AI agents are changing how we build software. They can handle complex tasks, make decisions, and even learn. For developers and builders looking to get into this space, picking the right tools is important. This article looks at some of the best open source agents available in 2025, focusing on what they do and how they can help you create your own AI-powered applications.

Key Takeaways

  • Many open source agent frameworks exist, each with different strengths for various project needs.
  • Tools like LangChain and CrewAI are good for building complex, multi-agent systems.
  • Visual interfaces such as FlowiseAI and Langflow make agent development more accessible.
  • Some platforms, like Botpress and Superagent, focus on specific applications like chatbots or self-hosted solutions.
  • Choosing the right open source agent depends on your project's complexity, desired control, and development approach.

1. LangChain

LangChain is a framework designed for building applications powered by large language models (LLMs). It's not a platform, but rather a Python and JavaScript library that gives developers detailed control over how their agents operate. Think of it as a toolkit for crafting AI agents from the ground up.

If you're aiming to create custom AI infrastructure and need more control than no-code solutions provide, LangChain offers the flexibility to design it your way. It's particularly useful for building inference workflows, multi-agent systems, or even generative AI app builders from scratch.

LangChain is powerful, but it's not a plug-and-play solution. It's ideal if you're building inference workflows, multi-agent systems, or gen AI app builders from scratch, with complete control.

LangChain offers a suite of components to streamline the development process:

  • Models: Interface with various language models.
  • Chains: Structure sequences of calls to LLMs and other utilities.
  • Data Augmented Generation: Connect LLMs to external data sources.
  • Agents: Allow LLMs to make decisions and take actions.
  • Memory: Add state to chains and agents.
  • Callbacks: Log and stream intermediate steps of any chain, making it easier to debug and observe the internal workings of your agents.
LangChain lets you serialize and version flows like code, making it good for production-grade releases.

For those looking to build AI agents with LangChain, a step-by-step guide can be invaluable. It covers everything from setting up your development environment to understanding the core concepts of AI agents and LangChain's role in simplifying their development.

LangGraph, built on top of LangChain, is useful when you need structured workflows instead of unpredictable chat sessions. It combines logic with smart models and offers built-in retry, error handling, and node control. It's great for devs building decision-tree agents or transactional agents.

2. FlowiseAI

FlowiseAI is an interesting option if you're looking for a visual way to build AI agents. I remember when I first stumbled upon it; the drag-and-drop interface seemed almost too good to be true. It's built for those who like to see how everything connects, which can be super helpful when you're trying to figure out a complex workflow.

FlowiseAI provides a drag-and-drop UI to construct agent workflows, connecting tools, prompts, vector stores, and LLMs in a single flow.

It's open-source, which is a big plus for anyone who likes to tinker and customize things. Plus, you can either self-host it or use their cloud option, giving you some flexibility in how you deploy it. I've found it particularly useful for creating MVPs and internal demos. It lets you iterate quickly and get feedback without a huge engineering lift. It's also built on LangChain, so if you're already familiar with that, you'll feel right at home.

One tech tip I picked up is that you can run FlowiseAI with Docker and expose it with an API. This makes it easy to hook it into your product backend, which is pretty neat.

Here's a quick rundown of what I like and don't like about FlowiseAI:

  • Open-source: Free to use and modify.
  • Visual Interface: Great for non-developers and fast testing.
  • Supports LangChain: Works well if you’re already using LangChain.
  • Flexibility: Self-host or use cloud.

FlowiseAI is one of the top AI agents that offers a balance between ease of use and powerful customization.

3. Superagent

Superagent is an interesting option for those who want a bit more control over their AI agent deployments. It's designed to be API-first and open-source, which makes it appealing to technical teams and startups. I think it's a good middle ground between DIY solutions and fully managed platforms.

It gives you flexibility without requiring you to build everything from scratch. It's like having a pre-built engine for your agents.

  • Open-source core: This gives you full control over the logic and hosting.
  • API-based deployment: You can easily integrate it into your existing frontend or product.
  • Agent memory, tools, and vector support: It functions as a complete agent engine.
  • Hosting options: You can choose between their cloud or self-hosting on your own infrastructure.
  • Best for: Technical teams, SaaS tools, and LLM-powered features.
Superagent supports multiple LLMs and lets you use Redis or PostgreSQL for memory and job queues. This is a great way to customize your setup and optimize performance.

Superagent lets you host and deploy AI agents using SDKs, APIs, and a dashboard. It offers prebuilt integrations and cloud deployment options, bridging the gap between frameworks and managed tools. If you need to run agents on your own infrastructure, Superagent provides a flexible middle ground. It's more structured than a DIY stack, but without sacrificing control.

Features

  • Agent creation via API, dashboard, or SDK.
  • Prebuilt integrations with OpenAI, Pinecone, Supabase, etc.
  • Offers storage, logging, scheduling, and memory out of the box.
  • Works with LangChain, LlamaIndex, and RAG pipelines.
  • Hosting options: cloud-hosted or self-hosted.

