Epictetus
5f917f4121
Publisher fixes: - Checkout new branch before push (was pushing main ref) - Verify files staged before commit - Handle duplicate files gracefully - Clean error reporting per stage Skills merged to main: - mcp-server-setup (from pipeshub-ai) - research-pipeline (from Blacknode) - multi-agent-sequential-workflow (from Fast-LLM-Agent-MCP) - unifai-workflow-execution (from UnifAI) - code-review-agent-workflow (from AgentKit)
85 lines
2.6 KiB
Markdown
85 lines
2.6 KiB
Markdown
---
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name: multi-agent-sequential-workflow
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version: 1.0.0
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description: Gather and process information from multiple agents to generate a comprehensive
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travel guide.
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inputs:
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- User query with location
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steps:
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- 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
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to gather raw facts about the destination.'
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- 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
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a structured travel guide based on the user''s request and raw information provided
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by the researcher.'
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- 'Step 3: Writer agent (agent.py) formats the final response, including the structured
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guide content and prominently features the ''Suggested Web Pages'' section.'
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outputs:
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- Structured travel guide with key sections
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- Final client response
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tags: []
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metadata:
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source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
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extracted_at: ''
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confidence: 0.95
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---
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# multi-agent-sequential-workflow
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Gather and process information from multiple agents to generate a comprehensive travel guide.
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## Setup
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**Dependencies:**
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```text
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pip install python3 fastapi uvicorn strands bedrock-model
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```
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**Setup steps:**
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1. Install required dependencies using pip
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1. Set up environment variables for API keys and model IDs
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## Key Files
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- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the multi-agent workflow logic.`
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- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
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## Steps
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1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
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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.
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3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
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## Implementation Details
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```python
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research_output = researcher_agent(query, stream=False)
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```
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```python
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guide_output = travel_guide_agent(planner_prompt, stream=False)
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```
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```python
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final_response = writer_agent(writer_prompt, stream=False)
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```
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## Inputs
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- User query with location
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## Outputs
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- Structured travel guide with key sections
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- Final client response
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## Failure Modes
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- Network issues during API calls could lead to incomplete data collection or processing failures
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## Source
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Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
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Confidence: 0.95
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