🚀 MageFlow Roadmap¶
Building the future of intelligent task orchestration, one feature at a time.
📋 Task Lifecycle Management¶
Complete control over task state, persistence, and recovery throughout their execution lifecycle.
🗑️ Delayed Signature Deletion¶
Difficulty:
HARD| Priority:HIGH| Status:Planned
Implement delayed deletion system for task signatures to enable rerun capabilities.
What's Coming:
- ⏰ Delayed Deletion - Mark signatures for deletion after configurable time period
- 🔄 Rerun Window - Allow users to rerun signatures before permanent deletion
- 🏷️ Soft Delete - Mark signatures as deleted while preserving data temporarily
- 🧹 Cleanup Jobs - Background processes to permanently remove expired signatures
- ⚙️ Configurable TTL - Adjustable time-to-live for deleted signatures
- 📊 Status Tracking - New MARKED_FOR_DELETION status with expiration timestamps
Critical Requirement:
Note: Tasks with signatures must continue to run even if the signature was marked for deletion. Active execution should not be affected by the deletion process.
Technical Challenges: - 🔐 Reference Integrity - Maintain task execution while signature is in deletion queue - 🕒 Time Management - Handle timezone-aware deletion scheduling - 💾 Storage Optimization - Balance rerun capability with storage efficiency - 🔄 Race Conditions - Prevent conflicts between deletion and rerun operations
Impact: Enable safe signature management with recovery options, reducing accidental data loss while maintaining system performance.
🧹 Signature Cleanup Callbacks¶
Difficulty:
MEDIUM| Priority:HIGH| Status:Planned
Implement cleanup callbacks for each signature that execute on both success and failure to ensure proper resource management.
What's Coming: - ✅ Success Cleanup - Execute cleanup operations after successful task completion - ❌ Failure Cleanup - Trigger cleanup on task failures or errors - 🔄 Guaranteed Execution - Ensure cleanup runs regardless of task outcome - 🎯 Per-Signature Config - Configure cleanup callbacks at signature level - 🧹 Resource Management - Properly release resources, close connections, clean temporary files - 📊 Cleanup Tracking - Monitor and log cleanup operations for debugging
Use Cases: - Database connection cleanup after query execution - Temporary file removal after processing - Lock release in distributed systems - Resource deallocation and memory cleanup - External API session termination - Notification sending after task completion
Technical Implementation: - Cleanup callbacks execute in finally-like semantics - Support both synchronous and asynchronous cleanup functions - Chain cleanup callbacks for nested operations - Handle cleanup callback failures gracefully
Impact: Ensure robust resource management and prevent resource leaks by guaranteeing cleanup operations execute regardless of task success or failure.
⛔ Support Interrupt Tasks¶
Difficulty:
MEDIUM| Priority:HIGH| Status:Planned
Implement the missing interrupt functionality for aggressive task termination.
What's Coming:
- 🛑 Aggressive Termination - Force stop tasks regardless of execution status
- 🔄 All Task Types - Support interrupt for signatures, chains, and swarms
- ⚠️ State Management - Handle interrupted task state transitions
- 🔧 Recovery Logic - Implement best-effort resume for interrupted tasks
- 📊 Status Tracking - Proper INTERRUPTED status handling
- 🛡️ Error Handling - Graceful handling of interrupt failures
Current Status:
- ❌ Method exists in API but raises NotImplementedError
- ❌ Task lifecycle documentation exists but functionality missing
- ❌ Chain and swarm interrupt operations not functional
Impact: Complete the task lifecycle management system with aggressive task termination capabilities, enabling better control over runaway or stuck tasks.
🚫 Cancel Tasks (Complete Deletion)¶
Difficulty:
MEDIUM| Priority:MEDIUM| Status:Planned
Implement task cancellation that completely removes the signature, unlike interrupt which preserves it.
