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Feature: Local/Remote Task Execution Tracking & Credits System

Date: 2026-02-15 Status: Approved (Option B) — V1.1 Version: V1.1 (post-launch)

1. Context & Motivation​

Problem​

Currently, Morphee's task system:

  • Only executes tasks in the Python backend (TaskProcessor)
  • Has no concept of where a task runs (locally on Tauri vs remotely on server)
  • Has no credits/usage tracking
  • No abstraction layer for alternative storage backends

Opportunity​

As Morphee evolves toward offline-first (Phase 3d M3), tasks should be:

  • Offloadable to local Tauri layer (ONNX embeddings, local LLM inference, file ops)
  • Tracked with location visibility (user sees "running locally" vs "running in cloud")
  • Accountable with credits (per-user, per-space quotas for API calls, compute, etc.)
  • Storage-agnostic (PostgreSQL now, Redis queue later, S3 archive, etc.)

Strategic Goals​

  1. Hybrid execution: Route tasks intelligently (local if capable, remote if needed)
  2. Cost visibility: Users understand what resources they're using
  3. Offline operation: Tasks queued/cached locally, synced when online
  4. Extensibility: Easy to swap storage backend without code changes

2. Options Investigated​

Option A: Minimal — Add Execution Location + Credits to Existing Task Table​

Approach:

  • Add columns to tasks table: execution_location (enum: local/remote/hybrid), credits_used (numeric)
  • Add columns to track execution times per location
  • Create simple TaskCredits model with user/space/interface quotas
  • Update TaskProcessor to mark location based on which backend executes
  • No storage abstraction—stay with PostgreSQL

Pros:

  • Minimal changes to existing system
  • Quick to implement (~2-3 weeks)
  • Straightforward frontend display
  • No new services or infrastructure

Cons:

  • No storage abstraction (hard to migrate later)
  • No credit enforcement mechanism (tracking only, no limits)
  • Task routing still manual (no intelligent dispatcher)
  • Scales poorly with many concurrent tasks (single TaskProcessor bottleneck)

Estimated Effort: M (2-3 weeks)

Option B: Task Router + Credits System + Storage Abstraction (Recommended)​

Approach:

  1. Execution Location Model:

    • Add execution_location (enum: LOCAL, REMOTE, HYBRID, AUTO)
    • Add executor_id (which Tauri instance or Python worker)
    • Add execution_metadata (JSONB: latency, memory, duration, location details)

  2. Credits System:

    • Create task_credits table (task_id, space_id, interface_id, amount, reason, timestamp)
    • Create space_quotas table (space_id, credit_limit, credit_used, reset_period)
    • Quota enforcement: reject task if space quota exceeded
    • Audit trail for compliance

  3. Task Router (New Service):

    • TaskRouter evaluates task → decides execution location
    • Rules: if local capability + offline mode → LOCAL
    • Otherwise: if quota available → REMOTE
    • Otherwise: PENDING (queue for later)
    • Emit task.routed event

  4. Storage Abstraction:

    • Create TaskStore ABC (abstract base class)
    • Implement PostgresTaskStore, RedisTaskStore (queue), S3TaskStore (archive)
    • TaskService uses TaskStore interface (not direct DB queries)
    • Config selects storage backend

  5. Frontend Updates:

    • Task card shows: "⚙️ Running locally" / "☁️ Running in cloud" / "⏳ Queued"
    • Credits used per task
    • Quota usage indicator
    • Estimated credits before execution (preview)

Pros:

  • Future-proof for offline operation
  • Intelligent routing based on capability + quota
  • Cost control (enforce limits, prevent runaway)
  • Storage flexibility (swap backends without code changes)
  • Audit trail (compliance-ready for GDPR/enterprise)
  • Scales (distributed task queuing with Redis)

Cons:

  • Larger scope (~6-8 weeks)
  • Requires migration script for existing tasks
  • More moving parts (router, quota service, multiple stores)
  • Needs careful design of routing rules

Estimated Effort: L (6-8 weeks)

Option C: Event-Driven Task Execution with Worker Pool​

Approach:

  1. Execution Model: Tasks → Event Bus → Worker Pool
  2. Workers: Python backend, Tauri instances, or external job servers
  3. Job Queue: Redis-backed queue (Celery-like) with priorities
  4. Location Tracking: Worker reports location after claiming task
  5. Credits: Per-worker billing (local cheaper, remote standard rate)

Pros:

  • Highly scalable (multiple workers)
  • Natural async/parallel execution
  • Easy to add new worker types (GPU servers, etc.)
  • Distributed tracing built-in

