AutomationSTATUS: Open Source 1 0

ThreadHarbor WhatsApp Hub

Centralized WhatsApp Business API orchestrator with NLP intent classification

ThreadHarbor enables businesses to automate customer messaging at scale. Built with FastAPI and Redis, it parses incoming WhatsApp webhooks, classifies conversational intent, executes automated inquiry flows, and provides fallback routing to human support agents.

Inspect GitHub Source Code

01 // THE BUSINESS PROBLEM & SITUATION

Operational Context

Service businesses relying on WhatsApp as a primary customer channel suffer from response delays and lost leads during peak message hours.

CLIENT / ENVIRONMENT: Developed for service businesses managing high-volume client communications on WhatsApp.

02 // SYSTEM ARCHITECTURE & SOLUTION

What Was Built

Engineered a scalable webhook orchestrator combining NLP intent classification with Redis state machines for real-time conversation management.

THREADHARBOR // WHATSAPP_APISession State Machine

WEBHOOK

WhatsApp API

NLP

FastAPI Classifier

CACHE

Redis Session

ESCALATION

Human Support

SYSTEMS_ARCHITECTURE_EXPLODER // LAYERED_STACKFULL-STACK SYSTEM DECOUPLING

Frontend Layer

Next.js 16 App Router, React 19, Tailwind CSS, TypeScript

API & Middleware

FastAPI, Pydantic Schema Validation, Rate Limiters

Orchestration

LangGraph Multi-Agent Supervisor & State Machines

Model Providers

OpenAI GPT-4o, Anthropic Claude 3.5, Vision AI

Data Stores

PostgreSQL (Prisma), Qdrant Vector Store, Redis Caching

Observability

LangSmith traces, Pydantic audit logs, Error retries

03 // ENGINEERING CONTRIBUTION & FEATURES

Exact Contribution

Developed high-throughput FastAPI webhook listener for WhatsApp Business API
Implemented intent classification module with fast fallback routing
Built Redis session state machine maintaining context across async webhooks
Designed seamless human-agent takeover protocols

Key Capabilities Built

✓ Real-Time WhatsApp Webhook Ingestion

✓ Intent-Driven Automated Response Routing

✓ Redis Session Context Caching

✓ Human-Agent Live Handoff Protocol

✓ Rich Interactive Message Template Support

DATA_PROVENANCE_TIMELINE // AUDIT_TRACETRACE_ID: #TR-992041

14:22:01.002Source Ingestion

Received webhook payloadOK

14:22:01.045Schema Validation

Pydantic structured field checkOK

14:22:01.210LangGraph Reasoning

Evaluated criteria rulesOK

14:22:01.350Human Audit Check

Confidence threshold evaluationPASSED

14:22:01.480Database Commit

Created audit record in PostgreSQLCOMMITTED

*Illustrative system trace — demonstration data, not a client result.

04 // EXECUTION PIPELINE

Step-by-Step Workflow

STEP 01Customer sends message to business WhatsApp account.

STEP 02FastAPI listener validates signature and parses payload.

STEP 03NLP module classifies customer intent and entities.

STEP 04Redis session state machine retrieves conversational context.

STEP 05System sends automated response or alerts human team if escalation is needed.

05 // ENGINEERING LESSONS & OUTCOMES

Technical Challenges Overcome

• Handling out-of-order webhook delivery during sporadic mobile network conditions.
• Managing WhatsApp 24-hour customer service session window constraints.

Measurable Outcomes

→ Automated tier-1 FAQ responses with sub-second response times in testing.

→ Standardized conversational session persistence across distributed worker instances.

Lessons & Engineering Rules

"Stateless webhook handlers require explicit Redis caching layers for reliable conversation continuity."
"Clear fallback paths must be triggered whenever intent classification confidence drops below 75%."

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PROJECT METADATA

ARSLAN'S ROLEBackend Automation Engineer

YEAR & STATUS2025 // Open Source

CATEGORYAutomation

TECHNOLOGY STACK

PythonWhatsApp Business APIFastAPIRedisNLPDocker

GITHUB METRICS

Stars: 1

Forks: 0

Verified GitHub Update: 8/27/2026

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