Hermes Pipeline
2382525f81
Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Score: 1.0
31 lines
2.3 KiB
JSON
31 lines
2.3 KiB
JSON
{
|
|
"name": "langgraph-multi-agent-sequential",
|
|
"version": "1.0.0",
|
|
"goal": "Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation",
|
|
"inputs": [
|
|
"BedrockModel with temperature=0.3, top_p=0.8",
|
|
"Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)",
|
|
"Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections",
|
|
"Writer agent with system prompt for formatting professional client responses"
|
|
],
|
|
"steps": [
|
|
"Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel",
|
|
"Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections",
|
|
"Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages"
|
|
],
|
|
"outputs": [
|
|
"Raw research data (JSON string containing destination facts and categories)",
|
|
"Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)",
|
|
"Final client response (formatted travel guide ready for delivery)"
|
|
],
|
|
"failure_modes": [
|
|
"Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data",
|
|
"Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output",
|
|
"Writer agent fails to format the final response correctly, producing garbled or incomplete output",
|
|
"Model timeouts or errors in any agent step causing the entire pipeline to fail"
|
|
],
|
|
"confidence": 0.95,
|
|
"explanation": "This workflow demonstrates a reusable LangGraph pattern where three specialized agents work sequentially: a Researcher agent gathers raw destination facts, a Travel Guide Generator agent structures those facts into a travel guide, and a Writer agent formats the final output for clients. The pattern is modular and can be adapted to other multi-step tasks by swapping agent roles and prompts while maintaining the same pipeline structure.",
|
|
"source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
|
|
"score": 1.0
|
|
} |