7 Best AI Agent Builders in 2024

The Large Language Models (LLMs) have taken centre stage in every conversation, and rightfully so. Given their exceptional reasoning and decision-making capabilities, these models can understand complex problem statements. However, confining them to a chat interface does not do justice to their potential. LLMs can not only understand problems but can also decompose and solve them. Also, with React and the function calling method, language models can use AI Agent tools like web search, file systems, and knowledge bases to solve problems requiring external environment interaction. This new workflow of augmenting LLMs with tools is called AI agents. The AI agents could solve problems that were not possible with vanilla language models. This led to the proliferation of frameworks for building AI agents.

This article discusses AI agents, their impact across industries, and frameworks for building them.

Learning Objectives

  • Understand what AI agents are.
  • Learn about AI agent builders.
  • Explore open-source frameworks for building AI agents.
  • Learn how AI agents can be beneficial in sales and marketing.

What are AI Agents?

AI agents are systems powered by AI models that can autonomously perform tasks, interact with their environment, and make decisions based on their programming and the data they process. The agents can receive input from their environment via sensors or software integrations, and with the help of the decision-making prowess of AI models, they can act to influence it. The input data could be texts, images, audio, or videos. The AI model, typically an LLM (Large Language Model) or an LMM (Large Multi-modal Model), is responsible for interpreting the data and taking the necessary steps to achieve a given task.

The Custom AI agents can be hardware or software-based. Robots and self-driving cars are some of the examples of hardware-based AI agents. Hardware-based agents have sensors and actuators to receive information and act on them. A self-driving car understands its surroundings by using a combination of cameras, LIDAR, RADARs, etc. An AI model processes the received information and actuators such as the steering wheel, brakes, and other mechanical components help execute the tasks. The AI model receives information via camera, mic, or text prompts in software-based agents. The model understands the input information and uses tools to accomplish a task. The agents use databases for long-term memory.

What are AI Agent Builders?

While AI agents offer numerous benefits and conveniences, building reliable and effective AI agents is challenging. The stochastic nature of language models often leads to inconsistent results, making it difficult to develop dependable agents. Additionally, many use cases require agents to access data stores, vector stores, or external applications. Over the past year, several agent builders have emerged to address these challenges, each with a unique approach and philosophy.

Agent builders or frameworks aggregate the necessary components to create reliable and capable agents. These components include data stores to access external data, data loaders, parsers, vector stores for indexing and querying vectorized data, and SQL and NoSQL databases for storing and querying data.

Developers can streamline building AI agents by using these agent builders, ensuring they have the tools and infrastructure needed to succeed.

Popular AI Agent Builders

The agent builders provide a unified approach to creating AI agents. In this article, we will discuss some of the popular open-source agent builders.

1. LangChain

LangChain has become the go-to platform for building AI agents. It supports popular vector stores, databases, tools and applications, parsers, and document readers. Developers can build AI agents by chaining LLMs with vector stores, tools, and dataloaders. LangChain has two value propositions.

  • Components: Composable and modular building blocks, tools, and integrations for building AI agents. It allows developers the flexibility to create custom workflows for executing complex agent tasks.
  • Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks.

Besides the integrations, LangChain also has an expression language called LangChain Expression Language (LCEL). It is a declarative way to compose chains together. The purpose of LCEL is to go from prototype to production faster.

2. LangGraph

LangGraph is another extension of the LangChain ecosystem. While LangChain allows for building AI agents with multiple tools and integrations, it cannot coordinate multiple agents to accomplish tasks. Collaboration among AI agents is crucial for building complex tasks. LangGraph makes up for this drawback in LangChain. It treats the Agent workflows as a cyclic Graph structure, where each node represents a function or a Langchain Runnable object, and edges are connections between nodes.

The LangGraph defines agentic workflows as state machines. The key components of LangGraph agents are

  • States: A shared data structure that keeps track of current snapshots of the AI agent. It can be TypedDict or a Pydantic BaseModel.
  • Nodes: Python functions that wrap up the logic of the agents. They receive the current State as input, operate on it, and return the updated state.
  • Edges: Edges direct the flow of execution based on the current state, the bridges between two different Nodes. They can be conditional branches or fixed transitions.

