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Add Skill: multi-agent-sequential-workflow

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Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
Score: 1.0
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2026-08-05 15:46:04 +00:00
parent 5f917f4121
commit dc9053fa1d
3 changed files with 14 additions and 15 deletions
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skills/multi-agent-sequential-workflow/SKILL.md
+9 -10
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@@ -9,10 +9,10 @@ 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 raw information provided
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
guide content and prominently features the ''Suggested Web Pages'' section.'
travel guide content and prominently featuring the ''Suggested Web Pages'' section.'
outputs:
- Structured travel guide with key sections
- Final client response
@@ -32,24 +32,23 @@ Gather and process information from multiple agents to generate a comprehensive
**Dependencies:**
```text
pip install python3 fastapi uvicorn strands bedrock-model
pip install python langchain strands
```
**Setup steps:**
1. Install required dependencies using pip
1. Set up environment variables for API keys and model IDs
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 multi-agent workflow logic.`
- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
- `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 raw information provided by the researcher.
3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
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
@@ -76,7 +75,7 @@ final_response = writer_agent(writer_prompt, stream=False)
## Failure Modes
- Network issues during API calls could lead to incomplete data collection or processing failures
- 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