learn-agentic-ai-from-low-code-to-code

This MCP server hosts the Learn Agentic AI course experience, providing access to no-code agent tools and a guided environment for building, testing, and deploying agent-driven workflows. The server exposes a suite of OpenAI AgentKit-based capabilities, including a visual Agent Builder for designing multi-step workflows, a Connector Registry for managing data connections, ChatKit for embedding branded chat interfaces, Evals for evaluating agent performance, and Guardrails for safety checks. Users can instantiate course labs, run guided labs, and publish working agents with transcripts and metrics to measure impact. The platform emphasizes visual, code-free interactions while offering pathways to more advanced configurations as learners progress from low-code to code-centric approaches.

Prerequisites:

• Docker installed and running on your machine or host
• Sufficient CPU/RAM to run containerized workloads (at least 2 cores and 4 GB RAM recommended)

Step-by-step:

1. Pull the MCP server image (one-time):

docker pull panaversity/learn-agentic-ai-from-low-code-to-code:latest

2. Run the MCP server container interactively:

docker run -it --rm panaversity/learn-agentic-ai-from-low-code-to-code:latest

3. Verify the server is running and accessible (port mappings may be defined by the image). If the image exposes a web UI or API, access it via the container’s published port as documented in the image docs.

4. Optional: set environment variables for configuration (example placeholders shown below). Run with -e flags if needed:

docker run -it --rm -e AGENTKIT_API_KEY="<your-api-key>" -e REGISTRY_URL="https://registry.example.com" panaversity/learn-agentic-ai-from-low-code-to-code:latest

5. For development, you can build from a Dockerfile if you fork the repository and want to customize the environment, then run the local image similarly.

Note: If you prefer non-Docker deployment, consult the project’s documentation for alternative install methods (npx, Node, or Python options) if provided by the maintainers.

Tips and considerations:

• Environment variables: AGENTKIT_API_KEY, REGISTRY_URL, CHATKIT_API, and any data connector credentials may be required for full feature access. Replace placeholders with your actual keys/URLs.
• If you encounter port conflicts, adjust the container’s -p mapping in the Docker run command as per the image’s documentation.
• This MCP server focuses on no-code to code workflows using AgentKit tooling; for deeper customization, you may need to integrate with your own data sources and authentication providers.
• Ensure you have sufficient permissions for data connectors (Drive, SharePoint, etc.) if you plan to attach knowledge sources.
• Review guardrails and eval configurations to tailor safety checks and evaluation criteria to your organization’s policies.