Agentic AI Tools
Cherry Studio Setup Guide
Cherry Studio is useful when a research workflow needs chat, files, models, and MCP servers in one interface.
Beginner · 20–30 min · API key required
Research starts with seeing. A unified GUI lets you explore before you commit to a terminal workflow.
Overview
Cherry Studio brings chat, knowledge bases, MCP servers, and CLI coding agents into one GUI. It is the central interface for researchers who want model comparison and file-based work without starting in a terminal.
Features:
- Multiple AI model access through one interface (100+ models)
- Knowledge Base for loading your literature corpus
- MCP servers for Zotero, web search, and other external tools
- Agent Workspace for a first file-touching agent without a terminal, and a Coding Companion for launching CLI agents such as Claude Code, Codex, and Kimi Code from within Cherry Studio
- Conversation forking for exploring different analytical paths
- Export to markdown for Obsidian integration
Official documentation: Cherry AI Docs and the installation guide.
Download and install Cherry Studio
Visit Cherry Studio GitHub Releases and download the version for your operating system.
- Open the downloaded
.dmgfile - Drag Cherry Studio to your Applications folder
- First launch: Right-click → Open (to bypass security warning)
Initial configuration
When you first open Cherry Studio, you'll see:
- Welcome screen with model selection
- API configuration section
- Settings panel
Navigate to settings by clicking the Settings icon (gear icon) in the sidebar, then select API Keys or Model Configuration. For API-key basics, free tiers, provider options, and setup instructions, see the API Keys Setup Guide.
Configure API provider
You'll need an API key from a provider to access AI models. See the API Keys Setup Guide for detailed instructions on getting keys from Google AI Studio (free), OpenRouter, or other providers. If you are following the Systematic Review course, use the shared OpenRouter key supplied by your instructor and skip the API setup guide.
Navigate to API settings
- In Cherry Studio, click the Settings icon (gear icon)
- In the settings menu, select API Keys
Add your provider
- In the API Keys panel, click the Add Provider button
- Select your provider, such as Google AI Studio or OpenRouter.
Enter and test API key
- A configuration window will appear
- Paste your API key into the field (starts with
sk-or similar) - Click the Test Connection button - you should see a green "Success" message
- Click Save
You're now ready to use AI models in Cherry Studio.
Optional: additional API providers
Optional Providers for Specific Models
OpenRouter provides access to most models you'll need. You may also want to configure additional providers for specific models or embedding services:
DeepSeek provider
Optional: if you want direct access to DeepSeek models:
- Click Add Provider → DeepSeek
- Create account at platform.deepseek.com
- Recommended Model:
deepseek-v4-flashif it is available; otherwise choose the DeepSeek model your provider marks as the default or recommended option. - Note: You can also access all DeepSeek models via OpenRouter.
Moonshot AI (Kimi)
Optional: for direct access to Kimi models:
- Click Add Provider → Custom
- Name: "Moonshot AI (Kimi)"
- Create account at platform.moonshot.ai
- Base URL:
https://api.moonshot.ai/v1 - Recommended Model:
kimi-k2.7-codefor coding tasks, orkimi-k2.6for general multimodal work - Note: You can also access Kimi models via OpenRouter.
Google AI Studio provider
Recommended free backup for generous daily limits:
- Create account at aistudio.google.com
- Get API key at aistudio.google.com/app/apikey
- In Cherry Studio: Add Provider → Google Gemini
- Paste your API key (starts with
AIza…)
Available models:
gemini-3.5-flash(stable default for strong general work)gemini-3.1-pro-preview(preview for complex analysis)gemini-3-flash-preview(preview for lower-cost frontier-class work)- Gemini embedding models (document similarity)
Use cases:
- Processing large literature collections (1M token context)
- Backup when course API credits are low
- Cost-free experimentation
- Document embeddings and semantic search
Daily limits reset at midnight Pacific Time.
Which models should I add?
Now that you have configured your API providers, choose which models to add.
For recommendations on models, costs, and configuration, see the AI Model Reference Guide.
MCP Servers for research access
What Are MCPs?
