agent-skills/skills/langgraph-multi-agent-sequential/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
langgraph-multi-agent-sequential	1.0.0	Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation	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	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	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)		source_repo extracted_at confidence https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git 0.95

langgraph-multi-agent-sequential

Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation

Setup

Dependencies:

pip install langchain langgraph bedrock-model pydantic

Setup steps:

1. Install langchain and langgraph packages
2. Configure BedrockModel with temperature=0.3 and top_p=0.8
3. Create three Agent instances with appropriate system prompts and tools
4. Deploy the FastAPI server with the LangGraph application

Key Files

• agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py
• agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/app.py

Steps

1. Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel
2. Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections
3. Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages

Implementation Details

Researcher agent with system_prompt for destination research and tools=[calculator, current_time]

Travel Guide Generator agent with system_prompt requiring structured sections (attractions, history, accommodations, cuisine, web pages)

Writer agent with system_prompt for client-facing response formatting

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

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

Source

Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Confidence: 0.95