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There’s a huge amount of hype around AI agents. Every day, we hear new reasons why they’re going to transform just about all aspects of daily life. But, practical information about how to make this a reality is less forthcoming.

The biggest question here is, how can we build agentic solutions of our own?

Today, we’re exploring an important subset of this by checking out the market for open-source agent builders.

Of course, this is a new, fast-moving space, with vendors rapidly bringing new solutions to market. While this brings huge opportunities for early adopters, it also makes it more challenging to assess our requirements and which tools are best positioned to meet them.

In this guide, we’ll give you the information you need to make a fully educated decision, including:

• What is an AI agent platform?
• What kinds of AI agent builders are available?
• Why opt for an open-source solution
• Top 6 open-source AI agent platforms

Let’s start with the basics.

What is an AI agent platform?

As the name suggests, an AI agent builder is any software tool that helps us to build, manage, and deploy AI agents.

Despite being a relatively new field, there’s already considerable variety across the market, with different vendors targeting a range of more specific use cases and personas.

However, they share the core use case of helping us to integrate various AI and non-AI tools to provide the logic, reasoning, memory, and actions required for use to create systems that can act autonomously.

In other words, agent builders do the heavy lifting of connecting language models, storage, external tools, APIs, and reasoning steps. Most also assist with orchestration, tool-chaining, persistent memory, goal tracking, and multi-agent collaboration.

Ultimately, the goal is to make the process of building AI agents faster and more accessible, both for experienced specialists and teams with less experience working with AI.

This works by enabling teams to focus on underlying goals and workflows, rather than building all aspects of their agents from scratch.

Take a look at our guide to AI agentic workflows to learn more.

What kinds of AI agent builders are available?

We mentioned a second ago that there’s quite a bit of variety across the market for AI agent builders. As a new technology, different vendors are trying to make their mark by targeting specific types of user personas.

This is because AI agents can mean different things to different people.

For example, a developer will likely require fine-grained control over how agents use tools, chain reasoning steps, and persist memory across steps. On the flip side, less technical teams may want a more visual experience for automating common, repetitive tasks.

To reflect these differences, we can point to three broad categories of AI agent builders:

1. Developer frameworks - Core libraries that expose the basic building blocks of an AI agent, including integrations, models, memory, and reasoning, via code, offering full customization at the cost of more manual setup.
2. Visual and low-code builders - Drag-and-drop or declarative tools that allow users to define agent behavior and workflows more quickly without extensive custom code.
3. End-to-end agent infrastructure platforms - Complete environments handling agent design, deployment, orchestration, and observability, enabling us to build production-ready agentic systems.

Each of these plays a distinct role, assisting distinct types of users to create AI agents for their own purposes. We’ll see examples from across the market a little later when we come to our top picks for AI agent platforms.

Why opt for an open-source solution?

Before we examine our options, it’s also important to consider some of the reasons that teams prioritize open-source solutions for building AI agents over closed-source equivalents.

Open-source solutions offer a range of potential benefits, which are particularly important in the context of AI agents, including:

• Licensing costs - Many open-source tools are free to use, making them a budget-friendly option for teams to experiment with agentic AI.
• Full customization - Depending on the specific license, access to source code can enable us to tweak the source code to perfect logic, integrations, and behavior to our specific needs.
• Transparency & security - Open-source solutions enable us to audit code, control updates, and manage our own data, making them ideal for use cases with tight security requirements.
• Avoiding vendor lock-in - Open standards, source-code, and portability can help to prevent us being locked in to one particular vendor or ecosystem.
• Active communities - Contributions from real-world users can mean faster developments, better documentation, and more alignment with real-world challenges.
• Easy integrations - Open-source tools typically offer a high level of connectivity for external platforms, including LLMs, tools, and APIs.

Top 6 open-source AI agent platforms

Now that we have a good grasp of what the market for open-source AI agent platforms looks like and what we’re looking for, we can begin to think about some of the top options available today.

As we said earlier, we’ve selected a range of options, from flexible developer-first frameworks to more accessible tools for building agents.

Here’s a summary of our top picks.

Let’s dive in to see what each one offers.

1. LangChain

First up, we have LangChain, perhaps the best-known and most widely adopted framework for building agentic workflows with LLMs. It’s a Python framework for building agents on top of existing LLMs.

Pros

One of LangChain’s biggest selling points is the granularity of its agent control, including a modular and composable architecture, with swappable elements like chains, memory, and tools, enabling custom logic, behavior, and reasoning.