MCP (Model Context Protocol) gives an AI client access to external tools and data. Without MCPs, the model only has the conversation context. With MCPs, it can:
Examples of what MCPs enable:
- search your Zotero library: "Find all papers about organizational learning from 2020-2024"
- read and write files: "Analyze the methodology section in Chapter3.docx"
- search the web in real time: "What's the latest research on AI in education published this month?"
- use structured reasoning: "Break down this complex theory comparison in 5 structured steps"
- ask another model: "Get Gemini's perspective on this analysis" (via Vox MCP)
MCPs let the model work with sources, files, and tools that are outside the chat window.
Recommended MCPs for Research
Essential MCPs:
@cherry/filesystem- lets the model read your files and analyze documents@cherry/sequentialthinking- supports step-by-step structured reasoning
Useful additions:
- Zotero MCP - direct library access and search
- Web Search MCP - current web information
Advanced MCPs:
- Lotus Wisdom MCP - contemplative problem-solving
- Vox MCP - multi-model access across 8+ providers
For installation instructions, see the MCP Explorer Guide. Session 2 covers the course setup in depth.
How to Install MCPs
In Cherry Studio:
- Settings → MCP Servers
- Click "Add Server"
- Choose from library or paste MCP URL
- Configure and test
To explore available MCPs, see MCP Explorer Guide for:
- Complete MCP catalog
- Installation instructions for each MCP
- Cherry Studio, Claude Code, and other client configs
- Use cases and examples
Official MCP docs: Cherry Studio MCP Guide | MCP Protocol
Create a first agent in the Agent Workspace
From chat to agent without a terminal
The chat view is for exploration, and nothing there touches your files. The Agent Workspace, under Work in the left sidebar, is where an agent gets a working directory, tools, and a multi-step task. For most participants this is their first agent, and it is the reason this guide still starts here rather than in a terminal.
Create one:
- Open Work and choose Create Agent.
- Pick a run mode. Enhanced: Claude Agent takes a main model, a plan model, and a small model for subtasks; Fast: Pi and DeepSeek Harness take a main model only. The run mode is locked after creation.
- Write the system prompt: the role, what the agent may read, and what it must hand back.
- Attach what it needs: built-in tools, MCP servers, Skills, a knowledge base.
Give it the same literature task you gave the chat, and compare. The difference between an answer and a worked file is the lesson.
Documentation: Agent Workspace.
Coding Companion for CLI agents
Access CLI Power from the GUI
Cherry Studio's Coding Companion (Code CLI) installs, configures, and launches command-line agents from inside the app. The current list includes Claude Code, OpenAI Codex, Gemini CLI, OpenCode, Qwen Code, Kimi Code, Qoder CLI, GitHub Copilot CLI, Pi, and DeepSeek Harness; the visible items change with product updates, so treat the page's own list as authoritative.
Why use it?
- Access CLI agent capabilities without leaving Cherry Studio
- No separate terminal setup needed
- Integrates with your API keys and models
- Works well for file-based research workflows
For the click path, follow the Coding Companion documentation: choose the tool, install it from the page, point it at your research project folder, and launch it in the embedded terminal.
For Research Memex, we recommend:
- Beginners → the Agent Workspace above, then Claude Code when a task outgrows it
- Advanced → Claude Code or Codex
- Experimenters → OpenCode or Qwen Code (open source)
Warning
Token usage: CLI agents can consume significant API tokens. Monitor usage carefully, especially with complex file operations.
When to use the Coding Companion vs a standalone CLI:
- use the Coding Companion when you want GUI convenience and integrated workflow
- use a standalone CLI when you prefer to work in a terminal and want full control
For standalone CLI setup, see:
Test your setup
Create test conversation
-
Click New Chat in the sidebar
-
Select a model (start with Gemini 3.7 Flash, Claude Sonnet 5, or your provider's current default DeepSeek model)
-
Type this test prompt:
Please summarize the key components of a systematic review according to PRISMA guidelines in 3 bullet points. -
Press Enter or click Send
Expected response: you should receive a concise summary within 5-10 seconds.
Verify model access
Test each configured model:
- Create new conversation
- Select different model from dropdown
- Send same test prompt
- Compare responses
Or select several models in the input area and send once; Cherry Studio answers side by side, the quickest way to see the same prompt go different places (multi-model comparison).
