Open-source AI agent platforms: how to choose one your team can own

Your team wants agents that do real work without handing the company's memory, prompts and connectors to a closed vendor. The hard part is choosing an open-source AI agent platform that lets you own the system and still ship. Five options range from a full management system you run yourself to developer frameworks you embed in an application. Licence, deployment, model choice and who approves the work separate them, and open-source Kortix is our recommendation.

The five platforms at a glance

The five options split into three kinds. Kortix is a full management system a company can run and own. LangGraph and CrewAI are developer frameworks you compose into an application. Dify and NocoBase are platforms for building workflows and business systems. The split matters because each layer has a different owner: a framework sits with engineering, an application platform with a product team, and a management system with the company. Licence, best fit and deployment are the fastest way to tell them apart, and the licence column decides what you may do with the code while deployment decides who runs it.

Kortix: one repo you own, end to end

Kortix is the open-source AI Management System, and our top pick for a team that needs to own the whole system. Agents, shared skills, company memory and connector configuration live in one git repo, so every change is versioned and diffable. A teammate can read the history instead of trusting a black box (Kortix on GitHub).

The platform reaches 3,000+ apps plus any MCP, OpenAPI, GraphQL or raw HTTP API, and it brokers connector credentials server-side so raw keys never reach the agent (Kortix on GitHub). That boundary matters once agents act on real systems: the credential stays with the platform, the agent only holds the access it was granted, and the catalog lets it reach the tools the company already uses.

Every session gets its own isolated Linux cloud computer on its own branch, and a team can run thousands in parallel. An agent can install packages, run code and break things inside that machine, and only committed work survives, so a failed run cannot quietly change the company.

Work reaches main through a change request a human approves. Merge is deny-by-default for agents, and approval gates can be switched on for individual actions, which places a reviewer between an agent and anything that cannot be undone. The request also gives the team a place to discuss a proposal before it becomes part of the system.

On models, Kortix takes any provider with your own API keys, or the ChatGPT or Copilot subscription you already pay for. Self-host on a laptop, a VPS, your own VPC or on-prem from one Docker Compose stack, or use managed Kortix Cloud. Self-hosting needs a sandbox provider such as Daytona, Platinum or E2B (self-hosting details). That range covers a solo developer on a laptop and a company that needs the control plane on its own network.

Pricing is published: Free at $0 with 200 credits a month for sandbox compute, Team at $40 per seat per month, and Enterprise custom (Kortix pricing). Kortix is open source (Elastic License 2.0): self-host, read and modify the code (license).

Together those choices point the same way. The company's memory and configuration are files you can read, a review is a diff, and it can all sit on your own network.

LangGraph: low-level orchestration for stateful agents

LangGraph is an MIT-licensed framework for building long-running, stateful agents and workflows (LangGraph on GitHub). It provides low-level infrastructure and deliberately does not abstract prompts or architecture, so the control flow stays explicit and you decide how much of the behaviour is deterministic.

LangGraph mixes hand-coded, deterministic steps with LLM-driven ones, adds persistence so a run can resume after a failure, and supports human-in-the-loop review by inspecting and modifying state at a chosen point. Short-term and long-term memory are built in. LangChain Inc builds it, though it can be used without LangChain, and observability and deployment come from LangSmith, a separate product (LangGraph overview).

The trade-off is assembly. LangGraph gives you the primitives, but the prompts, the architecture and the run environment stay your build. That is the point for teams that want the runtime inside their own code, and a reason to look elsewhere for teams that want a packaged interface.

CrewAI: role-based agent teams in Python

CrewAI is an MIT-licensed Python framework for orchestrating role-playing, autonomous agents (CrewAI on GitHub). A crew is a collaborative group of agents working through a set of tasks with a sequential or hierarchical process, while Flows handle event-driven workflows. Memory and checkpointing let long or interrupted runs resume (CrewAI crews).

It suits Python teams that want role-based agent teams they compose themselves. The process can be sequential or hierarchical, so the same crew model covers a fixed pipeline and a delegated one, and the surrounding application, run environment and review wiring belong to you.

Dify: visual workflows and RAG over your own models

Dify is an open-source platform for building agentic workflows and RAG pipelines across many models and tools, and it deploys on cloud, VPC or self-hosted (Dify on GitHub). Its visual interface is where the workflow and the retrieval pipeline are assembled, which shortens the path from a prototype to something a team can operate.

Check the edition boundary first. The licence is a modified Apache 2.0: running a multi-tenant service needs commercial permission, and the console logo and copyright cannot be removed. A free Community Edition is self-hosted, while Cloud and Enterprise are separate offerings (Dify license). It fits a team whose main deliverable is a workflow application over documents; a company-wide system is a different kind of project.

NocoBase: AI inside a no-code business system

NocoBase is an open-source AI plus no-code platform for building business systems on a WYSIWYG interface (NocoBase on GitHub). Teams reach for it when the system already exists and AI should work inside it, so the interface and the workflows are built visually.

It is licensed under its own NocoBase License Agreement, with a free Community Edition and commercial Standard, Professional and Enterprise editions (NocoBase license). The WYSIWYG interface means the system is built visually, and the AI layer works on top of that same structure. It fits teams that want agents acting inside a business system.

How to choose between them

Start with where finished work should land, then check the licence and the edition you will actually run. Choose Kortix when you want the whole system in one repo your team owns, with a human approving changes before they reach main. The system stays auditable, and the model is a configuration choice.

Choose LangGraph when you are embedding a custom, stateful agent runtime inside your own application and want to place every interrupt yourself. Choose CrewAI when role-based agent teams in Python match how you build. Choose Dify when the deliverable is a visual workflow or a retrieval pipeline over documents. Choose NocoBase when the agents need to act inside a no-code business system.

Kortix comes first for teams that want the whole system. A framework keeps the runtime in your code and an application platform keeps the interface in view, which is useful when that layer is the whole job. Kortix treats the company itself as the system, with the whole configuration in one repo a reviewer can read.

Two questions settle most of the choice. Can the work live in a repository your team owns, and can a reviewer stop a change before it ships? When both matter, Kortix is the pick. When your team is building one layer, a framework or an application platform is the better fit.

If you are weighing a specific alternative, read the open-source Claude Cowork alternative breakdown and building reliable AI agent workflows.

Start with one job and grow from there. Get started with open-source Kortix.