5 best open source AI agents I'm using in 2026

What is an open source AI agent?

An open source AI agent is an agentic workflow builder that leverages open source LLM models to do reasoning and tool calls. Being open source also means that all of the code is under a license (usually MIT) and anyone can look at it.

Most open source AI agents are built on top of agent frameworks like LangChain or CrewAI. These frameworks give developers the building blocks to connect large language models to tools, memory, and data, and then chain everything together into a workflow an agent can run on its own.

Since the code is open, you can host it yourself, change how it works, and see exactly what's happening with all the tool calls.

How I chose the best open source AI agents platforms

I could give you a wide range of open source AI agent platforms. But I first want to tell you how to find one for yourself.

This way, when I go over the top five I found that are actually worth your time, you can evaluate them based on your own use cases and needs.

Here is how I generally think about picking a platform to build open source AI agents:

Is it LLM agnostic? You want to be able to swap between open source and frontier large language models without rebuilding your whole setup.
Does it connect to MCP servers? Support for the Model Context Protocol (MCP) makes it much easier to give your agents access to your tools and data through a standard layer.
Can it handle RAG? Look for built-in support for vector databases and retrieval augmented generation (RAG) so your agents can pull from your own knowledge instead of guessing.
Can it execute code? The best platforms let your agents run code when a task needs real logic, not just text generation.
How is the ecosystem? A platform with lots of integrations, templates, and community support will get you building agentic systems faster.
Does it scale? Something that works for one workflow should still hold up when you are running hundreds of them across a team.
Is there a human in the loop? For anything high-stakes, you want the option to add approvals or review steps before an agent acts.
What is the building experience like? Some platforms are built for engineers and others for non-technical users, so pick the one that matches your team.

Now that you know what to look for, here are the five best open source AI agent platforms I would recommend.

5 best open source AI agent platforms and frameworks

Here are the top open source AI agents:

1. Gumloop

Best for: Building AI agents that run on open source LLM models without self-hosting any infrastructure
Pricing: Free plan available, then starts at $37/month
What I like: I can swap in open source models like GLM and cut my agent costs by over 70% with the same output

Gumloop is an AI agent platform built for automating work. Whether that's at a 10,000-person company or a small team, Gumloop is the AI operating system for all of your work.

The platform gives you access to open source LLM models from DeepSeek, Z.ai Moonshot, Qwen, and MiniMax. So you can create any agent that connects to your tools, has access to your skills, and can run on an open source LLM.

I have actually replaced Claude Opus with Z.ai's GLM models to run all of my growth analysis dashboards in Gumloop. And it has resulted in a 72% reduction in cost (while still having the same exact output).

Pros and cons

Here are some things I love about Gumloop:

The platform has a great harness for open source AI models. This is really important if you plan on giving your agent access to your tech stack to read and write data.
Each agent has Insights, which shows how many credits are being used and on what (whether that's reasoning or tool calling).
Gumloop can create skills based on any interaction you have with it. If you have a chat you did with an agent, you can just ask it to turn the output into a repeatable skill.

Here are some cons:

Gumloop is an agent builder that gives you access to open source LLMs. It is not a fully open source builder on its own.
While Gumloop has a lot of native integrations, if you need to connect to an MCP server it can have a bit of a learning curve.

Overall, Gumloop is the tool I would reach for first if you want to build agents on open source models without managing servers or API keys yourself.

Gumloop pricing

Here are Gumloop's pricing plans:

Free: $0/month with 5,000 credits, 1 seat, 1 active trigger, and unlimited agents and flows
Pro: Starts at $37/month with 20,000+ credits, unlimited seats, MCP server hosting, and connector policies and guardrails
Enterprise: Custom pricing with role-based access control, SCIM/SAML, audit logs, AI model access control, and a virtual private cloud

You can learn more about the pricing plans here.

Gumloop reviews

Here is what users rate Gumloop on third-party review sites:

G2: 4.8/5 star rating
Product Hunt: 5/5 star rating

2. LangChain

Best for: AI engineers, data engineers, or product teams building agents internally
Pricing: Free developer plan, then $39/seat per month
What I like: It is fully modular and LLM agnostic, so you can reuse components and switch models anytime

LangChain is an open source AI framework licensed under MIT. It is a tool for developers to build AI agents and apps powered by LLMs.

The platform gives you all the features you need to build agents, like observability, evals, debugging, prompt management, tool integrations, memory, and deployment support. This way, you can create, test, and ship AI agents end to end.

Just like Gumloop, LangChain is also LLM agnostic. So you can use any LLM, from a frontier lab or open source model, in your agents. But unlike Gumloop's platform that allows you to use open source AI models, LangChain is a full open source framework.

Pros and cons

Here are some of the pros of LangChain:

It is fully customizable and modular. You can reuse components like tools, models, prompts, and chains to make quick iterations and prototypes.
It lets you use a wide range of LLM providers, so you can switch at any time.
There is built-in support for RAG and agents that can do everything from vector stores to document processing and various tool calls.
There is a big community around the platform, as well as lots of docs and examples to help you get started.

Here are some of the cons of LangChain:

It does require heavy engineering infrastructure, so it is not a quick AI agent setup.
There can be a lot of abstraction overhead, which can make debugging and deeper customizations a bit complex.
Some users report that for complex workflows it can get a little messy, and it is a bit hard to create multi-agent orchestration workflows.

Overall, LangChain is a powerful, open source AI agent platform. It is great if you are looking for total flexibility, but if you need something with a bit more guardrails that is designed for security and reliability, then it might be worth looking into an alternative.

