traceAI

Open-source LLM tracing that speaks GenAI, not HTTP.

Open Source

Developer Tools

Artificial Intelligence

GitHub

Since this is fully OpenTelemetry-native, I assume it should work seamlessly with backends like SigNoz as well?

If yes might try it there too seems a cool tool

Open-source LLM tracing is exactly what was missing.

I run Claude API calls in a Celery worker — two calls per job,

one at temperature=0 (deterministic analysis),

one at temperature=0.7 (generative rewrites).

Right now I log both manually with structlog.

But correlating a specific trace across the two calls

when something fails in production is still painful.

Does traceAI handle multi-step pipelines where the same job

triggers two separate LLM calls with different parameters?

The OTel-native approach is the right call here. Most LLM tracing tools force you into a new dashboard and a new vendor relationship. The fact that this routes to Datadog, Grafana, Jaeger means teams can use what they already have instead of adding yet another pane of glass to monitor.

Curious about one thing: how does traceAI handle tracing across multi-agent workflows where one agent calls another? Do the traces compose into a single parent span, or do they stay isolated per agent?

Congrats on the launch.

Much needed! Since you’re positioning traceAI as a semantic layer over OpenTelemetry so do you see this becoming a standard like OTel itself or staying a developer-focused tool?

Hey TraceAI team, great product. was able to get started by giving claude your documentation in a single day. We use this with our internal grafana server so it was a small setup but loving it! thanks!

The OTel native approach is the right call imo. Every time I've tried an LLM observability tool it wants me to install yet another dashboard and I'm already drowning in Grafana tabs lol.

Two lines of code to instrument is bold. Does it handle multi-step agent chains well? Like if I have a LangChain agent that calls tools that call other models, does the trace show the full tree or does it flatten everything?

Two lines is impressive but curious - how does it handle agent decision tracking when you have nested tool calls 3-4 levels deep? Running a bunch of AI agents for project management workflows and the traces get messy fast. The GenAI semantic conventions piece is what's interesting here - most OTel solutions just treat LLM calls as HTTP and you lose all the context about what the model was actually doing.

How does the Trace AI handles long running tasks or loops apart from standard loops? does it have any reasoning steps added to it?

Really enjoyed building this solution for AI pros. It gives you a clear look at how your AI agents are performing without any vendor lock in

This is the one of the best open source open telemetry solution out there, no vendor lock in and one stop solution. Great Work team!

this is going to be a one stop solution for anyone who is building agents and exploring agentic architectures!

GenAI observability has been broken for too long. TraceAI gets it right and this is the kind of observability layer every AI team needs but rarely has. Smart to make this open source and build trust first. Congrats team! 🚀