Hermes Pipeline
dc9053fa1d
Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Score: 1.0
24 lines
1.3 KiB
JSON
24 lines
1.3 KiB
JSON
{
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"name": "multi-agent-sequential-workflow",
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"version": "1.0.0",
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"goal": "Gather and process information from multiple agents to generate a comprehensive travel guide.",
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"inputs": [
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"User query with location"
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],
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"steps": [
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"Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.",
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"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.",
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"Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section."
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],
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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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],
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"failure_modes": [
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"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."
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],
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"confidence": 0.95,
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"explanation": "This workflow is specific to generating travel guides but can be adapted for other types of structured content creation.",
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"source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
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"score": 1.0
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} |