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)
2.6 KiB
2.6 KiB
name, version, description, inputs, steps, outputs, tags, metadata
| name | version | description | inputs | steps | outputs | tags | metadata | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| multi-agent-sequential-workflow | 1.0.0 | Gather and process information from multiple agents to generate a comprehensive travel guide. |
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multi-agent-sequential-workflow
Gather and process information from multiple agents to generate a comprehensive travel guide.
Setup
Dependencies:
pip install python3 fastapi uvicorn strands bedrock-model
Setup steps:
- Install required dependencies using pip
- Set up environment variables for API keys and model IDs
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.
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 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.
Implementation Details
research_output = researcher_agent(query, stream=False)
guide_output = travel_guide_agent(planner_prompt, stream=False)
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
- Network issues during API calls could lead to incomplete data collection or processing failures
Source
Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Confidence: 0.95