From dc9053fa1dfbed7a8ed1e31c33debfff3647cd6c Mon Sep 17 00:00:00 2001 From: Hermes Pipeline Date: Wed, 5 Aug 2026 15:46:04 +0000 Subject: [PATCH] Add Skill: multi-agent-sequential-workflow Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Score: 1.0 --- .../multi-agent-sequential-workflow/SKILL.md | 19 +++++++++---------- .../examples.md | 2 +- .../metadata.json | 8 ++++---- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/skills/multi-agent-sequential-workflow/SKILL.md b/skills/multi-agent-sequential-workflow/SKILL.md index fd52bbb..026e691 100644 --- a/skills/multi-agent-sequential-workflow/SKILL.md +++ b/skills/multi-agent-sequential-workflow/SKILL.md @@ -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 diff --git a/skills/multi-agent-sequential-workflow/examples.md b/skills/multi-agent-sequential-workflow/examples.md index 15dae32..b3325ef 100644 --- a/skills/multi-agent-sequential-workflow/examples.md +++ b/skills/multi-agent-sequential-workflow/examples.md @@ -5,6 +5,6 @@ ```python # How to use this skill # Inputs: User query with location -# Process: 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. +# Process: 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 the raw information provided by the researcher. → Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section. # Outputs: Structured travel guide with key sections, Final client response ``` diff --git a/skills/multi-agent-sequential-workflow/metadata.json b/skills/multi-agent-sequential-workflow/metadata.json index 5128937..568dd7c 100644 --- a/skills/multi-agent-sequential-workflow/metadata.json +++ b/skills/multi-agent-sequential-workflow/metadata.json @@ -7,18 +7,18 @@ ], "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." + "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.", + "Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section." ], "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" + "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." ], "confidence": 0.95, - "explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.", + "explanation": "This workflow is specific to generating travel guides but can be adapted for other types of structured content creation.", "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git", "score": 1.0 } \ No newline at end of file -- 2.43.0