---
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