---
name: multi-agent-sequential-workflow
version: 1.0.0
description: Gather and process information from multiple agents to generate a comprehensive
  travel guide.
inputs:
- User query with location
steps:
- 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
  to gather raw facts about the destination.'
- 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
  a structured travel guide based on the user''s request and the raw information provided
  by the researcher.'
- 'Step 3: Writer agent (agent.py) formats the final response, including the structured
  travel guide content and prominently featuring the ''Suggested Web Pages'' section.'
outputs:
- Structured travel guide with key sections
- Final client response
tags: []
metadata:
  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
  extracted_at: ''
  confidence: 0.95
---

# multi-agent-sequential-workflow

Gather and process information from multiple agents to generate a comprehensive travel guide.

## Setup

**Dependencies:**

```text
pip install python langchain strands
```

**Setup steps:**

1. Install required dependencies using pip and ensure the BedrockModel is properly configured.

## Key Files

- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the sequential workflow logic for gathering and processing information.`
- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - Provides a FastAPI endpoint to interact with the multi-agent system.`

## Steps

1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and the raw information provided by the researcher.
3. Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section.

## Implementation Details

```python
research_output = researcher_agent(query, stream=False)
```

```python
guide_output = travel_guide_agent(planner_prompt, stream=False)
```

```python
final_response = writer_agent(writer_prompt, stream=False)
```

## Inputs

- User query with location

## Outputs

- Structured travel guide with key sections
- Final client response

## Failure Modes

- Specific failure scenario with mitigation: If any of the agents fail to process their tasks (e.g., network issues, model errors), the workflow will fail. Mitigation involves robust error handling and fallback mechanisms.

## Source

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