- Overview
- Project Structure
- Core Value Proposition
- Key Features
- Technology Stack
- Technical Architecture
- Installation & Setup
- Testing
- CI/CD Pipeline
- Documentation
- Contributing Guidelines
- License
Flowlet simplifies embedded finance by exposing payments, wallets, card issuance, and compliance through a single unified API. It is cloud-native and microservices-based, emphasizing scalability, security, and regulatory readiness while reducing operational overhead. Developer SDKs, documentation, and built-in AI for fraud detection and observability accelerate integration so teams can deliver auditable finance features without rebuilding core banking primitives.
Flowlet's strength lies in its comprehensive suite of embedded finance capabilities, meticulously implemented across its microservices architecture.
| Feature Domain | Core Functionality | Key Backend Modules |
|---|---|---|
| Digital Wallet Management | Wallet creation, transaction processing, multi-currency support, real-time notifications. | backend/src/routes/wallet.py, backend/src/models/account.py, backend/src/currency/ |
| Payment Processing | Payment routing, external gateway integration (e.g., Stripe), bank transfers, transaction validation. | backend/src/routes/payment.py, backend/src/integrations/payments/ |
| Card Issuance & Management | Card lifecycle events, transaction authorization, advanced card controls. | backend/src/routes/card.py, backend/src/models/card.py |
| KYC/AML Compliance | Identity verification, sanctions screening, regulatory compliance workflows, audit trails. | backend/src/routes/kyc_aml.py, backend/src/compliance/ |
| Ledger & Accounting | Meticulous recording of financial transactions, double-entry accounting, auditability. | backend/src/routes/ledger.py, backend/src/models/ledger.py, backend/src/utils/audit.py |
| AI-Enhanced Services | Real-time fraud detection, risk assessment, and intelligent support. | backend/src/ai/, backend/src/ml/fraud_detection/ |
| No-Code Workflow Engine | Configuration and execution of custom financial rules and workflows. | backend/src/nocode/ |
Flowlet utilizes a monorepo structure, separating the core backend services, frontend applications, and infrastructure configurations.
| Directory | Description |
|---|---|
code/ |
Contains the core Python microservices, shared libraries, and the main application logic. |
web-frontend/ |
The main web application built with React and TypeScript. |
scripts/ |
Essential shell scripts for setup, building, and running the application. |
tests/ |
Comprehensive test suite covering unit, integration, performance, and security testing. |
docs/ |
Documentation, including API reference and architecture specifications. |
infrastructure/ |
Comprehensive DevOps and Infrastructure-as-Code (IaC) configurations. |
.github/ |
Configuration for GitHub Actions and repository templates. |
Flowlet is built on a modern, high-performance, and cloud-native stack.
| Category | Component | Technology | Detail |
|---|---|---|---|
| Backend | Languages | Python | Primary language for all microservices. |
| Frameworks | Flask + flask-restx | REST API framework with automatic OpenAPI/Swagger documentation. | |
| Databases | PostgreSQL, Redis | PostgreSQL for transactional data; Redis for caching and session management. | |
| Messaging | Kafka/RabbitMQ | Event-driven architecture for inter-service communication. | |
| Frontend | Web | React, TypeScript | Main framework for the web dashboard. |
| Styling | Tailwind CSS | Utility-first CSS framework for rapid UI development. | |
| AI/ML | Frameworks | scikit-learn, XGBoost, LightGBM | Ensemble fraud detection; see ML Model Performance. |
| DevOps | Containerization | Docker | For packaging services. |
| Orchestration | Kubernetes, Helm | For scalable deployment and management of microservices. | |
| CI/CD | GitHub Actions, Ansible | Automated build, test, deployment pipelines, and configuration management. | |
| IaC | Terraform | Infrastructure-as-Code for provisioning cloud resources. |
Flowlet implements a Microservices Architecture with a strong focus on Event-Driven Design and Security-by-Design.
Flowlet/
├── API Gateway (Authentication, Rate Limiting)
├── Frontend Application (Web)
├── Core Microservices
│ ├── User Service (Auth, Profile)
│ ├── Wallet Service (Accounts, Transactions)
│ ├── Payment Service (Processing, Routing)
│ ├── Card Service (Issuance, Controls)
│ ├── Compliance Service (KYC/AML)
│ └── Ledger Service (Accounting, Audit)
├── AI/ML Engine
│ ├── Fraud Detection Service
│ └── Risk Assessment Service
├── Infrastructure
│ ├── Message Queue
│ ├── Database Cluster
│ └── Observability Stack (Prometheus, Grafana)
└── Integrations Layer
├── Open Banking (Plaid, FDX)
└── Payment Processors (Stripe)
Flowlet supports two primary deployment environments: Development (local setup) and Production (Kubernetes/Helm).
| Requirement | Detail |
|---|---|
| Python | 3.11+ |
| Node.js | 20+ |
| pnpm | Package manager for frontend dependencies. |
| Docker | Docker Engine and Docker Compose (for development setup). |
| Kubectl, Helm | Required for production setup. |
The setup.sh script is the primary tool for environment configuration. Use the --env development flag for a local setup.
