Give your agents tools and your own LLM
Goal. Make your agents more capable by giving them tools and choosing which LLM powers them.
Give the researcher agent a web-search tool so it can pull current information instead of relying only on the model's memory, and point the crew at the LLM you prefer.
CrewAI is model-agnostic and ships a large library of tools/integrations. You attach tools to agents in code; you pick the model via your .env / config.
- 1Add a tool to an agent (for example a search or file-reading tool from CrewAI's tools library). An agent with tools can take actions in the world, not just generate text.
- 2Choose your LLM: because CrewAI is model-agnostic, you can run the same crew on different providers or even a local model — you supply the key and model name yourself.
- 3Re-run with
crewai runand watch the researcher actually call its tool mid-task, then feed the result to the writer.
You'll see. An agent invoking a real tool during its reasoning, and the same crew running on whichever LLM you chose.
Cost. Still free at the framework level; tools that hit paid APIs and your LLM usage are billed by those providers. Bringing your own key means you control the spend.
Takeaway. Tools turn agents from talkers into doers, and model-agnostic config means you're never locked to one provider.