After an execution, the Nodes return their updated state to the graph. Based on the defined transitions, this updated state can be passed along the edges to other nodes. This cyclical and dynamic interaction allows the graph to continuously evolve and adapt based on the inputs and outputs of each node, effectively orchestrating complex and interdependent tasks across multiple agents.

Pregel, a system for large-scale graph processing, inspires the structural and operational architecture of LangGraph. The conceptual guide provides a detailed flow of LangGraph.

3. CrewAI

CrewAI is an open-source framework for building collaborative multi-agent systems. It allows developers to build complex agentic automation workflows where interaction among multiple agents is paramount. CrewAI allows individual AI agents to assume roles, delegate tasks, and share goals akin to a real-world crew. CrewAI mainly consists of five core features: Agents, Tasks, Tools, Processes, and Tasks.

  • Agents: Agents operate as autonomous entities tasked with reasoning, delegating tasks, and communicating with fellow agents, much like a team in the real world.
  • Tasks: Tasks are precise assignments allocated to agents. They outline the steps and actions required for an agent to achieve a specific goal.
  • Tools: Tools equip agents to carry out tasks that exceed the capabilities of LLMs, such as web scraping, email responses, and task scheduling.
  • Process: In CrewAI, processes manage the execution of tasks by agents, ensuring that tasks are allocated and performed effectively and systematically. These processes can be sequential, where tasks are completed one after another, or hierarchical, where tasks are carried out based on a tiered authority structure.
  • Crews: Crews within CrewAI consist of collaborative agents equipped with tasks and tools who work together to tackle complex tasks.

4. Autogen

Autogen is an open-source framework from Microsoft that enables the development of LLM applications by automating coordination and conversation between multiple LLM agents to solve complex tasks. Autogen agents are customizable, conversable, and can easily accommodate humans in the loop. It enables a variety of agent-building methods that integrate LLMs, tools, and human inputs.

Furthermore, Autogen agents are extensible and composable. you can extend simple agents with customized components like tools and integrations and create workflows that combine these agents to form a sophisticated agent.

5. Llama Index

Yet another open-source data framework for integrating external knowledge bases to LLMs. Despite being trained on vast datasets to serve organizational needs, they still require access to private data to function effectively. The Llama Index makes it easier to utilize data from various sources in various formats. It facilitates data ingestion, indexing, and querying to build LLM applications.

Llama Index allows easy integration of data stores, vector stores, graph stores, and SQL DB providers. Llama Hub has tools and integrations required for building advanced RAG apps and AI agents.

6. Julep

Julep is an open-source platform for building, managing, and deploying AI applications with advanced capabilities. It offers features to streamline the development process, such as stateful conversation management, support for various user-agent interaction models, and built-in retrieval-augmented generation (RAG). Julep allows seamless integration with over 90 third-party applications via Composio, facilitating smooth and efficient tool integration. Designed with flexibility and ease of use in mind, Julep enables developers to switch between different LLMs on the fly and deploy their applications locally or in production environments, making it an ideal solution for creating sophisticated and efficient AI-powered systems.

7. Griptape

Griptape is another open-source framework for building AI agents. It enforces structures like sequential pipelines, DAG (Directed Acyclic Graph)–based graphs, and long-term memory. It also allows developers to add tools to extend the agent’s flexibility.

Griptape’s design philosophy is based on the following tenets:

  1. Modularity and composability: All framework primitives are independently useful and usable and are easy to plug into each other.
  2. Technology-agnostic: Griptape is designed to work with any capable LLM, data store, and backend by abstracting drivers.
  3. Keep data off the prompt by default: When working with data through loaders and tools, Griptape aims to keep it off the prompt by default, making it easy to work with big data securely and with low latency.
  4. Minimal prompt engineering: It’s much easier to reason about code written in Python, not natural languages. Griptape aims to default to Python in most cases unless necessary.

How AI Agents Deliver Significant Benefits to Sales and Marketing

AI agents can be used in sales and marketing in various ways. They can automate many repetitive tasks and work in tandem to accomplish complex tasks involving reasoning and decision-making abilities.

Here are some examples of where AI agents can be beneficial.

