Add Skill: langgraph-workflow-creation

Extracted from: https://github.com/jkmaina/LangGraphProjects.git Score: 1.0
2026-08-05 15:53:02 +00:00
11 changed files with 2 additions and 368 deletions
@@ -34,8 +34,8 @@ scout:
    - 'rag agent workflow'
    - 'tool calling workflow'
  filters:
-    stars_min: 10
+    stars_min: 15
-    pushed_after: 2026-02-01
+    pushed_after: 2026-05-01
    language: Python
    archived: false
    size_max_kb: 10000
@@ -1,84 +0,0 @@
 ---
 name: agent-supervisor
 version: 1.0.0
 description: Demonstrate a supervisor-worker architecture for intelligent task delegation
  and real-time decision-making.
 inputs:
 - name: OPENAI_API_KEY
  description: OpenAI API key for language models.
 - name: TAVILY_API_KEY
  description: Tavily API key for search functionality.
 steps:
 - step: 1
  action: Load environment variables.
  details: Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.
 - step: 2
  action: Configure LangChain tools.
  details: Initialize TavilySearchResults and PythonREPLTool.
 - step: 3
  action: Define agent nodes.
  details: Create functions for the Researcher and Coder agents that process state
    through their respective tasks.
 - step: 4
  action: Set up supervisor agent.
  details: Create a supervisor agent function that decides which worker should act
    next based on user input.
 - step: 5
  action: Build state graph.
  details: Construct the state graph with nodes for each agent and edges connecting
    them to the supervisor node.
 - step: 6
  action: Add conditional edges.
  details: Define conditions for transitioning between agents based on their responses.
 - step: 7
  action: Compile graph.
  details: Compile the state graph into a runnable workflow.
 - step: 8
  action: Run example queries.
  details: Stream through the workflow with example inputs to demonstrate its functionality.
 outputs:
 - name: 'Example 1: Code Hello World'
  description: A demonstration of coding a simple hello world program.
 - name: 'Example 2: Research Report'
  description: A demonstration of researching and writing a brief report on pikas.
 tags: []
 metadata:
  source_repo: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
  extracted_at: ''
  confidence: 0.9
 ---
 # agent-supervisor
 Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.
 ## Steps
 1. {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'}
 2. {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'}
 3. {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
 4. {'step': 4, 'action': 'Set up supervisor agent.', 'details': 'Create a supervisor agent function that decides which worker should act next based on user input.'}
 5. {'step': 5, 'action': 'Build state graph.', 'details': 'Construct the state graph with nodes for each agent and edges connecting them to the supervisor node.'}
 6. {'step': 6, 'action': 'Add conditional edges.', 'details': 'Define conditions for transitioning between agents based on their responses.'}
 7. {'step': 7, 'action': 'Compile graph.', 'details': 'Compile the state graph into a runnable workflow.'}
 8. {'step': 8, 'action': 'Run example queries.', 'details': 'Stream through the workflow with example inputs to demonstrate its functionality.'}
 ## Inputs
 - {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}
 - {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
 ## Outputs
 - {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}
 - {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
 ## Failure Modes
 - {'mode': 'Invalid API keys', 'description': 'The workflow may fail if the provided API keys are invalid or expired.'}
 - {'mode': 'Insufficient permissions', 'description': 'The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality.'}
 ## Source
 Extracted from: [https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git](https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git)
 Confidence: 0.9
@@ -1,6 +0,0 @@
 # Commands: agent-supervisor
 ## Available Commands
 - `/skill agent-supervisor` — Load this skill
 - `/run agent-supervisor` — Execute workflow
@@ -1,10 +0,0 @@
 # Examples: agent-supervisor
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}, {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
 # Process: {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'} → {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'} → {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
 # Outputs: {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}, {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
 ```
@@ -1,81 +0,0 @@
 {
  "name": "agent-supervisor",
  "version": "1.0.0",
  "goal": "Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.",
  "inputs": [
    {
      "name": "OPENAI_API_KEY",
      "description": "OpenAI API key for language models."
