Core AI runtime - New encoach.ai.agent + encoach.ai.tool models with M2M tool binding, graph topology (simple|plan_review_revise|rag|react), model + fallback, temperature, max_tokens, response_format, max_revisions, quality checks and system prompt fields. - services/agent_runtime.py compiles a langgraph.StateGraph per agent and caches the build per (key, write_date). Emits a structured trace (output, tool_calls, retrieval_hits, revisions, quality_issues, ms, model_used, fallback_used) and auto-falls-back on rate-limit/5xx. - services/agent_tools.py registers 11 tool handlers wrapping existing services: resources.search, rubric.fetch, outcomes.fetch, student.profile, quality.cefr_check, quality.ai_detect, quality.content_gate, course_plan.save (mutates), course_plan.save_materials (mutates), scoring.grade_writing, scoring.grade_speaking. - 7 default agents seeded via data/agents_defaults.xml: course_planner, course_week_materials, exam_generator, exercise_generator, lms_tutor, writing_grader, speaking_grader. - Feature flag encoach_ai.use_langgraph_runtime (default True). - encoach_ai_course pipeline now routes through AgentRuntime when on, legacy SDK path kept as fallback. Admin UI - /admin/ai/prompts is now a tabbed Agents | Tools | Prompts console. - AIAgentsPanel: card grid + config dialog (model/temp/graph/tools/ system prompt) + built-in Test Runner showing live trace. - AIToolsPanel: registry table with category badges, mutates flag, schema viewer, edit dialog. - New /api/ai/agents* and /api/ai/tools* controller (list/get/update/ test, list-tools, toggle-tool). - Sidebar label nav.aiPrompts -> nav.aiAgents (AI Agents and Tools). - EN + AR (RTL) translations for ~80 new keys. Smart Wizard pages - /admin/quick-setup hub + CourseWizard, CoursePlanWizard, RubricWizard, ExamStructureWizard step-by-step flows. - /admin/course-plans list + detail pages. - /teacher/quick-setup mirror. Full demo seed + 8-role E2E - seed_full_demo.py adds the 5 missing user_types (approver, corporate, mastercorporate, agent, developer), activates a 2-stage exam-approval workflow with one pending request, creates a GE1-aligned 12-week B1 course plan with 6 detailed Week-1 materials (reading 400w, writing, listening 4-min script, speaking, grammar present simple vs continuous, vocabulary), and inserts sample ai.log + ai.feedback rows. - reset_demo_passwords.py forces every demo login back to canonical passwords (admin123/teacher123/student123/approver123/corporate123/ master123/agent123/dev123). - e2e_full_scenario.py: 46/46 PASS read-only API smoke across all 8 roles, including a live LangGraph round-trip on writing_grader. - e2e_approval_chain.py: 6/6 PASS mutation E2E - approver approves stage 1, admin approves stage 2, linked encoach.exam.custom flips to status=published, verified via psql. Docs - docs/PROJECT_SUMMARY.md updated to 2026-04-25: new Latest events bullets, refreshed credentials table, full sections 22 (LangGraph runtime) and 23 (full demo seed + 8-role E2E). - docs/ENCOACH_FULL_DEMO_QA_REPORT.md added with credentials, per-endpoint PASS/FAIL, mutation chain proof, LangGraph live output. - backend/GE1 Course Outline_ Fall AY25-26.pdf vendored as the reference outline the GE1 plan/materials are aligned to. Dependencies - requirements.txt: langgraph>=0.2.0, langchain-core>=0.3.0. - encoach_ai/__manifest__.py: external_dependencies updated. Made-with: Cursor
EnCoach Frontend — v2
Branching Workflow
This repo is connected to the staging server via a Git post-receive hook. All deployment is automatic — but only after code review approval.
How to contribute
- Never push directly to
main— branch protection will block it. - Push your changes to your feature branch (e.g.
feature/full-frontend-v1) - Open a Pull Request on Gitea targeting
main - Request review from devops (Talal)
- Once approved and merged, the staging server rebuilds and redeploys automatically.
Environment
The .env file is not committed. It lives only on the staging server at /opt/encoach/frontend-v2/.env.
