An AI assistant for your existing observability stack.
Worth being precise about what this is: it drives the observability tools you already run. It is not an LLM tracing platform, and if that is what you searched for, the tools below are the right ones.
What this is not
Searches for AI observability usually mean LLM tracing and evaluation: Langfuse, LangSmith, Arize Phoenix, Helicone, Braintrust, Opik and the rest. Those instrument an LLM application and record traces, prompts, token costs and eval results. Lucid Train does none of that and is not competing with them. If you are building an LLM application and need to see its traces, use one of those.
What it actually does
It connects to the observability stack you already run, Grafana, Prometheus and Loki, and puts an agent in front of it that can also read your repository. That combination is the point: an assistant that can query the metrics, read the logs, and then look at the code that produced them, in one place, without you moving between three tools and carrying context by hand.
From symptom to fix
The useful workflow is the whole path: a Prometheus query shows the error rate rising, a Loki query finds the log line, the agent locates the code that emits it, and then proposes a diff you review. Each of those steps exists in some tool already; doing them in one session with one context is the part that saves time.
It runs locally
Logs and metrics routinely contain data you would rather not send anywhere, which is an uncomfortable property of most hosted assistants in this space. Running against a local model means query results and log lines stay on your machine.
Where this sits
| Need | Use |
|---|---|
| Trace and evaluate an LLM application | Langfuse, LangSmith, Phoenix, Braintrust |
| Monitor LLM API cost and latency | Helicone, OpenRouter dashboards |
| Monitor agent execution | AgentOps |
| Collect metrics, logs and traces | Prometheus, Loki, OpenTelemetry |
| Dashboards over that data | Grafana |
| Query all of it and fix the code | Lucid Train |
Questions
Related
- Local-First AI Coding AgentSame agent core in a native desktop app and a 6 MB Rust binary, running whichever model you choose, including one on your own laptop.
- Local LLM Coding AgentFor when the constraint is not cost but that the code cannot leave the building.
- Diagram-Driven DevelopmentThe diagram stops being documentation produced after the fact and becomes the instruction the code is written from.
- Terminal Coding AgentOne binary, no runtime, no browser. Free and open source.