What's Coming: - 🗑️ Immediate Deletion - Completely remove task signature from system - 🛑 Force Stop + Delete - Stop execution and permanently delete all data - 🔄 All Task Types - Support cancellation for signatures, chains, and swarms - 🧹 Cleanup Operations - Remove all associated callbacks, logs, and metadata - ⚠️ Confirmation System - Require explicit confirmation for destructive operation - 📊 Audit Trail - Log cancellation events for debugging and compliance
Key Differences from Interrupt:
┌─────────────┬───────────┬─────────────┬──────────────┐
│ Operation │ Execution │ Signature │ Recovery │
├─────────────┼───────────┼─────────────┼──────────────┤
│ Interrupt │ ⏹️ Stops │ ✅ Preserved │ 🔄 Possible │
│ Cancel │ ⏹️ Stops │ ❌ Deleted │ ❌ Impossible │
└─────────────┴───────────┴─────────────┴──────────────┘
Use Cases: - Permanent removal of erroneous task submissions - Cleanup of test/development tasks - Resource cleanup when tasks are no longer needed - Emergency deletion of problematic workflows
Impact: Provide complete task lifecycle control with permanent removal capabilities for situations where tasks should never be resumed or recovered.
🔄 Auto-Resume Unfinished Tasks¶
Difficulty:
HARD| Priority:HIGH| Status:Planned
Automatically restart all unfinished tasks when the worker restarts after shutdown.
What's Coming: - 🔄 Automatic Recovery - Detect and resume tasks that were interrupted during shutdown - 📊 State Persistence - Track task execution state across worker restarts - 🎯 Selective Resume - Option to resume all or filter specific task types - 🛡️ Safety Checks - Validate task state before resuming to prevent corruption - ⚙️ Configuration Options - Enable/disable auto-resume per task type or globally - 📋 Resume Report - Generate summary of resumed tasks and any failures
Recovery Scenarios:
- 🔋 Worker Shutdown - Graceful shutdown with pending tasks
- 💥 Unexpected Crash - System failure during task execution
- 🔌 Network Issues - Connection loss during distributed execution
- 🔧 Maintenance - Planned restarts during maintenance windows
Task State Handling:
┌───────────┬──────────┬─────────────────────────────────┐
│ Status │ Action │ Behavior │
├───────────┼──────────┼─────────────────────────────────┤
│ RUNNING │ ▶️ Resume │ Continue from last checkpoint │
│ PENDING │ 🚀 Start │ Begin execution normally │
│ SUSPENDED │ ⏸️ Keep │ Maintain suspended state │
│ FAILED │ ❌ Skip │ Don't auto-resume failed tasks │
└───────────┴──────────┴─────────────────────────────────┘
Configuration: - 🎛️ Global toggle for auto-resume functionality - 🏷️ Task-type filters to control which types resume - ⏱️ Delay settings to stagger resume operations - 🔍 Validation rules for safe resumption
Impact: Provide seamless task continuity across worker restarts, ensuring no work is lost during planned or unplanned downtime.
🐝 Swarm Enhancements¶
Improvements to make swarm orchestration more powerful, efficient, and feature-rich.
🔥 Swarm Per-Task Callbacks¶
Difficulty:
MEDIUM| Priority:HIGH| Status:Planned
Add error and success callbacks that execute per individual task in swarm execution. This will provide:
What's Coming: - ✅ Success Callbacks - Triggered on individual task completion - ❌ Error Callbacks - Triggered when tasks fail - 📊 Real-time Monitoring - Live feedback during swarm execution - 🧹 Task Cleanup - Handle task-specific cleanup operations - 🔍 Enhanced Debugging - Better observability for task execution
Impact: Dramatically improve debugging and monitoring capabilities for complex swarm operations.
📦 Bulk Task Addition to Swarm¶
Difficulty:
EASY| Priority:MEDIUM| Status:Planned
Add multiple tasks to swarm in a single operation instead of one-by-one addition.
