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1 Commits
| Author | SHA1 | Date | |
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 Hermes Pipeline
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03b5beba17 |
@@ -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: 10
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pushed_after: 2026-02-01
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stars_min: 15
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pushed_after: 2026-05-01
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language: Python
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archived: false
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size_max_kb: 10000
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@@ -1,115 +0,0 @@
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---
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name: langgraph-agent-workflow
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version: 1.0.0
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description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
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RAG, code execution, and citation generation
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inputs:
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- LangGraph chain configuration files defining agent workflows
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- SerperDevTool integration for LLM tool access
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- React agent creation scripts via create_react_agent
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- Knowledge graph retrieval and citation generation pipelines
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steps:
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- 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
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a LangGraph chain that combines retrieval, reasoning, and response generation using
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SerperDevTool for tool access'
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- 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
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agent that can interact with the LangGraph chain'
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- 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
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graph retrieval (Neo4j/ArangoDB) with citation generation'
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- 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
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for code-related tasks'
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- "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
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\ \u2192 agent response in a single LangGraph workflow"
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outputs:
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- Reusable LangGraph chain definition (pyfile) with configurable steps
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- React agent frontend component that can be deployed independently
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- RAG pipeline that generates block citations and grounded answers
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- Code execution sandbox for artifact generation
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- Documentation for parameterizing workflows for different tasks
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tags: []
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metadata:
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source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-agent-workflow
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Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
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## Setup
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**Dependencies:**
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```text
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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
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```
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**Setup steps:**
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1. Install LangGraph and SerperDevTool dependencies
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1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
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1. Define chain topology with retrieval, reasoning, and response steps
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1. Build React agent frontend using create_react_agent
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1. Test multi-step agent workflows end-to-end
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## Key Files
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- `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
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- `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
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- `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
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- `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
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## Steps
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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
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2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
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3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
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4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
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5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
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## Implementation Details
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```python
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chain = LangGraph()
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```
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```python
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chain.add_step(SerperDevToolAgent())
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```
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```python
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agent = create_react_agent(chain, SerperDevToolAgent())
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```
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```python
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workflow = chain.start()
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```
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## Inputs
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- LangGraph chain configuration files defining agent workflows
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- SerperDevTool integration for LLM tool access
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- React agent creation scripts via create_react_agent
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- Knowledge graph retrieval and citation generation pipelines
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## Outputs
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- Reusable LangGraph chain definition (pyfile) with configurable steps
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- React agent frontend component that can be deployed independently
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- RAG pipeline that generates block citations and grounded answers
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- Code execution sandbox for artifact generation
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- Documentation for parameterizing workflows for different tasks
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## Failure Modes
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- GraphDB connection failures if Neo4j/ArangoDB is not properly configured
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- Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
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- LLM tool access errors if SerperDevTool is not properly initialized
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- Agent timeout if complex multi-step reasoning exceeds time limits
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- Sandbox execution failures if code has security vulnerabilities or infinite loops
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## Source
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Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
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Confidence: 0.95
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@@ -1,6 +0,0 @@
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# Commands: langgraph-agent-workflow
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## Available Commands
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- `/skill langgraph-agent-workflow` — Load this skill
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- `/run langgraph-agent-workflow` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: langgraph-agent-workflow
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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: 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
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# 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
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# 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
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```
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@@ -1,36 +0,0 @@
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{
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"name": "langgraph-agent-workflow",
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"version": "1.0.0",
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"goal": "Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation",
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"inputs": [
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"LangGraph chain configuration files defining agent workflows",
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"SerperDevTool integration for LLM tool access",
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"React agent creation scripts via create_react_agent",
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"Knowledge graph retrieval and citation generation pipelines"
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],
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"steps": [
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"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",
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"Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain",
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"Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation",
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"Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks",
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"Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research \u2192 agent response in a single LangGraph workflow"
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],
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"outputs": [
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"Reusable LangGraph chain definition (pyfile) with configurable steps",
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"React agent frontend component that can be deployed independently",
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"RAG pipeline that generates block citations and grounded answers",
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"Code execution sandbox for artifact generation",
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"Documentation for parameterizing workflows for different tasks"
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],
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"failure_modes": [
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"GraphDB connection failures if Neo4j/ArangoDB is not properly configured",
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"Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail",
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"LLM tool access errors if SerperDevTool is not properly initialized",
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"Agent timeout if complex multi-step reasoning exceeds time limits",
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"Sandbox execution failures if code has security vulnerabilities or infinite loops"
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],
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"confidence": 0.95,
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"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.",
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"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
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"score": 1.0
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}
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@@ -1,9 +0,0 @@
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# Tests: langgraph-agent-workflow
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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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@@ -1,83 +0,0 @@
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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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@@ -1,6 +0,0 @@
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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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@@ -1,10 +0,0 @@
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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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@@ -1,26 +0,0 @@
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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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@@ -1,9 +0,0 @@
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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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||||
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Block a user