Agentis MCP
A flexible multi-agent framework with native MCP server connectivity.
Install & Use
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This Skill addresses the challenge of tool integration in multi-agent collaborative development. In practice, developers often need to connect to multiple external MCP servers simultaneously to access tools and resources. However, manually managing each connection, switching contexts, and orchestrating multi-agent collaboration is cumbersome. Agentis MCP provides a unified framework that enables you to easily build complex AI agent workflows while keeping your code clean and maintainable.
Usage is straightforward: simply install the agentis-mcp package via pip, then create a YAML configuration file to define MCP server addresses, transport protocols (stdio or SSE), and agent behavior parameters. You can then command your agents using natural language instructions, such as "Check the weather in San Francisco" or "Analyze this sales data and generate a report." The framework automatically coordinates underlying MCP connections, tool calls, and resource retrieval, freeing you from low-level details.
It is ideal for teams or individuals needing to rapidly develop multi-agent systems, especially those who have already deployed multiple MCP tool services (e.g., databases, APIs, file systems). With Agentis MCP, you can combine these capabilities like building blocks to quickly create AI applications with sophisticated decision-making abilities. It is also suitable for engineers looking to upgrade existing single-agent systems to a multi-agent collaborative architecture.
We recommend using this framework uniformly when building multi-agent collaboration systems. Note that it primarily relies on the MCP protocol, so your tool services must support the MCP standard. Additionally, for very large-scale production environments, you may need to pay extra attention to connection pool management and timeout strategy tuning.
Key Features
Unlike LangChain's AgentExecutor, Agentis MCP natively supports MCP server connections, enabling direct consumption of MCP tools and resources without additional adaptation. It offers both stdin and SSE transport modes, making it better suited for scenarios requiring persistent connections or remote invocation.
Limitations
The current version requires Python 3.8+ and depends on the MCP protocol (0.1.0+). Tools that do not comply with the MCP standard require an additional adaptation layer for integration.
FAQ
What is the difference between Agentis MCP and CrewAI?
While CrewAI also supports multi-agent systems, Agentis MCP focuses more on native integration of the MCP protocol, allowing you to directly mount any MCP server as a tool source without the need for secondary API wrapping.
Does it support SSE (Server-Sent Events) transport?
Yes. You can specify the transport as 'sse' in the configuration file and provide the server endpoint. The framework will automatically establish a persistent connection and manage the message flow.
Installation guide for AI assistants
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