Chatbot APIs vs WebSockets

With the ongoing rise of generative AI, chatbots are becoming more and more prevalent. This includes a huge range of use cases across all industries. Chatbots are also becoming more sophisticated, including taking autonomous actions based on conversations with users.

So, developers must be familiar with the tools and techniques that are required to output chatbot solutions.

Today, we’re diving deep into an important subtopic within this, by checking out two of the most common communications techniques we’re going to encounter - Chatbot APIs and WebSockets.

More specifically, we’re going to outline what each one is, how and where it’s used, and what it achieves.

To achieve this, we’re covering:

• What is a Chatbot API?
• What is a WebSocket?
• Key differences
• Use cases
• Approaches combining APIs and WebSockets

To kick off, let’s check out some high-level definitions.

What is a Chatbot API?

API stands for application programming interface. This is a communications protocol that enables software applications to pass data back and forth via requests and responses. APIs power connections between all kinds of software tools, using standardized data formats.

So, one platform sends a HTTP request, either to trigger an action or to return information, and the other responds, confirming the success of the request and returning any relevant data in a specified format.

In the context of a chatbot, this can mean a couple of different things.

Firstly, a chatbot tool might offer its own platform API for triggering certain actions or accessing back-end functions from external tools.

Or, we might expose a chatbot to API endpoints for external systems, in order to enable it to trigger actions and functions. This relies on additional tool calling and reasoning capabilities.

For example, retrieving information requested by a user as part of an interaction.

Take a look at our guide to enterprise chatbots to learn more.

As such, APIs form a critical element of all kinds of chatbot development projects.

What is a WebSocket?

A WebSocket is another method for software tools to communicate with one another. However, it works slightly differently from a standard API.

WebSockets are a bidirectional communications protocol that enables low-latency, real-time data exchange between platforms, using a single persistent TCP connection.

This begins with a HTTP request from a client platform with Connection: Upgrade header. This requests for the server to upgrade to a TCP connection, initiating the WebSocket.

This is known as a handshake. Once a WebSocket is established, it is kept open until either the client or server closes the connection.

While the connection remains open, both the client and server can send data to each other. As a full-duplex connection, they can both send and receive data simultaneously and in real-time.

Messages are formatted using WebSocket frames. This can take the form of binary or any text data, such as strings, JSON, or XML. Binary data can be sent as Blobs or ArrayBuffers, enabling us to represent different types of media such as images or audio.

WebSockets are also stateful, meaning we can directly store state in our program for a given connection. In other words, context and information are retained across the session.

As we’ll see a little later, WebSockets are a crucial part of chatbot development, as well as other types of real-time, low-latency applications.

Key differences

To solidify our understanding of what chatbot APIs and WebSockets are, it’s helpful to outline some of the key functional differences between the two protocols.

That is, we can use what we’ve learned so far to identify the specific characteristics that differ between the two.

These include:

• Communication model - WebSockets utilize an event-based model. APIs are based on requests/responses.
• Directionality - WebSockets are bidirectional, whereas APIs are unidirectional, with only the client or server sending data at any one time.
• Persistence - Once initiated, a WebSocket remains open while APIs utilize individual requests/responses.
• Latency - WebSockets are designed for low-latency data transfer.
• Statefulness - API requests are stateless, while WebSockets are stateful, retaining information and context across open connections.

Of course, these are purely functional differences. In the following sections, we’ll refer back to some of these as we think more about the practical side of working with Chabot APIs vs WebSockets.

Pros and cons

To begin to elucidate how and when each communication method is used, we can start by thinking about some of the core benefits and challenges of working with each.

Once we understand their respective pros and cons, we’ll be in a better position to think about which kinds of use cases we’ll deploy each one for when building chatbots.

Chatbot APIs

First up, chatbot APIs. Some of the high-level benefits of using API requests in the context of chatbot tools include:

• Simplicity - APIs are relatively simple to design and work with, in an experience familiar to developers and other IT pros.
• Flexibility - Almost all platforms rely on APIs for integration, meaning we can connect to a huge range of tools.
• Consistency - APIs utilize consistent formatting and protocols, meaning we can use them across all kinds of platforms, without understanding the underlying systems.
• Scalability - APIs are highly scalable.
• Statelessness - APIs are stateless, meaning that each request is processed independently of others.
• Caching - RESTful APIs can implement caching, reducing loads on servers and improving performance.

However, these are all contingent on using APIs in the right context.

At the same time, there are a few important challenges and limitations to be aware of, including:

• Limited real-time support - API requests are generally not suitable for applications that require real-time communication between clients and servers.
• Performance overhead - Each individual request must include all of the information required to execute it, creating additional performance overhead.
• Latency - Chatbot APIs might encounter more latency while we wait for a response compared to WebSockets.
• Challenging for complex solutions - Designing, implementing, and managing complex solutions can require multiple API requests, which can be difficult to coordinate and oversee.

