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Agentic WorkFlow

LangChain Integration

Leverage the power of LangChain framework for sophisticated AI workflows
Getting Started Guide Browse Components

LangChain is the leading framework for building AI applications. Agentic WorkFlow integrates LangChain’s powerful components and patterns, making them accessible through visual workflows that run directly in your browser.

Getting Started

Learn the basics of using LangChain components in your workflows - from simple chains to complex agents.

Components Guide

Explore all available LangChain components - models, memory, tools, and more.

Workflow Patterns

Discover proven patterns for building intelligent workflows using LangChain components.

Browser Integration

Learn how to combine LangChain AI with browser automation for powerful web workflows.

Why LangChain in Agentic WorkFlow?

Section titled “Why LangChain in Agentic WorkFlow?”

Proven AI Patterns: LangChain provides battle-tested patterns for building AI applications, from simple text processing to complex multi-agent systems.

Visual Workflow Builder: Instead of writing code, you build LangChain workflows visually by connecting nodes that represent different components.

Browser-Optimized: All LangChain components are optimized to work in browser environments with proper security and performance considerations.

No Setup Required: Start using advanced AI patterns immediately without installing Python, managing dependencies, or setting up development environments.

Key capabilities

Section titled “Key capabilities”
  • Intelligent Chains: Connect multiple AI models and tools in sophisticated processing pipelines
  • Smart Agents: AI that can reason about which tools to use and when to use them
  • Memory Systems: Give your AI the ability to remember and learn from past interactions
  • Vector Search: Build knowledge bases that understand meaning, not just keywords
  • Tool Integration: Connect AI to external APIs, databases, and browser functionality

From code to visual workflows

Section titled “From code to visual workflows”

Traditional LangChain (Python/JavaScript):

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI


prompt = PromptTemplate(template="Analyze this content: {content}")
chain = LLMChain(llm=OpenAI(), prompt=prompt)
result = chain.run(content="...")

Agentic WorkFlow (Visual):

  • Drag a “Basic LLM Chain” node onto the canvas
  • Connect it to a “GetAllTextFromLink” node for content
  • Configure the prompt template in the UI
  • Run the workflow with one click

The visual approach makes LangChain accessible to everyone while maintaining all the power and flexibility of the framework.