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DESKTOP AI / MODELS & KNOWLEDGE
Cherry Studio
A free desktop workspace for comparing models, organizing reusable assistants and working with document knowledge, using local or cloud AI.
Official Cherry Studio documentation image of the desktop agent workspaceExplore the image ↓
What you can do with it
What this tool is for
Cherry Studio helps when your AI work is spread across several websites and repeated prompts. The desktop client keeps assistants, conversations and document knowledge together, while a local model or configured cloud provider supplies the intelligence. The community client is free to obtain. It organizes AI services; answer quality still depends on the selected model and source material.
A useful first project is a document-review assistant for one course, product or research topic. Give it a narrow remit and a small collection of current files. Compare answers without rebuilding the instructions each time. For occasional one-line questions, a simple web chat may be easier; this workspace becomes more useful as reference material and recurring tasks accumulate.
01
Compare technical explanations
02
Review a product manual
03
Maintain a writing assistant
04
Work with local notes
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
- Unified model access: Keep supported cloud providers and local endpoints in the same client. Check a new connection with a short task before relying on a complex document workflow.
- Reusable assistants: Save role instructions and model settings for recurring tasks. An editing assistant should know the reader, terminology and facts to preserve, not just a generic personality.
- Model comparison: Ask several models the same question. Compare omissions, evidence and manual editing effort; confident wording alone does not make an answer correct.
- Document knowledge: Connect a relevant knowledge base to an assistant. Separate projects and document versions so retrieval is less likely to mix unrelated or outdated material.
- Local supporting models: The settings guide documents downloadable local embedding and OCR models. They support indexing and text recognition; a conversational language model is still a separate component.
- Readable deliverables: Use Markdown, code and diagram rendering and the available conversation exports. Preserve the question and its source alongside the corrected output so another person can review your reasoning.
- Tool extensions: Agent and MCP capabilities can connect a task to additional tools. Add them for a concrete need and distinguish a generated explanation from an action that actually changes a file or service.
Get started, step by step
- 01
Install the community client
- 02
Choose a free route
- 03
Create an assistant and add a short source
- 04
Check and preserve the result
A practical first workflow
A practical first workflow
- Install the community client. Choose an official release for your operating system. Back up an existing workspace before a major-version upgrade.
- Choose a free route. Refresh the built-in CherryAI model list or connect a local provider such as Ollama. Basic conversation management does not require buying a third-party API subscription.
- Create an assistant and add a short source. Define its task and output format. If using a knowledge base, inspect a retrieval result before asking a broad question.
- Check and preserve the result. Compare the answer with the document, refine the instruction and export the useful conversation. Keep important source files separately.
Example prompt or task: Using only the supplied manual, list setup steps, prerequisites and unsupported cases. Identify the relevant section. If a fact is missing, say it was not found in the supplied material.
This is an editorial evaluation task. Include one question whose answer is absent; recognizing a gap is more useful than inventing a complete-looking procedure.
Find the right plan for you
Free client
Explore official plans ↗Free software Model services can cost extra
Built-in free models and rate limits change. External APIs and enterprise services are separate.
What is free and what can cost extra
The community desktop uses AGPL-3.0. The official CherryAI guide describes an integrated free model entry whose lineup, allowances and rate limits can change. Use the refreshed list inside the app as the current reference; no particular free model or fixed daily quota is promised here.
Local inference can avoid per-request cloud charges after downloading models, but uses your hardware, memory, storage and electricity. Paid APIs, aggregators and enterprise offerings remain separate. Account for document embedding and OCR as well as the conversational model when comparing the total cost.
A few things worth knowing
Frequently asked questions
Is the application itself free?
The community client is free. Connected model services are separate: built-in allowances can change and external paid APIs remain billable.
Can I begin without an API key?
The current CherryAI entry offers a built-in starting route. A configured local provider is another option. Check Model Services in the installed version.
Can its local OCR model answer questions?
OCR recognizes text and embeddings support retrieval. Neither replaces the conversational model selected for answering.
Does a local knowledge base mean everything stays offline?
Not automatically. Cloud chat or embedding services can receive material required for their requests. Review each component.
Does comparing models consume more credits?
With metered providers, several generated answers can mean several charges. Start with a small comparison and inspect provider usage.
Can an assistant edit local files?
Configured agent tools may do so with suitable permissions. Ordinary chat advice does not establish that a file has actually changed.
What should I back up?
Preserve important conversations, configuration and source files. Follow the current migration instructions before changing major versions.
The details behind your decision
Explore platforms, inputs and outputs, licensing, and access requirements.
Four useful starting projects
Four useful starting projects
- Compare technical explanations: Give two models the same paragraph and ask for an explanation for a beginner. Keep a short checklist of essential concepts and choose the result needing fewer corrections.
- Review a product manual: Create a knowledge base for one product version. Check installation and limitation answers against the original passage before turning them into customer guidance.
- Maintain a writing assistant: Save audience, tone and terminology instructions once. Start a fresh topic for each unrelated project to keep old context from contaminating a new draft.
- Work with local notes: Use a local chat provider and local embeddings for a bounded collection. Confirm that OCR, search and enabled tools also follow the data path you intended.
Platforms, inputs, outputs and setup
Platforms, inputs, outputs and setup
Desktop releases cover Windows, macOS and Linux. Match the download to your processor and operating system. Original documents, knowledge indexes and conversation exports are different objects; keep the originals and check the current export choices before planning a team handoff.
An assistant preset is reusable configuration, not a newly trained model. A knowledge base retrieves relevant material rather than permanently teaching every connected model. If a response overlooks a file, inspect extraction and retrieval before rewriting the whole prompt or switching providers.
Data, permissions and licensing
Data, permissions and licensing
The current policy describes local storage for conversations and configured keys, along with controls for anonymized operational and improvement data. Third-party providers receive their own requests, and built-in model services can involve a relay. A desktop interface does not by itself prove an offline data path.
Community code uses AGPL-3.0; enterprise arrangements differ. Keep the application license, individual model license and document rights separate when redistributing a modified client or sharing output.
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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