  • Personalized Customer Experience: AI agents can analyze customer data, such as past purchases, browsing behaviour, and social media interactions, to tailor marketing messages and offers to individual preferences. This level of personalization can increase the effectiveness of campaigns and improve customer satisfaction.
  • Automated Content Creation: AI agents can be used to automate social media content workflow from creating content to uploading and monitoring the performances. AI agents can speed up content marketing.
  • Email Marketing Optimizations: AI agents can optimize email marketing by personalizing each email sent based on known user data.
  • Competitor Research: AI agents can be used to research competitors in a particular domain. By collecting publicly available scattered data, agents can run analyses and prepare reports.
  • Lead Background Research: AI agents can be used to research the backgrounds of leads to enrich CRM and improve customer understanding. The agents can take users’ non-Gmail email IDs (john@doe.com) and prepare a report about the company, their ARR, industry, etc.

Challenges & Limitations of AI Agent Builders.

Discover the intricate landscape of AI agent builders, where cutting-edge technology meets formidable challenges in scaling language capabilities, optimizing healthcare recommendations, and automating complex tasks like content generation and market analysis.

  1. LangChain: LangChain faces challenges in scaling natural language understanding across diverse languages and contexts while maintaining accuracy and relevance. Limitations include the need for extensive data preprocessing and the complexity of capturing nuanced linguistic variations.
  2. Langraph: Langraph’s challenge lies in constructing robust semantic graphs that accurately represent the complexities of language. Limitations include the difficulty in managing graph size and complexity as well as ensuring real-time updates and responsiveness.
  3. Crew AI: Crew AI encounters challenges in automating complex crew management tasks while ensuring regulatory compliance and optimizing crew scheduling. Limitations include integrating with existing crew management systems and seamlessly handling real-time data updates.
  4. Autogen: Autogen faces challenges in generating high-quality, contextually relevant content at scale while maintaining coherence and originality. Limitations include the risk of producing repetitive or inaccurate content without human oversight and the difficulty in handling niche or specialized topics.
  5. Llama Index: Llama Index’s challenge lies in accurately predicting market trends and sentiment using AI-driven algorithms. Limitations include data accuracy and bias, the volatility of financial markets, and the need for continuous model adaptation to changing market conditions.
  6. Julep: Julep faces challenges in developing AI-powered solutions for personalized healthcare recommendations while ensuring privacy and regulatory compliance. Limitations include the complexity of integrating with electronic health records (EHR) systems and maintaining patient trust in AI-driven medical advice.
  7. Griptape: Griptape encounters challenges in automating software development tasks, such as code generation and bug detection, while ensuring code quality and security. Limitations include the variability in coding styles and languages, as well as the need for extensive training data to cover diverse software engineering scenarios.

Conclusion

We are witnessing an unprecedented rise in the development of AI agents and related technologies. The agents can autonomously interact with external applications to accomplish mundane and repetitive tasks. With the advancements in the quality of large language models, AI agents can handle even more complex tasks. However, to leverage the potential of LLMs, you might need a unified approach to integrate various tools and techniques with the LLMs.

Frameworks like LangChain, LangGraph, CrewAI, Autogen, Llama Index, and Julep offer the comprehensive infrastructure needed for this integration. These platforms provide modular components, robust APIs, and seamless interoperability with external systems, enabling developers to create AI agents that are not only capable but also versatile and scalable.

FAQs:

What is AI Builder used for?
AI Builder integrates various tools, data stores, and techniques to develop reliable and effective AI agents. It simplifies the creation and deployment of AI agents, enabling them to perform complex tasks and interact seamlessly with external applications.

What are the benefits of AI Builder?
The benefits of AI Builder include improved efficiency by automating repetitive tasks, enhanced personalization through data analysis, higher availability with 24/7 operations, and scalability to meet increasing demands without additional human resources. It also provides a unified approach to integrating necessary components for building capable AI agents.

What is the purpose of AI builder?

AI Builder provides the capability to create custom models or train the model according to your business needs.

Is Builder AI worth it?

Builder.ai might be cheaper than hiring developers, but it’s less time-consuming than using a no-code tool to build your app.

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