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Langflow

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78·Strong

Overall Trust Score

Visual, drag-and-drop interface for building LangChain-based AI applications and agents. Low-code platform that makes it easy to prototype and deploy LLM workflows, RAG systems, and conversational agents.

visual
low-code
open-source
Version: 1.5.1+
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

79
visual workflow execution
82
Methodology
Workflow execution testing
Evidence
Langflow UI
Drag-and-drop interface for building LangChain flows
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
langchain compatibility
88
Methodology
Compatibility testing
Evidence
LangChain Integration
Built on LangChain with access to all components
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
rapid prototyping
90
Methodology
Development speed assessment
Evidence
Low-Code Development
Fast prototyping with pre-built templates and components
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
llm support
84
Methodology
LLM integration testing
Evidence
Model Support
Supports OpenAI, Anthropic, Google, local models via LangChain
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
error handling
72
Methodology
Error recovery testing
Evidence
Flow Execution
Basic error handling, visual debugging available
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
latency
Value: Varies by flow (1-10s)
Methodology
Performance monitoring
Evidence
Performance
Performance depends on LangChain components and LLM calls
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

71
api key management
75
Methodology
Security configuration review
Evidence
Credentials
Environment variable-based credential management
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
self hosting
85
Methodology
Deployment security assessment
Evidence
Deployment
Self-hosting with Docker and cloud deployment options
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
data privacy
78
Methodology
Data flow analysis
Evidence
Self-Hosted Option
Data privacy depends on deployment and LLM choices
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
open source
90
Methodology
Open source assessment
Evidence
GitHub
MIT license, 30k+ stars, open source community
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
authentication
62
Methodology
Authentication assessment
Evidence
Security Features
Basic auth available, enterprise features in DataStax version
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

77
data retention
80
Methodology
Privacy architecture review
Evidence
Data Storage
Flow definitions and logs stored in configured database
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
gdpr compliance
76
Methodology
Compliance capabilities assessment
Evidence
Self-Hosted
GDPR compliance possible with self-hosted deployment
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment
88
Methodology
Deployment options assessment
Evidence
Deployment Options
Full local deployment with pip or Docker
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
llm data sharing
72
Methodology
Data flow analysis
Evidence
LLM Integration
Data sent to LLM providers unless using local models
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
flow export
82
Methodology
Data portability assessment
Evidence
Export Features
Export flows as JSON for version control and migration
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Trust & Transparency

85
documentation quality
84
Methodology
Documentation completeness review
Evidence
Langflow Docs
Good documentation with tutorials and component guides
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
visual debugging
88
Methodology
Debugging tools assessment
Evidence
UI Features
Visual flow debugging with step-by-step execution
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source
92
Methodology
Open source assessment
Evidence
GitHub
MIT license, 30k+ stars, very active community
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
community support
83
Methodology
Community engagement analysis
Evidence
Community
Active Discord and GitHub discussions
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09

Operational Excellence

78
ease of use
92
Methodology
Usability assessment
Evidence
UI/UX
Intuitive drag-and-drop interface for non-developers
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
scalability
70
Methodology
Scalability testing
Evidence
Deployment
Can scale with containerization, limited production features
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
90
Methodology
Pricing model analysis
Evidence
Open Source
Free MIT license, costs only for infrastructure and LLMs
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
68
Methodology
Monitoring features assessment
Evidence
Monitoring Features
Basic logging, limited production monitoring
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
production readiness
72
Methodology
Production readiness assessment
Evidence
Deployment Guide
Designed for prototyping, production use requires hardening
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
template library
86
Methodology
Template availability assessment
Evidence
Templates
Pre-built templates for common use cases
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

✨ Strengths

  • •Intuitive visual drag-and-drop interface for LLM workflows
  • •Open source (MIT) with very active community (30k+ stars)
  • •Built on LangChain ecosystem with access to all components
  • •Excellent for rapid prototyping and experimentation
  • •Low-code approach makes AI accessible to non-developers
  • •Free to use with flexible deployment options

⚠️ Limitations

  • •Limited production-grade features (auth, monitoring, scaling)
  • •Performance overhead from visual abstraction layer
  • •Primarily designed for prototyping, not enterprise deployment
  • •Security features less mature than enterprise platforms
  • •Limited control compared to code-based implementations
  • •Debugging complex flows can be challenging despite visual interface

📊 Metadata

license: MIT
supported models:
0: OpenAI
1: Anthropic
2: Google
3: Cohere
4: HuggingFace
5: Local LLMs
programming languages:
0: Python
1: TypeScript (UI)
deployment type: Self-hosted (pip, Docker) or cloud
tool support:
0: LangChain tools
1: Custom Python components
pricing model: Free open source (DataStax offers managed enterprise version)
github stars: 130000+
first release: 2023
ui framework: React-based visual interface
langchain version: Compatible with LangChain ecosystem
version: 1.5.1+
pricing: Free (open source), Cloud from $0/hour (usage-based)

Use Case Ratings

customer support

80

Good for prototyping support bots with visual design

code generation

72

Can build code agents but limited specialized features

research assistant

82

Good for RAG-based research workflows with visual design

data analysis

75

Can integrate analysis tools via LangChain components

content creation

78

Suitable for building content generation workflows

education

83

Easy for educators to build tutoring systems visually

healthcare

74

Prototyping viable, production needs security hardening

financial analysis

71

Self-hosted option possible but limited enterprise features

legal compliance

79

Good for building document analysis workflows

creative writing

76

Suitable for creative workflow prototyping