Cons:

  • Complex (Celery learning curve)
  • Operational overhead (worker health checks, dead letters, etc.)
  • Overkill for Phase 3e (MVP doesn't need distributed workers)
  • High effort (~10-12 weeks)

Estimated Effort: XL (10-12 weeks)

Option D: Lightweight — Storage Abstraction Only (No Router/Credits)​

Approach:

  • Add execution_location field to tasks
  • Implement TaskStore ABC with PostgreSQL + Redis queue support
  • Frontend shows location
  • No router, no credits (can add later)

Pros:

  • Medium effort (~3-4 weeks)
  • Unblocks future credit system
  • Gives storage flexibility without complexity

Cons:

  • Routing still manual
  • No cost control
  • Incomplete user-facing feature (location tracking without credits feels incomplete)

Estimated Effort: M (3-4 weeks)

3. Decision (APPROVED)​

Chosen: Option B (Task Router + Credits System + Storage Abstraction)

Approved Design Details:

3.1 Execution Location Routing (Configurable, User-Driven)​

  • Task specifies execution_preference: "local" | "remote" | "auto" | "cost_optimized" | "performance_optimized"
  • Router evaluates actual capability + preference + quota:
    • local → Force LOCAL if device is Tauri, error otherwise
    • remote → Always REMOTE (useful for high-security tasks)
    • auto → Default; router decides based on capability + efficiency
    • cost_optimized → Prefer LOCAL (free) over REMOTE (paid) if capable
    • performance_optimized → Prefer REMOTE (cloud resources) over LOCAL (device resource)
  • Router returns: execution location + estimated cost
  • User sees decision BEFORE task executes

3.2 Credit Model (Backend-Driven, Action + Token Multiplier)​

  • Base credit per action: Defined in IntegrationDefinition

    # Example
    {
      "gmail": {"send": 1.0, "read": 0.5},
      "llm": {"chat": 0.01, "embed": 0.005},  # multiplied by token count
      "local_embedding": 0,  # local = free
    }

  • LLM factor: credits_used = base_credit * (input_tokens + output_tokens) / 1000

    • E.g., 1000-token chat with base=0.01 → 1 token = 0.01 credits

  • Execution location discount:

    • LOCAL: 0% surcharge (free if action supports it)
    • REMOTE: 1x base cost (standard)
    • REMOTE + peak hours: 1.2x base cost (future: time-based pricing)

  • Backend decides cost via config file per integration

3.3 Quota Enforcement (Two-Tier: Monthly Hard + Weekly Soft)​

  • Hard limit: Monthly quota cap (e.g., 1000 credits/month)
    • Tasks blocked if monthly quota exceeded
    • Reset every month on billing cycle start
  • Soft limit: Weekly advisory quota (e.g., 250 credits/week)
    • Tasks allowed to proceed but trigger warning
    • User can override with one-click confirmation ("Use 50 extra credits?")
    • Goal: help users pace themselves without hard brick wall
  • Per-space quotas (not per-user)
  • Admin can override quota for urgent tasks

3.4 Storage Abstraction (MVP: PostgreSQL, Future: Redis/S3)​

  • Design TaskStore ABC now
  • Implement PostgresTaskStore fully for MVP
  • Future: RedisTaskStore (queue), S3TaskStore (archive) without code changes
  • Each backend is swappable via config

Reasoning:

  • Aligns with Morphee's offline-first vision (Phase 3d M3)
  • Provides user-visible value (quota control, cost transparency, execution choice)
  • Future-proof (storage abstraction enables Redis queue, S3 archive, etc.)
  • Moderate scope (6-8 weeks confirmed acceptable)
  • Sets up for Phase 3m (crypto marketplace) where credits → real value
  • Flexible: users can optimize for cost OR performance based on needs

Trade-offs we're accepting:

  • Larger upfront effort vs. Option A (6-8 weeks vs. 2-3 weeks)
  • Complexity added (router, quota service, multiple stores)
  • Requires migration script for existing tasks

If we do Option B, when would we do Option C (distributed workers)? — Later (Phase 4+), when we need to scale beyond a single backend instance.

