Skill: gechengling/ai-agent-orchestration-advisor (version 3.0.0)

--- name: AI Agent Orchestration Advisor description: > AI-powered multi-agent framework comparison and selection assistant — analyze use cases, compare LangGraph/CrewAI/OpenAI Agents SDK/Claude Agent SDK, generate architecture recommendations and starter code. Keywords: multi-agent, agent orchestration, LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Strands Agents, AutoGen, agent framework comparison, AI agent architecture, multi-agent system design, agent SDK, 多智能体, 智能体编排, 框架对比, 智能体架构. version: "3.0.0" --- # AI Agent Orchestration Advisor > Your expert co-pilot for designing, selecting, and implementing multi-agent AI systems. ## What This Skill Does In 2026, the agentic AI ecosystem exploded — LangGraph, CrewAI, AutoGen/AG2, OpenAI Agents SDK, Claude Agent SDK, and Strands Agents all compete for developer mindshare. Picking the wrong framework wastes weeks. This skill helps you: - **Choose the right framework** for your specific use case (workflow complexity, state management, team size, hosting requirements) - **Generate architecture diagrams** and data flow specs for multi-agent systems - **Produce starter code** scaffolds (Python) for the chosen framework - **Analyze trade-offs** across orchestration patterns (hierarchical, sequential, parallel, event-driven) - **Debug and optimize** existing multi-agent implementations ## Trigger Words Multi-agent, agent orchestration, LangGraph, CrewAI, AutoGen, AG2, OpenAI Agents SDK, Claude Agent SDK, Strands Agents, 多智能体, 智能体编排, 框架对比, 框架选型, 多代理, 智能体架构, agent framework, which agent framework, compare agent frameworks, build multi-agent system, agentic workflow ## Target Users - AI engineers building production agent systems - Data scientists exploring agentic automation - Product managers scoping agent-based features - Developers migrating from single LLM to multi-agent pipelines ## Workflow ### 新增内容（2026版） **Step 2 新增技术评估（2026）**： - LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K - CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档 - Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测 - MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施 - LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案 --- ## 新增内容（2026版） **Step 2 新增技术评估（2026）**： - LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K - CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档 - Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测 - MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施 - LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案 --- ## Step 1 — Understand the Use Case Ask the user to describe: - The task or workflow to automate (e.g., "research + summarize + post") - Number of distinct roles/agents needed - State persistence requirements (ephemeral vs. persistent) - Hosting preference (cloud / local / serverless) - Team's programming experience ### Step 2 — Framework Shortlist & Comparison Generate a focused comparison table of the top 2–3 frameworks suited to the use case: | Framework | Best For | State Mgmt | Learning Curve | Hosting | |-----------|----------|------------|----------------|---------| | LangGraph | Complex stateful workflows | ✅ Built-in | Medium | Any | | CrewAI | Role-based team simulations | Partial | Low | Any | | AutoGen/AG2 | Conversational agent loops | External | Medium | Any | | OpenAI Agents SDK | OpenAI ecosystem, handoffs | Built-in | Low | Cloud-first | | Claude Agent SDK | Anthropic native, tool use | Built-in | Low | Cloud-first | | Strands Agents | AWS/Bedrock integration | External | Medium | AWS | ### Step 3 — Architecture Recommendation Output a recommended architecture including: - Agent topology (who calls whom) - Tool assignments per agent - Memory / state strategy - Human-in-the-loop checkpoints - Error handling & fallback patterns ### Step 4 — Starter Code Generation Generate a complete, runnable Python scaffold: ```python # Example: CrewAI research + report pipeline from crewai import Agent, Task, Crew, Process from crewai_tools import SerperDevTool search_tool = SerperDevTool() researcher = Agent( role="Senior Research Analyst", goal="Uncover cutting-edge developments in {topic}", backstory="You are an expert researcher...", tools=[search_tool], verbose=True ) writer = Agent( role="Technical Writer", goal="Craft insightful, accurate reports from research", backstory="You transform raw research into executive summaries...", verbose=True ) research_task = Task( description="Research {topic} thoroughly...", agent=researcher, expected_output="Bullet-point research findings" ) write_task = Task( description="Write a 500-word report on the research findings", agent=writer, expected_output="Polished report with sections" ) crew = Crew( agents=[researcher, writer], tasks=[research_task, write_task], process=Process.sequential, verbose=True ) result = crew.kickoff(inputs={"topic": "agentic AI in 2026"}) ``` ### Step 5 — Production Checklist Provide a framework-specific production checklist: - [ ] Rate limiting & retry logic - [ ] Observability (LangSmith / Weights & Biases / custom logging) - [ ] Secrets management (never hardcode API keys) - [ ] Cost estimation per run - [ ] Human review gates for high-stakes outputs ## Example Interactions **User:** "I need to build a system where one agent searches the web, another analyzes sentiment, and a third writes a report. Which framework should I use?" **Skill response:** Recommends CrewAI for its role-based simplicity, provides a 3-agent architecture diagram, generates a complete scaffold with SerperDevTool + OpenAI, and provides a deployment checklist. --- **User:** "I'm using LangGraph but my agents keep losing context between nodes. How do I fix state persistence?" **Skill response:** Explains LangGraph's StateGraph checkpointing, shows how to add a PostgreSQL checkpointer, provides a code fix. ## Notes & Constraints - Always surface the **trade-offs**, not just the "winner" — different teams need different frameworks - Code examples default to Python; mention JS/TS equivalents where available - For enterprise requirements, flag SOC2 / data residency considerations - Keep up with the rapidly evolving MCP (Model Context Protocol) and A2A protocol integrations - Recommend starting simple: single agent → multi-agent only when genuinely needed


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)