What's Coming: - 📋 Batch Operations - Add multiple task signatures to swarm simultaneously - ⚡ Performance Boost - Reduce overhead of individual task additions - 🎯 Atomic Operations - All tasks added successfully or none at all - 🔄 Bulk Validation - Validate all tasks before adding any to swarm - 📊 Progress Tracking - Show progress during bulk addition operations - 🛠️ API Enhancement - New bulk methods for programmatic usage - 🚀 Add-and-Run - Single method to add task(s) and immediately start execution - ⚡ Streamlined Workflow - Eliminate separate add/run steps for immediate execution
Current vs Proposed:
# Current approach (inefficient)
for task in task_list:
await swarm.add_task(task)
await swarm.run()
# Proposed approach (efficient)
await swarm.add_tasks_bulk(task_list)
# Single task add-and-run (new)
await swarm.add_task_and_run(task)
# Bulk add-and-run (new)
await swarm.add_tasks_and_run(task_list)
Features: - 🎛️ Configurable batch size to prevent memory issues - ⚠️ Error handling with partial success reporting - 📈 Performance metrics for bulk operations - 🔍 Validation summary before execution
Use Cases: - Large-scale data processing with hundreds of similar tasks - Batch job submissions from external systems - Migration of tasks from other orchestration systems - Development and testing with multiple test scenarios
Impact: Dramatically improve efficiency for large-scale task orchestration by reducing API overhead and enabling atomic bulk operations.
⚡ Priority Swarm¶
Difficulty:
EASY| Priority:LOW| Status:Planned
Enable intelligent task prioritization for optimal resource utilization.
What's Coming: - 🏷️ Priority Levels - High, Medium, Low task classification - 📈 Smart Execution - Higher priority tasks execute first - 🔄 Dynamic Adjustment - Priority changes during runtime - 🎯 Resource Allocation - Priority-based scheduling
Impact: Optimize performance for time-sensitive workflows and resource-constrained environments.
🧠 Advanced Mageflow Patterns¶
Sophisticated workflow creation capabilities that go beyond basic chains and swarms.
🧠 Conditional Graph Tasks¶
Difficulty:
HARD| Priority:HIGH| Status:Planned
Introduce dynamic workflow execution with conditional routing based on task outputs.
What's Coming:
- 🔀 Conditional Nodes - Decision points that route execution based on output
- 📊 Output Analysis - Evaluate task results to determine next steps
- 🌊 Dynamic Flow - Runtime path selection like LangGraph
- 🎯 Smart Routing - Multi-path workflows with intelligent branching
- 🔧 Visual Editor - GUI support for building conditional workflows
- 📋 Condition Templates - Pre-built conditional logic patterns
Use Cases: - Data processing pipelines with success/failure paths - A/B testing workflows with result-based routing - Error handling with retry or alternative task paths - Multi-stage validation with conditional approvals
Impact: Enable sophisticated, intelligent workflows that adapt execution paths based on real-time results and conditions.
🏗️ Complex Task Signatures¶
Difficulty:
VERY HARD| Priority:HIGH| Status:Planned
Create special task type that can define complex workflows instead of simple tasks.
What's Coming: - 🧩 Composite Signatures - Signatures that contain entire workflows (swarms, chains, graphs) - 🎯 Unified Callbacks - Execute callbacks only when the entire complex task completes - 🔄 Nested Workflows - Allow swarms to contain complex signatures that are themselves swarms/chains - 📊 Hierarchical Status - Track status of both the wrapper signature and internal workflow - 🎮 Unified Control - Treat complex workflows as single units for lifecycle operations - 📋 Metadata Aggregation - Collect and aggregate results from all internal tasks
Use Cases: - Swarm of Workflows: Put a chain signature in a swarm that executes when the entire chain completes - Nested Processing: Create signatures that represent multi-stage data processing pipelines - Conditional Workflows: Embed graph tasks as signatures within other orchestration patterns - Reusable Components: Package complex workflows as reusable signature units
Technical Architecture:
Signature (Complex Type)
├── Internal Workflow (Chain/Swarm/Graph)
│ ├── Task 1 → Task 2 → Task 3
│ └── Callbacks execute only on internal completion
└── External Callbacks (execute on signature completion)
Callback Behavior: - ✅ Internal callbacks fire during internal workflow execution - 🎯 External callbacks fire only when entire complex signature completes - 🔄 Error propagation from internal workflows to signature level
Impact: Enable true composition of orchestration patterns, allowing users to build sophisticated nested workflows with proper callback semantics and lifecycle management.
🖥️ User Interface & Monitoring¶
Comprehensive visual interface and real-time monitoring capabilities for task orchestration.
