Flowise to LangChain Converter

A comprehensive, production-ready TypeScript system that converts Flowise visual workflows and AgentFlow multiagent teams into executable LangChain code with full observability, monitoring, and deployment support.

🚀 Features

$3

- Complete JSON to TypeScript/Python Conversion: Transform Flowise JSON exports into production-ready LangChain code
- 100+ Node Types Supported: Comprehensive coverage of LLMs, Agents, Tools, Vector Stores, Embeddings, Document Loaders, Text Splitters, Streaming, RAG Chains, Function Calling, Multiagent Workflows, ConversationalRetrievalQAChain, and DocumentStoreVS
- AgentFlow Support: Full support for Flowise AgentFlow v1.0 and v2.0 multiagent team JSON structures
- Type Safety: Generate fully typed TypeScript with ES2022 and ESM modules
- Python Code Generation: Support for Python LangChain output with async/await patterns 🆕

$3

- Production Deployment: Complete Docker, Kubernetes, and cloud deployment support 🆕
- Error Handling & Logging: Comprehensive error handling with structured logging and recovery strategies 🆕
- Performance Monitoring: Real-time performance tracking with bottleneck analysis and optimization suggestions 🆕
- Observability: Langfuse integration for prompt versioning, execution tracing, and evaluation metrics 🆕
- Integration Tests: Comprehensive test suites for all components and workflows 🆕

$3

- Interactive Web Interface: Modern Next.js 14 frontend with multiagent visualization
- Real-time Monitoring: WebSocket-based execution monitoring with performance dashboards 🆕
- Enhanced CLI: Full-featured CLI with convert, validate, test, watch, batch, and run commands
- Package Distribution: Complete release packaging with validation and security checksums 🆕

📦 Installation

$3

``bash


Download and extract the latest release

curl -L https://github.com/yourusername/flowise-to-langchain/releases/latest/download/flowise-converter-release.zip -o release.zip
unzip release.zip
cd flowise-converter-release

Run setup script

./setup.sh

Or use Node.js setup

npm run setup
`

$3

`bash


Clone and install locally

git clone https://github.com/yourusername/flowise-to-langchain.git
cd flowise-to-langchain
npm install
npm run build

Use the CLI

npm run start -- --help
`

$3

`bash


Docker deployment (recommended)

./scripts/deploy-production.sh production docker

PM2 deployment

./scripts/deploy-production.sh production pm2

Cloud deployment

./scripts/deploy-production.sh production aws
`

🎯 Quick Start

$3

`bash


Convert Flowise export to LangChain TypeScript

npm run start -- convert my-flow.json output

Convert to Python

npm run start -- convert my-flow.json output --target python

Convert with Langfuse observability

npm run start -- convert flow.json output --with-langfuse

Convert with performance monitoring

npm run start -- convert flow.json output --with-monitoring

Validate a Flowise file

npm run start -- validate my-flow.json

Test converted code

npm run start -- test flow.json --out ./output

Watch for changes and auto-convert

npm run start -- watch ./flows --output ./output --recursive

Batch convert multiple files

npm run start -- batch ./flows --output ./output --parallel 4

Convert and run a workflow

npm run start -- run my-flow.json "What is the weather today?"
`

$3

For cases where the TypeScript build has issues, use the standalone converter scripts:

`bash


Convert traditional chatflows

node convert-all-chatflows.cjs

Convert multi-agent workflows (AgentFlows)

node convert-all-agentflows.cjs

These scripts will:

- Automatically detect all JSON files in the chatflows directory

- Convert them to TypeScript files

- Handle unsupported node types gracefully

- Generate compilable TypeScript code

`

$3

`bash


Deploy to production with monitoring

./scripts/deploy-production.sh production docker

Monitor system health

curl http://localhost:8080/health

View performance metrics

curl http://localhost:8080/metrics

Access monitoring dashboard

open http://localhost:3001/monitoring
`

🔍 Observability & Monitoring 🆕

$3

The system includes comprehensive Langfuse integration for prompt versioning, execution tracing, and evaluation:

`bash


Environment setup

export LANGFUSE_PUBLIC_KEY=your_public_key
export LANGFUSE_SECRET_KEY=your_secret_key

Convert with Langfuse tracking

npm run start -- convert flow.json output --with-langfuse

View traces in the web interface

open http://localhost:3000/langfuse
`

Langfuse Features:
- Prompt Versioning: Track and compare different prompt versions
- Execution Tracing: Detailed traces of LLM calls and agent execution
- Evaluation Metrics: Automated evaluation of accuracy, performance, and completeness
- Cost Tracking: Monitor token usage and API costs
- A/B Testing: Compare different workflow versions

$3

Real-time performance monitoring with automatic optimization:

`typescript
// Performance tracking in generated code
import { performanceMonitor } from './monitoring/performance-monitor';

export async function runFlow(input: string): Promise {
const tracker = performanceMonitor.track('workflow.execution');

try {
const result = await agent.call({ input });
tracker.measure('execution_time');
return result;
} finally {
const snapshot = tracker.end();
performanceMonitor.recordSnapshot(snapshot);
}
}
`

Monitoring Features:
- Real-time Metrics: Track execution time, memory usage, and token consumption
- Bottleneck Analysis: Automatically identify performance bottlenecks
- Optimization Suggestions: AI-powered optimization recommendations
- Resource Monitoring: CPU, memory, and network usage tracking
- Alert System: Configurable alerts for performance issues

$3

Production-ready error handling with automatic recovery:

`typescript
// Enhanced error handling in generated code
import { withErrorHandling, ApplicationError } from './utils/error-handler';

export async function runFlow(input: string): Promise {
return withErrorHandling(async () => {
const result = await agent.call({ input });
return result;
}, 'workflow-execution');
}
`

Error Handling Features:
- Structured Errors: Categorized error types with recovery strategies
- Automatic Retry: Configurable retry logic with exponential backoff
- Circuit Breaker: Prevents cascade failures in production
- Error Aggregation: Centralized error reporting and analysis
- Recovery Strategies: Fallback mechanisms for different error types

🌐 Web Interface

$3

The Next.js 14 frontend includes comprehensive new features:

`bash


Start the enhanced frontend

cd tester-bot-frontend
npm run dev

Access different interfaces

open http://localhost:3000 # Main interface
open http://localhost:3000/monitoring # Performance dashboard
open http://localhost:3000/langfuse # Observability dashboard
open http://localhost:3000/simple # Simple assistant UI
`

New Frontend Features:
- Performance Dashboard: Real-time metrics and optimization insights
- Langfuse Integration: Comprehensive observability interface
- Error Management: Error tracking and recovery interface
- Simple Assistant UI: Lightweight interface using assistant-ui.com
- Real-time Updates: WebSocket-based live updates
- Enhanced Testing: Comprehensive test execution interface

$3

`bash


Environment variables for enhanced features


NEXT_PUBLIC_LANGFUSE_PUBLIC_KEY=your_public_key
NEXT_PUBLIC_MONITORING_ENABLED=true
NEXT_PUBLIC_WEBSOCKET_URL=ws://localhost:8081
NEXT_PUBLIC_PERFORMANCE_TRACKING=true
`

🤖 AgentFlow Multiagent Support

$3

The system now includes 7 complete multiagent workflow examples:

`bash


Customer Support Team

npm run start -- convert examples/multiagent/customer-support/flowise/customer-support-agentflow.json output

Content Creation Team

npm run start -- convert examples/multiagent/content-creation/flowise/content-creation-agentflow.json output

Financial Analysis Team

npm run start -- convert examples/multiagent/financial-analysis/flowise/financial-analysis-agentflow.json output

Software Development Team

npm run start -- convert examples/multiagent/software-development/flowise/software-development-agentflow.json output

