ReAct agents vs function calling agents

Large Language Models (LLMs) are transforming how we interact with technology, enabling tasks like generating creative content, summarizing text, and answering questions. However, they have limitations. Their knowledge is frozen at the time of training, and they cannot directly interact with external systems like APIs or databases. This means they lack access to real-time information and cannot directly influence the physical world. To address these limitations, two frameworks have emerged: ReACT agents and function calling agents. These frameworks enable LLMs to reason, take action, and interact with external systems, expanding their capabilities significantly. This article explores both frameworks, comparing their approaches, benefits, and limitations to guide developers in choosing the best approach for their needs.

• What are ReACT agents?
• How do ReACT agents work?
• What are the key components of a ReACT agent?
• What are the benefits of using a ReACT agent?
• Use cases of ReACT agents
• What is function calling?
• How does function calling work?
• Building function calling agents
• Benefits of the function calling agents
• Use cases of function calling agents
• ReACT agents vs. function calling agents: A comparative analysis

What are ReACT agents?

The reasoning and action agent, abbreviated as the ReACT agent, is a framework that incorporates the reasoning capabilities of LLMs with the ability to take actionable steps. This means they can not only understand and process information but also take concrete steps based on their understanding. These agents can comprehend and process information, assess situations, take apt actions, communicate responses and track ongoing situations. They are, thus, designed to interact with the real world through actions, such as searching the web, accessing databases, or controlling physical devices while leveraging the language abilities of LLMs for planning, decision-making, and knowledge acquisition.

A key aspect of ReACT-based agents lies in how they are prompted. ReACT prompting is a crucial technique that guides the LLM to alternate between reasoning steps and actions. ReACT prompts can effectively teach the model to adopt a ReACT-like behavior by providing examples of how to think and act. For instance, a prompt might include multiple examples of a task, followed by the agent’s thought process, actions taken, and the observed outcomes. This structured approach helps the LLM learn to break down problems, seek information, and take appropriate steps.

How do ReACT agents work?

Unlike traditional AI systems that separate decision-making from execution, ReACT agents follow a continuous loop of reasoning and action. Here’s how ReACT agents work:

1. Input: An agent receives a task description in natural language, which is fed into the core LLM.
2. Reasoning: The LLM breaks down the task into smaller steps, analyzes the situation, considers available information and plans the actions required to complete it.
3. Action: Based on the reasoning, the LLM decides which tool to use (e.g., search engine, database, API) and executes actions to gather information or interact with the external environment. This might involve querying Wikipedia for relevant facts or retrieving data from a company database.
4. Observation: The agent observes the results of the actions and updates its knowledge accordingly. The agent also uses this new information to refine its reasoning in the next iteration.
5. Response: The agent generates a final response based on reasoning and the information gathered.

ReACT process is iterative. The agent continuously cycles through reasoning and action, refining its plan as it gathers more information from external environments like Wikipedia. Based on the new information, it might decide to adjust its strategy or explore different avenues to reach the desired outcome. The agent may choose to use different tools or modify its approach based on the feedback from the environment. By interacting with the external world, the agent can continually update its knowledge base, improving its reasoning and decision-making in subsequent iterations.

This cycle continues until the agent successfully completes the task or reaches a satisfactory conclusion.

What are the key components of a ReACT agent?

The ReACT agent model is essentially a framework that allows AI models to think and act. It breaks down complex problems into smaller steps, considers different options, and then takes action based on its analysis. To achieve this, it relies on several key components:

• The Brain (Large language model): This is the brain of the operation. It is responsible for understanding the problem, thinking through solutions, and deciding what actions to take. It can be any language model, be it Llama 3, Gemini, GPT-4o, PaLM 2 or Gemma, that meets the requirement of generating coherent and contextually relevant output.
• Tools: These are the external resources the ReACT agent can use to interact with the world. It could be anything from a search engine (to look up information) to a calculator (to do math problems). The tools are chosen based on the task at hand.
• Agent types: Different ReACT agents have been designed for specific tasks. These include ReACT_DOCSTORE, ZERO_SHOT_ReACT_DESCRIPTION, CONVERSATIONAL_ReACT_DESCRIPTION, SELF_ASK_WITH_SEARCH, and OPENAI_FUNCTIONS. Some are good at searching for information, while others are better at having conversations.
• Prompt engineering for reasoning and action: Carefully crafted prompts are crucial for guiding the LLM’s behavior within this framework. Effective prompts encourage the LLM to not only think through a problem but also to generate specific actions and use available tools. ReACT agents leverage these two specific prompting techniques to enhance output quality:
  • Chain-of-thought (CoT) prompting: This is a way to help the LLM think through a problem step by step. It is similar to making the LLM write down its reasoning process, which makes its decisions clearer and easier to understand.
  • ReACT prompting: It is a technique that guides the LLM to produce both a thought process (like in CoT) and a set of actions. This makes sure the agent is always focused on what it needs to do and how it should do it.
• Memory and knowledge: Agents need a way to retain the information they learn, both from the LLM’s initial knowledge and from their interactions with the world. This might involve databases, knowledge graphs, or other storage mechanisms.

