfile myssl.key --ssl-password mypassword

Rasa by default listens on each available network interface. You can limit this to a specific network interface using the -i command line option.

rasa run -i 192.168.69.150

Rasa will by default connect to all channels specified in your credentials file. To connect to a single channel and ignore all other channels in your credentials file, specify the name of the channel in the --connector argument.

rasa run --connector rest

The name of the channel should match the name you specify in your credentials file. For supported channels see the page about messaging and voice channels. Run rasa run --help to see the full list of arguments. For more information on important additional parameters, see Model Storage See the Rasa REST API page for detailed documentation of all the endpoints.

rasa run actions

To start an action server with the Rasa SDK, run:

rasa run actions

Run rasa run actions --help to see the full list of arguments.

rasa visualize

To generate a graph of your stories in the browser, run:

rasa visualize

If your stories are located somewhere other than the default location data/, you can specify their location with the --stories flag. Run rasa visualize --help to see the full list of arguments.

rasa test e2e

v3.5

You can now use end-to-end testing to test your assistant as a whole, including dialogue management and custom actions.

To run end-to-end testing on your trained model, run:

rasa test e2e

This will test your latest trained model on any end-to-end test cases you have. If you want to use a different model, you can specify it using the --model flag.

By adding the --coverage-report flag you obtain a report describing how well your end-to-end tests cover the assistant’s flows in terms of share of steps tested per flow. The report includes a histogram of tested commands and allows you to specify the output path with the --coverage-output-path flag.This feature is currently released in a beta version. The feature might change in the future. If you want to enable this beta feature, set the environment variable RASA_PRO_BETA_FINE_TUNING_RECIPE=true.

New in 3.15.0

You can now specify a custom output path for the end-to-end test results using the -o or --e2e-results flag.
You can also now export failed end-to-end tests using the -f or --e2e-failed-tests flag. This also accepts an optional output path for the failed tests file. This file can then be used to re-run only the failed tests by passing it as an argument to the rasa test e2e command.

Here are some of the arguments available:

positional argument for the path to the test cases file or directory containing the test cases: rasa test e2e <path> If unspecified, the default path is tests/e2e_test_cases.yml.
optional argument for the trained model: -model <path>
optional argument for retrieving the trained model from remote storage: -remote-storage <remote-storage-location>
optional argument for the endpoints.yml file: -endpoints <path>
optional argument for stopping the test run at first failure: rasa test e2e --fail-fast
optional argument for exporting the test results to e2e_results.yml file: rasa test e2e -o or rasa test e2e -o <output-path>. When you specify the -o flag without an output path, the passed and failed test results yml files will be saved to the tests/ subdirectory in the current working directory.
optional argument for exporting failed tests to e2e_failed_tests_{timestamp}.yml file: rasa test e2e -f or rasa test e2e -f <output-path>. When you specify the -f flag without an output path, the failed test file will be saved to the tests/ subdirectory in the current working directory.
optional argument for creating a coverage report: rasa test e2e --coverage-report
optional argument for specifying the output directory for the coverage report: rasa test e2e --coverage-output-path
you can optionally dump results to stdout or to a file (o), plus coverage details with --coverage-report.

Run rasa test e2e --help to see the full list of arguments.

rasa llm finetune prepare-data

v3.10

This command is part of the fine-tuning recipe available starting with version 3.10.0. As this feature is a beta feature, please set the environment variable RASA_PRO_BETA_FINETUNING_RECIPE to true to enable it.

This command creates a dataset of prompt to commands pairs from E2E tests that can be used to fine-tune a base model for the task of command generation. To execute the command run

rasa llm finetune prepare-data <path-to-e2e-test-cases>

Here are some of the arguments available:

positional arguments:
  path-to-e2e-test-cases
                        Input file or folder containing end-to-end test cases. (default: e2e_tests)

options:
  -o OUT, --out OUT     The output folder to store the data to. (default: output)
  -m MODEL, --model MODEL
                        Path to a trained Rasa model. If a directory is specified, it will use the latest model in this directory. (default: models)

Rephrasing Module:

  --num-rephrases  {0, ..., 49}
                        Number of rephrases to be generated per user utterance. (default: 10)
  --rephrase-config REPHRASE_CONFIG
                        Path to config file that contains the configuration of the rephrasing module. (default: None)

Train/Test Split Module:
  --train-frac TRAIN_FRAC
                        The amount of data that should go into the training dataset. The value should be >0.0 and <=1.0. (default: 0.8)
  --output-format [{instruction,conversational}]
                        Format of the output file. (default: instruction)

Run rasa finetune prepare-data --help to see all available arguments.