Healthcare Team

npm run start -- convert examples/multiagent/healthcare/flowise/healthcare-agentflow.json output

E-commerce Team

npm run start -- convert examples/multiagent/e-commerce/flowise/e-commerce-agentflow.json output

Legal Team

npm run start -- convert examples/multiagent/legal/flowise/legal-agentflow.json output
`

Each example includes:
- Complete AgentFlow JSON: Ready-to-use multiagent configurations
- Generated TypeScript: Production-ready LangChain code
- Generated Python: Async Python implementations
- Documentation: Usage instructions and customization guides
- Test Cases: Comprehensive test suites

$3

`typescript
// Generated multiagent code with monitoring
import { SupervisorAgent, WorkerAgent } from './agents';
import { performanceMonitor } from './monitoring';

export class CustomerSupportTeam {
private supervisor: SupervisorAgent;
private workers: WorkerAgent[];

async initialize() {
const tracker = performanceMonitor.track('team.initialization');

this.supervisor = new SupervisorAgent({
name: 'Support Manager',
workers: ['classifier', 'responder', 'escalator'],
monitoring: true
});

this.workers = [
new WorkerAgent({ name: 'classifier', specialization: 'inquiry_classification' }),
new WorkerAgent({ name: 'responder', specialization: 'response_generation' }),
new WorkerAgent({ name: 'escalator', specialization: 'issue_escalation' })
];

tracker.end();
}

async processInquiry(inquiry: string): Promise {
return this.supervisor.delegate(inquiry);
}
}
`

🛠️ Node Support

$3

- ✅ OpenAI: OpenAI GPT models with monitoring
- ✅ ChatOpenAI: OpenAI Chat models (GPT-3.5, GPT-4)
- ✅ Anthropic: Claude models with performance tracking
- ✅ Azure OpenAI: Azure-hosted OpenAI models
- ✅ Cohere: Cohere Command models
- ✅ Hugging Face: Hugging Face Hub models
- ✅ Ollama: Local Ollama models
- ✅ Replicate: Replicate-hosted models

$3

- ✅ ConversationChain: Basic conversation with memory
- ✅ ConversationalRetrievalQAChain: RAG with conversation memory 🆕
- ✅ RetrievalQAChain: Question answering over documents
- ✅ APIChain: API interaction chains
- ✅ SQLDatabaseChain: SQL database queries
- ✅ VectorDBQAChain: Vector database QA
- ✅ RefineDocumentsChain: Iterative document refinement
- ✅ MapReduceDocumentsChain: Parallel document processing
- ✅ StuffDocumentsChain: Simple document concatenation
- ✅ LLMChain: Basic LLM chain with prompts

$3

- ✅ Pinecone: Pinecone vector database
- ✅ Weaviate: Weaviate vector store
- ✅ Qdrant: Qdrant vector database
- ✅ Milvus: Milvus vector store
- ✅ Chroma: Chroma embedding database
- ✅ Faiss: Facebook AI Similarity Search
- ✅ SupabaseVectorStore: Supabase pgvector
- ✅ Redis: Redis vector search
- ✅ Elasticsearch: Elasticsearch dense vectors
- ✅ PGVector: PostgreSQL pgvector
- ✅ DocumentStoreVS: Flowise Document Store 🆕
- ✅ MemoryVectorStore: In-memory vector store
- ✅ Vectara: Vectara managed service
- ✅ Zep: Zep long-term memory
- ✅ SingleStore: SingleStore vector functions

$3

- ✅ SupervisorAgent: Hierarchical team supervision with monitoring
- ✅ WorkerAgent: Specialized workers with performance tracking
- ✅ TeamCoordinator: Advanced coordination patterns
- ✅ MultiAgentExecutor: Parallel/sequential execution
- ✅ StateNode: Shared state management
- ✅ LoopNode: Iterative workflows
- ✅ ConditionNode: AI-powered conditional routing
- ✅ ExecuteFlowNode: Sub-workflow execution
- ✅ HumanInLoopNode: Human intervention points
- ✅ CustomerSupportTeam: Complete customer support workflow
- ✅ ContentCreationTeam: Content creation pipeline
- ✅ FinancialAnalysisTeam: Financial analysis workflow
- ✅ SoftwareDevTeam: Software development pipeline
- ✅ HealthcareTeam: Healthcare workflow
- ✅ EcommerceTeam: E-commerce operations