    },
    {
      "name": "TAVILY_API_KEY",
      "description": "Tavily API key for search functionality."
    }
  ],
  "steps": [
    {
      "step": 1,
      "action": "Load environment variables.",
      "details": "Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables."
    },
    {
      "step": 2,
      "action": "Configure LangChain tools.",
      "details": "Initialize TavilySearchResults and PythonREPLTool."
    },
    {
      "step": 3,
      "action": "Define agent nodes.",
      "details": "Create functions for the Researcher and Coder agents that process state through their respective tasks."
    },
    {
      "step": 4,
      "action": "Set up supervisor agent.",
      "details": "Create a supervisor agent function that decides which worker should act next based on user input."
    },
    {
      "step": 5,
      "action": "Build state graph.",
      "details": "Construct the state graph with nodes for each agent and edges connecting them to the supervisor node."
    },
    {
      "step": 6,
      "action": "Add conditional edges.",
      "details": "Define conditions for transitioning between agents based on their responses."
    },
    {
      "step": 7,
      "action": "Compile graph.",
      "details": "Compile the state graph into a runnable workflow."
    },
    {
      "step": 8,
      "action": "Run example queries.",
      "details": "Stream through the workflow with example inputs to demonstrate its functionality."
    }
  ],
  "outputs": [
    {
      "name": "Example 1: Code Hello World",
      "description": "A demonstration of coding a simple hello world program."
    },
    {
      "name": "Example 2: Research Report",
      "description": "A demonstration of researching and writing a brief report on pikas."
    }
  ],
  "failure_modes": [
    {
      "mode": "Invalid API keys",
      "description": "The workflow may fail if the provided API keys are invalid or expired."
    },
    {
      "mode": "Insufficient permissions",
      "description": "The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality."
    }
  ],
  "confidence": 0.9,
  "explanation": "This workflow demonstrates a hierarchical multi-agent system where a supervisor agent makes routing decisions based on user input, delegating tasks to specialized worker agents (Researcher and Coder). It is designed to be reusable for similar task delegation scenarios.",
  "source_repo": "https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git",
  "score": 1.0
 }
@@ -1,9 +0,0 @@
 # Tests: agent-supervisor
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -1,115 +0,0 @@
 ---
 name: langgraph-agent-workflow
 version: 1.0.0
 description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
  RAG, code execution, and citation generation
 inputs:
 - LangGraph chain configuration files defining agent workflows
 - SerperDevTool integration for LLM tool access
 - React agent creation scripts via create_react_agent
 - Knowledge graph retrieval and citation generation pipelines
 steps:
 - 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
  a LangGraph chain that combines retrieval, reasoning, and response generation using
  SerperDevTool for tool access'
 - 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
  agent that can interact with the LangGraph chain'
 - 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
  graph retrieval (Neo4j/ArangoDB) with citation generation'
 - 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
  for code-related tasks'
 - "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
  \ \u2192 agent response in a single LangGraph workflow"
 outputs:
 - Reusable LangGraph chain definition (pyfile) with configurable steps
 - React agent frontend component that can be deployed independently
 - RAG pipeline that generates block citations and grounded answers
 - Code execution sandbox for artifact generation
 - Documentation for parameterizing workflows for different tasks
 tags: []
 metadata:
  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
  extracted_at: ''
  confidence: 0.95
 ---
 # langgraph-agent-workflow
 Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
 ## Setup
 **Dependencies:**
 ```text
 pip install langgraph>=0.7.0 serper-dev-tool>=0.1.0 qdrant-client or opensearch-dsl neo4j-driver or arango-database-driver react, next.js
 ```
 **Setup steps:**
 1. Install LangGraph and SerperDevTool dependencies
 1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
 1. Define chain topology with retrieval, reasoning, and response steps
 1. Build React agent frontend using create_react_agent
 1. Test multi-step agent workflows end-to-end
 ## Key Files
 - `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
 - `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
 - `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
 - `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