LangChain is highly modular and works seamlessly alongside existing Python toolstacks. This offers flexible and easy deployment across apps, notebooks, production APIs, and other frameworks.

On top of this, LangChain benefits from a large, active community of users and developers. This helps to position it as one of the strongest players in this space for learning materials, pace of development, and community-driven improvements.

Cons

Many of the challenges of working with LangChain stem from its position as a more developer-focused platform. As such, it may present higher technical knowledge barriers than some competitors, requiring skills with Python, software development, and working with APIs.

On top of this, some users note that LangChain’s rich integration set leads to a heavy dependency footprint, including a variety of optional packages for models, vector databases, and other tools.

This may lead to issues with installation and management, especially for less technical teams. Similarly, some users report that building logic is a somewhat complex experience, due to LangChain’s layered abstractions. This may make more streamlined solutions more viable for such teams.

Licensing

The core LangChain framework is offered under the MIT license. This means that it is entirely free to use, modify, and distribute, without restriction.

However, there are two commercial services offered alongside this. LangSmith is a hosted service for tracing, debugging, and evaluating agent runs. It offers a free tier for a single user, with paid plans starting from $39 per user per month for up to 10,000 traces.

LangGraph is a graph-based orchestration framework build on top of LangChain. This is also open-source, although commercial licenses are available for enterprises too. Costs aren’t advertised, but this introduces premium support, SLAs, and hosting options.

2. CrewAI

Next up, we have CrewAI. This is another highly popular open-source AI agent platform, but compared to LangChain it takes a slightly different approach. That is, it focuses on providing code-based and visual experiences for building and managing multiple agents.

Pros

Compared to LangChain and similar platforms, one selling point of CrewAI is that it offers tools such as CrewAI studio, which make it more feasible for non-technical colleagues to create working agentic systems, using no-code development and templates.

On top of this, it’s a highly effective platform for creating solutions comprising multiple task-specific agents working together in crews. We can assign each agent its own role and responsibilities, making it easier to create coherent, predictable systems.

CrewAI is also highly effective from the point of view of reusability, offering a combination of prebuilt tools, requests to external services, event-driven flows, or connectivity for browser actions.

Cons

There are also aspects of working with CrewAI that some teams might find challenging. For one thing, its event-based architecture can introduce complexity, including when managing states and control flows. This might be out of reach of less technical teams with simpler use cases.

Similarly, CrewAI is a great option for scenarios that require multiple agents working in tandem, but the resources that are required to implement this might also make more straightforward solutions a better fit for basic workflows.

Lastly, although CrewAI offers a highly workable set of pre-built templates, some other platforms have a more extensive offering here, which might make them more suitable for getting started quickly with certain use cases.

Licensing

Like LangChain, CrewAI can be used, modified, and distributed with minimal restrictions under the open-source MIT license.

There are also commercial licenses available, although in-depth information about this is not publicly advertised.

Instead, at present, this is offered on an enterprise basis, meaning you’ll need to contact CrewAI directly to learn more about additional services or functionality that might be available.

3. AutoGPT

AutoGPT markets itself as a platform that empowers small businesses, non-technical colleagues, and developers alike to create AI agents using low-code tools.

Pros

AutoGPT is a low-code open-source agent platform, enabling teams to rapidly connect tools within workflows. This makes it a good option for non-developers who want to take advantage of the power of agentic AI.

It’s built around a visual, block-based drag-and-drop editor, which makes it easy to configure inputs, outputs, and actions, with relatively low technical skill barriers.

In particular, it’s a great pick for teams that want to deploy continuous agents to the cloud, that will run indefinitely and react based on defined triggers.

Cons

One potential downside is that, despite its position as a low-code offering, some users still report difficulties working with AutoGPT. Some users report difficulties with initial setup, while others state that some level of technical knowledge is still helpful to use the platform effectively.

There are also reports of issues with reliability. In particular, it has something of a reputation for becoming stuck in infinite loops, and may offer less insight into real-time agent behavior than some of its competitors.

AutoGPT may also lack some of the flexibility and advanced functionality of other platforms, with the likes of LangChain potentially offering more sophisticated customization options.

Licensing

The majority of the AutoGPT codebase is offered under the MIT license. However, some elements of this are also offered under the Polyform Shield license, restricting how it can be used within directly competing projects.

AutoGPT itself is free to use. However, we may incur costs for other elements we need to create working agents, including LLM API calls or hosting and infrastructure services.

Some third parties also offer hosted versions of AutoGPT on a commercial basis, although these offer their own pricing models.

