social-app/backend/AGENTS.md

Backend Development Rules

This document defines Python/FastAPI backend development constraints.

Scope and Precedence

• This file applies to all changes under backend/**.
• It extends root routing rules in AGENTS.md and workspace global runtime rules.
• If rules conflict, follow stricter requirements.
• Keep backend-only rules here; do not duplicate them in root AGENTS.md.

Python Environment

MUST use uv for dependency management and virtual environment execution.

• All Python commands: uv run <command>
• Add dependencies: uv add <package>
• All dependencies declared in pyproject.toml

Code Quality Checks

Git pre-commit hook enforces code quality before commit.

Pre-commit hook automatically runs on backend/ directory:

• ruff check - code style and linting
• basedpyright - type checking with error level

If any error detected, commit is rejected. Fix errors before committing. Do not bypass or weaken checks (no ignores, disables, or config relaxations). Resolve the underlying issues.

Logging

MUST use project logger for all runtime logging.

• Use project logger from backend/src/core/logging/*
• Prohibit: print(), logging.info/warning/error directly
• Required: structured logging with context
• Log levels: DEBUG, INFO, WARNING, ERROR, CRITICAL

HTTP API Standards

MUST follow RESTful conventions and RFC 7807 for error responses.

• Errors must use application/problem+json with RFC 7807 fields
• No custom response envelopes for HTTP APIs
• Request and response validation must use Pydantic models

Environment Variables

Backend env access MUST go through backend/src/core/config/settings.py.

• Only use Settings() / config from core.config.settings
• Do not call os.environ, os.getenv, dotenv, or manual parsing in backend runtime code
• Tests can set env vars via monkeypatch.setenv, and should read values via Settings() unless the test is explicitly validating env plumbing
• Canonical principle: one source of truth per setting; no duplicate/derived env vars in backend code

TDD Workflow

Coverage Requirements

• Minimum coverage: 80%
• Required test types:
  • Unit: isolated functions, utilities, components
  • Integration: API endpoints, database operations
  • E2E: critical user flows (Playwright)

Limited Exceptions

• Docs-only changes (README, comments, formatting) may skip integration/E2E
• Non-runtime config changes may skip E2E if no behavior changes
• Any runtime code change requires unit + integration + E2E
• If an exception is used, record the reason in the PR/test notes

Mandatory TDD Workflow

1. Write tests (RED) - they must fail
2. Run tests - confirm failure
3. Implement minimal code (GREEN) - only to pass
4. Run tests - confirm success
5. Refactor (IMPROVE)
6. Verify coverage - target 80%+

Enforcement

• Must use the tdd-guide agent for new features
• Do not write implementation before tests
• Do not lower coverage requirements
• Must include unit, integration, and E2E tests

Code Style

Immutability

ALWAYS create new objects, NEVER mutate.

# WRONG: Mutation
def update_user(user, name):
    user["name"] = name
    return user

# CORRECT: Immutability
def update_user(user, name):
    return {**user, "name": name}

File Organization

• Many small files over few large files
• 200-400 lines typical, 800 max per file
• Extract utilities from large components

Error Handling

Always handle errors comprehensively:

try:
    result = risky_operation()
    return result
except Exception as exc:
    logger.exception("Operation failed")
    raise RuntimeError("Detailed user-friendly message") from exc

Security

Mandatory Security Checks

Before ANY commit:

• No hardcoded secrets (API keys, passwords, tokens)
• All user inputs validated (use Pydantic)
• SQL injection prevention (parameterized queries)
• Authentication/authorization verified

Secret Management

# NEVER: Hardcoded secrets
api_key = "sk-proj-xxxxx"

# ALWAYS: Read through centralized settings
from core.config.settings import Settings

settings = Settings()
api_key = settings.openai_api_key
if not api_key:
    raise ValueError("OPENAI_API_KEY not configured in settings")

Database Development Rules

Architecture

• Supabase: authentication (JWT source of truth)
• Backend: business authorization (service layer)
• SQLAlchemy ORM: data access layer (async + asyncpg, service_role connection)

Code Organization

Use schemas / repository / service pattern:

• schemas.py — Pydantic models
• repository.py — CRUD only, no auth, no commit (only flush), must receive session (never create session/engine)
• service.py — authorization + business logic + transaction boundary (must commit/rollback)
• dependencies.py — DI (get_db, get_current_user)

Auth & Data Access

• Backend must verify JWT signature and expiration (not just decode)
• Extract user_id from JWT sub claim
• Backend connects with service_role (bypasses RLS)
• owner_id always derived from JWT, never from client
• Scope queries by owner/org; public access must be explicit
• service_role key is backend-only; never expose credentials
• Prohibit calling Supabase Admin API (service_role key) from repository/service layers

Soft Delete

Soft delete marks data as invisible, not cascade delete.

• Use deleted_at: datetime | None column (via SoftDeleteMixin)
• Query filtering: Repository _apply_soft_delete_filter() auto-excludes deleted records
• No automatic cascade: Related data stays intact; visibility controlled by JOIN filtering
• Cascade only for strong dependencies: When parent deletion must invalidate children, implement in Service layer explicitly
• Recovery: Only restore the record itself; related data visibility restored automatically via queries
• Unique constraints: Use partial indexes excluding deleted_at IS NOT NULL to allow re-creation

# Partial unique index in migration
op.execute("""
    CREATE UNIQUE INDEX ux_user_email 
    ON users(email) 
    WHERE deleted_at IS NULL
""")

Migrations

• Alembic is the single source of truth for schema migrations
• ORM model changes → alembic revision --autogenerate
• Raw SQL (policies, triggers, functions) → op.execute()
• Migrations must be reversible; no reliance on generated IDs

Enum Storage Convention

Store enum names (strings), not integer values.

• Use VARCHAR(20) + CHECK constraint in database
• Use Python Enum class with str base in code

class AgentType(str, Enum):
    INTENT_RECOGNITION = "INTENT_RECOGNITION"
    TASK_EXECUTION = "TASK_EXECUTION"
    RESULT_REPORTING = "RESULT_REPORTING"

RLS Policy

• Backend does not rely on RLS for correctness (uses service_role), but RLS is mandatory as a defensive boundary for tables in PostgREST-exposed schemas.
• Mandatory default: any new business table in public must enable RLS in the same Alembic migration.
• The same migration must create policies covering SELECT/INSERT/UPDATE/DELETE (minimum requirement).
• Recommended default policy set for anon, authenticated: deny all operations first, then open explicit access only when required.
• alembic_version must not be exposed to anon or authenticated.

Exemption Rule (strict)

• Exemptions are allowed only when a new public table is guaranteed not to be exposed to PostgREST clients.
• Exemptions must be explicit in the migration file with rationale and verification notes.
• If exposure is uncertain, do not exempt: enable defensive RLS by default.

Migration Checklist

• New public business table has ALTER TABLE ... ENABLE ROW LEVEL SECURITY in migration
• Policies for SELECT/INSERT/UPDATE/DELETE are present in migration
• Policy target roles are explicit (anon, authenticated, or both)
• Downgrade path is reversible and does not silently weaken intended production security
• Any exemption is documented with clear non-exposure evidence

Backend Startup

Always use ./infra/scripts/app.sh to start/stop the backend. Do not start uvicorn directly. Always use ./logs/*.log to check the backend log output.

Agent Loop (AG-UI Protocol)

Agent loop functionality MUST follow the AG-UI protocol. Use the ag-ui skill for protocol reference and implementation guidance.

Multi-Agent Orchestration (AgentScope Framework)

Multi-agent orchestration MUST use the AgentScope framework. Use the agentscope-skill for framework reference and implementation guidance.

Core Principles

• Use AgentScope for orchestrating multiple agents working together
• Define clear agent roles, stage responsibilities, and pipeline boundaries
• Leverage AgentScope built-in workflow and tool middleware mechanisms
• Follow AgentScope best practices for agent configuration

Key Components

• Agents: Autonomous units with specific roles and goals
• Tasks: Stage-specific prompts and execution goals
• Pipelines: Ordered orchestration flow between agents
• Tools: Capabilities available to agents
• Flows: Workflow orchestration and state management