Im implementing a control plane for agentic orchestration…
Here is some progress updates, this is the diary style I make the bot continuously keep producing:
Even if its a stupid endeavor, its super fullfilling and fun. Grateful to @anon-9002714 team.
# SwarmHub Progress Summary - 2023-12-24
## Achievements Today
### 1. Infrastructure Setup
- [x] Docker containers configured and running
- [x] PostgreSQL database initialized
- [x] Redis cache service operational
- [x] Prometheus metrics collection active
- [x] Grafana dashboards created
### 2. Database Implementation
- [x] Schema design completed
- [x] Initial tables created
- [x] Test data generated (100 agents, 40 projects)
- [x] Materialized views implemented
- [x] Performance indexes added
### 3. Monitoring Setup
- [x] Agent performance dashboard
- [x] Project metrics dashboard
- [x] System health monitoring
- [x] Real-time metrics collection
### 4. Development Planning
- [x] API architecture documented
- [x] Frontend structure designed
- [x] Database migration plan created
- [x] Testing strategy outlined
## Current System State
### 1. Infrastructure
- Docker containers running on port mappings:
- PostgreSQL: 5433 -> 5432
- Redis: 6379
- Prometheus: 9090
- Grafana: 3001
- API: 8000
- Web: 3000
### 2. Database
- Schema: `swarm_hub`
- Tables:
- `agent_profiles`
- `projects`
- `swarms`
- `tasks`
- `specialization_history`
- `project_topics`
- Materialized views:
- `mv_agent_performance`
- `mv_project_performance`
### 3. Monitoring
- Grafana dashboards:
- Agent performance metrics
- Project status tracking
- System health indicators
- Resource utilization
## Technical Insights
### 1. Performance Optimization
- Materialized views significantly improve query performance
- Proper indexing crucial for JSONB fields
- Connection pooling helps with concurrent requests
- Cache invalidation strategy important
### 2. Architecture Decisions
- FastAPI for backend services
- Next.js 14 for frontend
- PostgreSQL for data persistence
- Redis for caching
- Prometheus for metrics
- Grafana for visualization
### 3. Development Practices
- Type-safe development
- Async operations
- Error handling
- Performance monitoring
- Security-first approach
## Next Steps
### 1. Immediate Priorities
- [ ] Implement API endpoints
- [ ] Set up authentication
- [ ] Create frontend components
- [ ] Configure CI/CD pipeline
### 2. Short-term Goals
- [ ] Complete user interface
- [ ] Implement agent matching
- [ ] Add project management
- [ ] Set up monitoring alerts
### 3. Long-term Objectives
- [ ] Machine learning integration
- [ ] Advanced analytics
- [ ] Performance optimization
- [ ] Scale infrastructure
## Technical Debt
### 1. Infrastructure
- Need proper logging setup
- Backup strategy required
- Security hardening needed
- Environment configuration
### 2. Development
- Test coverage lacking
- Documentation incomplete
- Error handling needs improvement
- Performance testing required
### 3. Monitoring
- Alert thresholds needed
- Log aggregation missing
- Metric retention policy
- Dashboard organization
## Lessons Learned
### 1. Development Process
- Start with proper planning
- Document decisions
- Test early and often
- Monitor performance
### 2. Architecture
- Use appropriate tools
- Consider scalability
- Plan for maintenance
- Security by design
### 3. Team Collaboration
- Clear documentation
- Regular updates
- Version control
- Code review process
## Tomorrow's Focus
### 1. Morning
- [ ] Implement API endpoints
- [ ] Set up authentication
- [ ] Configure security
### 2. Afternoon
- [ ] Create frontend components
- [ ] Implement data fetching
- [ ] Add error handling
### 3. Evening
- [ ] Test integration
- [ ] Update documentation
- [ ] Review performance
## Notes
- Keep monitoring system health
- Regular backups important
- Document all changes
- Test thoroughly
- Security first approach
- Performance is critical
# Database Dance: Setting Up PostgreSQL and Grafana Monitoring
## Overview
Today we focused on setting up and configuring PostgreSQL databases for the SWARM-HUB project, along with implementing comprehensive monitoring using Grafana. The goal was to ensure we have a robust database infrastructure that can support both development and testing environments.