🏗️ PROJECT: GUI Interface for Tasks¶
Difficulty:
VERY HARD| Type:META-PROJECT| Priority:MEDIUM| Status:Planned
Tasks:5| Completion:0%
┌─────────────────────────────────────────────────────────────┐
│ A comprehensive web-based interface for visualizing │
│ and controlling mageflow workflows │
└─────────────────────────────────────────────────────────────┘
📋 Project Tasks:¶
Task 1: 📊 Graph Visualization Engine¶
Status:
Not Started| Complexity:High- 🕸️ Build interactive task graph display - 🔗 Show visual connections between tasks and callbacks
- 🎨 Implement color-coded status nodes - 🔍 Add zoom/pan for complex workflows
Task 2: 🎮 Task Control Center¶
Status:
Not Started| Complexity:Medium- ⏹️ Stop/halt running tasks - ▶️ Resume paused task execution - 🚀 Execute new tasks from UI - 🔄 Retry failed tasks with one click
Task 3: 📋 Task Data Dashboard¶
Status:
Not Started| Complexity:Medium- 📈 Display task metadata and configuration - 📊 Show performance metrics and statistics - 🏷️ Implement task categorization - 🔍 Build advanced filtering system
Task 4: 📜 Live Logging System¶
Status:
Not Started| Complexity:Medium- 📝 Stream real-time task logs - 🎯 Filter by log levels (INFO, WARNING, ERROR) - 📥 Export logs functionality - 🔍 Full-text search across logs
Task 5: ⏱️ Real-time Progress Tracking¶
Status:
Not Started| Complexity:High- 📊 Live progress bars and status updates - 🔔 Push notifications for state changes - 📈 Timeline visualization of execution - 🎯 ETA calculations for running tasks
🎯 Project Impact: Transform task orchestration from command-line tool to powerful visual interface with complete workflow control.
🗄️ Enterprise Scale & Persistence¶
Large-scale orchestration capabilities for enterprise deployments and massive workflows.
💾 Persistent DB for Extremely Large Workflows¶
Difficulty:
VERY HARD| Priority:HIGH| Status:Planned
Enable support for extremely large workflows using persistent database storage with Redis as a caching layer.
What's Coming: - 🗄️ Dual Storage Architecture - Redis for hot cache, persistent DB for complete workflow storage - 💾 Automatic Tiering - Smart data movement between cache and persistent storage - 🔄 Lazy Loading - Load workflow segments on-demand from persistent storage - 📈 Infinite Scale - Handle workflows with millions of tasks without memory constraints - 🔍 Query Optimization - Efficient retrieval patterns for large workflow data - 🏗️ Database Abstraction - Support multiple persistent backends (PostgreSQL, MySQL, MongoDB) - 🔐 ACID Guarantees - Ensure workflow integrity with transactional storage
Architecture Overview:
┌──────────────────────────────────────────────────────┐
│ Application Layer │
├──────────────────────────────────────────────────────┤
│ Redis Cache (Hot Data) │
│ • Active task signatures │
│ • Running workflow metadata │
│ • Recent execution results │
├──────────────────────────────────────────────────────┤
│ Persistent Database (Cold Storage) │
│ • Complete workflow history │
│ • Archived task results │
│ • Long-term audit logs │
│ • Workflow definitions & metadata │
└──────────────────────────────────────────────────────┘
Technical Challenges: - 🔄 Cache Cohekrency - Maintain consistency between Redis and persistent DB - ⚡ Performance - Minimize latency for cache misses - 🔍 Smart Prefetching - Predictively load data before it's needed - 🧹 Garbage Collection - Efficient cleanup of completed workflow data - 🔐 Transaction Management - Handle distributed transactions across storage layers - 📊 Monitoring - Track cache hit rates and storage performance
Use Cases: - Data Pipeline Orchestration - Process petabytes of data with millions of parallel tasks - ML Training Workflows - Manage complex model training pipelines with extensive checkpointing - ETL Operations - Handle enterprise-scale data transformation workflows - Distributed Computing - Coordinate massive distributed computation tasks - Long-Running Workflows - Support workflows that run for days or weeks
Impact: Enable MageFlow to scale to enterprise-level deployments handling millions of concurrent tasks while maintaining performance through intelligent caching and persistent storage strategies.
💡 Have Ideas?¶
We'd love to hear your suggestions! Feel free to open an issue or contribute to the discussion.