Resulting file structure

output/
├── 1_command_annotations/          # conversations extracted from your E2E tests
├── 2_rephrasings/                  # same conversations + generated rephrasings
├── 3_llm_finetune_data/
│   └── llm_ft_data.jsonl           # single JSONL file consumed by the fine-tuner
├── 4_train_test_split/
│   ├── e2e_tests/                  # subsets of the original tests
│   │   ├── train.yaml              # test cases that fall into the training split
│   │   └── validation.yaml         # test cases that fall into the validation split
│   └── ft_splits/
│       ├── train.jsonl             # training data for the LLM
│       └── test.jsonl              # held-out evaluation data
├── params.yaml                     # run parameters
└── result_summary.yaml             # short report of what was generated

rasa inspect

v3.7

This command is part of Rasa’s new Conversational AI with Language Models (CALM) approach and available starting with version 3.7.0.

Opens the Rasa Inspector, a debugging tool that offers developers an in-depth look into the conversational mechanics of their Rasa assistant. The default Inspector includes voice support, so you don’t need the --voice flag. Using --voice on its own raises an error. To test voice with the legacy Inspector, combine it with the --legacy flag:

rasa inspect --legacy --voice

Run rasa inspect --help to see the full list of arguments.

rasa inspect —nextgen

v3.16 Opens a preview of the nextgen Rasa Inspector, which will include both the legacy inspector features from Pro, Studio and additional features for better debugging and development. This will replace the old Rasa Inspector in 3.17.

rasa data validate

You can check your domain, NLU data, flows or story data for mistakes and inconsistencies. To validate your data, run this command:

rasa data validate

The validator searches for errors in the data, e.g. two intents that have some identical training examples. The validator also checks if you have any stories where different assistant actions follow from the same dialogue history. Conflicts between stories will prevent a model from learning the correct pattern for a dialogue. To learn more about the checks performed by the validator on flows, continue reading in the next section.

Searching for the assistant_id key introduced in 3.5The validator will check whether the assistant_id key is present in the config file and will issue a warning if this key is missing or if the default value has not been changed.

If you pass a max_history value to one or more policies in your config.yml file, provide the smallest of those values in the validator command using the --max-history <max_history> flag.

Validate flows

The validator will perform the following checks on flows:

determine whether flow names or descriptions are unique after stripping punctuation
verify whether logical expressions in conditions or collect step rejections are valid pypred expressions
determine whether slots used in flows are defined in the domain
disallow list slots from being used in flows collect steps: CALM supports only filling slots with values of type int, string or bool in flows.
disallow dialogue_stack internal slot from being used in flows
ensure that bool and categorical slots are validated against acceptable values in conditions

For every failure, the validator will log an error and exit the command with exit code 1. You can validate flows only by running this command:

rasa data validate flows

rasa export

To export events from a tracker store using an event broker, run:

rasa export

You can specify the location of the environments file, the minimum and maximum timestamps of events that should be published, as well as the conversation IDs that should be published. Run rasa export --help to see the full list of arguments.

rasa license

v3.3 Use rasa license to display information about licensing in Rasa, especially information about 3rd party dependencies licenses. Run rasa license --help to see the full list of arguments.

Requesting a Developer License

If you run a licensed Rasa CLI command without setting RASA_LICENSE, the CLI prompts you for an email address in an interactive terminal and submits a Rasa Developer Edition license request on your behalf. Licensed commands include rasa train, rasa run, rasa inspect, rasa license, rasa tools, rasa telemetry, and rasa --version. The CLI exits after submitting the request. Set RASA_LICENSE to the returned key and rerun the command. Submitting the email opts you into the Rasa Developer Terms and the marketing consent attached to the license request form. rasa --help, rasa -h, and subcommand help (for example rasa train --help) do not require a license and never trigger the prompt. To disable the interactive prompt (for example in CI), pass --no-prompt or run the command in a non-interactive shell. The CLI then exits with the license error as before. See Licensing for details on setting RASA_LICENSE.

Rasa Studio Commands

The CLI commands for Rasa Studio enable you to manage updates between your local project and changes made by your team in Studio.

