Table of Contents
Who Should Read This Post?
If you are a programmer who wants to learn how to build AI agents and is confused between LangGraph and LangChain, this post is for you and will help you decide which one is best for you!
Introduction
What is LangChain?
LangChain is a framework designed to simplify the development of AI applications that rely on large language models (LLMs).
It provides tools to build, chain, and manage interactions with LLMs, making it easier to create AI-driven workflows
What is LangGraph?
LangGraph is an extension of LangChain that introduces graph-based workflows.
It enables more complex and dynamic agent behavior.
Unlike LangChain’s linear flow, LangGraph allows for loops, state persistence, and multi-agent interactions.
Capabilities
LangChain Capabilities
- Composability: LangChain is a composable framework allowing developers to build LLM applications.
- Model Integration: Supports over 60 models, providing flexibility in choosing the most suitable model for your application.
- Tool Integration: Offers over 100 tool integrations, enabling seamless interaction with various external services and APIs.
- Integration with Data Sources: LangChain enables seamless connection between LLMs and various data sources, allowing applications to access and process information from multiple APIs and databases.
- Application Suitability: LangChain is particularly beneficial for tasks such as chatbots, retrieval-augmented generation, document summarization, and synthetic data generation.
LangGraph Capabilities
- Stateful Orchestration: LangGraph is a stateful orchestration framework designed for building complex, agentic systems.
- Agent Workflows: It enables the creation of agent and multi-agent workflows, offering features like cycles, controllability, and persistence.
- Human-in-the-Loop: Interrupt graph execution to approve or edit the next action planned by the agent, enhancing control and reliability.
- Flow Definition: LangGraph allows developers to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions
- Control and State Management: As a low-level framework, it provides fine-grained control over both the flow and state of applications, crucial for creating reliable agents.
- Streaming Support: Stream outputs as they are produced by each node, including token streaming, for real-time insights into agent reasoning and actions.
- Persistence: LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
When to Use?
When to Use LangChain?
- Linear Workflows: Ideal for linear workflows where tasks are executed sequentially.
- State Management: Provides straightforward state management with implicit input-output handling.
- Basic Agents: Suitable for developing basic agents with simple chains or tool integrations.
- Moderate Complexity: Works well for applications with moderate complexity and standard deployment needs.
When to Use LangGraph?
- Complex Workflows: Designed for complex, stateful workflows with multi-actor and cyclical processes.
- Advanced State Management: Offers explicit state management for better control over the agent’s decision-making.
- Human-in-the-Loop: Supports human-in-the-loop and memory persistence features for advanced interaction.
- Multi-Stage Applications: Ideal for multi-stage applications with looping, recursive, or branching workflows.
- Advanced Architectures: Best for advanced agent architectures where tracking and manipulating state is essential.
Use Case Examples
LangChain Use Cases
- Conversational AI Assistants: Developing a chatbot that handles customer inquiries, provides product recommendations, and assists with order processing.
- Document Analysis and Summarization: Creating a tool that reads and summarizes lengthy legal documents or research papers.
- Personalized Content Generation: Generating personalized email responses or social media posts based on user input.
- Automated Data Extraction: Extracting structured data from unstructured text sources like news articles or reports.
LangGraph Use Cases
- Multi-Agent Collaborative Systems: Developing a system where multiple AI agents collaborate to solve complex problems, such as coordinating tasks in a project management tool.
- Hierarchical Task Management: Managing a series of tasks that require sequential execution, like processing a customer service request with multiple steps.
- Dynamic Workflow Automation: Automating business processes that require decision-making based on changing conditions, like adjusting marketing strategies in real-time.
- Complex Data Processing Pipelines: Processing and analyzing large datasets that require multiple stages, such as data cleaning, transformation, and visualization.
What should you do?
- If you are an absolute beginner go with LangChain and build simple agents.
- If you are already familiar with AI agents go with LangGraph for the most flexible and dynamic agents.
- If you’re building a simple AI agent with predefined workflows, LangChain is the better choice. If you need complex, multi-agent, stateful, and dynamically adaptive agents, LangGraph is the superior option.