So, the ReACT agent model combines these key parts to create a smart and capable system that can not only understand things but also take action in the real world.

What are the benefits of using a ReACT agent?

The ReACT agent framework is more than just a fancy name for a language model – it’s a real game-changer when it comes to making LLMs smarter and more useful. Here are some of the key advantages:

• Thoughtful problem solving: ReACT enhances large language models by enabling them to approach problems more like humans. It encourages breaking down complex tasks, reasoning through each step, and making informed decisions. This method is particularly effective for handling complex tasks that require extensive knowledge and careful planning.
• Integration with external tools: ReACT allows LLMs to use external tools, such as web search engines, calculators, or APIs. This capability significantly expands their potential to interact with the real world.
• Coordinated thinking and action: ReACT seamlessly integrates reasoning with action. The framework not only devises solutions but also executes them and learns from the outcomes. This continuous cycle of thinking, acting, and learning enhances the adaptability and reliability of ReACT agents.
• Adaptive learning: ReACT agents excel at adapting to new situations. They learn from mistakes, adjust their strategies, and manage unforeseen challenges, making them more resilient and effective over time.
• Transparency and trust: ReACT agents are designed with transparency in mind, allowing users to observe their reasoning processes and actions. This transparency builds trust and makes the agents easier to understand.
• Improved accuracy: By encouraging careful consideration before generating responses, ReACT often yields more accurate and reliable results, functioning as a built-in double-check mechanism.
• Versatility and explainability: ReACT agents are more versatile and interpretable than traditional LLMs. They can manage a broader range of tasks, and their reasoning processes are easier to follow, enhancing user-friendliness.

While ReACT agents may require more computational resources due to its additional reasoning and action steps, the resulting improvements in accuracy, reliability, and overall intelligence typically justify the cost.

Use cases of ReACT agents

ReACT agents, with their ability to combine reasoning and action, offer a wide range of applications. Here are some potential use cases:

Customer service

• Handling complex inquiries: ReACT agents can process intricate customer queries, access relevant information, and provide accurate and helpful responses.
• Resolving issues efficiently: By combining reasoning with actions like checking order status or initiating refunds, these agents can expedite problem resolution in customer service.

Information retrieval

• Answering complex questions: ReACT agents can tackle multi-hop questions by breaking them down into smaller steps, searching for relevant information, and synthesizing answers.
• Summarizing complex documents: By understanding the content of a document, ReACT agents can generate concise and informative summaries.

Personal assistants

• Scheduling and planning: ReACT agents can assist users in managing their schedules, setting reminders, and planning events.
• Providing recommendations: By analyzing user preferences and available options, these agents can offer personalized recommendations for products, services, or activities.

Education

• Personalized tutoring: ReACT agents can adapt to individual learning styles and provide tailored explanations and exercises.
• Grading and feedback: By analyzing student responses, these agents can offer constructive feedback and assess performance.

Other potential applications

• Financial analysis: ReACT agents can process financial data, identify trends, and generate insights.
• Medical diagnosis: By combining medical knowledge with patient data, these agents can assist in diagnosis and treatment planning.
• Creative writing: ReACT agents can generate creative text formats such as poems, scripts, code, musical pieces, emails, letters, etc.

It’s important to note that these are just a few examples, and the potential applications of ReACT agents are vast and continually expanding as the technology evolves.

What is function calling?

Function calling is a powerful technique that extends the capabilities of large language models beyond text generation, allowing them to interact with external tools and APIs in a structured way. It enables you to equip an LLM with a set of functions, also known as tools, which represent actions the applications connected to the models can perform. These functions can be anything from making API calls, querying databases, executing code, or even accessing external knowledge sources.

Function calling allows models to interact with external tools or APIs by generating the necessary parameters to call specific functions. These LLMs do not execute the functions themselves but provide the arguments needed for them, which are then executed by the application or system they’re integrated with.

Popularized by OpenAI, function calling has become a standard feature in many language models and frameworks. By leveraging this capability, developers can create a wide range of custom agents and solutions that enhance productivity, efficiency, and user experience.

How does function calling work?

