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You can programmatically enable automatic tracing of LLM calls to a supported LLM model like OpenAI or a framework like LangChain by setting `integrations_enabled` to `true` in the `LLMOBs.enable()` function. In addition to capturing latency and errors, the integrations capture the input parameters, input and output messages, and token usage (when available) of each traced call.
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-`chat.send_message()`
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-`chat.send_message_async()`
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## LangGraph
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The LangGraph integration automatically traces `Pregel/CompiledGraph` and `RunnableSeq (node)` invocations made through the [LangGraph Python SDK][33].
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### Traced methods
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The LangGraph integration instruments synchronous and asynchronous versions of the following methods:
The Crew AI integration automatically traces execution of Crew kickoffs, including task/agent/tool invocations, made through [CrewAI's Python SDK][26].
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-[Tool Invocation][30]:
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-`tool.invoke()`
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## OpenAI Agents
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The OpenAI Agents integration converts the [built-in tracing][33] from the [OpenAI Agents SDK][34] into
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LLM Observability format and sends it to Datadog's LLM Observability product by adding a Datadog trace processor.
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The following operations are supported:
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-[`traces`][35]
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-[`agent`][36]
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-[`generation`][37] using Datadog's [OpenAI](#openai) integration
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-[`response`][38]
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-[`guardrail`][39]
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-[`handoff`][40]
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-[`function`][41]
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-[`custom`][42]
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## LangGraph
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The LangGraph integration automatically traces `Pregel/CompiledGraph` and `RunnableSeq (node)` invocations made through the [LangGraph Python SDK][33].
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### Traced methods
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The LangGraph integration instruments synchronous and asynchronous versions of the following methods:
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