Agent Orchestration
Pick a multi-agent framework (LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, AG2) with a structured checklist before you commit architecture.
Overview
Agent-orchestration is an agent skill most often used in Build (also Validate scope, Operate infra) that guides multi-agent framework selection via structured checklists for LangGraph, CrewAI, OpenAI SDK, Microsoft Agent
Install
npx skills add https://github.com/yonatangross/orchestkit --skill agent-orchestrationWhat is this skill?
- Requirements analysis checklist: use case, complexity, state, human-in-the-loop, observability
- Side-by-side evaluation gates for LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, and AG2
- Technical fit criteria: team expertise, maturity, docs, community, and production readiness
- Maps patterns to needs: persistence/checkpoints vs role delegation vs OpenAI handoffs vs Azure enterprise
- Prevents premature framework lock-in without documenting HITL and observability needs
- Five framework evaluation sections: LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, AG2
Adoption & trust: 537 installs on skills.sh; 183 GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
You know you need multiple agents but lack a structured way to match state, HITL, and observability needs to the right orchestration framework.
Who is it for?
Indie builders prototyping or shipping agent features who must choose among mainstream orchestration stacks before writing orchestration code.
Skip if: Teams that already standardized on one framework with running production agents, or use cases that are strictly single-call LLM wrappers with no coordination.
When should I use this skill?
Use when starting or refactoring a multi-agent architecture and you need a structured framework comparison before implementation.
What do I get? / Deliverables
You complete requirements and framework evaluation checklists with a defensible primary framework choice and documented tradeoffs for implementation.
- Completed requirements analysis checklist
- Framework shortlist with documented tradeoffs
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Build because framework choice is a core agent-tooling decision while designing orchestration. Agent-tooling is where stateful workflows, handoffs, persistence, and team structure get locked in for solo shipped products.
Where it fits
Decide whether the MVP needs multi-agent delegation or a single agent with tools before writing the prototype spec.
Score LangGraph persistence vs CrewAI role teams against your HITL and streaming requirements.
Re-evaluate Microsoft Agent Framework when compliance or Azure hosting becomes a hard requirement post-launch.
How it compares
Decision checklist for picking a framework—not a code generator or runtime deployment skill.
Common Questions / FAQ
Who is agent-orchestration for?
Solo and small-team builders designing multi-agent systems who need a disciplined comparison of LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, and AG2.
When should I use agent-orchestration?
During Validate when scoping agent complexity, in Build before implementing agent-tooling, and in Operate when reassessing framework fit for enterprise or Azure requirements.
Is agent-orchestration safe to install?
It is documentation-style guidance with no inherent runtime; still review the Security Audits panel on this Prism page and the OrchestKit source before chaining it with skills that use shell or network.
SKILL.md
READMESKILL.md - Agent Orchestration
# Framework Selection Checklist Choose the right multi-agent framework. ## Requirements Analysis - [ ] Use case clearly defined - [ ] Complexity level assessed (single vs multi-agent) - [ ] State management needs identified - [ ] Human-in-the-loop requirements defined - [ ] Observability needs documented ## Framework Evaluation ### LangGraph - [ ] Need complex stateful workflows - [ ] Require persistence and checkpoints - [ ] Want streaming support - [ ] Need human-in-the-loop - [ ] Already using LangChain ecosystem ### CrewAI - [ ] Role-based collaboration pattern - [ ] Hierarchical team structure - [ ] Agent delegation needed - [ ] Quick prototyping required - [ ] Built-in memory preferred ### OpenAI Agents SDK - [ ] OpenAI-native ecosystem - [ ] Handoff pattern fits use case - [ ] Need built-in guardrails - [ ] Want OpenAI tracing - [ ] Simpler agent definition preferred ### Microsoft Agent Framework - [ ] Enterprise compliance requirements - [ ] Using Azure ecosystem - [ ] Need A2A protocol support - [ ] Want AutoGen+SK merger features - [ ] Long-term Microsoft support preferred ### AG2 (Community AutoGen) - [ ] Open-source flexibility priority - [ ] Community-driven development OK - [ ] AutoGen familiarity exists - [ ] Custom modifications needed ## Technical Considerations - [ ] Team expertise with framework - [ ] Framework maturity level acceptable - [ ] Community support adequate - [ ] Documentation quality sufficient - [ ] Production readiness validated ## Integration Assessment - [ ] Observability tool compatibility (Langfuse, etc.) - [ ] LLM provider compatibility - [ ] Existing codebase integration - [ ] Testing framework support - [ ] CI/CD pipeline compatibility ## Risk Mitigation - [ ] Fallback strategy defined - [ ] Framework lock-in assessed - [ ] Migration path understood - [ ] Version update strategy - [ ] Community health evaluated ## Decision Documentation - [ ] Framework choice documented - [ ] Rationale recorded - [ ] Alternatives considered listed - [ ] Trade-offs acknowledged - [ ] Review date scheduled # Multi-Agent Orchestration Checklist ## Architecture - [ ] Define agent responsibilities - [ ] Plan communication patterns - [ ] Set coordination strategy - [ ] Design failure handling ## Agent Design - [ ] Single responsibility per agent - [ ] Clear input/output contracts - [ ] Independent operation - [ ] Stateless when possible ## Communication - [ ] Message format definition - [ ] Async message passing - [ ] Result aggregation - [ ] Error propagation ## Coordination - [ ] Central orchestrator - [ ] Task queue management - [ ] Priority handling - [ ] Deadlock prevention ## Monitoring - [ ] Agent health checks - [ ] Task completion tracking - [ ] Performance metrics - [ ] Error rates { "version": "2.0.0", "organization": "OrchestKit", "date": "February 2026", "abstract": "Agent orchestration patterns covering agentic loops (ReAct, plan-and-execute), multi-agent coordination (supervisor, debate, synthesis), alternative frameworks (CrewAI, AutoGen, OpenAI SDK, GPT-5.2-Codex), and multi-scenario orchestration (parallel difficulty scaling, milestone sync, result aggregation).", "ruleCount": 10, "categories": 4, "consolidatedFrom": [ "agent-loops", "alternative-agent-frameworks", "multi-agent-orchestration", "multi-scenario-orchestration" ] } # Architectural Patterns for Multi-Scenario Orchestration **Deep patterns and design decisions for production multi-scenario demos.** ## Pattern 1: Three-Tier Synchronization ### Tier 1: Free-Running (Baseline) **Each scenario runs independently, no blocking.** ``` Time → ─────────────────────────────────────────────────┐ Simple ███████████████ Complete at 1.2s │ └─────────────────────────────────────┘ │ │ Medium ██████████████████████░░░░ In progress at 3.5s └──────────────────────────────────┘ │