When you ask an LLM a question or give it a task, the LLM analyzes the request and identifies the most relevant function to call based on its training data, the prompt provided and any available context. The LLM does not actually execute the function itself; it simply outputs a JSON object containing the function name and arguments (the necessary input parameters). Your application then takes this JSON output, executes the function, and sends the result back to the LLM. This process enables the LLM to integrate with external systems and act on your behalf.

Let us understand how it works through an example.

Imagine you have a chatbot that acts as a travel assistant. You ask the chatbot, “What’s the weather like in Paris next week?” The chatbot needs to access a weather API to get the information. Here’s how function calling makes this possible:

1. The LLM analyzes the prompt: The LLM understands you’re asking about weather, and it knows there’s a tool (function) called get_weather that can fetch weather data from an API.

2. The LLM outputs a JSON object: The LLM sends you a JSON object like this:

{
"function": "get_weather",
"arguments": {
"location": "Paris",
"date": "next week"
}
}

This JSON tells your chatbot to call the get_weather function, providing the location (“Paris”) and the date (“next week”) as input.

3. Your application executes the function: Your chatbot application receives the JSON, identifies the get_weather function, and calls it with the provided arguments. It then sends a request to a weather API, providing “Paris” and “next week” as parameters.

4. The API returns a response: The weather API receives the request and returns a response, potentially including information like temperature, humidity, and precipitation for Paris next week.

5. The LLM receives the response: The API response is sent back to your chatbot, which then sends it back to the LLM.

6. The LLM generates a human-readable response: The LLM now has the weather information for Paris next week. It generates a human-readable response like, “The weather in Paris next week is expected to be…” using the specific details from the API response. This response is passed to the chatbot application, which users can access.

Building function calling agents

Let’s break down the process of creating agents based on function calling:

1. Define tools: Identify the functions (tools) you want your LLM to use. Each tool should have a descriptive name, a clear description of its purpose, and a well-defined schema for its input parameters (often defined using JSON Schema).
2. Provide tools to the LLM: Pass the tool definitions to your chosen LLM. Most LLMs now support function calling, including OpenAI’s GPT models, Google’s Gemini, and Anthropic’s Claude.
3. Prompt the LLM: Ask the LLM a question or give it a task. The LLM will analyze the prompt and identify which tool (function) is most appropriate.
4. Generate tool call: The LLM outputs a JSON object containing the name of the tool to call and the required arguments.
5. Execute the tool: Your application takes the LLM’s output, finds the corresponding function, executes it with the provided arguments, and retrieves the result.
6. Return results to LLM: The applicaion sends the result of the tool execution back to the LLM. This allows the LLM to use the new information in its final response to the user.
7. Generate final response: The LLM incorporates the tool results and provides a human-readable response to the user.

Benefits of the function calling agents

Function calling agents represent a significant leap forward in the capabilities of LLMs, offering a wealth of benefits that extend beyond simply providing information. The benefits of function calling include:

• Beyond the training data: LLMs can now access up-to-date information from external sources like APIs, databases, and knowledge bases, surpassing the limitations of their original training data. This makes them more relevant to current events and evolving information.
• Actionable intelligence: LLMs are empowered to perform actions in the real world. They can interact with external systems, execute tasks, and even influence physical systems, going beyond simply providing passive responses.
• Structured output: Function calling uses structured JSON for communication, ensuring consistency and minimizing errors in interpretation.
• Targeted data: LLMs can precisely request specific information through function calls, eliminating the risk of receiving irrelevant or misleading information.
• Personalized assistance: By accessing user-specific data, LLMs can offer tailored recommendations and assistance, catering to individual preferences and contexts.
• Modular design: Function calling promotes modularity in development, allowing developers to easily integrate new tools and functionalities without disrupting existing code.
• Reusable tools: Functions can be defined and reused across different applications, promoting efficiency and code reusability.
• Beyond chatbots: Function calling enables the creation of sophisticated agents that can perform complex tasks, automate workflows, and assist in various industries.
• Autonomous decision making: LLMs can make intelligent choices about which tools to use, rather than relying solely on their pre-trained knowledge.

Use cases of function calling agents

Function calling agents open up a world of possibilities, transforming LLMs from passive knowledge providers to active problem solvers and assistants. Here are some compelling use cases across various domains:

1. Customer service and support:

• Personalized assistance: Chatbots equipped with function calling agents can access customer databases, retrieve order details, shipping information, or account balances. They can provide tailored support, address specific queries, and even offer proactive solutions based on past interactions.
• Efficient issue resolution: Bots can interact with internal systems to troubleshoot issues, check inventory, or escalate tickets to the appropriate team, streamlining the customer service workflow.

