Everyone from indie hackers to Fortune 500s is building AI agents. A 2025 Gartner report confirms this shift. It states that by the end of the year, over 40% of enterprises will have dedicated teams for AI agent development.
But with so many frameworks popping up, which one actually works best? We tested the top frameworks to find out.
An AI agent framework is the software layer that turns LLMs into goal-oriented agents.
It handles all the annoying plumbing so you don’t have to, like:
By providing this scaffolding, a framework lets you stop worrying about the low-level mechanics and start focusing on the interesting part: what your agent should actually do.
Imagine going from building one car to managing a whole Formula 1 pit crew. That’s what a multi-agent framework does.
Essentially, a multi-agent framework is a system for creating a team of specialized AI agents that can work together, communicate, and delegate tasks to solve a problem that would be too big for any single agent.
Let's use a real-world example: planning a marketing campaign. You could have:
A multi-agent framework manages the intricate dance between them, ensuring the researcher passes its findings to the director, who then briefs the copywriter. It's about orchestrating collaboration at machine speed.
Honestly? To save your sanity. Anyone who has tried to build an agent from pure API calls knows the pain. Ever tried to parse a messy JSON output from an LLM that decided to add a friendly but code-breaking preamble? Or build a reliable loop that doesn't spiral into an expensive, infinite vortex of API calls?
Frameworks are born from these exact struggles. Using a framework means you’re building on top of thousands of hours of trial-and-error from the community. You get speed, reliability, and the peace of mind that comes from not having to reinvent the wheel.
Enterprises are moving from RAG (Retrieval-Augmented Generation) to agent-based architectures because of one key realization:
RAG is great at answering questions. But agents can get things done.
RAG systems, like chatbots with access to external knowledge, are designed to retrieve and summarize. They’re reactive. You ask a question, and they give you an answer. But agents are different.
AI agents are goal-driven. They:
For example, an agent might use RAG as one of its tools, but it doesn’t stop there. After reading, it can pick up the phone (call an API), use a calculator (run a Python script), and send an email (use a Zapier integration).
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PRS is one of the classic blueprints for an autonomous agent. Think of it as the AI equivalent of a chef's muscle memory. The chef has a set of beliefs (the current state of the kitchen, what ingredients are fresh) and a library of plans or procedures (recipes). Based on the goal ("make a risotto"), the chef instinctively executes a series of steps, reacting to changes in the environment, like the heat of the pan. It's one of the earliest and most influential models in agent reasoning.
If PRS is about muscle memory, the Belief-Desire-Intention (BDI) model is where agents start to "think" more like us. It’s a fantastic model for practical reasoning:
This structure allows an agent to focus on a committed plan (its intention) without being constantly distracted by all its other desires.
dMARS is a famous, real-world implementation of the BDI model. While it's an older system, its relevance today is that it proved these concepts could work in complex, distributed environments—like controlling a fleet of autonomous vehicles. It’s part of the solid academic foundation that makes modern frameworks possible.
The Open Agent Architecture (OAA) solved a huge problem: how do you get specialized agents, maybe even written in different programming languages, to talk to each other? OAA created a standardized communication layer—a "universal translator." It's like the Babel Fish from The Hitchhiker's Guide to the Galaxy, allowing a Python-based data agent to seamlessly delegate a task to a Java-based notification agent.
The rule of thumb is simple: if you need to automate a multi-step process, not just a single Q&A, you should be looking at an agentic framework.
When: Your goal involves interacting with the outside world (APIs, databases, filesystems), requires planning and self-correction, or could benefit from breaking a large task down into smaller sub-tasks.
How: Don't try to boil the ocean. Start with a very narrow, specific goal. Map out the steps a human would take to achieve it. Then, find the tools and agents that can replicate that process. Iterate and expand from there.
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Enterprises are moving fast into agent-based systems, and five frameworks stand out in 2025: LangChain is the most flexible framework for developers building custom agent logic with tools and memory.
Voiceflow is a visual platform, which means you can build a powerful AI agent without writing a single line of code. This guide will create a simple Customer Support Agent that can answer questions about a company's return policy.
Start on the Canvas. Drag a Text step for your agent's greeting (e.g., "How can I help?"). Next, drag a Listen step to capture the user's reply. Finally, connect the two steps with an arrow to create your basic flow.
Go to the Knowledge Base tab and upload your data (e.g., paste your return policy). Back on the Canvas, drag an AI Response step after your Listen step. In the prompt field, simply tell the AI to answer the user's last message using the knowledge base.
Click the blue Run button to open the Prototype window. Ask a question related to the data you uploaded (e.g., "How long do I have for a return?"). The agent will use your document to generate a precise answer in real-time.
And that's it! In just a few minutes, you've built and tested a knowledge-aware AI agent without writing any code.
In short, to save time and avoid common pitfalls. Frameworks give you a battle-tested structure for things like memory, tool use, and error handling. This lets you focus on your application's unique logic rather than spending months building and debugging the foundational plumbing.
They typically include LLM connectors (to easily swap models like GPT or Claude), tools for managing short-term and long-term memory, parsers for interpreting what the model says, and an agent "executor" or "runtime" that manages the main loop of reasoning and taking action.
CrewAI and LangChain are all fantastic open-source choices. Your best bet depends on your goal: CrewAI is great for collaborative workflows, while LangChain is the incredibly flexible and mature incumbent.
Start with your business goal, not the tech. First, define the process you want to automate. Then, consider your team's skills (e.g., Python expertise vs. design and product). This will naturally guide you to the right choice, whether it's a code-first framework or a low-code platform like Voiceflow.
Absolutely. Voiceflow is the leader in this space. It provides a visual, collaborative platform that allows teams with varying technical skills to come together to design, build, and launch sophisticated, enterprise-ready AI agents.
Want to explore how Voiceflow can be a valuable resource for you? Let's talk.
