Multi Agent Orchestration
Architect coordinated agent teams with delegation, messaging, and workflow patterns when a single agent cannot cover the whole problem.
Overview
Multi-Agent Orchestration is an agent skill most often used in Build (also Ship) that designs how specialized agents communicate, delegate tasks, and aggregate results in coordinated workflows.
Install
npx skills add https://github.com/qodex-ai/ai-agent-skills --skill multi-agent-orchestrationWhat is this skill?
- Covers sequential, parallel, hierarchical, and consensus orchestration patterns with example modules
- Framework-oriented templates for CrewAI, AutoGen, LangGraph, and Swarm-style teams
- Helper scripts for message brokers, shared memory, workflow execution, and monitoring
- Benchmarking utilities for team performance and per-agent effectiveness metrics
- Explicit scope: task delegation, result aggregation, and cross-domain specialized agents
- Example tree references orchestration_patterns.py, framework_implementations.py, and benchmarking.py modules
Adoption & trust: 1.7k installs on skills.sh; 26 GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your agent product needs several expert roles but you only have ad-hoc prompts with no clear handoffs, shared state, or way to measure the team.
Who is it for?
Indie builders shipping multi-step agent apps where tasks naturally split across research, implementation, QA, or ops specialists.
Skip if: Simple single-tool automations or one-shot chat skills that never delegate work to peer agents.
When should I use this skill?
Building coordinated agent teams, complex multi-step workflows, or systems requiring specialized expertise across domains.
What do I get? / Deliverables
You leave with an orchestration pattern, communication layout, and starter implementations you can extend before wiring frameworks like CrewAI, AutoGen, or LangGraph.
- Orchestration topology choice (sequential, parallel, hierarchical, or consensus)
- Communication and workflow module layout
- Benchmark or monitoring hook for agent team effectiveness
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Multi-agent orchestration is implemented while building agent products, but the same patterns inform how you test and operate complex agent workflows in production. Communication brokers, orchestration modes, and framework templates are agent-platform tooling rather than generic frontend or marketing tasks.
Where it fits
Pick hierarchical orchestration when a planner agent delegates coding and review subtasks.
Wire a message broker so CRM and support agents share customer context without duplicate API calls.
Run benchmarking scripts to compare parallel fan-out vs sequential pipelines before launch.
How it compares
Skill package for orchestration design and reference code—not a hosted multi-agent runtime or MCP server by itself.
Common Questions / FAQ
Who is multi-agent-orchestration for?
Solo and small-team builders creating agent systems that require multiple specialized roles, structured handoffs, and measurable team performance.
When should I use multi-agent-orchestration?
During Build → agent-tooling when defining team topology, and during Ship → testing when you benchmark parallel vs hierarchical flows before release.
Is multi-agent-orchestration safe to install?
Review example scripts and dependencies on the Security Audits panel for this page; orchestration code often needs network and API access when you connect live models.
SKILL.md
READMESKILL.md - Multi Agent Orchestration
# Multi-Agent Orchestration Design and orchestrate sophisticated multi-agent systems where specialized agents collaborate to solve complex problems, combining different expertise and perspectives. ## Quick Start Get started with multi-agent implementations in the examples and utilities: - **Examples**: See [`examples/`](examples/) directory for complete implementations: - [`orchestration_patterns.py`](examples/orchestration_patterns.py) - Sequential, parallel, hierarchical, and consensus orchestration - [`framework_implementations.py`](examples/framework_implementations.py) - Templates for CrewAI, AutoGen, LangGraph, and Swarm - **Utilities**: See [`scripts/`](scripts/) directory for helper modules: - [`agent_communication.py`](scripts/agent_communication.py) - Message broker, shared memory, and communication protocols - [`workflow_management.py`](scripts/workflow_management.py) - Workflow execution, optimization, and monitoring - [`benchmarking.py`](scripts/benchmarking.py) - Team performance and agent effectiveness metrics ## Overview Multi-agent systems decompose complex problems into specialized sub-tasks, assigning each to an agent with relevant expertise, then coordinating their work toward a unified goal. ### When Multi-Agent Systems Shine - **Complex Workflows**: Tasks requiring multiple specialized roles - **Domain-Specific Expertise**: Finance, legal, HR, engineering need different knowledge - **Parallel Processing**: Multiple agents work on different aspects simultaneously - **Collaborative Reasoning**: Agents debate, refine, and improve solutions - **Resilience**: Failures in one agent don't break the entire system - **Scalability**: Easy to add new specialized agents ### Architecture Overview ``` User Request ↓ Orchestrator ├→ Agent 1 (Specialist) → Task 1 ├→ Agent 2 (Specialist) → Task 2 ├→ Agent 3 (Specialist) → Task 3 ↓ Result Aggregator ↓ Final Response ``` ## Core Concepts ### Agent Definition An agent is defined by: - **Role**: What responsibility does it have? (e.g., "Financial Analyst") - **Goal**: What should it accomplish? (e.g., "Analyze financial risks") - **Expertise**: What knowledge/tools does it have? - **Tools**: What capabilities can it access? - **Context**: What information does it need to work effectively? ### Orchestration Patterns #### 1. Sequential Orchestration - Agents work one after another - Each agent uses output from previous agent - **Use Case**: Steps must follow order (research → analysis → writing) #### 2. Parallel Orchestration - Multiple agents work simultaneously - Results aggregated at the end - **Use Case**: Independent tasks (analyze competitors, market, users) #### 3. Hierarchical Orchestration - Senior agent delegates to junior agents - Manager coordinates flow - **Use Case**: Large projects with oversight #### 4. Consensus-Based Orchestration - Multiple agents analyze problem - Debate and refine ideas - Vote or reach consensus - **Use Case**: Complex decisions needing multiple perspectives #### 5. Tool-Mediated Orchestration - Agents use shared tools/databases - Minimal direct communication - **Use Case**: Large systems, indirect coordination ## Multi-Agent Team Examples ### Finance Team ``` Coordinator Agent ├→ Market Analyst Agent │ ├ Tools: Market data API, financial news │ └ Task: Analyze market conditions ├→ Financial Analyst Agent │ ├ Tools: Financial statements, ratio calculations │ └ Task: Analyze company financials ├→ Risk Manager Agent │ ├ Tools: Risk models, scenario analysis │ └ Task: Ass