4. SuperAgent

SuperAgent is an open-source framework for creating AI assistants, as well as a cloud-based platform for deploying them into production with ease.

Pros

SuperAgent offers a highly performant, customizable solution for creating AI agents. This includes support for a wide range of LLMs, vector stores, traceability tools, third-party data, and more.

It’s also optimized for development teams that need to iterate quickly, with SuperAgent Markup Language (SAML) acting as an expedient tool to configure key agent configurations and attributes.

It also supports TypeScript and Python SDKs, making it a great choice for teams that need to leverage the power of an open-source AI agent platform in the context of wider development projects and applications.

Cons

One clear downside of SuperAgent is that it’s highly geared towards developers and other more technical colleagues. For instance, it does not offer a visual builder, which will preclude most non-engineers from using it to ship agentic solutions.

Even within its core persona of developers, some users may still find that SuperAgent offers a relatively steep learning curve.

Some users also report that observability and tracing functions are more limited, although there is support for a decent range of third-party solutions here.

Licensing

Once again, SuperAgent is offered under the MIT license for use, modification, and distribution.

According to their documentation, SuperAgent also offers a cloud platform that allows us to deploy agents without managing hosting for ourselves.

However, as with many of the other platforms we’ve seen today, more specific information about using this isn’t forthcoming.

5. MetaGPT

MetaGPT is a somewhat novel spin on the AI agent platform market. It bills itself as an AI software company.

Really, what this means is that it simulates a development team by orchestrating LLM tools into specialized roles - like product manager, architect, and engineer. We can then interact with this through a single prompting interface, resulting in complex software artifacts.

Pros

MetaGPT is a very strong offering for its core use case of mimicking software teams. Once it has received a prompt, it assigns roles to agents, each of which follows Standard Operating Procedures (SOPs). This means it can predictably carry out complex tasks.

Another key strength is MetaGPT’s ability to output full-stack prototypes, including requirements documents, UML diagrams, API designs, and runnable code. This makes it a great fit for teams that need to build custom tools but lack the required human development skills and resources.

MetaGPT ships with a library of pre-defined agents for specific software development roles and tasks, making it comparatively easy to get up and running, even for users with less technical experience.

Cons

One obvious downside of MetaGPT is its tight focus on software development tasks. While it’s highly effective in this respect, the flip side of this specialization is that it might lack the general applicability of some of the other tools we’ve seen so far.

Some users also state that MetaGPT presents higher compute costs and resource overheads than some other tools. So, for more basic tasks, we might be better off considering a more lightweight solution.

On top of this, MetaGPT is strongest on single-shot workflows, but may be less effective for human-in-the-loop or iterative refinement agentic systems, compared to some other platforms.

Licensing

Again, MetaGPT is offered under the MIT license, meaning it’s offered on highly permissive terms.

However, unlike some of the other platforms we’ve seen, there’s currently no commercial version of the platform.

As with all tools we’ve seen, we’ll need to factor in costs associated with hosting and LLM usage.

6. CAMEL

Lastly, we have CAMEL. This stands for Communicative Agents for Mind Exploration of Large Language Models.

Essentially, it’s an open-source framework and community focused on building and studying data-driven multi-agent systems.

Pros

CAMEL stands out by focusing on communication between agents. This works by enabling agents to negotiate and reason together in natural language rather than relying on static chains. This makes it possible to build adaptive, human-like decision-making processes.

Another selling point is CAMEL’s comparatively small footprint. It requires less extensive infrastructure and orchestration logic than some other open-source AI agent platforms, making it ideal for experimentation.

It’s also impressive for use cases that require some element of data generation. This includes several advanced modules for the likes of Chain of Thought Generation, Instruction Generation, and more.

Cons

However, CAMEL also presents some important downsides. For one thing, its primary use case is research and exploration. Because of this, it’s not really optimized for production use around real-world business workflows.

Unlike some of the other tools we’ve seen, CAMEL lacks extensive native support for tool use or built-in actions. This means we’re more likely to need to build specific pieces of functionality from scratch.

There’s also minimal user interface, with interactions primarily conducted through code or a CLI. This may pose barriers for teams without a programming background.

Licensing

CAMEL is free to use, although, as ever, we’ll need to factor in the costs of hosting and LLM usage.

The source code itself is offered under the Apache 2.0 license, which permits free usage, modification, and distribution, including for commercial purposes.

Some datasets are offered under CC BY-NC 4.0, allowing use for non-commercial purposes with appropriate attribution.

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