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Comment by lumost | Hacker News Reader

Curious what people see in these frameworks in 202(6). My experience has been that an agent is a simple while loop over tools/instructions/dialog. More complex integrations generally lie in the tools/context retrieval - but those have so far been so domain specific that it’s not worth pulling in a framework.

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OpenAI agent sdk makes it extremely simple to get started with function calling and subagent-as-tools delegations.

If you use it with OpenAI responses api, there’s not even any need to store input items in your own DB

> Agent Framework offers two primary categories of capabilities:

> AI agents: Individual agents that use LLMs to process user inputs, call tools and MCP servers to perform actions, and generate responses. Agents support model providers including Azure OpenAI, OpenAI, and Azure AI.

> Workflows: Graph-based workflows that connect multiple agents and functions to perform complex, multi-step tasks. Workflows support type-based routing, nesting, checkpointing, and request/response patterns for human-in-the-loop scenarios.

You can do specialized SLMs with different roles working on problems. Also deterministic workflows. That is what I gathered its use. I know last year, multi-agent scenarios were topping to benchmarks but I don't know if 2025 has been the same.

Anything beyond this is usually a play to trap you into an ecosystem. No reason to adopt any of these frameworks, especially if you already have a mature workflow system.

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