2. Travel and tourism:

• Travel planning: Function calling agents can access travel APIs to compare flight options, book hotels, suggest itineraries, and even provide real-time updates on flight delays.
• Personalized recommendations: By integrating with travel review platforms, function calling-based applications can offer tailored restaurant, attraction, and activity recommendations based on user preferences and budget.

3. Research and knowledge management:

• Academic research: Function calling agents can access academic databases, extract relevant papers, summarize key findings, and even generate citations. This empowers researchers to quickly gather information and build knowledge graphs.
• Document analysis: LLMs with function calling capability can be used to analyze complex documents, extract key information, and generate summaries or reports, facilitating faster and more efficient knowledge acquisition.

4. Business automation:

• Workflow automation: With function calling, LLMs can automate tasks like scheduling appointments, creating invoices, or sending reminders, freeing up human employees for more complex work.
• Data extraction and analysis: LLMs that possess function calling features can extract data from unstructured documents, tables, and forms, analyze trends, and generate insights, helping businesses make data-driven decisions.

5. Healthcare and life sciences:

• Medical diagnosis support: LLMs that support function calling can access patient medical records, analyze symptoms, and suggest potential diagnoses, aiding healthcare professionals in their decision-making.
• Drug discovery and development: Function calling agents can accelerate the drug discovery process by searching through vast databases of scientific literature, identifying potential drug candidates, and simulating their effects.

6. Smart home and IoT:

• Voice control: LLMs with function calling capability can be used to control smart home devices, adjust thermostat settings, dim lights, or even order groceries based on voice commands.
• Predictive maintenance: Applications with function calling capability can analyze data from sensors to predict potential failures in appliances or systems, allowing for proactive maintenance and preventing expensive downtime.

7. Education and training:

• Personalized learning: Function calling agents can adapt their teaching style and content based on individual student needs, providing personalized lessons and targeted feedback.
• Interactive learning: Function calling can enable LLMs to create interactive simulations and games that enhance learning and engagement.

Function calling agents are rapidly expanding the potential of LLMs, offering powerful solutions for a wide range of applications. As these agents continue to evolve, we can expect to see even more innovative and transformative uses across various industries.

ReACT agents vs. function calling agents: A comparative analysis

Both ReACT and function calling agents are powerful frameworks that extend the capabilities of LLMs, allowing them to interact with the real world. However, they differ in their approach and specific strengths. Here’s a comparative analysis:

Additional information

• ReACT agents
  • ReACT agent is more flexible than function calling agent, as it doesn’t rely on pre-defined functions. The LLM can choose its own actions based on its reasoning.
  • ReACT agents are often used in scenarios where the LLM needs to gather information from multiple sources or perform complex reasoning tasks.
  • ReACT agents are still under development, and there are ongoing efforts to improve their efficiency and robustness.
  • ReACT agents can be accessed through frameworks and platforms like LangChain and ZBrain or by building custom implementations with LLMs, tools, and reasoning mechanisms.
• Function calling agents
  • Function calling agent is generally easier to implement as it relies on pre-defined functions and their parameters.
  • Function calling agents are ideal for tasks that involve well-defined actions and external APIs, such as data extraction, automation, and interaction with specific systems.
  • Function calling agents are already being widely adopted by LLM providers like OpenAI and Google.
  • Function calling agents can be accessed through frameworks and platforms like LangChain and ZBrain or by building custom implementations using LLMs like OpenAI’s GPT-4o.

Ultimately, the choice between ReACT and function calling agents depends on the specific task at hand. If the task involves complex reasoning and actions that are not easily defined as functions, ReACT agents might be a better choice. If the task involves calling specific APIs or executing well-defined actions, function calling agents could be more suitable. Both approaches offer powerful features for extending the capabilities of LLMs and opening up new possibilities for human-machine collaboration.

Final thoughts

ReACT and function calling agents represent two distinct but powerful approaches to extending the capabilities of LLMs, each with its own strengths and weaknesses. ReACT agent excels in complex, open-ended tasks requiring multi-step reasoning, while function calling agent is ideal for interacting with specific APIs and executing well-defined actions. Ultimately, the choice between these frameworks depends on the specific task at hand and the desired level of flexibility and control. As LLM technology continues to evolve, both approaches are poised to drive innovation and unlock new possibilities for human-machine collaboration.

Whether you want to enhance decision-making capabilities or integrate AI with external systems, LeewayHertz’s expert team can help you build intelligent, responsive agents that drive real-world impact. Contact us to start developing your AI agent-powered solutions today.