Test MCP tools
Verify MCP servers are working:
- Test Zotero: "Search my Zotero for systematic review papers"
- Test Sequential Thinking: "Help me plan a literature review in 5 steps using sequential thinking"
- Test Web Search: "Find recent papers on AI in management"
Expected: each command should return relevant results.
Set up Knowledge Base
Prepare your documents
- Export your curated papers from Zotero as PDFs
- Create a folder:
systematic-review-papers - Place all PDFs in this folder
Create Knowledge Base in Cherry Studio
- Click Knowledge Base in sidebar
- Click Create New Collection
- Name it: "My Systematic Review"
- Click Add Documents
- Select your PDF folder
- Wait for processing (1-2 min per 10 papers)
Advanced options:
- OCR processing: enable for scanned PDFs (requires v1.4.8+)
- intent recognition: improves search accuracy with stronger models
- multiple formats: supports PDF, TXT, Markdown, Word, and other formats
Enable Knowledge Base in conversations
- Start new conversation
- Click the Knowledge icon in the chat toolbar
- Select your collection
- The AI now has access to your papers.
Tip: enable "Intent Recognition" in knowledge base settings for more accurate search results when asking complex research questions.
Knowledge management with Obsidian
Why Obsidian?
Obsidian is a markdown editor for connected research notes:
- local storage: your notes stay on your computer
- bidirectional linking: connect ideas across papers
- Zotero integration: citation management
- graph view: visualize connections in your research
- MCP accessibility: AI can read your knowledge base
Quick setup overview
For detailed instructions, see the Obsidian Setup Guide.
Essential steps:
- Install Obsidian from obsidian.md
- Create vault: "Systematic-Review-Research"
- Install plugins:
- Zotero Integration (multiple options available)
- Dataview (for literature tables)
- Local REST API (for MCP access)
- Configure integration with Zotero (requires Better BibTeX)
- Set up MCP for Cherry Studio access
Cherry Studio to Obsidian workflow
Set Up Folder Structure
Create this structure in your Obsidian vault:
/Research/
/01-Literature-Notes/ # Individual paper notes from Zotero
/02-AI-Conversations/ # Exported Cherry Studio chats
/03-Synthesis/ # Your analysis and connections
/04-Protocol/ # Review protocol development
/05-Daily-Notes/ # Research journal
/Templates/ # Note templatesExport from Cherry Studio to Obsidian
-
In Cherry Studio conversation:
- Click Export button (or
Cmd/Ctrl + E) - Choose Markdown format
- Select Save to Folder
- Navigate to
/02-AI-Conversations/ - Name format:
YYYY-MM-DD-Topic.md
- Click Export button (or
-
The exported file includes:
- Complete conversation history
- Model used and timestamps
- Any code blocks or tables
- Referenced papers (if using Zotero MCP)
Create Literature Note Template
Save this in /Templates/literature-note.md:
# {{title}}
## Metadata
- **Authors**: {{authors}}
- **Year**: {{year}}
- **Journal**: {{publicationTitle}}
- **DOI**: {{DOI}}
- **Tags**: {{tags}}
- **Zotero**: [Open in Zotero]({{zoteroLink}})
## Summary
*AI-generated or your summary*
## Key Contributions
-
## Methodology
-
## Limitations
-
## Relevance to My Research
-
## Connections
- Related papers:
- Contradicts:
- Extends:
## Annotations
{{annotations}}Bidirectional workflow benefits
- From Zotero to Obsidian: Import papers with annotations
- From Cherry Studio to Obsidian: Export AI analysis
- Within Obsidian: Link papers, find patterns, build arguments
- Back to Cherry Studio: Copy synthesis for further AI analysis
Advanced integration: Cherry Studio can also connect directly to Obsidian via MCP or data settings. See: Cherry Studio Obsidian Integration
Troubleshooting
'API Key Invalid' Error
- Double-check you copied the key completely, with no extra spaces
- Ensure you have credits in your account
- Try regenerating the API key
'Rate Limit Exceeded'
- Wait 60 seconds and try again
- Switch to a different model temporarily
- Check your API provider's rate limits
