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

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

name	version	description	inputs	steps	outputs	tags	metadata
langgraph-multi-agent-router	1.0.0	Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response	User query string (e.g., destination location) BedrockModel configuration (model_id, temperature, top_p) Pre-configured agents with specific system prompts and tool sets	Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time) Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages) Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages	Raw research data (JSON string containing gathered facts) Structured guide content (markdown-formatted travel guide with labeled sections) Final client response (professional formatted response ready for delivery)		source_repo extracted_at confidence https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git 0.95

langgraph-multi-agent-router

Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response

Setup

Dependencies:

pip install langchain langgraph bedrock-model pydantic

Setup steps:

1. Install langchain and langgraph packages
2. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
3. Create three Agent instances with specific system prompts and tool sets
4. Initialize LangGraph with the agent chain and run the workflow

Key Files

• agents/langchain_langgraph/00-basic-agent/agent.py
• agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py
• agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py

Steps

1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages

Implementation Details

Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts

Travel guide agent receives raw output and formats into 5 labeled sections

Writer agent takes structured guide and writes professional client response

Inputs

• User query string (e.g., destination location)
• BedrockModel configuration (model_id, temperature, top_p)
• Pre-configured agents with specific system prompts and tool sets

Outputs

• Raw research data (JSON string containing gathered facts)
• Structured guide content (markdown-formatted travel guide with labeled sections)
• Final client response (professional formatted response ready for delivery)

Failure Modes

• Researcher agent fails to gather sufficient data or returns incomplete results
• Travel guide agent fails to structure information correctly or produces unreadable output
• Writer agent fails to format the final response properly or loses key information from the guide

Source

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