M

MCP Agentis

Agentis MCP is a flexible multi - agent framework for building powerful AI agents, supporting MCP server connection, and providing functions such as tool access, resource retrieval, and multi - agent workflow orchestration.
2 points
17

What is Agentis MCP?

Agentis MCP is a multi - agent framework that allows developers to build AI agents that can connect to MCP servers. It provides functions such as tool access, resource retrieval, and multi - agent workflow orchestration, making AI application development more efficient.

How to use Agentis MCP?

Through a simple Python API, you can create custom agents, connect to the MCP server, and run various tasks. The framework provides a configuration system and supports multiple transport mechanisms.

Use cases

Suitable for application scenarios that require integrating multiple AI services, building complex workflows, or flexibly configuring agent behaviors, such as intelligent customer service and data analysis automation.

Main features

MCP server connectionSupports connecting to MCP servers to obtain tools and resources
Multi - agent workflowBuild and manage complex processes where multiple agents work together
Simple APIAn intuitive Python interface for easily creating custom agents
Flexible configurationSupports system configuration through YAML files
Multiple transport mechanismsSupports multiple communication methods such as stdio and SSE
Multi - server aggregationCan connect to and use resources from multiple tool servers simultaneously

Advantages and limitations

Advantages
Simplifies the development and integration process of AI agents
Supports complex multi - agent collaboration scenarios
Flexible configuration options to meet different needs
A lightweight and easily extensible architecture
Limitations
Requires basic Python programming knowledge
The MCP server needs to be set up and configured separately
Complex workflows may require more debugging

How to use

Install the framework
Install the Agentis MCP package using pip
Create a configuration file
Prepare a configuration file in YAML format to define parameters such as server connection
Write agent code
Use the framework API to create and run agents
Run tasks
Execute the required tasks through the agent

Usage examples

Weather queryObtain weather information for a specified city by connecting to the weather service API
Data analysisUse multiple agents to collaborate to complete data analysis tasks

Frequently Asked Questions

What Python version is required?
How to connect to a custom MCP server?
What types of tools are supported?
How many agents can be run simultaneously?

Related resources

Official documentation
Complete framework documentation and API reference
Example code library
Example code for various use cases
MCP protocol description
Official specification of the Model Context Protocol
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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