Connect to a Studio Deployment: rasa studio config
Login and authenticate: rasa studio login
Upload or Download a full project: rasa studio upload/download
Link a specific assistant project: rasa studio link <assistant-project-name>
Push and pull updates between Studio and your local project: rasa studio push/pull

rasa studio config

v3.7

This command is available from Rasa Pro 3.7.0 and requires Rasa Studio

This command prompts for parameters of Rasa Studio installation and configures rasa to target that Rasa Studio instance when executing rasa studio commands. Configuration is saved to: $HOME/.config/rasa/global.yml The command will use default arguments for the configuration of the authentication server (realm name, client id and authentication url). If you want to use a different configuration, you can specify the parameters by running the command with rasa studio config --advanced. The command will overwrite the existing configuration file with the new configuration. Example:

rasa studio config

The command will use SSL strict verification by default to verify the connection to the Rasa Studio authentication server. If you want to skip the strict verification of this connection, you can use the --disable-verify or -x flag:

rasa studio config --disable-verify

Run rasa studio config --help to see the full list of arguments. v3.7 This command is used to retrieve the access token from Rasa Studio. All other studio commands use this token to authenticate with Rasa Studio. The token is saved to: $HOME/.config/rasa/studio_token.yaml Example:

rasa studio login --username my_user_name --password my_password

Run rasa studio login --help to see the full list of arguments.

rasa studio upload

v3.13

new in 3.13You can now upload and download a full project using the Rasa CLI as well as link a Studio project to a local project for easier syncing.

Uploads an assistant from local files to Rasa Studio.

Import of NLU-based assistants

For NLU-based assistants, it will upload the intent and entity definitions to Rasa Studio to an existing assistant in Rasa Studio. When arguments for specifying which intents or entities to upload are not given, all intents and entities get uploaded. When uploading an intent, all entities used in annotations of that intent’s utterance examples are uploaded as well.

At the moment, only some intents and entities can be uploaded to Studio. The following can’t be uploaded:

Retrieval intents
Entities that have entity_group
Intents with use_entities and ignore_entities
Entities with influence_conversation

Example:

rasa studio upload

Run rasa studio upload --help to see the full list of arguments.

Overwriting an existing assistant

new in 3.16You can now delete an existing assistant automatically before uploading by using the --dangerously-delete-existing flag.

By default, if an assistant with the same name already exists in Studio, the CLI will ask whether you want to link your local project to that existing assistant. To skip this prompt and automatically delete the existing assistant before uploading, use the --dangerously-delete-existing flag:

rasa studio upload --dangerously-delete-existing

This deletes the existing assistant and all its data without any confirmation prompt, then proceeds with the upload. It is intended for CI/CD workflows where the assistant must be replaced on every run. Use with caution.

Possible errors

Assistant name errors

These include the following:

Assistant with name <assistant_name> already exists
<assistant_name> is not a valid name

A valid assistant name will not exceed the length of 128 characters and will not contain spaces.

Invalid YAML errors

If something is wrong with the YAML files structure, a specific error will be logged. You will see these errors when, for example, a required field is missing for an action, slot, response, config or flow. Examples:

Invalid domain: responses.utter_greeting.0.text: Required
Invalid flows: flows.transfer_money.description: Required

Reference errors

If a flow references a response, slot, action or another flow (with a link step), the following errors will be logged:

Can't find <response/slot/action> utter_ask_add_contact_handle in domain
Can't find flow <flow_name> in flows

Unsupported feature errors

Not all the features available in Rasa Pro are supported by Rasa Studio. Trying to import an assistant with unsupported features will result in an error. To find out which versions of Studio support the version of Rasa Pro you are using, check the compatibility matrix. Examples:

Flows with cycles are not supported, flow: <flow_name>
Comparing two slots is not supported. Condition: slots.recurrent_payment_end_date < slots.recurrent_payment_start_date
Having multiple rejections on one slot is not supported, collect: <slot_name>

Authentication errors

User needs to be logged into Rasa Studio before uploading. Use the rasa studio login command.

rasa studio download

v3.13 This command downloads a specified assistant project from Rasa Studio and creates a folder using the assistant name. The following data is supported:

configuration
endpoints
custom prompts
flows (for CALM assistants)
responses
slots
custom action declarations
intents
entities

Example:

rasa studio download my_awesome_assistant

Creates a folder at ./my_awesome_assistant Run rasa studio download --help to see the full list of arguments.

rasa studio link

v3.13 Links your local assistant to a project in Rasa Studio. You can specify the assistant name as an argument:

rasa studio link my_assistant_name

Once linked, all subsequent commands (like download, upload, pull, and push) will refer to this assistant.

rasa studio pull

v3.13 Pulls the latest changes from your Rasa Studio assistant into your local project. You can either pull the entire assistant:

rasa studio pull

Or pull a specific section (e.g., just the configuration or endpoints):

rasa studio pull config

Current supported sections include: config, endpoints.

rasa studio push

v3.13 Pushes the latest changes from your local project to your Rasa Studio assistant. You can either push everything:

rasa studio upload

Or push a specific section:

rasa studio push config

Supported sections include: config, endpoints.