'Connection Failed'
- Check internet connection
- Verify firewall isn't blocking Cherry Studio
- Try using a different API provider
Knowledge Base not working
- Ensure PDFs are text-based (not scanned images)
- Check file size (max 10MB per file recommended)
- Try re-importing documents
Model not responding
- Check API key configuration
- Verify you have credits remaining
- Try a different model to isolate issue
Quick reference card
Keyboard shortcuts
- New Chat:
Cmd/Ctrl + N - Fork Conversation:
Cmd/Ctrl + Shift + F - Search Conversations:
Cmd/Ctrl + F - Export Chat:
Cmd/Ctrl + E - Settings:
Cmd/Ctrl + ,
Document processing options comparison
| Feature | MinerU (Free) | Mistral API | Direct Text |
|---|---|---|---|
| Daily Limit | 10,000 files/day* | Unlimited | Unlimited |
| Cost | Free* | $4/1,000 pages* | Free |
| Quality | Good | Excellent | Basic |
| Math Formulas | ✓ LaTeX | ✓ LaTeX | ✗ |
| Tables | ✓ Preserved | ✓ Enhanced | Partial |
| Images | ✓ Extracted | ✓ OCR | ✗ |
| Multi-column | ✓ | ✓ | ✗ |
| Speed | Fast | Moderate | Instant |
*Published limits and prices are endpoint-, plan-, and page-count dependent. Check MinerU's current limits and Mistral's current OCR pricing before a batch.
Best practices
- Choose the right model for your task. See the AI Model Reference Guide for recommendations. If you are unsure, experiment with cheaper or free models first. Use expensive models when your analysis requires more capability.
- Fork conversations before trying different approaches. This feature is available in Cherry Studio, ChatWise, ChatGPT, and Claude.ai.
- Export important conversations immediately.
- Cherry Studio and ChatWise support native export.
- For ChatGPT, Claude.ai, or the Gemini web interface, use the Save my Chatbot Chrome and Firefox extension or the Obsidian Web Clipper.
- Start a new conversation if the responses become slow or low quality.
What's next
Cherry Studio is the GUI hub. Once you're comfortable, the natural next step is a CLI agent. See the Claude Code Setup Guide for paired terminal work.
External references
- Cherry AI Docs
- MCP Installation
- Knowledge Base Guide
- GitHub Repository
- OpenRouter API Docs
- MCP Protocol Docs
If Cherry Studio doesn't fit, ChatWise (chatwise.ai) is a close alternative. The OpenRouter playground and provider web UIs (ChatGPT, Claude.ai, Gemini) cover the gap for one-off chats.
Checklist
By the end of this guide, you should have:
- Downloaded and installed Cherry Studio
- Created at least one API account (OpenRouter recommended)
- Added $5-10 in API credits
- Successfully sent a test message to any added model
- Enabled and tested at least one MCP server (Zotero, Sequential Thinking, Web Search)
- (Optional) Created an agent in the Agent Workspace, or launched a CLI agent from the Coding Companion
- Installed Obsidian and created a vault (a folder on your computer)
- Exported a conversation to markdown format and imported it to Obsidian
- Created and tested a knowledge base in Cherry Studio with your seed papers
- (Optional) Configured OCR and intent recognition for knowledge base
How Do You Know It's Working?
For more information
If you plan to experiment with command‑line tools or provider‑specific keys later, see the CLI Setup Guide (optional).
For API key setup and provider configuration, see the API Keys Setup Guide.
For model selection and recommended settings, refer to the AI Model Reference Guide. The guide covers reasoning effort, thinking level, and when to leave sampling defaults alone.
cite this page
Lin, X. (2026). Cherry Studio Setup Guide. Research Memex. https://research-memex.org/docs/implementation/agentic-ai-tools/cherry-studio-setup-guide
@misc{docs-implementation-agentic-ai-tools-cherry-studio-setup-guide-2026,
author = {Xule Lin},
title = {Cherry Studio Setup Guide},
year = {2026},
howpublished = {\url{https://research-memex.org/docs/implementation/agentic-ai-tools/cherry-studio-setup-guide}},
note = {ORCID: 0000-0001-7885-4194}
}one renderingthe source remains