Add Skill: langgraph-multi-agent-sequential

Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git Score: 1.0
2026-08-06 14:41:45 +00:00
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+---
+name: langgraph-multi-agent-sequential
+version: 1.0.0
+description: 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)
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-multi-agent-sequential
+
+Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph bedrock-model pydantic
+```
+
+**Setup steps:**
+
+1. Install langchain and langgraph packages
+1. Configure BedrockModel with temperature=0.3 and top_p=0.8
+1. Create three Agent instances with appropriate system prompts and tools
+1. Deploy the FastAPI server with the LangGraph application
+
+## Key Files
+
+- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
+- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/app.py`
+
+## Steps
+
+1. Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel
+2. Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections
+3. Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
+
+## Implementation Details
+
+```python
+Researcher agent with system_prompt for destination research and tools=[calculator, current_time]
+```
+
+```python
+Travel Guide Generator agent with system_prompt requiring structured sections (attractions, history, accommodations, cuisine, web pages)
+```
+
+```python
+Writer agent with system_prompt for client-facing response formatting
+```
+
+## 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
+
+## 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
+
+## Source
+
+Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
+Confidence: 0.95