Agent Frameworks

Agent frameworks are the libraries and SDKs you build on to orchestrate LLM calls, tools, memory, and multi-agent workflows — the scaffolding between a raw model API and a working agent. The decision usually hinges on your language, how much control versus abstraction you want, and whether you need production features like observability, retries, durable execution, and human-in-the-loop checkpoints. Lightweight libraries give you fine-grained control and stay out of the way but leave more to wire up yourself; higher-level frameworks move faster at the cost of flexibility and can be harder to debug when an abstraction leaks. Check first-class support for your model providers and for tool and function calling, then look at how state and memory are handled and whether streaming works end to end.

Most frameworks are open source and free; many companies pair an open-source core with a paid hosted platform for tracing, deployment, and scale, so factor in where this will actually run in production. In a field moving this fast, ecosystem and momentum matter more than feature checklists — a framework with active maintainers, real adoption, and solid docs will save you more time than one with a longer feature list and a stalled repository. Prototype your hardest workflow in two candidates before committing; the one that fights you least is usually the right answer.