# Clone the repository
git clone https://github.com/quantsingularity/Flowlet.git
cd Flowlet
# Run the setup script for the development environment
./scripts/setup.sh --env development
# To start all services (backend and frontend)
./dev-start.shFor production, Flowlet is designed to be deployed using Helm charts to a Kubernetes cluster.
# Run the setup script for the production environment
# This will check for kubectl/helm, create secrets, and deploy via Helm
./scripts/setup.sh --env production --namespace flowlet-prod
# To check the status of the deployment
kubectl get pods -n flowlet-prodThe fraud detection ensemble (XGBoost + LightGBM + Random Forest stacking) achieves:
| Metric | Value |
|---|---|
| AUC-ROC | 0.987 |
| Precision | 97.3% |
| Recall | 96.1% |
| F1 Score | 96.7% |
| Inference latency (p50) | 8 ms |
See docs/ML_MODEL_PERFORMANCE.md for full tearsheets, confusion matrices, walk-forward validation, fairness analysis, and operational benchmarks.
Flowlet maintains a high standard of code quality with 91% test coverage. The testing framework is comprehensive and covers all layers of the application.
| Test Type | Location | Purpose |
|---|---|---|
| Unit Tests | tests/unit/ |
Isolated testing of individual functions and classes. |
| Integration Tests | tests/integration/ |
Validating inter-service communication and external API integrations. |
| Functional Tests | tests/functional/ |
Testing core business logic and user flows. |
| Performance Tests | tests/performance/ |
Benchmarking API response times and system throughput. |
| Security Tests | tests/security/ |
Automated checks for common security vulnerabilities. |
AlphaMind uses GitHub Actions for continuous integration and deployment:
| Stage | Control Area | Institutional-Grade Detail |
|---|---|---|
| Formatting Check | Change Triggers | Enforced on all push and pull_request events to main and develop |
| Manual Oversight | On-demand execution via controlled workflow_dispatch |
|
| Source Integrity | Full repository checkout with complete Git history for auditability | |
| Python Runtime Standardization | Python 3.10 with deterministic dependency caching | |
| Backend Code Hygiene | autoflake to detect unused imports/variables using non-mutating diff-based validation |
|
| Backend Style Compliance | black --check to enforce institutional formatting standards |
|
| Non-Intrusive Validation | Temporary workspace comparison to prevent unauthorized source modification | |
| Node.js Runtime Control | Node.js 18 with locked dependency installation via npm ci |
|
| Web Frontend Formatting Control | Prettier checks for web-facing assets | |
| Mobile Frontend Formatting | Prettier enforcement for mobile application codebases | |
| Documentation Governance | Repository-wide Markdown formatting enforcement | |
| Infrastructure Configuration | Prettier validation for YAML/YML infrastructure definitions | |
| Compliance Gate | Any formatting deviation fails the pipeline and blocks merge |
For detailed documentation, please refer to the following resources:
| Document | Path | Description |
|---|---|---|
| README | README.md |
High-level overview, project scope, and quickstart |
| API Reference | API.md |
Detailed documentation for all API endpoints |
| CLI Reference | CLI.md |
Command-line interface usage, commands, and examples |
| Installation Guide | INSTALLATION.md |
Step-by-step installation and environment setup |
| User Guide | USAGE.md |
Comprehensive guide for end-users, workflows, and examples |
| Contributing Guidelines | CONTRIBUTING.md |
Contribution process, coding standards, and PR requirements |
| Architecture Overview | ARCHITECTURE.md |
System architecture, components, and design rationale |
| Configuration Guide | CONFIGURATION.md |
Configuration options, environment variables, and tuning |
| Feature Matrix | FEATURE_MATRIX.md |
Feature capabilities, coverage, and roadmap alignment |
| Troubleshooting | TROUBLESHOOTING.md |
Common issues, diagnostics, and remediation steps |
We welcome contributions to Flowlet. Please follow the organization's standard contribution process:
- Open an Issue: Discuss your proposed feature or bug fix before starting work.
- Fork and Branch: Fork the repository and create a new branch for your changes.
- Code Standards: Adhere to the existing code style and ensure all tests pass.
- Documentation: Update the relevant documentation for any new features or changes.
- Pull Request: Submit a pull request with a clear description of your changes and reference the related issue.
Flowlet is released under the MIT License. For full details, see the LICENSE file in the repository root.