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3 Commits
| Author | SHA1 | Date | |
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 Hermes Pipeline
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5593e60b91 | ||
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Epictetus
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d81ddeda88 | ||
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Epictetus
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a14f09bec2 |
@@ -34,8 +34,8 @@ scout:
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- 'rag agent workflow'
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- 'tool calling workflow'
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filters:
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stars_min: 15
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pushed_after: 2026-05-01
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stars_min: 10
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pushed_after: 2026-02-01
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language: Python
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archived: false
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size_max_kb: 10000
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@@ -0,0 +1,96 @@
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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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@@ -0,0 +1,6 @@
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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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@@ -0,0 +1,10 @@
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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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@@ -0,0 +1,29 @@
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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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@@ -0,0 +1,83 @@
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---
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name: langgraph-workflow-creation
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version: 1.0.0
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description: Create a LangGraph workflow to gather facts using SerperDevTool and process
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them with an AI agent.
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inputs:
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- API Key for SerperDevTool
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- Search Query
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steps:
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- 'Step 1: Import necessary modules from langgraph and langchain libraries'
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- 'Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function
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with SerperDevTool as the tool node'
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- 'Step 3: Define the search query and pass it to the agent for fact gathering'
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- 'Step 4: Process the gathered facts within the AI agent'
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outputs:
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- Processed Facts
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tags: []
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metadata:
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source_repo: https://github.com/jkmaina/LangGraphProjects.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-workflow-creation
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Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.
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## Setup
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**Dependencies:**
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```text
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pip install langchain serperdev
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```
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**Setup steps:**
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1. Install required libraries: pip install langchain serperdev
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1. Add API key to .env file: OPENAPI_API_KEY=your_api_key
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## Key Files
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- `agent.py - Contains the LangGraph agent creation logic`
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- `tool_node.py - Defines the SerperDevTool node`
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## Steps
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1. Step 1: Import necessary modules from langgraph and langchain libraries
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2. Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node
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3. Step 3: Define the search query and pass it to the agent for fact gathering
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4. Step 4: Process the gathered facts within the AI agent
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## Implementation Details
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```python
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import langgraph
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from serperdev import SerperDevTool
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def create_agent(api_key, query):
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tool = SerperDevTool(api_key)
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agent = langgraph.create_react_agent(tool=tool)
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facts = agent.run(query)
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return process_facts(facts)
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```
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## Inputs
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- API Key for SerperDevTool
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- Search Query
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## Outputs
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- Processed Facts
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## Failure Modes
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- API Key not provided
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- Invalid Search Query
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## Source
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Extracted from: [https://github.com/jkmaina/LangGraphProjects.git](https://github.com/jkmaina/LangGraphProjects.git)
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Confidence: 0.95
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@@ -0,0 +1,6 @@
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# Commands: langgraph-workflow-creation
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## Available Commands
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- `/skill langgraph-workflow-creation` — Load this skill
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- `/run langgraph-workflow-creation` — Execute workflow
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@@ -0,0 +1,10 @@
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# Examples: langgraph-workflow-creation
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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: API Key for SerperDevTool, Search Query
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# Process: Step 1: Import necessary modules from langgraph and langchain libraries → Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node → Step 3: Define the search query and pass it to the agent for fact gathering
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# Outputs: Processed Facts
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```
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@@ -0,0 +1,26 @@
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{
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"name": "langgraph-workflow-creation",
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"version": "1.0.0",
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"goal": "Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.",
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"inputs": [
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"API Key for SerperDevTool",
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"Search Query"
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],
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"steps": [
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"Step 1: Import necessary modules from langgraph and langchain libraries",
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"Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node",
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"Step 3: Define the search query and pass it to the agent for fact gathering",
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"Step 4: Process the gathered facts within the AI agent"
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],
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"outputs": [
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"Processed Facts"
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],
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"failure_modes": [
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"API Key not provided",
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"Invalid Search Query"
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],
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"confidence": 0.95,
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"explanation": "This workflow is specific to fact gathering and can be adapted for different search queries or tools.",
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"source_repo": "https://github.com/jkmaina/LangGraphProjects.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-workflow-creation
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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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Reference in New Issue
Block a user