Dust AI alternatives: choose a team workspace or build your own

Replacing Dust can mean two very different projects. You might want another place where your team can use AI with company knowledge. Or you might want to build an application whose behavior you control yourself.

If you only need a different team workspace, a developer library can turn a software switch into a development project. Someone still has to build the interface, manage access, and keep it running. That work belongs in the comparison alongside the subscription price.

Start with Langdock if you want to compare another managed team platform. Consider Onyx for company knowledge search with a self-hosting option, or Dify for a visual app builder. Look at LangChain or smolagents when writing and maintaining the application is part of the plan.

This is a documentation-based comparison. The recommendations describe which options deserve a closer look, not results from a hands-on performance test.

Decide what you need to replace

Dust connects agents to company knowledge and tools in a shared workspace. If your team already relies on those connections, replacing its chat screen is only a small part of the migration.

List the agents people actually use, their data sources, and the actions they can take. Then choose the route that matches the work you want to own.

Langdock: compare the workflow, not just the seat price

Langdock combines chat, custom agents, integrations, knowledge libraries, and multi-step workflows. That makes it worth evaluating when the replacement needs to serve a team, rather than only the developer building an agent.

The cost comparison needs more care. Its pricing documentation separates Chat & Agents seats, workflow runs, and API usage. AI usage inside workflows is charged separately even when model usage is included with chat seats.

For example, a team that mainly asks questions in chat has a different cost profile from one that runs agents over every incoming support ticket. Price the workload you expect to run, including busy periods. A cheaper seat does not settle that comparison.

Langdock also offers bring-your-own-key configurations. You supply model-provider keys and pay those providers separately from the platform license. This gives you another way to manage model access and spending; it does not mean you are hosting Langdock yourself.

Before switching, rebuild one useful Dust workflow in Langdock. Check the answer, the connected action, the permissions, and the cost together. A working demonstration should cover all four.

Check what an agent can access before sharing it

Both products document access controls, but you need to understand what each control governs.

Dust organizes connected data into open and restricted spaces. Its default Company Data space cannot be restricted. Agents using data from a restricted space are available to that space's members. That makes the placement of data part of the setup, not an administrative detail to postpone.

Langdock separates integration access from action access. An administrator can limit an integration to selected people and control individual actions. Some native actions cannot be disabled. These settings do not, by themselves, tell you how every source system's document permissions will behave.

Use a simple acceptance test: one employee should be able to read a document, and another should not. Try the same question as each person. Then test a read-only task and a task that changes something in the connected system. Record whose account authorizes the action.

Do this before inviting the wider team. Finding a permission problem during a small trial is easier to handle than finding it after colleagues have connected sensitive documents.

Onyx: when finding company knowledge is the main job

Onyx combines chat, internal search, and custom agents. It offers both a cloud service and a self-hosting path. Consider it when the main problem is finding answers across company documents, especially if you also need control over where the application runs.

Check the edition and each connector before treating it as a permission-preserving replacement. Onyx's connector documentation limits automatic source-permission syncing to specified connectors and identifies it as an Enterprise Edition feature. Some connectors also require particular credentials.

There is an important setup distinction: a connector marked Public makes its indexed data available to all Onyx users, even when the source data is private. Self-hosting does not change that access setting.

For a trial, connect a small document set with different access levels. Test both who can retrieve a document and what happens after you remove someone's access. Compare the required edition, model costs, and hosting work with your current Dust setup.

Dify: a visual builder with deployment choices

Dify lets you build workflows visually, connect agents to documents, and configure the models they use. Consider it when you want to design a particular process, such as drafting support replies from an approved knowledge base.

Its Docker Compose guide provides a self-hosted deployment path. That is useful when running the application in your own environment is a requirement. It also means assigning responsibility for updates, access, backups, and recovery. Include that work in the comparison with a managed service.

Check the license before planning a customer-facing service. It includes additional conditions for multi-tenant operation and frontend branding. Public source code does not make every hosting or resale arrangement unrestricted.

For an internal workflow, start with the process itself: what enters, which decisions require a person, what gets written back, and what happens when a step fails. A visual canvas helps you express that process; it does not remove the need to test it.

LangChain: choose it when the application is yours to build

LangChain's agent interface lets developers combine models, tools, and prompts in code. Related tools serve different jobs: LangGraph gives developers more control over how an agent's steps run, while LangSmith helps them inspect and evaluate those runs.

This route makes sense when your product needs behavior that you want to express and test in code. It is a larger commitment if the original goal was simply to give a sales or support team a working assistant.

Estimate the surrounding work before comparing costs: the user interface, authentication, document access, tool credentials, monitoring, and support. Flexibility is valuable when it solves a specific constraint. It can be expensive when the team did not need the extra control.

smolagents: a current option for Python-based agents

Older comparisons often recommend “Hugging Face Agents.” The Transformers documentation says that agents and tools moved to the separate smolagents library and were removed from Transformers in version 4.52.

smolagents supports agents that write code to carry out actions, as well as agents that call predefined tools. Its documentation includes options for running that code in an isolated environment. Consider it when your developers want to build a Python application around either approach.

Keep code execution narrowly scoped. Decide which files, network destinations, credentials, and tools the agent should be able to reach before giving it real work. The library is a building block, not a substitute for those decisions or for a team-facing product.

Make the decision with one representative workflow

Choose a task that people already repeat: answering a customer question from approved documentation, preparing an account brief, or drafting a report from connected systems.

Evaluate the same task with the same access rules in your current setup and one alternative. Include an incomplete source, a restricted document, and a failed tool call. Check whether someone can understand and correct the result without needing the developer who built it.

Then compare the complete cost: seats, usage, workflow charges, hosting, and maintenance. If a managed workspace handles the task well, custom development needs a clear reason. If a product constraint demands control over the application, budget for that control from the start.