rasa

library3.6.21pypypiunverified

Rasa is an active open-source machine learning framework (version 3.6.21) designed to automate text and voice-based conversations. It provides tools for Natural Language Understanding (NLU) and dialogue management, enabling developers to create contextual chatbots and voice assistants that can integrate with various platforms like Slack and Facebook. The project maintains a regular release cadence with frequent updates.

llm-agents ai-ml

pip install rasa

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · RASA

rasa

llm-agentspythonv3.6.21

Install

—

Import

—

Disk

—

Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v3.6.21 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

4/8 runs

py 3.11

✕ build_error

py 3.12

✕ build_error

py 3.13

✕ build_error

py 3.9

✕ build_error

4/8 runs

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Agent

✓ from rasa.core.agent import Agent

Action

✓ from rasa_sdk import Action

CollectingDispatcher

✓ from rasa_sdk.executor import CollectingDispatcher

TrackerStore

✓ from rasa.core.tracker_store import TrackerStore

✗ from rasa.core.tracker_store import MongoTrackerStore

For custom tracker stores, inherit from base `TrackerStore`. Specific implementations like `MongoTrackerStore` are part of the internal API or configured via `endpoints.yml`.

Interpreter

✓ from rasa.nlu.model import Interpreter

✗ from rasa_nlu.model import Interpreter

The `rasa_nlu` package was deprecated; NLU functionalities are now integrated into the main `rasa` package.

The standard way to get started with Rasa is through its command-line interface. This quickstart initializes a new project, trains a basic model, and then allows interactive conversation with the bot in the shell. Custom actions require running a separate action server.

import os # Ensure you are in an empty directory for this quickstart # In a real project, replace 'tutorial' with your desired template or omit for default # os.system('rasa init --template tutorial') # os.system('rasa train') # os.system('rasa shell') print("To start a new Rasa project, open your terminal and run:") print("1. rasa init --template tutorial") print("2. cd <your-project-name>") print("3. rasa train") print("4. rasa shell") print("For custom actions, run 'rasa run actions' in a separate terminal after step 3.")

rasa --version

Debug

Known issues

breakingRasa 3.6.21 introduced a model breaking change by replacing `pickle` and `joblib` with safer alternatives like `json`, `safetensors`, and `skops` for component serialization. Models trained with older versions will not be compatible.

fix

Retrain your Rasa models after upgrading to Rasa 3.6.21 or newer. If you have custom components that override `persist` or `load` methods, update your code to use the new serialization mechanisms.

affects: >=3.6.21

gotchaRasa 3.x has specific Python version requirements. The `rasa` PyPI package currently requires Python <3.11, >=3.8. Using incompatible Python versions can lead to `ModuleNotFoundError` or other installation issues.

fix

Always install Rasa in a virtual environment with a supported Python version (e.g., `python3.10 -m venv .venv && source .venv/bin/activate`). Check the official documentation for the exact compatible Python versions for your Rasa release.

affects: 3.x

deprecatedRasa X, the UI for Rasa, does not support Rasa Open Source versions 3.x and above. Attempting to use `rasa x` with Rasa 3.x will result in an error.

fix

If you need to use Rasa X, you must downgrade your Rasa Open Source installation to version 2.8.x. For Rasa 3.x and newer, consider alternative UI tools or develop custom interfaces.

affects: >=3.0

breakingRasa 3.0 introduced a major architecture revamp, transitioning from a sequential training pipeline to a graph architecture. This significantly changed how NLU and policy components interact and can affect custom components, particularly the `process` method signature for NLU components (now accepting `List[Message]` instead of `Message`).

fix

Review and update any custom NLU components or policies to align with the new graph architecture and method signatures, especially for `process` methods that handle incoming messages.

affects: >=3.0

Upgrade

Version history

3.6.21latest on PyPI · released Jan 14, 2025

Audit

Dependencies

pythonrequiredRasa 3.x requires Python <3.11, >=3.8.

rasa-sdkrequiredRequired for implementing custom actions.

tensorflowoptionalCore dependency for many NLU and dialogue models, included with `rasa[full]`.

spacyoptionalCommonly used for language modeling in NLU pipelines, included with `rasa[full]`.

Agent activity

31 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/rasahq/rasa ↗

packagehttps://pypi.org/project/rasa/ ↗

homepagehttps://rasa.com ↗

docshttps://rasa.com/docs ↗