B
Blue
January 17, 2026
Introduction to Eigent
Eigent is a multi-agent desktop application based on CAMEL-AI, with 3.7k+ Stars on GitHub.Core Features
- Multi-Agent Collaboration: Specialized Agents like Developer, Search, Document work in parallel
- Custom Agents: Support for creating custom Agents based on your needs
- Clear Task Monitoring: Clean and elegant interface for clearly monitoring the multi-Agent collaboration process
Quick Start
Step 1: Get MiniMax API Key
How to get:
- Users subscribed to Coding Plan can use Coding Plan API Key
- New users without subscription can try the free quota from Pay-as-you-go
Step 2: Download and Install Eigent
1
Install Node.js
Make sure Node.js (version 18 ~ 22) and npm are installed
2
Clone Repository
3
Install Dependencies
Step 3: Launch Eigent Application
Run the following command in the terminal to start the Eigent application:This article runs Eigent in self-hosted (community edition) mode. First-time users need to register on the Eigent website, then log in with your account in the local Eigent application.
First-time startup requires installing environment dependencies, which may take some time. Please be patient.
Step 4: Configure MiniMax M2.1 Model
Click: Logo in upper left → Settings → Models tab to enter the model configuration interface.
Scroll down to the MiniMax model configuration area and configure as follows:
| Configuration Item | Value |
|---|---|
| API Key Setting | Your MiniMax API Key |
| API Host Setting | https://api.minimax.io/v1 |
| Model Type Setting | MiniMax-M2.1 |
Step 5: Create Project and Start Task
Enter the Project interface, click the ”+” button in the upper right corner to create a new Project, and enter the new Project interface. Input your task requirements and send them to the Agent for execution.
Demo
Task Requirements
We will test a complex travel planning task:Based on personal travel preferences, desired attractions, travel dates, and total budget, provide a detailed itinerary including attractions, hotels, transportation, dining, etc., ultimately creating an HTML travel handbook.
Task Execution Process
1
Phase 1: Task Planning
The MiniMax M2.1 model has built-in thinking capability. After sending the task requirements in Eigent, it first goes through deep thinking to clarify task requirements and goals, dividing them into 7 steps:
- Search for attractions
- Search for panda attractions
- Search for dining and restaurants
- Search for transportation
- Search for practical tips
- Create 7-day itinerary
- Create HTML handbook
2
Phase 2: Conducting Search
Based on task planning, the MiniMax M2.1 model acts as a Search Agent calling search tools to conduct search tasks. In the Eigent framework, 5 search tasks execute in parallel.
3
Phase 3: Compiling into Handbook
After the Search Agent completes information gathering, the overall task progress updates. MiniMax M2.1 takes on the role of Document Agent, receiving the gathered materials and arranging the itinerary and writing the HTML travel handbook according to task requirements.
Summary
Through this tutorial, we demonstrated how to integrate the MiniMax M2.1 model into the Eigent multi-agent framework and complete complex tasks:- MiniMax M2.1’s thinking capability enables it to autonomously plan execution steps for complex tasks
- Multi-Agent collaboration allows search, document generation, and other tasks to execute in parallel, significantly improving efficiency
- The final output is a complete HTML travel handbook, demonstrating MiniMax M2.1’s excellent performance in long-horizon planning tasks