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Rasa Open Source

Rasa Technologies

82·Strong

Overall Trust Score

Leading open-source conversational AI framework for building contextual assistants and chatbots. Provides full control over NLU, dialogue management, and deployment with on-premises and cloud options.

nlp
conversational
open-source
Version: 3.x
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

82
intent classification
85
Methodology
Intent accuracy benchmarking
Evidence
Rasa NLU
Customizable NLU pipeline with multiple classifiers
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
dialogue management
87
Methodology
Conversation flow testing
Evidence
Rasa Core
Machine learning-based dialogue policies and story-based training
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
entity extraction
83
Methodology
Entity extraction testing
Evidence
Entity Extractors
Multiple entity extractors (CRF, DIET, SpaCy, Duckling)
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
custom actions
88
Methodology
Custom action testing
Evidence
Custom Actions
Full Python SDK for custom business logic and API calls
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
context handling
79
Methodology
Context retention testing
Evidence
Slot Management
Slot-based context tracking with influence on dialogue
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
latency
Value: 100-500ms (self-hosted)
Methodology
Performance benchmarking
Evidence
Performance
Latency depends on infrastructure and model complexity
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

78
self hosted control
92
Methodology
Deployment security assessment
Evidence
Deployment Options
Full control with on-premises or private cloud deployment
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
authentication
75
Methodology
Authentication capabilities review
Evidence
Security Features
Token-based authentication, requires custom implementation
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
data privacy
85
Methodology
Data flow analysis
Evidence
Open Source
All data stays on your infrastructure, no third-party sharing
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
open source transparency
95
Methodology
Open source assessment
Evidence
GitHub Repository
Apache 2.0 license, 18k+ stars, full source code available
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
encryption
62
Methodology
Security features review
Evidence
Documentation
Encryption implementation is user's responsibility
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

87
data retention
90
Methodology
Privacy architecture review
Evidence
Tracker Store
Full control over conversation storage and retention
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
gdpr compliance
88
Methodology
Compliance capabilities assessment
Evidence
Self-Hosted
GDPR compliance achievable with proper configuration
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
on premise deployment
95
Methodology
Deployment options assessment
Evidence
Deployment
Complete on-premises deployment with no external dependencies
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
pii handling
82
Methodology
PII protection assessment
Evidence
PIIEntityExtractor
PII detection available, anonymization requires custom actions
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
no data sharing
95
Methodology
Data flow analysis
Evidence
Open Source Model
No telemetry or data sharing with third parties
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Trust & Transparency

86
documentation quality
90
Methodology
Documentation completeness review
Evidence
Rasa Docs
Excellent documentation with tutorials and best practices
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
open source code
95
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0, 18k+ stars, active community contributions
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
conversation debugging
82
Methodology
Debugging tools assessment
Evidence
Debug Mode
Interactive learning and conversation debugging tools
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
community support
85
Methodology
Community engagement analysis
Evidence
Community
Active forum, Discord, and community contributions
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
explainability
78
Methodology
Explainability features assessment
Evidence
Policy Insights
Policy confidence scores available but limited explainability
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

77
ease of integration
80
Methodology
Integration complexity assessment
Evidence
Connectors
Built-in connectors for Slack, Facebook, Telegram, REST API
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
scalability
76
Methodology
Scalability testing
Evidence
Scaling Guide
Horizontal scaling possible with container orchestration
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
95
Methodology
Pricing model analysis
Evidence
Open Source
Free open source, costs only for infrastructure
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
73
Methodology
Monitoring features assessment
Evidence
Monitoring
Basic metrics endpoint, requires external monitoring tools
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
production readiness
74
Methodology
Production readiness assessment
Evidence
Deployment Guide
Requires DevOps expertise for production deployment
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
training pipeline
81
Methodology
Training capabilities assessment
Evidence
Training
Flexible training pipeline with customizable components
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

✨ Strengths

  • •Open source (Apache 2.0) with complete control over code and data
  • •On-premises deployment option for maximum privacy and compliance
  • •No vendor lock-in or usage-based pricing, only infrastructure costs
  • •Highly customizable NLU pipeline and dialogue policies
  • •Active community with 18k+ GitHub stars and extensive resources
  • •Machine learning-based dialogue management for contextual conversations

⚠️ Limitations

  • •Requires significant ML and DevOps expertise for deployment
  • •Limited enterprise features out-of-box (auth, monitoring, analytics)
  • •Steeper learning curve compared to managed cloud services
  • •Production deployment and scaling requires infrastructure knowledge
  • •No built-in analytics dashboard, requires external tools
  • •Training data quality critical for good performance

📊 Metadata

license: Apache 2.0
supported models:
0: DIET
1: TED Policy
2: Transformer Embedding Dialogue
programming languages:
0: Python
deployment type: Self-hosted (on-premises, cloud, containers)
tool support:
0: Custom actions (Python)
1: REST API
2: Webhooks
pricing model: Free open source (Rasa Pro available for enterprise)
github stars: 20700+
first release: 2016
supported channels: Slack, Facebook, Telegram, Web, Custom REST
enterprise version: Rasa Pro (commercial license available)
pricing: Free Developer Edition (1,000 conversations/month), Entry-level from $100-500, Rasa Pro from $35,000+
version: 3.x

Use Case Ratings

customer support

88

Excellent for custom support bots with full control

code generation

61

Not designed for code generation use cases

research assistant

67

Conversational focus, limited research capabilities

data analysis

72

Can integrate with analytics via custom actions

content creation

64

Focused on conversations, not content generation

education

80

Good for educational chatbots with customization

healthcare

86

On-premises deployment ideal for HIPAA compliance

financial analysis

85

Self-hosted option meets compliance requirements

legal compliance

70

Would need significant custom development

creative writing

60

Not optimized for creative tasks