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Creator of Dirac here. Glad to see it mentioned and even more glad that you found it useful.

I am currently in deep refactor mode to introduce modular tooling to Dirac since the concept of 'fixed' set of tools is starting to feel antiquated, adding tools on demand would be super convenient and a likely replacement for MCP (I understand not all use-cases of it)

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Curious how you’re handling prompt caching, as I understand it most LLM providers essentially inject tool definitions in the system prompt, so changing tools dynamically breaks the cache. This has been a big annoyance for me in a separate project; I currently just implemented my own tool-ish system that defines schemas in user messages and instructs the LLM to return matching JSON, but it’s less reliable than using the native tool calling + structured outputs available in the API.

Native tool calling indeed. By modular, I meant the tool defs are loaded dynamically per task and stay the same during the task

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