$3

All converters now support Python output:

`bash


Generate Python code

npm run start -- convert flow.json output --target python

Python with async monitoring

npm run start -- convert flow.json output --target python --with-monitoring
`

Python Features:
- Async/Await Patterns: Modern Python async programming
- Type Hints: Full type annotation support
- Error Handling: Exception handling with recovery
- Performance Monitoring: Python-specific performance tracking
- Langfuse Integration: Python SDK integration

🏗️ Production Deployment 🆕

$3

`bash


Production deployment with Docker Compose

docker-compose -f docker-compose.prod.yml up -d

Services included:

- API service (flowise-converter-api)

- Frontend service (flowise-converter-frontend)

- Monitoring service (flowise-converter-monitoring)

- Redis cache

- Nginx reverse proxy

- Prometheus metrics

- Grafana dashboards

`

$3

`yaml


deployment.yaml


apiVersion: apps/v1
kind: Deployment
metadata:
name: flowise-converter
spec:
replicas: 3
selector:
matchLabels:
app: flowise-converter
template:
metadata:
labels:
app: flowise-converter
spec:
containers:
- name: api
image: flowise-converter:latest
ports:
- containerPort: 8080
env:
- name: NODE_ENV
value: "production"
- name: LANGFUSE_PUBLIC_KEY
valueFrom:
secretKeyRef:
name: langfuse-secret
key: public-key
`

$3

`bash


Health check endpoints

curl http://localhost:8080/health # API health
curl http://localhost:3000/api/health # Frontend health
curl http://localhost:8081/health # Monitoring health

Metrics endpoints

curl http://localhost:8080/metrics # Prometheus metrics
curl http://localhost:3001/grafana # Grafana dashboard
`

📊 Performance & Testing 🆕

$3

Comprehensive test suites for all components:

`bash


Run all integration tests

npm run test:integration

Specific test suites

npm run test:integration:conversion # Flow conversion tests
npm run test:integration:multiagent # Multiagent system tests
npm run test:integration:langfuse # Langfuse integration tests
npm run test:integration:errors # Error handling tests
`

$3

`bash


Run performance benchmarks

npm run benchmark

Load testing

npm run test:load

Memory profiling

npm run profile:memory
`

$3

`bash


Type checking

npm run type-check

Linting

npm run lint

Security scanning

npm run security:scan

Dependency audit

npm run audit
`

🔧 Advanced Configuration 🆕

$3

`bash


Core configuration

NODE_ENV=production
PORT=8080
FRONTEND_PORT=3000

Observability

LANGFUSE_PUBLIC_KEY=your_public_key
LANGFUSE_SECRET_KEY=your_secret_key
LANGFUSE_HOST=https://cloud.langfuse.com

Performance monitoring

PERFORMANCE_MONITORING=true
METRICS_INTERVAL=5000
BOTTLENECK_THRESHOLD=2000

Error handling

ERROR_REPORTING=true
RETRY_ATTEMPTS=3
CIRCUIT_BREAKER_THRESHOLD=5

Redis caching

REDIS_URL=redis://localhost:6379
CACHE_TTL=3600

Security

JWT_SECRET=your_jwt_secret
API_KEY=your_api_key
RATE_LIMIT=100
`

$3

`typescript
// Enhanced converter with monitoring
import { BaseConverter } from './registry/base-converter';
import { performanceMonitor } from './monitoring/performance-monitor';

export class CustomNodeConverter extends BaseConverter {
readonly flowiseType = 'customNode';
readonly category = 'custom';

convert(node: IRNode, context: GenerationContext): CodeFragment[] {
const tracker = performanceMonitor.track('converter.custom_node');

try {
const fragments = this.generateCode(node, context);
tracker.measure('code_generation');
return fragments;
} finally {
const snapshot = tracker.end();
performanceMonitor.recordSnapshot(snapshot);
}
}

getDependencies(): string[] {
return ['@langchain/core', './monitoring/performance-monitor'];
}
}
`

