Add 5 skills from LFM + 12 skills total
New skills: - blacknode-graph-workflow - multi-agent-workflow-execution - langgraph-agent-workflow - langgraph-multi-agent-router - three-tier-evaluation-pipeline Config: LLM pipeline uses LFM on llama.cpp (8080)
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Epictetus
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---
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name: langgraph-multi-agent-router
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version: 1.0.0
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description: Orchestrate a multi-agent workflow where specialized agents collaborate
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sequentially to gather information, structure it, and generate a final response
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inputs:
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- User query string (e.g., destination location)
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- BedrockModel configuration (model_id, temperature, top_p)
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- Pre-configured agents with specific system prompts and tool sets
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steps:
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- Researcher agent executes with system prompt to gather raw destination facts (places,
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history, accommodations, food, web pages) using BedrockModel and available tools
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(calculator, current_time)
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- Travel guide agent receives raw research output and structures it into labeled sections
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(Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights,
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Suggested Web Pages)
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- Writer agent receives the structured guide and synthesizes it into a professional
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client-facing response with clear formatting and emphasis on the suggested web pages
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outputs:
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- Raw research data (JSON string containing gathered facts)
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- Structured guide content (markdown-formatted travel guide with labeled sections)
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- Final client response (professional formatted response ready for delivery)
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tags: []
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metadata:
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source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-multi-agent-router
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Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response
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## Setup
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**Dependencies:**
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```text
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pip install langchain langgraph bedrock-model pydantic
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```
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**Setup steps:**
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1. Install langchain and langgraph packages
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1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
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1. Create three Agent instances with specific system prompts and tool sets
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1. Initialize LangGraph with the agent chain and run the workflow
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## Key Files
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- `agents/langchain_langgraph/00-basic-agent/agent.py`
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- `agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py`
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- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
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## Steps
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1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
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2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
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3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
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## Implementation Details
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```python
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Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts
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```
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```python
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Travel guide agent receives raw output and formats into 5 labeled sections
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```
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```python
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Writer agent takes structured guide and writes professional client response
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```
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## Inputs
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- User query string (e.g., destination location)
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- BedrockModel configuration (model_id, temperature, top_p)
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- Pre-configured agents with specific system prompts and tool sets
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## Outputs
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- Raw research data (JSON string containing gathered facts)
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- Structured guide content (markdown-formatted travel guide with labeled sections)
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- Final client response (professional formatted response ready for delivery)
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## Failure Modes
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- Researcher agent fails to gather sufficient data or returns incomplete results
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- Travel guide agent fails to structure information correctly or produces unreadable output
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- Writer agent fails to format the final response properly or loses key information from the guide
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## Source
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Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
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Confidence: 0.95
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# Commands: langgraph-multi-agent-router
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## Available Commands
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- `/skill langgraph-multi-agent-router` — Load this skill
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- `/run langgraph-multi-agent-router` — Execute workflow
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# Examples: langgraph-multi-agent-router
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## Usage Example
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```python
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# How to use this skill
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# Inputs: User query string (e.g., destination location), BedrockModel configuration (model_id, temperature, top_p), Pre-configured agents with specific system prompts and tool sets
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# Process: Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time) → Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages) → Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
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# Outputs: Raw research data (JSON string containing gathered facts), Structured guide content (markdown-formatted travel guide with labeled sections), Final client response (professional formatted response ready for delivery)
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```
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{
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"name": "langgraph-multi-agent-router",
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"version": "1.0.0",
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"goal": "Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response",
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"inputs": [
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"User query string (e.g., destination location)",
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"BedrockModel configuration (model_id, temperature, top_p)",
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"Pre-configured agents with specific system prompts and tool sets"
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],
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"steps": [
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"Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)",
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"Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)",
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"Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages"
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],
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"outputs": [
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"Raw research data (JSON string containing gathered facts)",
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"Structured guide content (markdown-formatted travel guide with labeled sections)",
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"Final client response (professional formatted response ready for delivery)"
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],
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"failure_modes": [
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"Researcher agent fails to gather sufficient data or returns incomplete results",
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"Travel guide agent fails to structure information correctly or produces unreadable output",
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"Writer agent fails to format the final response properly or loses key information from the guide"
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],
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"confidence": 0.95,
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"explanation": "This workflow demonstrates a reusable multi-stage agent pattern where specialized agents collaborate in sequence. The Researcher agent gathers raw information using a domain-specific model, the Travel Guide agent structures that information into a consistent format, and the Writer agent synthesizes the final output. This pattern can be adapted to other domains (e.g., code generation, data analysis, research workflows) by swapping the agent types and system prompts while maintaining the same three-step structure.",
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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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}
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@@ -0,0 +1,9 @@
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# Tests: langgraph-multi-agent-router
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## Test Checklist
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- [ ] Workflow has at least 3 steps
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- [ ] All inputs are defined
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- [ ] All outputs are defined
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- [ ] Failure modes are documented
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- [ ] Skill can be loaded without errors
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