Tsinghua University Future Lab: Open source multi-agent collaboration framework!
Cooragent, launched by Tsinghua University Future Lab, opens a new era of AI collaboration.
Core content:
1. Cooragent: Zero threshold to create a collaborative community of intelligent agents
2. Five core advantages, surpassing traditional AI development frameworks
3. Actual combat cases: Full process automation of stock analysis and travel planning
Introduction: When AI learns “teamwork”
Imagine that you only need to say one sentence to the computer: "Help me analyze the trend of Xiaomi's stock", and you can immediately get a complete analysis report; or enter "Plan a five-day tour in Yunnan", and the system will automatically generate a complete plan including scenic spot recommendations, itinerary arrangements and even PDF guides. This sounds like a scene from a science fiction movie, but CoorAgent developed by the Tsinghua University team has made such a future within reach.
1. Redefine AI collaboration
1.1 What is a Cooragent?
Cooragent is not an ordinary AI tool, but a collaborative community of intelligent agents . Its special features are:
Zero threshold creation : Use everyday language to describe your needs, and the system will automatically generate a professional AI assistant
Autonomous collaboration : complex tasks will be broken down and completed by different AI agents
For example, if you want to analyze stocks, you don’t need to know programming. Just say “create a stock analysis expert” and the system will automatically call modules such as data crawlers, analysis models, and report generators to complete the entire process just like forming a professional team.
1.2 Two working modes
Agent Factory builds a customized AI assistant just like a custom robot. Users only need to describe their needs, and the system will automatically optimize prompts and select tools without writing any code. The generated agent can also be adjusted and optimized at any time, just like pre-job training for employees.
When Agent Workflow handles complex tasks, the system coordinates multiple AI agents like a project manager. For example, when planning a trip, the crawler agent is responsible for collecting information, the browser agent simulates manual selection of attractions, the route planning expert designs the itinerary, and finally the file agent generates a PDF report - the whole process is fully automated.
2. Technological breakthrough: How is it better than traditional tools?
Compared with common AI development frameworks (such as LangChain), the innovation of Cooragent can be understood by a metaphor: traditional tools are Swiss Army knives with a single function, while Cooragent is a robot team that can autonomously divide labor.
2.1 Five core advantages
| Feature Comparison | Cooragent | Traditional tools |
|---|---|---|
2.2 Analysis of key technologies
MCP protocol : Like a shared notebook for team members, ensuring that each AI agent has access to the complete task context
A2A communication : supports direct communication between agents and dynamically adjusts the division of labor like a human team
Dual-mode architecture : can quickly create a single agent, but also form a cross-domain collaborative network
3. Real Case: How the AI Team Works
3.1 Stock Analysis Practice
When the user enters the command:
run -t agent_workflow -u user01 -m "Analyze the trend of Xiaomi stock in the past month, predict tomorrow's stock price and give investment advice"
The AI team behind it works like this:
Data Scout : Crawler Agents Capture Historical Data from Financial Websites
Analyst : Code agent runs predictive model to generate trend chart
Strategic Advisor : Generate graphic reports with buying and selling suggestions based on market sentiment
The whole process is like having a team of financial analysts on call 24 hours a day, and the user only needs to speak.
3.2 The whole process of travel planning
In the case of planning the 2025 May Day Yunnan tour, the collaboration of the AI team is textbook-like:
Information collection team : Crawler agents scan the travel platform and collect information on more than 500 attractions
Screening expert group : The browser agent simulates human browsing behavior and screens out 30 candidates with a score of > 4.8
Route designer : Optimize the 5-day itinerary based on transportation distance and tour duration
Document Specialist : Automatically generate PDF guides with map annotations and budget estimates
This is equivalent to hiring a travel planner, a data analyst, and a copy editor at the same time, and the cost is just typing a line of command.
4. Three steps to get started: Even a novice can master AI collaboration
4.1 Installation Guide (Windows/Mac)
# Create a dedicated environment (to avoid software conflicts)
git clone https://github.com/LeapLabTHU/cooragent.git
cd cooragent
conda create -n cooragent python=3.12
pip install -e .
# Install browser support (for web page manipulation agents)
playwright install The whole process is like assembling Lego blocks - all the modules are prefabricated and you just need to assemble them according to the instructions.
4.2 Key configuration tips
copy.env.examplefor.envAfter the file, it is recommended to focus on:
API key : It is recommended to configure multiple large models (such as OpenAI+Gemini) at the same time. The system will automatically select the best solution.
Proxy settings : Domestic users can add a transit service to increase access speed
Log level : set to DEBUG for debugging and INFO for daily use
4.3 Quick Lookup of Common Commands
run -t agent_factory -u your ID -m "Requirement description" | |
list-agents -u yourID | |
run -t agent_workflow -u your ID -m "task description" |
5. Why is it worth paying attention to?
5.1 Value to ordinary users
Liberate productivity : Use natural language to replace complex programming and handle professional-level tasks
Creative Experimental Field : Free combination of intelligent entities, such as letting poetry generator + illustration AI collaborate to create picture books
5.2 What it means to developers
Modular development : Existing tools (such as LangChain components) can be directly embedded
API scalability : Automated management is achieved through RESTful interfaces, such as timed triggering of intelligent maintenance
5.3 Prospects of Enterprise Applications
Cross-departmental collaboration : HR agents automatically synchronize attendance data to the financial system
Intelligent customer service upgrade : Combined with the business system to achieve a complete link of pre-sales consultation-order processing-after-sales follow-up
6. The Future is Here: The Prototype of AI Collaborative Ecosystem
The project roadmap shows that Cooragent is promoting three major upgrades:
Visual editor : Design agent workflows by dragging and dropping, no need to use command lines
App Store : One-click deployment of community-contributed intelligent solutions, such as the "Short Video Script Generation Kit"
Long-term memory : The intelligent body will remember user preferences and provide personalized services
This means that in the near future, everyone will be able to configure an AI assistant like forming a digital team - data analysts, travel planners, and copywriters are on call at all times.
