AgentHub - Multi-Agent DAG Orchestration

AgentHub provides patterns and tools for orchestrating multiple AI agents as a directed acyclic graph (DAG). Instead of one agent doing everything sequentially, AgentHub lets you decompose complex tasks into sub-tasks, assign each to a specialized agent, define dependencies between them, and merge their outputs into a coherent result.

The core insight: complex tasks decompose better than they scale. A 10-step sequential task run by one agent hits context limits and quality degradation. Five parallel agents with clear scopes and a merge step produce better results faster.

Core Capabilities

• DAG workflow design — model tasks as nodes with explicit input/output contracts and dependency edges.
• Parallel execution — topological sort, parallel groups, and max_parallel scheduling for real speedup.
• Agent lifecycle — spawn, monitor (board), and track states from PENDING through COMPLETED/FAILED.
• Quality gates — evaluate outputs against thresholds and rank competing results.
• Output merging — synthesize, rank-select, or chain terminal outputs into a coherent deliverable.

When to Use

• A task needs multiple specialized agents with distinct scopes.
• You want to parallelize AI work that would otherwise run sequentially.
• A single agent hits context limits or quality degradation on a long task.
• You need quality gates and merge strategies across agent outputs.