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Add Skill: langgraph-multi-agent-sequential

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Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
Score: 1.0
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2026-08-06 14:41:45 +00:00
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skills/langgraph-multi-agent-sequential/metadata.json
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{
"name": "langgraph-multi-agent-sequential",
"version": "1.0.0",
"goal": "Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation",
"inputs": [
"BedrockModel with temperature=0.3, top_p=0.8",
"Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)",
"Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections",
"Writer agent with system prompt for formatting professional client responses"
],
"steps": [
"Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel",
"Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections",
"Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages"
],
"outputs": [
"Raw research data (JSON string containing destination facts and categories)",
"Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)",
"Final client response (formatted travel guide ready for delivery)"
],
"failure_modes": [
"Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data",
"Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output",
"Writer agent fails to format the final response correctly, producing garbled or incomplete output",
"Model timeouts or errors in any agent step causing the entire pipeline to fail"
],
"confidence": 0.95,
"explanation": "This workflow demonstrates a reusable LangGraph pattern where three specialized agents work sequentially: a Researcher agent gathers raw destination facts, a Travel Guide Generator agent structures those facts into a travel guide, and a Writer agent formats the final output for clients. The pattern is modular and can be adapted to other multi-step tasks by swapping agent roles and prompts while maintaining the same pipeline structure.",
"source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
"score": 1.0
}