- Explore MCP Servers
- autogen-langchain-mcp-mix
Autogen Langchain Mcp Mix
What is Autogen Langchain Mcp Mix
autogen-langchain-mcp-mix is a multi-agent system that integrates Autogen, Langchain, and MCP to assist with Kubernetes configuration tasks by enabling agents to collaborate on researching, modifying, and applying YAML configurations.
Use cases
Use cases include automating the setup of Kubernetes clusters, managing Istio configurations, and facilitating collaborative tasks among agents to streamline YAML file management.
How to use
To use autogen-langchain-mcp-mix, clone the repository, set up a virtual environment, install the required dependencies, configure environment variables, and then set up a Kubernetes environment before running the main script to initiate the multi-agent system.
Key features
Key features include a PlanningAgent for task decomposition, a WebSearchAgent for information gathering, a FixerAgent for YAML file manipulation, and a RunnerAgent for executing terminal commands related to Kubernetes configurations.
Where to use
autogen-langchain-mcp-mix can be used in DevOps, cloud computing, and any environment requiring automated management of Kubernetes configurations.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Autogen Langchain Mcp Mix
autogen-langchain-mcp-mix is a multi-agent system that integrates Autogen, Langchain, and MCP to assist with Kubernetes configuration tasks by enabling agents to collaborate on researching, modifying, and applying YAML configurations.
Use cases
Use cases include automating the setup of Kubernetes clusters, managing Istio configurations, and facilitating collaborative tasks among agents to streamline YAML file management.
How to use
To use autogen-langchain-mcp-mix, clone the repository, set up a virtual environment, install the required dependencies, configure environment variables, and then set up a Kubernetes environment before running the main script to initiate the multi-agent system.
Key features
Key features include a PlanningAgent for task decomposition, a WebSearchAgent for information gathering, a FixerAgent for YAML file manipulation, and a RunnerAgent for executing terminal commands related to Kubernetes configurations.
Where to use
autogen-langchain-mcp-mix can be used in DevOps, cloud computing, and any environment requiring automated management of Kubernetes configurations.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Multi-Agent System that connects Autogen, Langchain, and MCP
This project demonstrates a multi-agent system using Autogen that assists with Kubernetes configuration tasks by combining
popular frameworks such as Autogen, langchain and shortly to be added too MCP.
The agents collaborate to research, modify, and apply YAML configurations.
Agents
The system comprises four specialized agents:
- PlanningAgent: Decomposes complex tasks into smaller subtasks and delegates them to other agents.
- WebSearchAgent: Gathers information from the web using DuckDuckGo search.
- FixerAgent: Creates, modifies, or corrects YAML files based on gathered information and best practices.
- RunnerAgent: Executes commands on the terminal, such as applying YAML configurations using
kubectl.
Installation
-
Clone the repository:
git clone https://github.com/rinormaloku/autogen-langchain-mcp-mix.git cd autogen-langchain-mcp-mix -
Install dependencies in a
venv:python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt
Configuration
-
Set up environment variables:
Copy the values from
.env.exampleto a new file named.env:cp .env.example .envUpdate the environment variables in the
.envfile as needed.
Usage
Setup a k8s environment and run the main script:
kind create cluster istioctl install -y python main.py
This will start the multi-agent system and initiate a conversation based on the predefined user_question in main.py. The agents will collaborate to address the issue related to the Istio VirtualService configuration.
Notes
- This project is a demonstration and may require adjustments for real-world applications.
- The
RunnerAgent’s ability to execute commands depends on the environment and permissions. - The termination condition is based on a maximum message count (30) or the mention of the word “TERMINATE”.
- Ensure that you have
kubectlconfigured correctly if you intend to apply the generated YAML configurations.
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.