Pros

  • Open-source and free under the MIT license.
  • Offers a balance between control and ease of use.
  • Supports multiple LLMs and databases.

Cons

  • Still evolving, so documentation and stability may vary.

Pricing

  • Free, open-source under the MIT license.
  • Requires external APIs (OpenAI, Anthropic, or any other LLM model).
  • Paid plans start at $99/month for features like a no-code builder and advanced monitoring.

4. CrewAI

CrewAI is an open-source framework designed for building AI agent teams. It lets you define "crews" of agents, each with specific roles and responsibilities. Instead of relying on a single agent for everything, you can distribute tasks across a team—researcher, writer, planner, executor—and have them collaborate toward a common goal. It's a useful tool if you're experimenting with role-based delegation or building a custom AI agent builder for more structured multi-step projects.

CrewAI provides a platform to model collaboration between autonomous agents if you're looking to create an AI agent team. It’s more abstract than most platforms, but powerful once you get the hang of it. CrewAI is best for orchestrating collaborative multi-agent systems ai agents.

Here's a breakdown of what CrewAI offers:

  • Define multiple agents with roles, goals, and tool access.
  • Assign tasks and dependencies between agents.
  • Simulate autonomous reasoning.
  • Leverage a massive open-source community.
  • Customize extensively for developers.
Crew AI lets you control agent “thinking time” and task handoff timing if you need fine-tuned flow control.

However, there are some drawbacks:

  • It's not ideal for real-world workflows.
  • There's a risk of hallucinations or infinite loops.
  • It needs setup, hosting, and monitoring.
  • There's no UI or business integrations.

Regarding pricing:

  • It's free and open-source.
  • You must pay for LLM API usage (OpenAI, Anthropic, or any other model).

5. AutoGPT

AutoGPT is an open-source AI agent that really took off, and people are still talking about it. It's built with Python and uses GPT models to automate tasks. The idea is that you give it a goal, and it figures out how to get there. It's designed to be autonomous, so it can make decisions and take actions without needing you to constantly tell it what to do.

One of the big draws is that it's low-code, meaning you don't need to be a coding expert to get started. However, setting it up can be a bit tricky, and some users have reported reliability issues. It's free to use, but keep in mind that you'll probably have to pay for LLM API calls, which can add up depending on how much you're using it.

AutoGPT aims to be a fully autonomous agent. It's designed to chain together LLM "thoughts," to independently achieve whatever goal you set. It's an interesting concept, but it's still early days, and it's not always the most reliable tool.

Here's a quick rundown of some key aspects:

  • Autonomy: It tries to handle tasks from start to finish.
  • Goal-Oriented: You set the objective, and it figures out the steps.
  • Python-Based: It's built on Python, so you'll need some familiarity with the language if you want to customize it.
  • Experimentation: It's still under development, so expect some bugs and changes.

AutoGPT is a cool project, but it's not a magic bullet. It's best suited for people who are comfortable with a bit of technical setup and don't mind troubleshooting. If you're looking for something that just works out of the box, you might want to check out some of the other options on this list. But if you're up for a bit of experimentation, it can be a powerful tool for automating tasks.

6. Botpress

Botpress is an open-source AI agent platform that uses OpenAI to help you build chatbots and agents. It gives you a visual workflow designer, solid integration, and support for different channels. I think it's a pretty cool tool.

It's one of the better open-source AI agent builders out there. Let's take a closer look.

Botpress is a leading open-source AI agent platform. It's powered by OpenAI, which is pretty neat. It lets you build chatbots and agents with a visual workflow designer. Plus, it has robust integration and supports multiple channels. It's a solid choice if you're looking for something like that.

There's no single best AI agent builder. The right one depends on your team, your project, and your goals. If you're a startup, go lean and move fast.

Here are some of its key features:

  • Drag-and-drop interface
  • Code flexibility for advanced logics
  • Enterprise-level security

Botpress is a cost-effective option for startups.

Choosing the right AI agent platform depends on your needs. Botpress is a good option if you want something open-source and flexible.

7. n8n

n8n is a workflow automation platform that lets you connect various apps and services to create automated processes. It's designed for technical users who want to build complex workflows without extensive coding. Think of it as a glue that holds different parts of your digital world together.

n8n is pretty cool because it's open source and self-hostable. This gives you a lot of control over your data and how the platform is used. You can run it on your own servers, which is a big plus for privacy and security. It's also extensible, meaning you can add custom functions and integrations to fit your specific needs. This makes it a solid choice for developers who want a flexible automation tool.

n8n's visual interface makes it easier to design and manage workflows. You can drag and drop nodes, connect them, and configure each node to perform a specific action. This visual approach can speed up the development process and make it easier to understand complex automations.