Legacy commands

rasa studio upload

v3.7 Uploads an assistant from local files to Rasa Studio.

Import of NLU-based assistants

At the moment, only some intents and entities can be uploaded to Studio. The following can’t be uploaded:

Retrieval intents
Entities that have entity_group
Intents with use_entities and ignore_entities
Entities with influence_conversation

Example:

rasa studio upload

Run rasa studio upload --help to see the full list of arguments.

Import of CALM assistants

To upload a CALM assistant to Rasa Studio, run this command with --calm flag.

Important!

When uploading a CALM assistant, a new Rasa Studio assistant with specified name will be created. This is different from the NLU-based assistant upload, which will reuse an existing Rasa Studio assistant.
During CALM upload, we also upload config and endpoints that can be edited in the UI.

Example:

rasa studio upload --calm

rasa studio download

v3.7 This command downloads the data from Rasa Studio and saves it to files inside data folder. If local files use a single domain file, it is updated accordingly. If there is a domain folder instead, domain changes are written to <domain_folder>/studio_domain.yml. The command downloads Studio data that is available in Studio but not in local files. The following data is supported:

configuration
endpoints
custom prompts
flows (for CALM assistants)
responses
slots
custom action declarations
intents
entities

The --overwrite flag can be used to overwrite the existing data in the existing files when a primitive has the same ID as the one downloaded from Rasa Studio. Special cases:

If an intent exists in local files, but Studio has examples missing locally, they will be downloaded.
If local config and endpoints files exist during the download of a CALM assistant, the user needs to confirm their intent to overwrite them, even when the --overwrite flag is provided.

Example:

rasa studio download my_awesome_assistant -d my_domain_folder

Run rasa studio download --help to see the full list of arguments.

rasa studio train

v3.7 This command is analogous to rasa train. This command combines data from local files and Rasa Studio to train a model. In case both Studio and local files have a primitive with the same ID, the local one is used for training. Example:

rasa studio train my_awesome_assistant -d my_domain_folder

Run rasa studio train --help to see the full list of arguments.

Other

For a full list of legacy commands, please head over to this reference.

​Cheat Sheet

​Pro specific

​Studio specific

​Rasa Pro Commands

​Logging

​Setting log levels

​Log Level LLM Components

​Custom logging configuration

​Custom logging configuration example

​rasa init

​rasa train

​rasa shell

​rasa run

​rasa run actions

​rasa visualize

​rasa test e2e

​rasa llm finetune prepare-data

​Resulting file structure

​rasa inspect

​rasa inspect —nextgen

​rasa data validate

​Validate flows

​rasa export

​rasa license

​Requesting a Developer License

​Rasa Studio Commands

​rasa studio config

​rasa studio login

​rasa studio upload

​Import of NLU-based assistants

Overwriting an existing assistant

Possible errors

Assistant name errors

Invalid YAML errors

Reference errors

Unsupported feature errors

Authentication errors

​rasa studio download

​rasa studio link

​rasa studio pull

​rasa studio push

​Legacy commands

​rasa studio upload

​Import of NLU-based assistants

​Import of CALM assistants

​rasa studio download

​rasa studio train

​Other

Cheat Sheet

Pro specific

Studio specific

Rasa Pro Commands

Logging

Setting log levels

Log Level LLM Components

Custom logging configuration

Custom logging configuration example

rasa init

rasa train

rasa shell

rasa run

rasa run actions

rasa visualize

rasa test e2e

rasa llm finetune prepare-data

Resulting file structure

rasa inspect

rasa inspect —nextgen

rasa data validate

Validate flows

rasa export

rasa license

Requesting a Developer License

Rasa Studio Commands

rasa studio config

rasa studio login

rasa studio upload

Import of NLU-based assistants

rasa studio download

rasa studio link

rasa studio pull

rasa studio push

Legacy commands

rasa studio upload

Import of NLU-based assistants

Import of CALM assistants

rasa studio download

rasa studio train

Other