LangChain pricing

Here are LangChain's pricing plans:

Developer is $0/seat per month with up to 5,000 base traces per month, community support, and 1 seat
Plus is $39/seat per month with up to 10,000 base traces per month, access to deployment and sandboxes, email support, and unlimited seats
Enterprise is custom pricing with self-hosted and hybrid deployment, custom SSO and RBAC, and a support SLA

You can learn more about what each plan has to offer on their pricing page.

LangChain reviews

Here is what users rate LangChain on third-party review sites:

G2: 4.6/5 star rating
Product Hunt: 4.9/5 star rating

3. Dify

Best for: Non-technical and low-code users building AI apps and agents, often in marketing or ops
Pricing: Free Sandbox plan, then $59 per workspace/month
What I like: The visual no-code interface plus the option to self-host your agents and chatbots

Dify is an open source agentic workflow builder. Similar to LangChain, it gives you everything from agentic workflows, RAG pipelines, observability, and a wide range of integrations to help you build AI agents.

The platform lets you build AI apps, agents, chatbots, and workflows in a visual no-code interface. And because of this approach, it is better suited for non-technical or low-code users, typically in use cases around marketing or operations. But developers and product teams can also use it as a place to collaborate on different workflows and check evaluations across their org.

Pros and cons

Here are some of the pros of Dify:

It is great for prototyping and building agents.
The low-code and no-code interface makes it easier for non-technical people to figure out how to use it.
It is LLM agnostic, so you can use anything from frontier labs to open source models.
The platform also has a cool marketplace full of different models, agent templates, and tools.

Here are some of the cons of Dify:

The platform can be a bit confusing when you are trying to build workflows that go through parallel steps, and it can be a bit hard to manage how your agents are logically moving through the steps in a workflow.
Some users report that the customization is a bit limited compared to fully custom platforms like LangChain.

Overall, Dify is a solid tool if you are looking for an all-in-one platform that can help you leverage open source models. It does sit in this middle ground of being developer friendly while also being non-technical friendly.

Dify pricing

Here are Dify's pricing plans:

Sandbox is free with 200 message credits, 1 team workspace, 1 team member, and 5 apps
Professional is $59 per workspace/month with 5,000 message credits, 3 team members, and 50 apps
Team is $159 per workspace/month with 10,000 message credits, 50 team members, and 200 apps

You can learn more about each plan on the Dify pricing page.

Dify reviews

Here is what users rate Dify on third-party review sites:

G2: 4.1/5 star rating
Product Hunt: 4.7/5 star rating

4. n8n

Best for: Technical teams that want control, flexibility, and the option to self-host
Pricing: Starts at $24/month, billed monthly
What I like: The self-hosting option gives you full control over your data with no vendor lock-in

n8n is a visual AI automation platform and agent builder. It has become popular among business and technical teams that are trying to build and prototype agents internally. n8n is LLM agnostic, so you can use your own open source models. The only thing is you have to bring your own API keys.

I would say it is a comparable platform to Zapier or Make, these traditional automation tools. But n8n offers greater flexibility and control over your data.

Pros and cons

Here are some of the pros with n8n:

It is built for technical teams that want a lot of control but also want an easy-to-use agent abstraction tool.
It is good for multi-agent orchestration and building complex workflows.
The self-hosting open source option means you can have full control over your data and not be locked in to any vendor.

Here are some of the cons with n8n:

There can be a slight learning curve to the platform, especially for non-technical users who have not used drag and drop visual tools.
The platform feels more like a workflow builder than an agent platform, and it does require some manual work to get the agents just right.

Overall, n8n is a great platform, and I wanted to include it in this list because it is so flexible and allows you to use open source LLMs. If you are an engineer who likes the customizability of something like LangChain but you want a harness that is quick to set up, then this is a great platform.

n8n pricing

Here are n8n's pricing plans:

Starter is $24/month with 2,500 workflow executions, 1 shared project, and unlimited users
Pro is $60/month with 10,000 workflow executions, 3 shared projects, and admin roles
Business is $960/month with 40,000 workflow executions, SSO and SAML, and self-hosting
Enterprise is custom pricing with unlimited shared projects, 200+ concurrent executions, and log streaming

Check out their pricing page to learn more about all the features included (or not) in each plan.

n8n reviews

Here is what users rate n8n on third-party review sites:

G2: 4.7/5 star rating
Capterra: 4.6/5 star rating

5. CrewAI

Best for: AI engineers and biz ops teams building multi-agent systems for research, lead gen, or support
Pricing: Free Basic plan, then custom Enterprise pricing
What I like: You can build visually and then export your agents to Python code

CrewAI is what they call "an open platform" that allows you to build and deploy AI agents. The platform is similar to n8n in that it has a visual workflow builder where you can build multi-agent systems.

You can also export all of your agents to Python code. If you are a developer, this is also a great platform for building open source AI agents.

Pros and cons

Here are some of the pros with CrewAI:

It is LLM agnostic, so you can use any of the open source or frontier models.
It has a built-in knowledge base that can enable RAG patterns and create context over your agents.
It is designed to help you build from idea to production quickly, so it has a lot of guardrails, observability, and enterprise features built in.

Here are some of the cons:

It is very much focused around Python, so if you are using JavaScript or other languages, it might be a little confusing, at least for those who are technical and want to explore all the open platform features.
The platform is a bit overkill for simple use cases that are just triggers and automations, so I would not recommend it if you do not already have complex workflows that you want to automate.

Overall, CrewAI is a promising tool for creating a "crew" of AI agents. The platform allows you to use open source LLM models, and it is also an open source platform itself. But if you want something a bit more self-serve friendly, you might want to look at an alternative.