Here's why n8n might be a good fit for your AI agent projects:

  • Integration Capabilities: n8n can connect to a wide range of AI services, databases, and APIs. This allows you to create workflows that combine AI capabilities with other business processes.
  • Customization: You can extend n8n with custom functions and integrations, making it possible to tailor the platform to your specific needs. This is especially useful if you're working with niche AI technologies or custom models.
  • Self-Hosting: Running n8n on your own infrastructure gives you full control over your data and security. This is a major advantage for organizations with strict compliance requirements.

n8n's flexibility and extensibility make it a powerful tool for building sophisticated AI agent workflows. It's a great option if you need to integrate AI with other systems and want control over your data. For example, you could use n8n to automate the process of training a machine learning model, deploying it to a production environment, and monitoring its performance. Or, you could use it to build a chatbot that integrates with your CRM system and provides personalized customer support. You can use it to automate AI agent workflows.

8. Langflow

Minimalistic Langflow logo on a simple background.

Langflow is a visual tool designed to simplify the creation of LangChain workflows. It provides a user-friendly interface, making it suitable for rapidly prototyping agents or chains.

Langflow is open-source and self-hostable, offering flexibility and control over your experiments. It's a solid choice for developers looking to explore LLM chains and quickly validate ideas.

  • Drag-and-drop Interface: Build logic using visual blocks.
  • LangChain Integration: Seamlessly build, test, and export chains.
  • Self-Hosted: Ideal for keeping experiments local.
Langflow allows you to export flows as JSON, which can then be loaded directly into LangChain-powered applications. This feature streamlines the integration process and enhances the reusability of your workflows.

9. OpenAgents

OpenAgents, developed by former Google researchers, is designed to let you build AI agents that can communicate with each other, utilize external tools, execute custom code, and create workflows. It's pretty cool, and it's got some interesting features.

The main thing is that OpenAgents allows agents to collaborate on tasks.

It's also open-source, giving you full access to the engine and logic. Plus, it supports APIs, plugins, and custom tools, which is always a plus. It also handles memory and context, which is super important for keeping things on track.

OpenAgents is still evolving, so documentation and stability might vary. It's not exactly plug-and-play, so expect some setup. Also, it might not have as much tone customization or memory logic as some LLM-native tools.

Here's a quick rundown of what you get:

  • Multi-agent orchestration
  • Open-source access
  • Support for APIs and plugins

As for pricing, it's free and open-source under the MIT license. You'll need external APIs like OpenAI or Anthropic, or any other LLM model. Paid plans start around $99/month for features like a no-code builder and advanced monitoring. If you're looking to build initial AI agents, this could be a good option to explore.

10. Reworkd Agent-LLM

Minimalist robot head, glowing circuits.

Reworkd Agent-LLM is another interesting option in the open-source AI agent space. It's designed to be a development framework, giving you the tools to build autonomous agents. Think of it as a toolkit for creating AI that can handle tasks on its own.

Agent-LLM is aimed at developers who want to build custom AI agents. It provides the basic structure and components needed, but you'll need to do some coding to get it working the way you want. It's not a plug-and-play solution, but it offers a lot of flexibility.

One of the key features of Reworkd Agent-LLM is its focus on data analysis. It allows you to create agents that can analyze data using natural language. This means you can ask questions and get insights without having to write complex code. It's like having a conversation with your data.

Reworkd Agent-LLM is still under development, so it may not be as polished as some of the other options on this list. However, it has the potential to be a powerful tool for building custom AI agents.

It's worth checking out if you're a developer looking for a flexible and customizable framework. It might take some effort to set up, but the results could be worth it.

Conclusion

So, there you have it. Picking the right open-source AI agent for your work really comes down to what you need. Are you building something simple or something super complex? Do you want to get a project off the ground fast, or are you looking to customize every little detail? The good news is, there are lots of great options out there. Each one has its own strengths, whether it's making things easy to use, giving you lots of control, or helping you work with a team. Take some time to check them out, see what fits your project best, and then just start building. The open-source AI world is always changing, so staying updated on these tools will help you keep making cool stuff.

Frequently Asked Questions

What are AI agent builders?

AI agent builders are special tools that help you create, run, and manage AI agents. They make it easier to build these agents without starting from scratch. This means you don't have to write all the code yourself, which saves a lot of time and effort.

Can I build AI agents without writing code?

Yes, many AI agent builders are available that do not require you to write code. Tools like FlowiseAI allow users to build agents using a simple drag-and-drop interface. This makes it possible for people without programming skills to create powerful AI agents.

Are there any open-source AI agent builders?

There are several excellent open-source AI agent builders. Some notable examples include FlowiseAI, Superagent, Reworkd Agent-LLM, and Langflow. These tools are free to use and modify, offering great flexibility for developers.

Which AI agent builder is best for my project?

The best AI agent builder depends on your specific needs. For small businesses, Lindy is often recommended. If you need to automate business operations without coding, Relevance AI is a strong choice. For developers, LangChain provides powerful frameworks.

What are the advantages of using open-source AI agent builders?

Open-source AI agent builders offer several benefits. They are typically free to use, allowing for cost savings. They also provide transparency, as you can view and modify the underlying code. This flexibility is valuable for customization and learning.

What should I look for when choosing an AI agent builder?

When choosing an AI agent builder, consider factors such as ease of use, available features, community support, and whether it is open-source or commercial. It is important to select a tool that matches your technical skill level and project requirements.

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