So Anthropic sees its CLI (in TypeScript) as the first-class product and maybe planning to expand the claude code with more JS based agents / ecosystem? Especially owning the runtime gives a lot of control over developer experience.
Yes, we can connect for example CC with MCPs. But this may not work well for example if user wants to check the latency for previous 10 days error log on function A. By using MCP the agent needs to get 10 days error logs at first and then somehow get the latency and correlates them, apply filters for function A. IMO it will hallucinate a lot if there are too many tools, logs and traces. But on TraceRoot platform we "mixed" all necessary data at first, and based on user's query apply filters on structured data, which is more accurate, straightforward and efficient. Here is the README of the general design https://github.com/traceroot-ai/traceroot/tree/main/rest/age...
We provide an easy to use solution that Sentry is quite complex to use by connecting code context to corresponding loggings and tracings. Also, directly using MCP with LLMs may hallucinate if there are too many tool candidates and if there are a lot of loggings (which is very common) We need to have some optimizations to improve the both of the efficiency and reduce the context fed into the LLMs. An example is shown in this README https://github.com/traceroot-ai/traceroot/tree/main/rest/age... There is also some cursor like UI in TraceRoot to better involve human in the loop which is crucial to minimize the context length and other platforms such as Sentry does not have.
Yes, there is a roadmap to support more models. For now there is a in progress PR to support Anthropic models https://github.com/traceroot-ai/traceroot/pull/21 (contributed by some active open source contributors) Feel free to let us know which (open source) model or framework (VLLM etc.) you want to use :)
