agent-skills/skills/langgraph-agent-workflow/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
langgraph-agent-workflow	1.0.0	Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation	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	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 → deep research → agent response in a single LangGraph workflow	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		source_repo extracted_at confidence https://github.com/pipeshub-ai/pipeshub-ai.git 0.95

langgraph-agent-workflow

Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation

Setup

Dependencies:

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
2. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
3. Define chain topology with retrieval, reasoning, and response steps
4. Build React agent frontend using create_react_agent
5. 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

chain = LangGraph()

chain.add_step(SerperDevToolAgent())

agent = create_react_agent(chain, SerperDevToolAgent())

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 Confidence: 0.95