[xinweihe]

Fair question. Here’s how TraceRoot is different.

- We don’t just stream raw logs/traces into an LLM, we build execution trees and correlate data across services and threads. That gives our agent causal context, not just pattern matching.

- It’s designed to debug real issues in production, where things are messy, not just dev or staging.

- We are aiming for automatic bug detection and remediation soon, not just copiloting, but a debugging agent that can spot regressions and trigger fixes proactively.

- We are working on persist historical incidents, fixes, and infra quirks, so the agent improves with each investigation, and doesn’t start from scratch every time.

Happy to dive deeper! Let me know if you have more questions.

[sand_9999]

Sentry does that. Also most observability platforms have tracing built in. All of this can be fed into LLM using MCP.

I saw your video...and I see that it makes things easy to understand (in right panel) at any node.

[zecheng]

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.