doccano vs DocsGPT
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source tool for humans to label text data for machine learning projects.
Open source · MIT
Self-hosted platform for AI agents that answer from your own documents, with any model.
The short answer
Choose doccano if you need…
- Machine learning teams building labeled text datasets
- Researchers creating named entity recognition or sentiment datasets
- Small groups who want to annotate text together in one shared project
- Developers who want to automate labeling workflows through the RESTful API
Choose DocsGPT if you need…
- Teams that want an internal assistant over their own documents and wikis
- Developers who want to embed a documentation chatbot on a product site
- People who need to keep data inside their own infrastructure
- Users who want to run local models through Ollama or vLLM
Side-by-side
| doccano | DocsGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 10.8k | 18.4k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | See the README |
| Main language | Python | Python |
| Last code update | 7 months ago | 4 days ago |
What each one is
doccano
doccano is a web-based tool for annotating text, meaning people add labels to sentences or passages to create training data. It supports text classification, sequence labeling such as named entity recognition, and sequence-to-sequence tasks such as summarization. You create a project, upload data, and label it in the browser, with collaboration and a RESTful API available.
DocsGPT
DocsGPT turns your documents, websites and connected apps into AI agents that answer questions and cite their sources. You can build agents and visual workflows, give them tools, and embed them in a website, an OpenAI-compatible API, or an MCP server. The README says it can run entirely in your own environment with cloud or local models.
Best for
doccano
- Machine learning teams building labeled text datasets
- Researchers creating named entity recognition or sentiment datasets
- Small groups who want to annotate text together in one shared project
- Developers who want to automate labeling workflows through the RESTful API
DocsGPT
- Teams that want an internal assistant over their own documents and wikis
- Developers who want to embed a documentation chatbot on a product site
- People who need to keep data inside their own infrastructure
- Users who want to run local models through Ollama or vLLM
Strengths
doccano
- Covers several common text annotation task types in one tool
- Can run locally with pip or Docker, with SQLite by default and PostgreSQL optional
- Released under the permissive MIT license
DocsGPT
- Self-hosting and the MIT license let you keep data in your environment
- Supports many model providers, including local models
- Answers include citations to the source documents
Not ideal for
doccano
- People who need to label images, audio, or video, since the README only describes text annotation
- Users who need a fully managed hosted service, because the README covers self-hosting options and a demo only
- Teams needing enterprise support guarantees, since the README does not describe any support plans
DocsGPT
- People who only want a quick hosted chatbot and no setup, since the cloud option is a separate service and the self-hosted route needs Docker and configuration
- Non-technical users without someone to run the install and manage upgrades, backups and security settings
- Teams that need guarantees about model quality, as the README does not make claims about answer accuracy beyond citing sources
Trade-offs
doccano
- The README is truncated and gives limited detail on specific annotation features and their setup
- The demo is hosted on a free Heroku address that may not be reliable for real work
- Docker Compose setup requires a .env file and some familiarity with Docker
DocsGPT
- Self-hosting involves several services (API, worker, Postgres, Redis, vector store), so it takes real setup effort
- The README is truncated here, so detailed requirements and limits are not fully visible
- Features like guardrails and deep research depend on configuration the README does not fully detail
Getting started
doccano
Install with pip
pip install doccanoInitialize the database
doccano initDocsGPT
Install on macOS or Linux (one command, from README)
curl -fsSL https://docs.ac/install | bashInstall on Windows PowerShell (from README)
irm https://docs.ac/install.ps1 | iexFrequently asked questions
What is the difference between doccano and DocsGPT?
doccano: Open-source tool for humans to label text data for machine learning projects. DocsGPT: Self-hosted platform for AI agents that answer from your own documents, with any model. doccano suits machine learning teams building labeled text datasets, while DocsGPT suits teams that want an internal assistant over their own documents and wikis.
Are doccano and DocsGPT free?
doccano: Free and open source. DocsGPT: Free and open source.
Which is easier to set up, doccano or DocsGPT?
doccano is easier to get started with (rated beginner); DocsGPT is rated intermediate and usually needs more setup.
Is doccano or DocsGPT more popular?
DocsGPT has more GitHub stars (18.4k) than doccano (10.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to doccano and DocsGPT?
Other options people compare them with include Hoppscotch, Krayin CRM, Vue Admin Better, Vuestic Admin, CVAT.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.