ContextQMD
Back to Dd Trace Js

LLMObsPlugin Architecture Reference

.agents/skills/llmobs-integration/references/plugin-architecture.md

6.9.04.5 KB
Original Source

LLMObsPlugin Architecture Reference

Guide to implementing LLMObs plugins in dd-trace-js.

Base Class

Leaf plugins extend LLMObsPlugin at packages/dd-trace/src/llmobs/plugins/base.js. A composite root can extend CompositePlugin and select among leaf implementations, as ai/index.js does.

The LLMObs base class handles span registration, context management, and lifecycle hooks. Leaf plugins implement two methods.

Required Methods

getLLMObsSpanRegisterOptions(ctx)

Defines span metadata for registration with LLMObs. Called at span start.

Returns an object with:

  • kind (string) — span type. SPAN_KINDS lists 'llm', 'agent', 'workflow', 'task', 'tool', 'embedding', and 'retrieval'; plugin extensions such as 'step' also exist
  • name (string, optional) — operation name (e.g. 'openai.chat.completions'); the event falls back to the APM span name when omitted
  • modelProvider (string, optional) — provider name (e.g. 'openai', 'anthropic', 'google')
  • modelName (string, optional) — model identifier (e.g. 'gpt-4', 'claude-3-sonnet')
  • sessionId (string, optional) — session identifier when the integration supplies one

Return nothing to skip recording an LLMObs span for a given ctx entirely — base.js only tests the result for truthiness, and the plugins use a bare return (openai/index.js does this for the methods it does not trace).

setLLMObsTags(ctx)

Extracts and tags LLM-specific data after the operation completes. Called in asyncEnd.

Responsibilities:

  1. Extract the kind-specific input from the channel's ctx fields
  2. Extract the kind-specific output from the channel's ctx fields
  3. Extract token usage metrics when available
  4. Extract model parameters when available
  5. Tag all data via this._tagger methods (see below)

Tag the input when the channel provides it. Error output is integration-specific: OpenAI and GenAI use an empty message, while integrations without a result omit output. Pin that contract in the integration's spec.

Plugin Lifecycle

  1. start(ctx) — registers the LLMObs span and captures parent context
  2. The wrapped operation is invoked
  3. end(ctx) — restores parent context after the wrapped call returns
  4. asyncEnd(ctx) — calls setLLMObsTags() after a promise-backed operation settles

Tagger Methods

Tag data using this._tagger, which provides:

  • tagLLMIO(span, inputMessages, outputMessages) — for llm spans
  • tagEmbeddingIO(span, inputDocuments, outputValue) — for embedding spans
  • tagRetrievalIO(span, inputValue, outputDocuments) — for retrieval spans
  • tagTextIO(span, inputValue, outputValue) — for workflow, agent, task, step, and tool spans
  • tagMetadata(span, metadata) — model parameters (temperature, max_tokens, etc.)
  • tagMetrics(span, metrics) — token usage (input_tokens, output_tokens, total_tokens)
  • tagSpanTags(span, tags) — arbitrary key/value span tags
  • tagPrompt(span, prompt, strictValidation = false) — prompt tracking metadata
  • tagToolDefinitions(span, toolDefinitions) — the tools a request declared, for tool-calling integrations
  • tagModelName(span, modelName) — a model name discovered after registration

Static Properties

Each leaf plugin class needs:

  • static integration — integration name for LLMObs telemetry ('openai', 'google_genai')
  • static id — unique plugin ID. Often the same string as the integration ('openai'), but not necessarily: genai pairs id = 'google-genai' with integration = 'google_genai'. A package that hooks several operations qualifies it per operation ('llmobs_langgraph_pregel_stream')
  • static prefix — diagnostic channel prefix (e.g. 'tracing:apm:openai:request')

Error Handling

OpenAI and GenAI tag an empty output message on error:

javascript
if (ctx.error) {
  this._tagger.tagLLMIO(span, inputMessages, [{ content: '' }])
  return
}

That is not a base-class invariant. Anthropic omits output when no result exists, and non-llm integrations follow their own kind-specific contract.

Reference Implementations

See existing plugins for complete working examples:

  • packages/dd-trace/src/llmobs/plugins/openai/index.js — simple messages array, standard token usage
  • packages/dd-trace/src/llmobs/plugins/anthropic/ — nested content in util.js, usage extraction in index.js
  • packages/dd-trace/src/llmobs/plugins/genai/util.js — contents/parts format, role normalization
  • packages/dd-trace/src/llmobs/plugins/langgraph/index.js — orchestration, workflow span kind, no messages