What does an AI agent framework provide?
It packages the mechanics surrounding a model, such as tool schemas, state passing, retries, hooks, and agent handoffs. The framework owns the recurring control flow, while the application owns the business rules and data. Tool calling remains an application decision, and an agent harness may add permissions and execution tools around the framework.
Frameworks differ most in how visibly they represent control flow. LangGraph makes state transitions explicit. Other libraries favour a compact agent loop. Neither choice removes the need for human approval when actions can change customer or production data.
When is a framework worth using?
Use one when several agents share repeatable runtime needs, or when a workflow needs state, checkpoints, and inspectable orchestration. A framework should reduce repeated plumbing, not conceal the decisions that need review. A small request-response agent with two tools may be safer as direct code, because every abstraction adds behavior the team must understand.
The real selection question is not which project has the longest feature list. Check the provider boundary, state model, debugging path, and exit cost. That turns a build versus buy decision into an engineering choice rather than a popularity contest.