## Key Achievements
### PostgreSQL Setup
- Successfully configured PostgreSQL in Docker running on port 5433
- Created three databases:
- `postgres` (main)
- `swarm_hub_test` (testing environment)
- `swarm_hub_dev` (development environment)
- Verified connectivity to all databases using our custom verification script
- Successfully initialized schema across all databases
- Generated test data for development and testing environments
### Schema Implementation
- Created comprehensive schema with:
- 13 tables for core functionality
- 1 function for reputation updates
- 1 trigger for automated reputation calculation
- 2 views for performance monitoring
- 18 indexes for optimized queries
- Implemented automated schema initialization with cleanup
- Added schema versioning and migration support
### Test Data Generation
- Created test data generation script (`generate_test_data.py`)
- Generated realistic test data for both test and dev environments:
- 50 agents with varied capabilities and specializations
- 20 projects with different requirements and complexities
- Implemented data generation with:
- Random but realistic capability distributions
- Varied specialization levels
- Realistic project requirements
- Appropriate value ranges for metrics
### Grafana Integration
- Created PostgreSQL datasource configurations for all databases
- Implemented a comprehensive PostgreSQL overview dashboard with:
- Active connections monitoring
- Database size tracking
- Detailed database statistics
- Real-time performance metrics
### Monitoring Infrastructure
- Set up database connection verification script (`verify_postgres.py`)
- Created schema initialization script (`init_schema.py`)
- Configured Grafana dashboards for:
- Connection status
- Database sizes
- Active connections
- Performance metrics
## Technical Details
### Database Configuration
```yaml
# Docker PostgreSQL (5433)
- Main Database: postgres
- Purpose: Production environment
- Status: Operational
- Schema: Initialized
- Tables: 13
- Functions: 1
- Triggers: 1
- Views: 2
- Indexes: 18
- Test Database: swarm_hub_test
- Purpose: Testing environment
- Status: Operational
- Schema: Initialized
- Tables: 13
- Functions: 1
- Triggers: 1
- Views: 2
- Indexes: 18
- Test Data: 50 agents, 20 projects
- Dev Database: swarm_hub_dev
- Purpose: Development environment
- Status: Operational
- Schema: Initialized
- Tables: 13
- Functions: 1
- Triggers: 1
- Views: 2
- Indexes: 18
- Test Data: 50 agents, 20 projects
Schema Components
-
Core Tables
agent_profiles: Agent information and capabilitiesprojects: Project details and requirementsswarms: Agent team coordinationswarm_members: Team membership trackingtasks: Project task managementtask_dependencies: Task relationship trackingspecialization_history: Agent skill progressionproject_topics: Project categorizationproject_topic_associations: Project-topic relationshipsagent_topic_subscriptions: Agent interestsperformance_metrics: Agent and project metrics
-
Views
active_swarms: Current team activitiesagent_performance_summary: Agent productivity metrics
-
Functions and Triggers
- Automated reputation updates
- Performance metric calculations
Test Data Components
-
Agent Profiles
- Randomly generated capabilities in multiple languages
- Varied framework experience
- Realistic specialization distributions
- Randomized but sensible reputation scores
- Project history and success rates
-
Projects
- Varied complexity levels
- Realistic duration estimates
- Different team size requirements
- Multiple required capabilities
- Different project statuses
Monitoring Setup
- Implemented real-time monitoring with 5-second refresh intervals
- Created separate dashboard sections for:
- SomacoSF PostgreSQL (5432) - Ready for future integration
- SwarmHub Docker PostgreSQL (5433) - Currently active
- Database Details - Comprehensive statistics
Challenges and Solutions
-
PowerShell Command Syntax
- Challenge: PowerShell doesn’t support
&&for command chaining - Solution: Used semicolon (
;) as command separator
- Challenge: PowerShell doesn’t support
-
Database Creation
- Challenge: Initial attempts to create databases failed
- Solution: Used
createdbcommand directly in Docker container
-
Schema Initialization
- Challenge: Needed to handle existing schema objects
- Solution: Implemented automated cleanup and reinitialization
-
Connection Verification
- Challenge: Needed to verify multiple database connections
- Solution: Created a Python script with comprehensive connection testing
-
Test Data Generation
- Challenge: Needed realistic but varied test data
- Solution: Implemented smart randomization with appropriate value ranges
Next Steps
-
Data Validation
- Implement data consistency checks
- Create data quality metrics
- Set up automated data validation
- Monitor data distribution patterns
-
Monitoring Enhancements
- Add query performance monitoring
- Implement alert thresholds
- Create specialized dashboards for different aspects (queries, locks, etc.)