4. Implementation Plan (Option B)​

StepDescriptionEffortDependencies
4.1Design TaskStore ABC + PostgresTaskStoreSNone
4.2Create DB migrations (execution_location, task_credits, space_quotas)SNone
4.3Implement TaskRouter service (decision logic)M4.1
4.4Implement TaskCreditsService (track, enforce quotas)M4.2
4.5Integrate router/credits into TaskProcessorM4.3, 4.4
4.6Refactor TaskService to use TaskStore ABCM4.1
4.7Add TaskStore-backed Redis queue supportM4.1, 4.6
4.8Frontend: TaskCard location displayS4.5
4.9Frontend: Credits/quota indicatorsS4.4
4.10Migration script: backfill execution_locationS4.2
4.11Integration tests (router, credits, stores)M4.3-4.7
4.12E2E tests (task execution, quota enforcement)M4.8-4.9
4.13Documentation (architecture, quotas, examples)SAll

Total: ~8 weeks (M+M+M+M+S+S+S+S+M+M+S)

5. Key Questions & Decisions (APPROVED ANSWERS)​

Q1: What determines "local" vs. "remote"?

  • A (APPROVED): Task specifies execution_preference (local/remote/auto/cost_optimized/performance_optimized)
    • local: Force LOCAL if device is Tauri, error otherwise
    • remote: Always REMOTE
    • auto (default): Router decides based on capability + efficiency
    • cost_optimized: Prefer LOCAL (free) → REMOTE (paid) if capable
    • performance_optimized: Prefer REMOTE (cloud) → LOCAL (device) for speed
    • User sees cost estimate BEFORE execution

Q2: Credit model — how is it determined?

  • A (APPROVED): Backend-driven, per-action with LLM token multiplier
    • Base credit per action defined in IntegrationDefinition (config file)
    • LLM: credits = base * (input_tokens + output_tokens) / 1000 (e.g., 0.01 credits per 1000 tokens for chat)
    • Local tasks: free (0 credits) to incentivize offline use
    • REMOTE surcharge: 1x base (standard), 1.2x during peak hours (future)
    • Example: Gmail send=1 credit, LLM chat=0.01 per 1000 tokens, local embedding=0

Q3: What if quota exceeded mid-task?

  • A (APPROVED):
    • Hard monthly limit: Task BLOCKED with blocked_reason = "monthly_quota_exceeded"
    • Soft weekly limit: Task proceeds with warning, user can confirm override
    • No partial rollback (credits charged upfront based on estimate)

Q4: Do we enforce quotas or just track?

  • A (APPROVED): ENFORCE both hard and soft limits
    • Hard monthly: rejects execution
    • Soft weekly: allows but warns
    • Admin override available for urgent tasks
    • Compliance-ready for B2B (teams, companies have budgets)

Q5: Storage abstraction — do we actually need RedisTaskStore now?

  • A (APPROVED): Not now. Design ABC for it, implement PostgresTaskStore only (MVP)
  • RedisTaskStore (queue) + S3TaskStore (archive) in Phase 4 without code changes to TaskService

Q6: How do we handle task retry and credits?

  • A: Original task_id charged credits once; retries don't re-charge
  • Failed → Pending → Retry = no additional credit charge (same task, same debit)
  • Re-run (manual execution, new task_id) = new task, new credits charged

Q7: Should we track credits per user or per space?

  • A: Per SPACE (not per user)
  • Rationale: spaces are the billing unit (family has 1 quota, school has 1 quota)
  • Sub-spaces inherit parent quota by default but can override per sub-space

Q8: What about historical reports and predictions?

  • A: Deferred to Phase 3e.5+ (UI for quota management, usage reports, predictions)

6. Open Items / Deferred​

  • Crypto integration (Phase 3m) — how do credits convert to MorphCoin/payments?
  • User-facing quota UI — settings page, quota management (Phase 3e.5+)
  • Quota reset schedules — monthly/weekly/custom
  • Multi-tenant enterprise quotas — shared vs. per-seat
  • Prediction API — estimate credits before executing task
  • Alert system — notify when quota approaching 80%
  • Historical reports — usage over time (audit/billing)

7. References​

  • Existing: backend/tasks/models.py, backend/tasks/service.py, backend/tasks/processor.py
  • Storage Pattern: Similar to Python's abc.ABC + SQLAlchemy
  • Credits Model: Inspired by OpenAI's token counting, Stripe's credit system
  • Router Logic: Similar to Kubernetes scheduler (request-based dispatch)
  • Phase 3d M3: docs/features/2026-02-12-mobile-ios-android.md (local execution baseline)
  • Phase 3m: MEMORY.md (crypto marketplace reference)
  1. Get approval on Option B — Does the scope and timeline work?
  2. Clarify routing rules — Which tasks go local vs. remote by default?
  3. Define credit costs — Per-interface/action credit amounts
  4. Timeline alignment — Does 6-8 weeks fit the current sprint?
  5. Begin Step 4.1 — TaskStore ABC design (most critical dependency)

Last Updated: 2026-02-20