---
name: langgraph-multi-agent-router
version: 1.0.0
description: Orchestrate a multi-agent workflow where specialized agents collaborate
  sequentially to gather information, structure it, and generate a final 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
steps:
- 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
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)
tags: []
metadata:
  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
  extracted_at: ''
  confidence: 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:**

```text
pip install langchain langgraph bedrock-model pydantic
```

**Setup steps:**

1. Install langchain and langgraph packages
1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
1. Create three Agent instances with specific system prompts and tool sets
1. 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

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

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

```python
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](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
Confidence: 0.95