So, it’s important to understand which use cases are appropriate for API-based communications, and where we’ll want to look to other solutions, such as WebSockets.

WebSockets

Next, we can consider the relative strengths and weaknesses of WebSockets. Their high-level benefits include:

• Bidirectionality - WebSockets enable us to pass data back and forth between clients and servers with a single connection.
• Real-time data support - WebSockets are capable of facilitating real-time communication between applications.
• Statefulness and persistence - WebSockets are stateful and can retain information throughout a session.
• Efficiency - By persisting a connection, WebSockets can have lower overheads compared to chatbot APIs.
• Scalability - WebSockets can often handle thousands of connections per server.

But, as always, achieving these relies on utilizing WebSockets in appropriate contexts.

So, at the same time, we’ll want to be aware of the following challenges and limitations:

• Complexity - WebSockets are comparatively difficult to design and implement, potentially requiring additional development skills.
• Lack of support - Not all systems, or even browsers, support WebSockets.
• Security - WebSockets can introduce additional security considerations and vulnerabilities.
• Scalability challenges - Maintaining large numbers of persistent connections can lead to scalability issues.
• Lack of caching - WebSockets don’t support intermediary or edge caching, which can hinder performance compared to APIs.

With a good grasp of the relative pros and cons of both chatbot APIs and WebSockets, we can begin to flesh this out by considering some of the core use cases for each communication method.

Use cases

As we said earlier, both APIs and WebSockets have an important role to play in chatbot development.

So, our next task is to outline the specific tasks, use cases, and situations where each is most effective.

Chatbot APIs

In the context of chatbot solutions, APIs are typically leveraged in a few key ways. Of course, in large part, this relates to connecting chatbot tools to external systems.

There are essentially two configurations to this. Either a chatbot client sends an API request to an external system, or vice versa.

For example, a chatbot might make an API request to an external data source in order to return a piece of information that’s required to answer a user’s question.

For example, if the user wants to know what the weather forecast is, the chatbot will need to hit an appropriate API to get this information.

Alternatively, modern chatbots may use API requests in order to trigger actions in external systems. For instance, if a user asks the chatbot to take a particular action, or the chatbot independently reasons that this is required.

Take a look at our guide to agentic AI workflows to learn more about how this works.

Alternatively, external systems might need to use API requests to retrieve information or trigger actions via our chatbot’s server.

Naturally, what’s possible here depends on what’s supported by our chatbot solution. Common examples include things like querying analytics data, performing system functions, or any other tasks that our API server provides an endpoint for.

WebSockets

WebSockets are often fundamental to chatbot projects. By facilitating low-latency, real-time, bidirectional communications, they’re a key part of how developers build tools to enable users to communicate with AI.

To understand how this works, it’s helpful to consider the architecture behind chatbots, at least at a high level.

A basic chatbot features a UI - the client - where users can interact with our application. This is powered by a chatbot backend - the server - where inputs are interpreted and responses are generated, using AI.

WebSockets are ideal for handling communications between these two layers.

In fact, the characteristics and benefits of WebSockets that we’ve already seen are required in order to build effective chatbots.

For instance, we’ll need bidirectional, real-time connections between the client and server in order to seamlessly create experiences that mimic conversations with other humans via messaging platforms.

As well as this, WebSockets are capable of streaming responses. This means they can respond incrementally, enabling users to view the response as it is being generated, which is particularly useful for longer or more complex interactions.

For certain use cases, we might also want to connect to external platforms via WebSockets. For example, if we needed real-time data to use as context or as part of a response to a user query.

You might also like our guide to digital workers .

Approaches combining APIs and WebSockets

As you may have gathered, given the respective roles of chatbot APIs and WebSockets, it’s not the case that we should straightforwardly use one or the other.

In fact, most modern chatbot solutions will utilize both communication methods to a greater or lesser extent.

To wrap up, we can consider how this might work, drawing on what we’ve learned so far.

So, we could leverage WebSockets in order to handle the core communication between our chatbot’s client and server, enabling end users to send messages back and forth from a UI to our backend.

At the same time, our chatbot could connect to a whole range of external systems, services, and data sources via API requests.

For instance, to facilitate retrieval augmented generation (RAG) or tool calling within answers, or to send data from our chatbot to external systems, for monitoring, analytics, or other purposes.

As such, having a strong grasp of how both APIs and WebSockets work and how they can be used in the context of building a chatbot solution, we’re in a much stronger position to choose the appropriate communication methods for specific tasks and use cases.

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