From: aidotengineer
OpenTelemetry is an open-source project, maintained by the CNCF, that standardizes cloud observability [00:00:28]. It is supported by all major observability platforms, including Splunk, Datadog, Dynatrace, New Relic, Grafana, and Honeycomb [00:00:50]. TraceLoop’s OpenElemetry project extends OpenTelemetry to specifically support Gen frameworks, foundation models, and vector databases, making it highly beneficial for LLM applications [00:06:16].
Core Components of OpenTelemetry for LLMs
OpenTelemetry defines a protocol for collecting and transmitting three primary types of telemetry data:
- Logging An arbitrary event that can be sent at any time during an application’s lifecycle, which is emitted as is and can be viewed later, often with metadata [00:01:29].
- Metrics Data observed at an aggregate level to track behavior over time, across users, or other dimensions [00:01:44]. For Gen-based applications, relevant metrics include token usage, latency, and error rate [00:02:08].
- Traces Used to track multi-step processes, common in cloud environments with microservices [00:02:18]. In Gen applications, tracing is particularly useful for multi-step processes like chains, workflows, or agents interacting with tools [00:02:54].
Beyond the protocol, the OpenTelemetry ecosystem includes:
- SDKs (Software Development Kits) Provide ways to manually send logs, metrics, and traces from an application, with support for 11 different languages [00:03:32].
- Instrumentations Automatically gather observability data without manual code [00:03:55]. They work by “monkey patching” client libraries used within an application, emitting desired data with negligible latency impact [00:04:54].
- Collectors Self-deployable components that allow for pre-processing of observability data before it’s sent to a platform. This includes filtering data, obscuring sensitive information (like PII), and sending data to multiple providers simultaneously [00:05:25].
Specific Benefits for LLM Applications
The extension of OpenTelemetry for Gen frameworks offers significant advantages:
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Broad Coverage of LLM Ecosystem The project has built over 40 instrumentations to support a wide range of providers [00:07:09]:
- Foundation Models: Including OpenAI, Anthropic, Cohere, Gemini, and Bedrock [00:07:16].
- Vector Databases: Such as Pinecone and Chroma [00:07:23].
- Frameworks: Including LangChain, LlamaIndex, CrewAI, and Haystack [00:07:28].
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Automatic Data Collection Because these are instrumentations, data collection for LLM-based applications is largely automatic [00:07:45]. For instance, a Pinecone instrumentation can automatically track queries, indexing processes, and allow investigation of returned vectors, including distances, scores, and latencies [00:07:58].
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Vendor Agnostic Observability By relying on the OpenTelemetry standard, users can gain observability in any platform that supports it, such as Datadog, Sentry, Grafana Tempo, or Dynatrace [00:06:42]. This means users automatically get logs, metrics, and traces in their preferred platform by configuring OpenElemetry correctly [00:06:56].
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Prevention of Vendor Lock-in The use of a standard protocol ensures that users are not tied to a specific observability platform [00:08:41]. Switching between platforms is straightforward, often requiring only a configuration change, as all platforms supporting OpenTelemetry use the exact same data format [00:08:44]. This allows easy integration of LLM applications with existing telemetry and monitoring setups [00:08:31].