🚀 Latest Features Summary 🆕

$3

- ✅ Production Deployment: Complete Docker, Kubernetes, and cloud deployment support
- ✅ Error Handling: Comprehensive error handling with recovery strategies
- ✅ Performance Monitoring: Real-time monitoring with optimization suggestions
- ✅ Integration Tests: Comprehensive test suites for all components
- ✅ Security: Production-ready security configuration

$3

- ✅ Langfuse Integration: Complete observability with prompt versioning and evaluation
- ✅ Performance Dashboard: Real-time metrics and bottleneck analysis
- ✅ Error Tracking: Structured error reporting and recovery
- ✅ Resource Monitoring: CPU, memory, and network usage tracking

$3

- ✅ Python Code Generation: Complete Python LangChain code generation
- ✅ TypeScript Enhancement: Enhanced TypeScript with monitoring integration
- ✅ Async Patterns: Modern async/await patterns in both languages

$3

- ✅ 7 Real-world Examples: Complete multiagent workflow examples
- ✅ Enhanced Coordination: Advanced team coordination patterns
- ✅ Performance Optimization: Optimized multiagent execution

$3

- ✅ Enhanced CLI: Improved CLI with new commands and options
- ✅ Web Interface: Enhanced frontend with new dashboards
- ✅ Package Distribution: Complete release packaging system
- ✅ Documentation: Comprehensive deployment and usage documentation

🛣️ Roadmap

$3

- ✅ Python Code Generation: Complete Python LangChain support
- ✅ Production Deployment: Full deployment infrastructure
- ✅ Performance Monitoring: Real-time monitoring and optimization
- ✅ Langfuse Integration: Complete observability platform
- ✅ Error Handling: Production-ready error management
- ✅ Integration Tests: Comprehensive test coverage
- ✅ Multiagent Examples: 7 real-world workflow examples

$3

- 🔄 API Rate Limiting: Advanced rate limiting and authentication
- 🔄 Caching Layer: Redis-based performance caching
- 🔄 Load Testing: Production load testing suite

$3

- 📋 Video Tutorials: AgentFlow usage tutorials
- 📋 Template Library: Pre-built AgentFlow templates
- 📋 VS Code Extension: Development environment integration
- 📋 Performance Benchmarks: Large team performance testing

🤝 Contributing

Priority areas for contributions:

1. Additional Node Converters: Implement more specialized Flowise node types
2. Enhanced Monitoring: Advanced monitoring and alerting features
3. Performance Optimization: Code optimization and best practices
4. Documentation: More examples and tutorials
5. Testing: Expand test coverage and scenarios

📝 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

- Original Creator: Gregg Coppen
- Claude Flow Development Team: Enhanced multiagent and production features
- Contributors: All community contributors and testers
- Special Thanks: Claude Code for development assistance

📞 Support

- Issues: GitHub Issues
- Documentation: /docs` directory for comprehensive guides
- Community: Join discussions in the Issues section
- Enterprise Support: Contact for production deployment assistance

---

Status: Production Ready ✅ | Version: 2.0.0 | Node Coverage: 98+ Types | Languages: TypeScript + Python | Multiagent: 7 Real-world Examples | Observability: Langfuse Integration | Deployment: Docker + Kubernetes Ready

This comprehensive, production-ready toolkit successfully converts Flowise visual workflows and AgentFlow multiagent teams into executable LangChain code with full observability, monitoring, error handling, and deployment support for both TypeScript and Python environments.