- Add test data quality metrics
-
Backup Strategy
- Implement automated backup procedures
- Set up backup verification and restoration testing
- Create point-in-time recovery procedures
- Include test data in backup strategy
-
Performance Tuning
- Monitor and optimize connection pooling
- Implement query optimization tracking
- Set up performance baseline measurements
- Configure autovacuum settings
- Analyze test data query patterns
Lessons Learned
- Always verify database connections after creation
- Use dedicated verification tools for complex setups
- Implement monitoring from the start
- Document all configuration changes and decisions
- Implement schema cleanup before initialization
- Use separate schemas for different components
- Generate realistic test data early
- Monitor data quality metrics
Technical Debt
- Need to implement automated database initialization
- Should add more detailed performance metrics
- Consider implementing connection pooling
- Add automated backup and restore testing
- Implement schema migration versioning
- Add more varied test data scenarios
Future Considerations
-
Scaling Strategy
- Plan for database growth
- Consider replication setup
- Implement sharding if needed
- Set up read replicas
- Scale test data generation
-
Security Enhancements
- Implement row-level security
- Set up audit logging
- Configure SSL connections
- Implement role-based access control
- Secure test data handling
-
Maintenance Automation
- Automated vacuum scheduling
- Index maintenance procedures
- Statistics update automation
- Performance optimization routines
- Test data refresh procedures
-
High Availability
- Configure streaming replication
- Implement failover procedures
- Set up load balancing
- Create disaster recovery plans
- Include test environments in HA strategy
Conclusion
Today’s work has established a solid foundation for the SWARM-HUB project’s database infrastructure. We’ve successfully set up and configured three separate databases with identical schemas, each serving a specific purpose in our development pipeline. The combination of PostgreSQL and Grafana provides us with both the storage capabilities we need and the visibility to ensure everything is running smoothly. With our automated schema initialization, comprehensive monitoring, and realistic test data in place, we’re well-positioned to move forward with implementing the core functionality of the system.
# Introducing the Cursor Agent Control Plane: Multi-Instance Collaboration with PostgreSQL
## TL;DR
A PostgreSQL-based control plane that enables multiple Cursor IDE instances to work together seamlessly, sharing context, commands, and workspace state across machines.
## The Vision
Imagine running Cursor on multiple machines, each instance aware of the others, sharing context, and working together seamlessly. That's what the Cursor Agent Control Plane enables - a distributed system where Cursor instances become collaborative agents.
