langflow-ai/langflow

Langflow is a powerful tool for building and deploying AI-powered agents and workflows.

146,437 stars Python 10 components 5 connections

Visual AI agent and workflow builder with web UI and API

User creates workflows in React frontend, which sends requests to FastAPI backend for execution, with results stored in database and streamed back to UI

Under the hood, the system uses 2 data pools, 2 control points to manage its runtime behavior.

Structural Verdict

A 10-component fullstack with 5 connections. 3713 files analyzed. Loosely coupled — components are relatively independent.

How Data Flows Through the System

User creates workflows in React frontend, which sends requests to FastAPI backend for execution, with results stored in database and streamed back to UI

Workflow Creation — User drags and connects nodes in React Flow interface to build AI workflows
API Request — Frontend sends workflow definition to FastAPI backend endpoints
Workflow Execution — Backend processes workflow, executing AI components and managing state
Database Storage — Results and workflow metadata stored in Aurora MySQL database
Response Streaming — Execution results streamed back to frontend via WebSocket or HTTP

System Behavior

How the system actually operates at runtime — where data accumulates, what loops, what waits, and what controls what.

Data Pools

Aurora Database (database)
User accounts, workflows, execution results, and API keys

Flow Store (state-store)
React state management for workflow nodes and edges

Delays & Async Processing

ECS Task Startup (async-processing, ~2-5 minutes) — Backend API unavailable during container initialization
CloudFront Cache (cache-ttl, ~24 hours default) — Frontend updates delayed until cache expiration

Control Points

RAG Feature Toggle (feature-flag) — Controls: Enables/disables Kendra-based retrieval augmented generation. Default: ragEnabled context variable
CPU Architecture (env-var) — Controls: ECS task CPU architecture (ARM64 vs X86_64). Default: X86_64

Technology Stack

React (framework)
Frontend framework with React Flow for visual workflow building

FastAPI (framework)
Backend API server with automatic OpenAPI documentation

Pydantic (library)
Data validation and serialization for API schemas

AWS CDK (infra)
Infrastructure as code for cloud deployment

Aurora MySQL (database)
Managed relational database for workflow and user data

Docusaurus (framework)
Documentation site with custom plugins and analytics

Segment (library)
Analytics tracking and user behavior monitoring

Code Hike (library)
Syntax highlighting for documentation code blocks

Key Components

DefaultEdge (component) — React component for rendering visual connections between workflow nodes with context menu actions src/frontend/src/CustomEdges/index.tsx
ChatWidget (component) — Embeddable chat widget that loads external script and renders langflow-chat element docs/src/components/ChatWidget/index.tsx
LangflowAppStack (class) — Main AWS CDK stack orchestrating VPC, ECS, RDS, and other infrastructure components scripts/aws/lib/cdk-stack.ts
BackEndCluster (class) — Creates Fargate service and task definition for running backend API containers scripts/aws/lib/construct/backend.ts
trackEvent (function) — Enhanced analytics tracking function that merges common properties with event data docs/src/plugins/segment/analytics-helpers.js
CodeSnippet (component) — Syntax-highlighted code block component using Code Hike lighter for line-range extraction docs/src/components/CodeSnippet.tsx
initializeDataAttributeTracking (function) — Sets up automatic click tracking for elements with data-event attributes docs/src/plugins/segment/data-attribute-tracking.js
Network (class) — Creates VPC, ECS cluster, security groups, and application load balancer infrastructure scripts/aws/lib/construct/network.ts
Rds (class) — Creates Aurora MySQL cluster with auto-generated credentials stored in Secrets Manager scripts/aws/lib/construct/db.ts
Web (class) — Creates S3 bucket and CloudFront distribution for hosting static frontend assets scripts/aws/lib/construct/frontend.ts

Sub-Modules

Frontend Application (independence: high)
React-based visual workflow builder with drag-and-drop interface

Backend API (independence: high)
FastAPI server handling workflow execution and database operations

Documentation Site (independence: high)
Docusaurus-based documentation with embedded chat widgets

AWS Infrastructure (independence: medium)
CDK deployment scripts for cloud infrastructure provisioning

Configuration

codecov.yml (yaml)

codecov.require_ci_to_pass (boolean, unknown) — default: false
codecov.notify.after_n_builds (number, unknown) — default: 1
coverage.precision (number, unknown) — default: 2
coverage.round (string, unknown) — default: down
coverage.range (string, unknown) — default: 30...100
coverage.status.project.backend.target (string, unknown) — default: 55%
coverage.status.project.backend.threshold (string, unknown) — default: 5%
coverage.status.project.backend.flags (array, unknown) — default: backend
+22 more parameters

render.yaml (yaml)

services (array, unknown) — default: [object Object]

src/backend/base/langflow/alembic/migration_validator.py (python-dataclass)

type (str, unknown)
message (str, unknown)
line (int, unknown)
severity (str, unknown) — default: "error" # error or warning

src/backend/base/langflow/api/health_check_router.py (python-pydantic)

status (str, unknown) — default: "nok"
chat (str, unknown) — default: "error check the server logs"
db (str, unknown) — default: "error check the server logs"

Explore the interactive analysis

See the full architecture map, data flow, and code patterns visualization.

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Related Fullstack Repositories

Frequently Asked Questions

What is langflow used for?

Visual AI agent and workflow builder with web UI and API langflow-ai/langflow is a 10-component fullstack written in Python. Loosely coupled — components are relatively independent. The codebase contains 3713 files.

How is langflow architected?

langflow is organized into 4 architecture layers: Frontend, Backend, Documentation, Infrastructure. Loosely coupled — components are relatively independent. This layered structure keeps concerns separated and modules independent.

How does data flow through langflow?

Data moves through 5 stages: Workflow Creation → API Request → Workflow Execution → Database Storage → Response Streaming. User creates workflows in React frontend, which sends requests to FastAPI backend for execution, with results stored in database and streamed back to UI This pipeline design reflects a complex multi-stage processing system.

What technologies does langflow use?

The core stack includes React (Frontend framework with React Flow for visual workflow building), FastAPI (Backend API server with automatic OpenAPI documentation), Pydantic (Data validation and serialization for API schemas), AWS CDK (Infrastructure as code for cloud deployment), Aurora MySQL (Managed relational database for workflow and user data), Docusaurus (Documentation site with custom plugins and analytics), and 2 more. A focused set of dependencies that keeps the build manageable.

What system dynamics does langflow have?

langflow exhibits 2 data pools (Aurora Database, Flow Store), 2 control points, 2 delays. These runtime behaviors shape how the system responds to load, failures, and configuration changes.

What design patterns does langflow use?

5 design patterns detected: React Flow Integration, Pydantic Schemas, CDK Infrastructure as Code, Analytics Tracking, Docusaurus Plugins.

Analyzed on March 31, 2026 by CodeSea. Written by Karolina Sarna.