OpenAI Assistants Vs LangChain Agents: What Are They & How To Build Them?

From the renowned “Attention is all you need” paper that explained the concept of transformers to the world to preset-day AI assistants, we have come a long way.

OpenAI released AI assistants API enabling everyone, even the non-tech people to customize and build their own AI assistants.

But OpenAI was not the only one in the market to offer custom built smart assistants. LangChain in January 2024 published their update introducing LangChain Agents. Similar to OpenAI assistants, LangChain in Agents can be trained and customized to do specific tasks.

While both have the same purpose, the ease of development, features and cost-effectiveness varies. In the stand-off – OpenAI Assistants Vs LangChain Agents, let’s explore which is better.

What are OpenAI Assistants?

Who doesn’t like smart assistants?

We all need someone who can handle and execute our tasks efficiently as we want them. But seldom we get such productive assitants.

OpenAI with its AI assistants API feature is giving us a tool to build smart AI assistants. You can build a specialized AI assistant who can –

• Write blogs and ad copies
• Make marketing strategies
• Plan your trips
• Analyze business data
• Or even write code

The best thing is that you don’t need to be a tech geek to build your AI assistant. Plus, these AI assistants comes with features like Code Interpreter, Retrieval and Function Calling. These tools, especially the function calling tool enables the assistants to access advanced third-party tools to efficiently carry out their tasks.

Moreover, you can integrate AI assistants within your own application or popular apps like Slack or Google Sheets.

Sounds amazing right?

But before you come to a decision, here are few factors you need to know about OpenAI assistants.

OpenAI assistants only uses OpenAI models and functions to process the NLP tasks.

• OpenAI assistants only uses OpenAI models and functions to process the NLP tasks.
• Though no coding is needed, you should be very careful and clear in your instructions.
• You will always have to keep an eye on the performance and give feedbacks for improvements.
• ChatGPT is not connected to internet and cannot fetch real-time data. You can connect external APIs in your assistants to give access to updated information.
• Assistants can access multiple tools in parallel to ensure its accuracy and efficiency. They can access both OpenAI hosted tools and external tools (via Function Calling).
• The high performance of these AI assistants comes from Threads. Threads are responsible for managing and storing message history.

How To Build OpenAI Assistants?

Follow the below mentioned steps to create your own AI assistant.

• Choose the model you want to use.
• Define the assistant’s personality and goals using the instruction parameter.
• Use the tools parameter to give assistant access to OpenAI hosted or external tools. You can add upto 128 tools.
• Use the file_ids parameter to enable the tools like code interpreter and knowledge retrieval access files.
• You can easily upload the file to using the File upload endpoint.

Please note that you must first upload the file before defining the purpose of the assistant.

For example, if you want to create an assistant for data visualization, you must first upload the file. Once you have all your file uploaded, you can then create the assistant using that file.

Important note: You can attach up to 20 files of 512 MB each. The total size of the uploaded file should not exceed 100 GB.

What are LangChain Agents?

LangChain Agents are components in the LangChain framework that uses LLMs to decide a sequence of actions required to perfectly execute NLP tasks.

The core purpose of LangChain Agents is the ability to decide/choose the course of actions to perform a task. The flexibility that you won’t get with Chains.

In Chains the sequence of actions are hardcoded. It means that you cannot change the sequence of actions depending on the task at hand.

On the contrary, agents uses LLMs as reasoning engine to decide which actions to take and in which order to complete the task given.

This advantage of the agent’s architecture makes it ideal for applications such as personal assistants, question answering, chatbots, querying tabular data, interacting with APIs, extraction, summarization, and evaluation.

Now, let’s look at three key components of LangChain agents.

• Agent: At the heart of the structure lies the core functionality, tasked with processing inputs, formulating action strategies, and executing them.
• Tools: The agent relies on these external aids to carry out tasks, ranging from other LLMs to web APIs, covering a broad spectrum of resources.
• Toolkits: These assemblies of tools are meticulously crafted for distinct purposes, such as question answering, text generation, and natural language processing.

When you look at OpenAI assistants Vs LangChain Agents, the latter comes forward with unique benefits.

Firstly, LangChain agents are beginner-friendly as developers with basic knowledge of LLMs can also build an agent.

Secondly, these agents are versatile. They can handle simple response generation task to complex context-relevant interactions.

Thirdly, LangChain agents are not limited to only one type of Large language models. Unlike OpenAI assistants, you can use any LLM of your choice to build your custom agent.

This also enables you to give advanced functionalities to your agents for performing a wide range of tasks.

How To Build LangChain Agents?

Before you start, you will need the following to build agent:

• Tavily API token
• OpenAI API token
• Python v3.11
• Pip packages: langchain (at least v0.1.0), openai, wikipedia, langchain-community, tavily-python, langchainhub, langchain-openai, python-dotenv

Here’s the step-by-step process to create a Langchain agent capable of generating marketing strategies.

1. Installation

Install LangChain and the necessary tools for this task:

2. Import Necessary Libraries

Import the required libraries for setting up the agent:

3.Setting up the Tools:

Initialize tools like DuckDuckGo search, Arxiv, and Wikipedia for gathering information:

4.Setting up the Marketing Strategy Tool:

Configure the ChatOpenAI model and define a prompt template for generating marketing strategies:

5.Initializing the Agent:

Define the tools required for the agent, including the marketing strategy tool:

6.Running the Agent:

Execute the agent to generate a marketing strategy based on the provided input:

By following these steps, you can create a LangChain agent capable of generating marketing strategies by leveraging tools like DuckDuckGo, Arxiv, Wikipedia, and the ChatOpenAI model for intelligent content generation.

OpenAI Assistants Vs LangChain Agents

LangChain Agents and OpenAI Assistants are both advanced AI systems designed to perform intelligent tasks, but they have distinct characteristics and functionalities:

LangChain Agents

• LangChain Agents leverage language models along with a sequence of actions to process natural language input and generate responses.
• In LangChain, creating new agents is recommended using AgentExecutor, passing agents and tools for defining the executor.
• LangChain allows for the creation of custom agents tailored to specific use cases by defining prompts and utilizing various tools.
• LangChain categorizes agents based on model type, support for chat history, multi-input tools, parallel function calling, and required model parameters.
• Agents in LangChain can dynamically choose actions based on the context and reasoning model, enabling them to fetch specific data efficiently.

OpenAI Assistants

• OpenAI Assistants are like personal AI assistants that use OpenAI models and tools such as Functions, Code Interpreter, and Retrieval.
• The Assistants API provides a user-friendly way to create AI applications without extensive coding skills.
• OpenAI Assistants can interact with external APIs like travel APIs to gather real-time information for tasks like travel recommendations.
• Assistants come with built-in functions like code interpreter, retrievals, and more for seamless interaction with users.
• The Assistants API manages memory and context window automatically, making it easier to build applications compared to more manual setups like in LangChain.

To sum up OpenAI Assistants Vs LangChain Agents, the latter offer flexibility in processing natural language input and choosing actions based on context. While OpenAI Assistants provide a user-friendly interface with built-in functions for seamless interaction and integration with external APIs.

Ultimately the model you choose will depend on your task requirements and purpose. So select your assistant wisely!

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