Humata
ProductivitySearch, summarize and compare uploaded documents with source-linked answers. Humata adds team-oriented document access and page-based plans for ongoing knowledge work.
Questions across filesCited passages
ANYTHINGLLM / GIVE YOUR DOCUMENTS A USEFUL WORKSPACE
AnythingLLM
A local-first AI workspace for chatting with documents, organizing knowledge and connecting model-backed agents. The free desktop app and MIT-licensed self-hosted project can use local models, while external providers and hosted services are optional costs.
Frame from the official AnythingLLM repository demonstration showing the workspace chat interface; the source is a product demo rather than a FindGoodAI performance testExplore the image ↓
What you can do with it
What this tool is for
AnythingLLM provides a workspace around models and documents. Instead of treating every question as a new standalone chat, users can organize reference material and ask questions in a context that is easier to reuse. It is a practical option for people who want a document assistant with local deployment choices and room to connect different model providers.
The application is only one part of the system. Document extraction, embeddings, retrieval, generation and optional tools may use different components. A useful setup makes each of those choices explicit, so a user knows why an answer was produced, which material was searched and whether any part of the workflow left the machine.
01
Build a personal reference desk
02
Organize a support knowledge base
03
Compare document workflows
04
Prototype a team assistant
A closer look at the product
Official product image. Click to inspect the details.
Product interface and examples
Key features, practical tasks
Capabilities in practice
- Document ingestion: The project supports common document types such as PDF, text and office documents. Check the extracted content after import, especially tables and scanned pages, because a retrieval system cannot reliably answer from text that was never captured correctly.
- Workspace organization: Separate material by topic or purpose so a conversation has a coherent reference set. Keeping old policy versions apart from current instructions helps prevent an apparently well-cited answer from quoting the wrong edition.
- Answers with sources: The chat interface can show source citations for document-backed responses. Use those references to inspect the relevant passage and confirm that the answer did not extend a limited statement into a broader claim.
- Local model choices: The desktop route includes a built-in local model option and can connect to other local providers. Select a model that fits available memory and verify that embeddings are also configured as intended if local processing is the goal.
- External provider support: Cloud model and embedding services can be connected when a workflow requires them. This offers flexibility, but provider fees, credentials and data handling remain separate from the free application itself.
- Agent and tool workflows: The project includes agent features and integration routes. A useful first agent performs one bounded action with a clear review point; adding many tools before the task is understood can make failures harder to diagnose.
- Deployment-specific collaboration: The repository distinguishes Docker features such as multi-user access and website embedding from the personal desktop route. Choose the deployment based on the actual audience rather than assuming every desktop setting is a team administration feature.
Get started, step by step
- 01
Choose the deployment
- 02
Set the data path
- 03
Import a small collection
- 04
Evaluate before expanding
A practical first workflow
A practical first workflow
- Choose the deployment. Use the desktop app for a personal starting point or review the self-hosted route for shared use. Follow the current official setup for the operating system instead of mixing old installation instructions.
- Set the data path. Select the chat model, embedding provider and storage configuration. Write down which components are local and which are hosted, and keep provider credentials out of documents intended for retrieval.
- Import a small collection. Start with a handful of current files, inspect their extracted text and organize them into a coherent workspace. Use filenames and dates that make later source checks straightforward.
- Evaluate before expanding. Ask several answerable questions and one unanswerable question, inspect the citations and correct the source set or settings. Add more documents only after the small workflow behaves predictably.
Example prompt or task: Answer only from this workspace. Quote the document title and relevant passage for each policy claim. If the current documents disagree, show the disagreement and dates instead of choosing silently.
This is an editorial starting workflow, not a measured performance result.
Find the right plan for you
Free software
Explore official plans ↗Free software Models, hardware and hosting are separate
The project uses the MIT license and offers a free desktop download. Local models and local embeddings can support a workflow without a mandatory hosted-model subscription, but the computer still supplies memory, storage and processing resources.
What is free and what can cost extra
The project uses the MIT license and offers a free desktop download. Local models and local embeddings can support a workflow without a mandatory hosted-model subscription, but the computer still supplies memory, storage and processing resources.
Cloud model APIs, external vector services and managed hosting can cost money. Check each configured provider rather than assuming that a free chat interface makes all downstream requests free. Shared deployments also require someone to maintain updates, backups and access controls.
A few things worth knowing
Frequently asked questions
Is AnythingLLM free software?
The project is MIT licensed and offers a free desktop route.
Do I need a paid model API?
Not for a suitable all-local configuration, though hardware resources are still needed.
Does choosing a local chat model make everything local?
No. Embeddings, tools and other providers must be checked too.
Can it answer from PDFs?
PDF is among supported document types; extraction quality should be inspected.
Are team features identical to desktop features?
No. Some capabilities are specific to the Docker deployment.
Does a source citation guarantee correctness?
No. Check that the passage supports the complete claim.
Can a website widget expose private material?
Its access and source scope must be designed and reviewed before public use.
The details behind your decision
Explore platforms, inputs and outputs, licensing, and access requirements.
Four useful starting projects
Four useful starting projects
- Build a personal reference desk: Create a workspace from your own notes and a few trusted manuals. Ask questions with known answers and keep track of outdated files, so the assistant remains useful as the reference set grows.
- Organize a support knowledge base: Use public help articles to explore whether questions lead to the correct article. Include a question that has no documented answer and require the assistant to say so rather than inventing a support policy.
- Compare document workflows: Try the same small source set with two model configurations. Evaluate retrieved passages and final answers separately, because a good model cannot compensate for every retrieval mistake.
- Prototype a team assistant: Use a self-hosted test instance with non-sensitive sample documents and a small set of user roles. Check what each user can access before connecting real internal material or embedding a public chat widget.
Platforms, inputs, outputs and setup
Platforms, inputs, outputs and setup
Official desktop downloads cover Windows, macOS and Linux. The built-in local model engine documented for Desktop is not interchangeable with every self-hosted configuration; follow the guide for the deployment actually in use.
Document chat quality depends on extraction, chunking, retrieval and model behavior. When an answer is wrong, first inspect the source passage that was retrieved. Replacing the language model immediately can hide a simpler problem such as an outdated file or a table that imported incorrectly.
Data, permissions and licensing
Data, permissions and licensing
A local-first configuration gives control over where documents and model requests are processed, but the actual provider settings determine the route. Review chat, embedding and agent connections individually before importing private files.
For shared use, separate workspaces and roles deliberately and verify access with non-sensitive examples. Keep backups of source documents and configuration, and remove obsolete material from the knowledge set so users do not receive confidently cited but outdated instructions.
Sources, documentation and help
Official sources and review date
Official sources and review date
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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