---
name: langgraph-multi-agent-sequential
version: 1.0.0
description: 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)
tags: []
metadata:
  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
  extracted_at: ''
  confidence: 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:**

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

**Setup steps:**

1. Install langchain and langgraph packages
1. Configure BedrockModel with temperature=0.3 and top_p=0.8
1. Create three Agent instances with appropriate system prompts and tools
1. 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

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

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

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