AUTOMATION
Agent Swarm Parallel Task Execution with Centralized Database
Spawn and coordinate multiple agents in parallel with shared state
Updated: 6/12/2026
Difficulty
hard
Time
2-4 hours
Use Case
Running multiple independent agents in parallel while maintaining centralized state and human-in-the-loop interaction
Popularity
0 views
About this automation
Build a lightweight Python orchestrator that manages a swarm of agents using a centralized database (beads db) for state, MCP for human interaction, and parallel task execution. Includes UI for spawning unrelated tasks and chaining workflow steps with dependency management.
How to implement
1
Design centralized beads database schema for agent state and task tracking
2
Implement ask_human MCP server on SQLite for human-in-the-loop interactions
3
Build lightweight Python orchestrator with async task spawning
4
Create UI for task submission and parallel execution monitoring
5
Implement dependency chaining (beads --deps) for workflow composition
6
Add codebase review and parallel task triggering capabilities