AI Observability

Since Camel 4.23

The camel-ai-observability module provides GenAI observability for Camel AI components following the OpenTelemetry semantic conventions for GenAI (stable subset aligned with Spring AI).

AI producers depend on the lightweight camel-ai-observability-api module. Add camel-ai-observability to your application classpath together with a tracing or metrics backend to enable span and metric emission.

When camel-opentelemetry2 and/or camel-micrometer is on the classpath, langchain4j, OpenAI, and Spring AI chat producers emit child spans and metrics per LLM call with attributes such as gen_ai.operation.name, gen_ai.system, gen_ai.request.model, gen_ai.usage.input_tokens, and gen_ai.usage.output_tokens.

Global toggle: set camel.aiObservability.enabled=false (default is true when a tracing or metrics backend is present). Camel Main exposes the same setting via camel.aiObservability.enabled in application.properties or programmatically with main.configure().aiObservability().withEnabled(false).

Supported producers: langchain4j-chat, langchain4j-agent, langchain4j-embeddings, openai, and spring-ai-chat.

When using spring-ai-chat with only a pre-built chatClient (no chatModel on the endpoint), Camel attempts to read the backing Spring AI ChatModel via a private field on Spring AI’s internal ChatClientRequestSpec implementation. That field is not public API and may change between Spring AI releases. If extraction fails, gen_ai.system and gen_ai.request.model span attributes may report unknown. Configure chatModel explicitly on the endpoint URI or component when GenAI observability is enabled so model metadata is always populated.

New exchange headers for model identification on langchain4j components:

  • CamelLangChain4jChatRequestModel / CamelLangChain4jChatResponseModel

  • CamelLangChain4jAgentRequestModel / CamelLangChain4jAgentResponseModel

  • CamelLangChain4jEmbeddingsRequestModel / CamelLangChain4jEmbeddingsResponseModel

Metrics recorded (when Micrometer is available):

  • gen_ai.client.operation — operation duration timer

  • gen_ai.client.token.usage — token usage counter (tags: gen_ai.token.type=input|output)

Micrometer Observation

When a non-NOOP io.micrometer.observation.ObservationRegistry is in the Camel registry (for example a Spring Boot Actuator bean), each GenAI client call is recorded as a Micrometer io.micrometer.observation.Observation named gen_ai.client.operation. The contextual name uses the same {operation} {model} form as GenAI telemetry spans (for example chat test-model).

That is not the same type as Camel’s org.apache.camel.component.ai.observability.GenAiObservation handle used by AI producers.

camel-micrometer-observability is not required. Bind the ObservationRegistry in the Camel registry; Spring Boot does this automatically when observation support is enabled.

When Observation is used:

  • Camel does not create the GenAI CLIENT span through camel-telemetry / camel-opentelemetry2.

  • Camel does not record the direct gen_ai.client.operation timer.

  • Tracing is emitted only if the registry has a tracing handler. Without one, the Observation path produces no GenAI trace.

  • The operation timer is emitted only if the registry has a meter handler. Without one, the Observation path produces no operation timer.

  • Token usage counters (gen_ai.client.token.usage) still use MeterRegistry when one is present.

When no ObservationRegistry is available, existing OpenTelemetry spans and Micrometer timers continue to work as before.

Low-cardinality keys: gen_ai.operation.name, gen_ai.system, gen_ai.request.model, camel.component, and error.type on failure. Prompts, completions, and token counts are not used as Observation keys.

Camel TUI integration

When monitoring a running integration with camel tui and observability enabled, the AI panel usage view (Ctrl+U while the AI panel is open) combines:

  • TUI ask token usage from camel ask / the embedded AI prompt

  • Route GenAI usage extracted from exported OpenTelemetry spans with gen_ai.* attributes

This gives a single dashboard for developer CLI usage and production route LLM calls.

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