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name: agenthub description: > Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work. license: MIT + Commons Clause metadata: version: 1.1.0 author: borghei category: engineering domain: ai-agents updated: 2026-06-17 tags: [multi-agent, orchestration, dag, workflow, parallel, agent-hub]

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.

Clarify First

Before designing the workflow, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Task decomposition — how the work splits into agent sub-tasks and their dependencies (defines the DAG nodes and edges in Init)
• Parallelism budget — how many agents may run concurrently (sets max_parallel scheduling)
• Merge strategy — synthesize, rank-select, or chain (determines how the Merge stage combines outputs)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Sub-Skills

This skill uses compound sub-skill architecture. Each sub-skill in skills/ handles a stage of the orchestration lifecycle:

Sub-Skill	File	Purpose
Init	skills/init.md	Initialize a multi-agent workflow definition
Run	skills/run.md	Execute a defined workflow end-to-end
Spawn	skills/spawn.md	Spawn individual agents within a workflow
Board	skills/board.md	Dashboard showing agent status and progress
Eval	skills/eval.md	Evaluate agent outputs for quality and consistency
Merge	skills/merge.md	Merge outputs from multiple agents into final result
Status	skills/status.md	Show workflow execution status and health

Lifecycle: Init defines the workflow DAG, Run orchestrates execution, Spawn creates individual agents, Board provides real-time visibility, Eval checks output quality, Merge combines results, and Status reports overall health (Init → Run → Spawn (parallel) → Eval → Merge, with Board/Status reading state throughout).

Tools

Tool	Purpose	Command
dag_analyzer.py	Validate DAG definitions (cycles, unreachable nodes, critical path)	python scripts/dag_analyzer.py --workflow workflow.json --validate --critical-path
session_manager.py	Manage orchestration sessions and state	python scripts/session_manager.py create --json
board_manager.py	Manage agent task boards with status tracking	python scripts/board_manager.py --session session.json --view board
result_ranker.py	Rank and merge outputs from multiple agents	python scripts/result_ranker.py --session session.json --rank --merge synthesize

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/orchestration-core.md — workflow DAG concepts, the full workflow-definition JSON format, agent states, execution strategy, the define/execute/evaluate workflows, and the common DAG patterns (fan-out/fan-in, pipeline, reducer, validator chain). Read when designing or running a workflow.
• references/multi-agent-patterns.md — the deep pattern catalog (fan-out/fan-in, pipeline, reducer, validator chain, map-reduce, diamond dependency), agent design principles, quality-gate patterns, failure handling, scaling table, and metrics targets. Read when choosing a pattern or designing quality gates and failure handling.
• references/operations-and-quality.md — best practices, common pitfalls, troubleshooting table, and success criteria. Read when debugging a workflow or validating it against the quality bar.

Scope and Limitations

This skill covers:

• Multi-agent workflow design with DAG dependency graphs
• Agent spawning, monitoring, and lifecycle management
• Output quality evaluation and ranking
• Result merging strategies for coherent final deliverables

This skill does NOT cover:

• Individual agent design or prompt engineering (see agent-designer)
• Agent memory and self-improvement (see self-improving-agent)
• Infrastructure for running agents (compute, scheduling, deployment)
• Real-time streaming communication between agents

Integration Points

Skill	Integration	Data Flow
agent-designer	Defines individual agent capabilities that become DAG nodes	Agent specs flow in; execution results flow back for agent tuning
self-improving-agent	Each agent can use self-improvement patterns to get better	Session feedback from orchestration feeds into agent learning loops
prompt-engineer-toolkit	Agent task prompts benefit from prompt engineering	Optimized prompts improve individual agent quality within the DAG
context-engine	Manages what context each agent sees	Context retrieval provides relevant inputs to each spawned agent
observability-designer	Monitors workflow execution and agent health	Agent state transitions and timing metrics feed into dashboards