EnCoach — Adaptive Learning Platform (Frontend)
The frontend application for the EnCoach Adaptive Learning Platform, serving UTAS university students and freelance learners across IELTS, Mathematics, and IT courses.
Built with React 18, TypeScript, and Vite. Connects to an Odoo 19 backend via REST API.
Tech Stack
| Layer | Technology |
|---|---|
| Framework | React 18 + TypeScript |
| Build | Vite 5 (SWC) |
| Routing | React Router v6 |
| State / Data | TanStack Query v5, React Context |
| UI Components | shadcn/ui + Radix UI |
| Styling | Tailwind CSS 3 |
| Forms | react-hook-form + Zod validation |
| Charts | Recharts |
| Icons | Lucide React |
Architecture
src/
├── components/ # Shared UI, layouts, AI components
│ ├── ai/ # 14 AI-powered components (coaching, grading, insights)
│ └── ui/ # shadcn/ui primitives
├── contexts/ # AuthContext (JWT session management)
├── hooks/
│ ├── queries/ # TanStack Query hooks (exams, assignments, LMS, adaptive)
│ └── usePermissions # Entity-scoped permission checks
├── lib/
│ ├── api-client.ts # Centralized HTTP client with JWT + 401 interception
│ └── query-client.ts # TanStack Query configuration
├── pages/
│ ├── admin/ # Admin & LMS management (courses, batches, taxonomy, resources)
│ ├── student/ # Student portal (dashboard, courses, adaptive learning flow)
│ └── teacher/ # Teacher portal (courses, assignments, attendance, grading)
├── services/ # 21 API service modules mapped to Odoo endpoints
└── types/ # 14 TypeScript type definition files
Key Features
- 7 User Roles — Student, Teacher, Admin, Corporate, Master Corporate, Agent, Developer
- Adaptive Learning Engine — Diagnostic assessment, proficiency profiling, AI-generated learning plans, topic-level content delivery
- LMS Integration — Course management, batches, timetables, attendance, grades (OpenEduCat via API)
- Exam Portal — IELTS-style exams with AI grading, writing evaluation, speaking assessment
- 14 AI Components — Study coach, writing helper, grading assistant, risk detection, insights panel, batch optimizer, report narratives
- Entity-Scoped Permissions — Fine-grained access control with 77+ permission types
- Multi-Subject Support — IELTS (English), Mathematics, IT — all using a universal subject taxonomy
Getting Started
Prerequisites
- Node.js 18+ (or Bun)
- Odoo 19 backend running (see
docs/ODOO_BACKEND_SRS_v3.md)
Setup
# Clone the repository
git clone https://git.albousalh.com/devops/encoach_frontend_new_v1.git
cd encoach_frontend_new_v1
# Install dependencies
npm install
# Configure environment
cp .env.example .env.development
# Start development server
npm run dev
The app runs on http://localhost:8080 by default.
Environment Variables
| Variable | Description | Default |
|---|---|---|
VITE_API_BASE_URL |
Odoo backend API base URL | http://localhost:8069/api |
VITE_APP_NAME |
Application display name | EnCoach |
Scripts
| Command | Description |
|---|---|
npm run dev |
Start dev server with HMR |
npm run build |
Production build |
npm run build:dev |
Development build |
npm run preview |
Preview production build |
npm run lint |
Run ESLint |
npm run test |
Run tests |
npm run test:watch |
Run tests in watch mode |
Documentation
All project documentation is in the docs/ folder:
| Document | Purpose |
|---|---|
ENCOACH_UNIFIED_SRS.md |
Master Software Requirements Specification |
ODOO_BACKEND_SRS_v3.md |
Backend developer handoff (Odoo modules + API contract) |
ENCOACH_PRODUCT_DESCRIPTION.md |
Non-technical product overview |
UTAS_MASTER_PLAN.md |
Project timeline and milestones |
MATH_IT_ADAPTIVE_LEARNING_SRS.md |
Adaptive learning engine specification |
Backend API
The frontend expects an Odoo 19 backend exposing REST endpoints under /api/. All service modules in src/services/ map directly to the API contract defined in docs/ODOO_BACKEND_SRS_v3.md.
Authentication uses JWT tokens stored in localStorage, with automatic 401 interception and redirect to login.
License
Proprietary — EnCoach Platform. All rights reserved.