[jira] [Created] (CAMEL-23861) camel-langchain4j - Add OpenTelemetry span attributes for AI/LLM observability

["Claus Ibsen (Jira)" <ji...@apache.org>]

Claus Ibsen created CAMEL-23861:
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             Summary: camel-langchain4j - Add OpenTelemetry span attributes for AI/LLM observability
                 Key: CAMEL-23861
                 URL: https://issues.apache.org/jira/browse/CAMEL-23861
             Project: Camel
          Issue Type: Improvement
          Components: camel-langchain4j-agent, camel-langchain4j-embeddings, camel-langchain4j-chat, camel-langchain4j-tools
            Reporter: Claus Ibsen


The camel-langchain4j components currently have no OpenTelemetry integration for AI/LLM-specific observability. Token usage, model name, finish reason, and latency are not captured as span attributes.

Spring AI exposes this data through OpenTelemetry spans following the OpenTelemetry Semantic Conventions for GenAI (https://opentelemetry.io/docs/specs/semconv/gen-ai/), which enables tools like Boot UI to build AI Usage dashboards showing token consumption, costs, model distribution, and conversation tracking.

Camel should follow the same approach. When camel-opentelemetry (or camel-opentelemetry2) is active, the langchain4j producers should enrich spans with standard GenAI semantic convention attributes such as:

- gen_ai.system (e.g. openai, anthropic)
- gen_ai.request.model
- gen_ai.response.model
- gen_ai.usage.input_tokens
- gen_ai.usage.output_tokens
- gen_ai.response.finish_reasons

This would allow standard OpenTelemetry tooling (Jaeger, Grafana, etc.) and Camel TUI to visualize AI/LLM usage across routes.

See also CAMEL-23860 which adds token data as exchange headers - this ticket is about also propagating that data into OTel spans for observability tooling.



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