Framework Choices Are Sticky
Switching an MCP server is straightforward. You swap one for another, update your config, and move on. Switching an agent framework is a much bigger deal. Your workflows, your custom logic, your tool integrations, and potentially your team's mental model of how things work are all tied to the framework. That's why it's worth evaluating carefully before you commit.
The agent frameworks listed on Skillful.sh range from lightweight orchestrators to full platforms with their own deployment infrastructure. The right choice depends on what you're building and how much of the stack you want the framework to own.
Architecture Fit
Some frameworks are opinionated about architecture: they want you to structure agents in a specific way with specific patterns. Others are more flexible, giving you primitives and letting you compose them however you want. Neither approach is universally better, but one will fit your existing codebase and team preferences more naturally than the other.
If you already have infrastructure you're happy with and just need an orchestration layer, a lightweight, flexible framework will cause less friction. If you're starting from scratch and want a batteries-included experience, a more opinionated framework saves you from making a dozen architectural decisions yourself.
MCP Support and Tool Ecosystem
How well does the framework integrate with MCP servers? Some frameworks have first-class MCP support where connecting a new server is a single configuration change. Others require adapters or custom code to bridge between the framework's tool interface and the MCP protocol. The difference matters as your toolkit grows.
Check the MCP server directory for compatibility notes if they exist, and look for examples of people using the framework with the specific servers you plan to use. Community experience is the best predictor of how smooth the integration will actually be.
Community and Maintenance
A framework's community health tells you a lot about its future. Active maintainers, responsive issue tracking, regular releases, and a growing contributor base are all positive signs. A framework that's technically impressive but maintained by one person in their spare time is a risk you should weigh carefully.
Look at the trajectory, not just the current state. A smaller framework with growing momentum might be a better bet than a larger one that's losing contributors. The trending page can help you spot which frameworks are gaining versus losing traction.
Try Before You Invest
Build a small prototype with your top two candidates before committing. A day spent prototyping will reveal integration issues, developer experience problems, and architectural mismatches that no amount of documentation reading will surface. The cost of a day is trivial compared to the cost of switching frameworks six months into a project.
Related Reading
- The Developer Experience Gap in AI Tooling
- How Open Source AI Tools Compare to Proprietary Alternatives
- Building an AI-First Development Workflow from Scratch
Explore AI agent frameworks. Search for specific tools and integrations.