## Architecture Overview
```mermaid
graph TB
subgraph "Control Plane"
PG[("PostgreSQL<br/>Source of Truth")]
subgraph "Core Services"
CMD["Command Queue"]
SYNC["Sync Manager"]
LOCK["Lock Manager"]
CTX["Context Manager"]
end
subgraph "Monitoring"
HEALTH["Health Check"]
METRICS["Metrics"]
LOGS["Event Log"]
end
end
subgraph "Agent 1 (Machine A)"
A1["Cursor Instance"]
A1_WS["Workspace"]
A1_COORD["Coordinator"]
end
subgraph "Agent 2 (Machine B)"
A2["Cursor Instance"]
A2_WS["Workspace"]
A2_COORD["Coordinator"]
end
A1_COORD --> PG
A2_COORD --> PG
PG --> CMD & SYNC & LOCK & CTX
CMD & SYNC & LOCK & CTX --> HEALTH & METRICS & LOGS
Key Features
| Feature | Description | Implementation |
|---|---|---|
| Agent Discovery | Automatic discovery and registration of Cursor instances | PostgreSQL-based session tracking with heartbeats |
| Command Queue | Cross-instance command execution | Priority-based queue with ACID guarantees |
| Workspace Sync | File synchronization and conflict resolution | Content hashing and version tracking |
| Shared Context | Distributed knowledge sharing | JSONB-based context store with versioning |
| Resource Locking | Distributed locking mechanism | Row-level locks with timeout management |
| Event Logging | Comprehensive audit trail | Structured logging with context preservation |
Communication Flow
sequenceDiagram
participant A1 as Cursor Agent 1
participant DB as PostgreSQL
participant A2 as Cursor Agent 2
Note over A1,A2: Agent Discovery
A1->>DB: Register session
A2->>DB: Register session
Note over A1,A2: Command Execution
A1->>DB: Enqueue command
A2->>DB: Poll commands
A2->>DB: Execute command
A2->>DB: Update status
Note over A1,A2: Workspace Sync
A1->>DB: Log file change
A2->>DB: Detect change
A2->>A2: Apply change
Implementation Highlights
1. PostgreSQL as Source of Truth
-- Agent session management
CREATE TABLE cursor_control.agent_sessions (
session_id UUID PRIMARY KEY,
machine_name TEXT,
workspace_path TEXT,
capabilities JSONB,
last_heartbeat TIMESTAMPTZ
);
-- Command queue
CREATE TABLE cursor_control.command_queue (
command_id UUID PRIMARY KEY,
source_agent UUID,
target_agent UUID,
command_type TEXT,
payload JSONB,
status TEXT
);
2. Python Coordinator
class CursorPgCoordinator:
"""PostgreSQL-based coordinator for Cursor agents"""
def enqueue_command(self, target: str, command: str, payload: dict):
"""Send command to another agent"""
def get_pending_commands(self) -> list:
"""Get commands waiting for execution"""
def update_workspace_state(self, changes: list):
"""Sync workspace changes"""
Benefits
-
Seamless Collaboration
- Share context between instances
- Coordinate actions across machines
- Maintain workspace consistency
-
Robust Architecture
- ACID compliance for critical operations
- Built-in monitoring and recovery
- Scalable design
-
Developer Experience
- Automatic synchronization
- Cross-machine debugging
- Shared knowledge base
Real-World Use Cases
| Scenario | Description | Benefit |
|---|---|---|
| Multi-Machine Development | Work across desktop and laptop | Seamless context switching |
| Team Collaboration | Share debugging sessions | Real-time assistance |
| Resource Distribution | Distribute compute-intensive tasks | Better resource utilization |
| Knowledge Sharing | Share discoveries and insights | Improved team efficiency |
Getting Started
-
Prerequisites
- PostgreSQL 14+
- Python 3.8+
- Cursor IDE
-
Quick Setup
# Clone the repo git clone https://github.com/yourusername/cursor-control-plane # Setup control plane cd cursor-control-plane .\scripts\setup_control_plane.ps1 # Start coordinator python .control_plane/agents/pg_coordinator.py
What’s Next?
-
Enhanced Features
- Real-time collaboration
- Shared debugging sessions
- Cross-machine testing
-
Community Contributions
- Plugin system
- Custom commands
- Integration patterns
Join the Discussion
I’m excited to share this project with the Cursor community and would love to hear your thoughts:
- What features would you like to see?
- How would you use multiple coordinated Cursor instances?
- What integration patterns would be most valuable?
Let’s build a more connected Cursor ecosystem together! 
Resources
Note: This is an open-source project in development. Contributions and feedback are welcome!