feat(ai): LangGraph as core runtime + AI Agents/Tools console + full-demo seed

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

backend/requirements.txt

@@ -7,3 +7,8 @@ reportlab>=4.0.0
 Pillow>=10.0.0
 pgvector>=0.2.0
 psycopg2-binary>=2.9.0
+# LangGraph is the core runtime for EnCoach AI agents (course planning, exam/exercise
+# generation, grading, LMS tutor). We still call OpenAI via the existing
+# `OpenAIService` wrapper; LangGraph only adds the state machine + tool routing on top.
+langgraph>=0.2.0
+langchain-core>=0.3.0