 ## Steps
 1. Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access
 2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
 3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
 4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
 5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
 ## Implementation Details
 ```python
 chain = LangGraph()
 ```
 ```python
 chain.add_step(SerperDevToolAgent())
 ```
 ```python
 agent = create_react_agent(chain, SerperDevToolAgent())
 ```
 ```python
 workflow = chain.start()
 ```
 ## Inputs
 - LangGraph chain configuration files defining agent workflows
 - SerperDevTool integration for LLM tool access
 - React agent creation scripts via create_react_agent
 - Knowledge graph retrieval and citation generation pipelines
 ## Outputs
 - Reusable LangGraph chain definition (pyfile) with configurable steps
 - React agent frontend component that can be deployed independently
 - RAG pipeline that generates block citations and grounded answers
 - Code execution sandbox for artifact generation
 - Documentation for parameterizing workflows for different tasks
 ## Failure Modes
 - GraphDB connection failures if Neo4j/ArangoDB is not properly configured
 - Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
 - LLM tool access errors if SerperDevTool is not properly initialized
 - Agent timeout if complex multi-step reasoning exceeds time limits
 - Sandbox execution failures if code has security vulnerabilities or infinite loops
 ## Source
 Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
 Confidence: 0.95
@@ -1,6 +0,0 @@
 # Commands: langgraph-agent-workflow
 ## Available Commands
 - `/skill langgraph-agent-workflow` — Load this skill
 - `/run langgraph-agent-workflow` — Execute workflow
@@ -1,10 +0,0 @@
 # Examples: langgraph-agent-workflow
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: LangGraph chain configuration files defining agent workflows, SerperDevTool integration for LLM tool access, React agent creation scripts via create_react_agent, Knowledge graph retrieval and citation generation pipelines
 # Process: Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access → Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain → Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
 # Outputs: Reusable LangGraph chain definition (pyfile) with configurable steps, React agent frontend component that can be deployed independently, RAG pipeline that generates block citations and grounded answers, Code execution sandbox for artifact generation, Documentation for parameterizing workflows for different tasks
 ```
@@ -1,36 +0,0 @@
 {
  "name": "langgraph-agent-workflow",
  "version": "1.0.0",
  "goal": "Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation",
  "inputs": [
    "LangGraph chain configuration files defining agent workflows",
    "SerperDevTool integration for LLM tool access",
    "React agent creation scripts via create_react_agent",
    "Knowledge graph retrieval and citation generation pipelines"
  ],
  "steps": [
    "Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access",
    "Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain",
    "Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation",
    "Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks",
    "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research \u2192 agent response in a single LangGraph workflow"
  ],
  "outputs": [
    "Reusable LangGraph chain definition (pyfile) with configurable steps",
    "React agent frontend component that can be deployed independently",
    "RAG pipeline that generates block citations and grounded answers",
    "Code execution sandbox for artifact generation",
    "Documentation for parameterizing workflows for different tasks"
  ],
  "failure_modes": [
    "GraphDB connection failures if Neo4j/ArangoDB is not properly configured",
    "Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail",
    "LLM tool access errors if SerperDevTool is not properly initialized",
    "Agent timeout if complex multi-step reasoning exceeds time limits",
    "Sandbox execution failures if code has security vulnerabilities or infinite loops"
  ],
  "confidence": 0.95,
  "explanation": "PipesHub provides a reusable LangGraph-based agent workflow framework that can be parameterized for different tasks. The core pattern involves defining a LangGraph chain with SerperDevTool integration for tool access, creating a React agent wrapper, and configuring RAG pipelines with citation generation. This framework can be reused across RAG, code execution, and research workflows by adjusting the chain definition and agent configuration.",
  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
  "score": 1.0
 }
@@ -1,9 +0,0 @@
 # Tests: langgraph-agent-workflow
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors