# Semantic Conventions for GenAI agent and framework spans **Status**: [Development][DocumentStatus] - [Semantic Conventions for GenAI agent and framework spans](#semantic-conventions-for-genai-agent-and-framework-spans) - [Spans](#spans) - [Create agent span](#create-agent-span) - [Invoke agent client span](#invoke-agent-client-span) - [Invoke agent internal span](#invoke-agent-internal-span) - [Invoke workflow span](#invoke-workflow-span) - [Plan span](#plan-span) - [Execute tool span](#execute-tool-span) Generative AI models can be trained to use tools to access real-time information or suggest a real-world action. For example, a model can leverage a database retrieval tool to access specific information, like a customer's purchase history, so it can generate tailored shopping recommendations. Alternatively, based on a user's query, a model can make various API calls to send an email response to a colleague or complete a financial transaction on your behalf. To do so, the model must not only have access to a set of external tools, it needs the ability to plan and execute any task in a self-directed fashion. This combination of reasoning, logic, and access to external information that are all connected to a Generative AI model invokes the concept of an agent. This document defines semantic conventions for GenAI agent calls that are defined by this [whitepaper](https://www.kaggle.com/whitepaper-agents). It MAY be applicable to agent operations that are performed by the GenAI framework locally. The semantic conventions for GenAI agents extend and override the semantic conventions for [Gen AI Spans](gen-ai-spans.md). ## Spans ### Create agent span **Status:**  Describes GenAI agent creation and is usually applicable when working with remote agent services. The `gen_ai.operation.name` SHOULD be `create_agent`. **Span name** SHOULD be `create_agent {gen_ai.agent.name}`. Semantic conventions for individual GenAI systems and frameworks MAY specify different span name format. **Span kind** SHOULD be `CLIENT`. **Span status** SHOULD follow the [Recording Errors](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/general/recording-errors.md) document. **Requirement level:** [Recommended](https://github.com/open-telemetry/semantic-conventions/blob/main/docs/general/signal-requirement-level.md). **Attributes:** | Key | Stability | [Requirement Level](https://opentelemetry.io/docs/specs/semconv/general/attribute-requirement-level/) | Value Type | Description | Example Values | | --- | --- | --- | --- | --- | --- | | [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The name of the operation being performed. [1] | `chat`; `generate_content`; `text_completion` | | [`gen_ai.provider.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The Generative AI provider as identified by the client or server instrumentation. [2] | `openai`; `gcp.gen_ai`; `gcp.vertex_ai` | | [`error.type`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/error.md) |  | `Conditionally Required` If the operation ended in an error. | string | Describes a class of error the operation ended with. [3] | `timeout`; `java.net.UnknownHostException`; `server_certificate_invalid`; `500` | | [`gen_ai.agent.description`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If provided by the application. | string | The free-form description of the GenAI agent created during this operation. | `Helps with math problems`; `Generates fiction stories` | | [`gen_ai.agent.id`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If applicable. | string | The stable unique identifier of the GenAI agent created during this operation. [4] | `asst_5j66UpCpwteGg4YSxUnt7lPY`; `arn:aws:bedrock:us-east-1:123:agent/42`; `urn:agent:projects-123:projects:123:locations:us-east1:aiplatform:reasoningEngines:456` | | [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If provided by the application. | string | The human-readable name of the GenAI agent created during this operation. | `Math Tutor`; `Fiction Writer` | | [`gen_ai.agent.version`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If provided by the application. | string | The version of the GenAI agent created during this operation. | `1.0.0`; `2025-05-01` | | [`gen_ai.request.model`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If available. | string | The name of the GenAI model a request is being made to. [5] | `gpt-4` | | [`server.port`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) |  | `Conditionally Required` If `server.address` is set. | int | GenAI server port. [6] | `80`; `8080`; `443` | | [`server.address`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) |  | `Recommended` | string | GenAI server address. [7] | `example.com`; `10.1.2.80`; `/tmp/my.sock` | | [`gen_ai.system_instructions`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The system message or instructions provided to the GenAI model separately from the chat history. [8] | [
  {
    "type": "text",
    "content": "You are an Agent that greet users, always use greetings tool to respond"
  }
]; [
  {
    "type": "text",
    "content": "You are a language translator."
  },
  {
    "type": "text",
    "content": "Your mission is to translate text in English to French."
  }
] | **[1] `gen_ai.operation.name`:** If one of the predefined values applies, but specific system uses a different name it's RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value. **[2] `gen_ai.provider.name`:** Semantic conventions for individual GenAI operations SHOULD clarify which kinds of providers (e.g. inference, embeddings, retrieval, memory, hosted agent providers) apply when it is not clear from context. The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual upstream provider. For example, a client SDK may be configured against a proxy or hosting platform that transparently relays requests to a different provider. The `gen_ai.provider.name` attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the `gen_ai.provider.name` set to `aws.bedrock` and include applicable `aws.bedrock.*` attributes and are not expected to include `openai.*` attributes. **[3] `error.type`:** The `error.type` SHOULD match the error code returned by the Generative AI provider or the client library, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report. **[4] `gen_ai.agent.id`:** For hosted agents, this SHOULD be the provider-assigned stable identifier of the agent resource such as [AWS Bedrock agent ARN](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_Agent.html) or [GCP Agent Registry identifier](https://docs.cloud.google.com/agent-registry/concepts#agent-identifier). It's NOT RECOMMENDED to record in-memory agent instance ids on this attribute due to their transient nature. **[5] `gen_ai.request.model`:** The name of the GenAI model a request is being made to. If the model is supplied by a vendor, then the value must be the exact name of the model requested. If the model is a fine-tuned custom model, the value should have a more specific name than the base model that's been fine-tuned. **[6] `server.port`:** When observed from the client side, and when communicating through an intermediary, `server.port` SHOULD represent the server port behind any intermediaries, for example proxies, if it's available. **[7] `server.address`:** When observed from the client side, and when communicating through an intermediary, `server.address` SHOULD represent the server address behind any intermediaries, for example proxies, if it's available. **[8] `gen_ai.system_instructions`:** Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-system-instructions.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. The following attributes can be important for making sampling decisions and SHOULD be provided **at span creation time** (if provided at all): * [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.provider.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.request.model`](/docs/registry/attributes/gen-ai.md) * [`server.address`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) * [`server.port`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) --- `error.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `_OTHER` | A fallback error value to be used when the instrumentation doesn't define a custom value. |  | --- `gen_ai.operation.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `chat` | Chat completion operation such as [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) |  | | `create_agent` | Create GenAI agent |  | | `create_memory` | Create new memory records |  | | `create_memory_store` | Create or initialize a memory store |  | | `delete_memory` | Delete memory records |  | | `delete_memory_store` | Delete or deprovision a memory store |  | | `embeddings` | Embeddings operation such as [OpenAI Create embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create) |  | | `execute_tool` | Execute a tool |  | | `generate_content` | Multimodal content generation operation such as [Gemini Generate Content](https://ai.google.dev/api/generate-content) |  | | `invoke_agent` | Invoke GenAI agent |  | | `invoke_workflow` | Invoke GenAI workflow |  | | `plan` | Agent planning or task decomposition phase |  | | `retrieval` | Retrieval operation such as [OpenAI Search Vector Store API](https://platform.openai.com/docs/api-reference/vector-stores/search) |  | | `search_memory` | Search/query memories from a memory store |  | | `text_completion` | Text completions operation such as [OpenAI Completions API (Legacy)](https://platform.openai.com/docs/api-reference/completions) |  | | `update_memory` | Update existing memory records |  | | `upsert_memory` | Create or update memory records without the caller choosing which |  | --- `gen_ai.provider.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `anthropic` | [Anthropic](https://www.anthropic.com/) |  | | `aws.bedrock` | [AWS Bedrock](https://aws.amazon.com/bedrock) |  | | `azure.ai.inference` | Azure AI Inference |  | | `azure.ai.openai` | [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) |  | | `cohere` | [Cohere](https://cohere.com/) |  | | `deepseek` | [DeepSeek](https://www.deepseek.com/) |  | | `gcp.gemini` | [Gemini](https://cloud.google.com/products/gemini) [9] |  | | `gcp.gen_ai` | Any Google generative AI endpoint [10] |  | | `gcp.vertex_ai` | [Vertex AI](https://cloud.google.com/vertex-ai) [11] |  | | `groq` | [Groq](https://groq.com/) |  | | `ibm.watsonx.ai` | [IBM Watsonx AI](https://www.ibm.com/products/watsonx-ai) |  | | `mistral_ai` | [Mistral AI](https://mistral.ai/) |  | | `moonshot_ai` | [Moonshot AI](https://www.moonshot.ai/) |  | | `openai` | [OpenAI](https://openai.com/) |  | | `perplexity` | [Perplexity](https://www.perplexity.ai/) |  | | `x_ai` | [xAI](https://x.ai/) |  | **[9]:** Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API. **[10]:** May be used when specific backend is unknown. **[11]:** Used when accessing the 'aiplatform.googleapis.com' endpoint. ### Invoke agent client span **Status:**  Describes GenAI agent invocation over a remote service. The `gen_ai.operation.name` SHOULD be `invoke_agent`. **Span name** SHOULD be `invoke_agent {gen_ai.agent.name}` if `gen_ai.agent.name` is readily available. When `gen_ai.agent.name` is not available, it SHOULD be `invoke_agent`. Semantic conventions for individual GenAI systems and frameworks MAY specify different span name format. Examples: OpenAI Assistants API, AWS Bedrock Agents. **Span kind** SHOULD be `CLIENT`. **Span status** SHOULD follow the [Recording Errors](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/general/recording-errors.md) document. **Requirement level:** [Recommended](https://github.com/open-telemetry/semantic-conventions/blob/main/docs/general/signal-requirement-level.md). **Attributes:** | Key | Stability | [Requirement Level](https://opentelemetry.io/docs/specs/semconv/general/attribute-requirement-level/) | Value Type | Description | Example Values | | --- | --- | --- | --- | --- | --- | | [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The name of the operation being performed. [1] | `chat`; `generate_content`; `text_completion` | | [`gen_ai.provider.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The Generative AI provider as identified by the client or server instrumentation. [2] | `openai`; `gcp.gen_ai`; `gcp.vertex_ai` | | [`error.type`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/error.md) |  | `Conditionally Required` If the operation ended in an error. | string | Describes a class of error the operation ended with. [3] | `timeout`; `java.net.UnknownHostException`; `server_certificate_invalid`; `500` | | [`gen_ai.agent.description`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The free-form description of the invoked GenAI agent. | `Helps with math problems`; `Generates fiction stories` | | [`gen_ai.agent.id`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If applicable. | string | The stable unique identifier of the invoked GenAI agent. [4] | `asst_5j66UpCpwteGg4YSxUnt7lPY`; `arn:aws:bedrock:us-east-1:123:agent/42`; `urn:agent:projects-123:projects:123:locations:us-east1:aiplatform:reasoningEngines:456` | | [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The human-readable name of the invoked GenAI agent. | `Math Tutor`; `Fiction Writer` | | [`gen_ai.agent.version`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The version of the invoked GenAI agent. | `1.0.0`; `2025-05-01` | | [`gen_ai.conversation.id`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The unique identifier for a conversation (session, thread), used to store and correlate messages within this conversation. [5] | `conv_5j66UpCpwteGg4YSxUnt7lPY` | | [`gen_ai.data_source.id`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If applicable. | string | The data source identifier. [6] | `H7STPQYOND` | | [`gen_ai.output.type`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` [7] | string | Represents the content type requested by the client. [8] | `text`; `json`; `image` | | [`gen_ai.request.choice.count`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If available, in the request, and !=1. | int | The target number of candidate completions to return. | `3` | | [`gen_ai.request.seed`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If applicable and if the request includes a seed. | int | Requests with same seed value more likely to return same result. | `100` | | [`server.port`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) |  | `Conditionally Required` If `server.address` is set. | int | GenAI server port. [9] | `80`; `8080`; `443` | | [`gen_ai.request.frequency_penalty`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The frequency penalty setting for the GenAI request. | `0.1` | | [`gen_ai.request.max_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The maximum number of tokens the model generates for a request. | `100` | | [`gen_ai.request.model`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` If applicable. | string | The name of the GenAI model configured for the agent. [10] | `gpt-4` | | [`gen_ai.request.presence_penalty`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The presence penalty setting for the GenAI request. | `0.1` | | [`gen_ai.request.stop_sequences`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | string[] | List of sequences that the model will use to stop generating further tokens. | `["forest", "lived"]` | | [`gen_ai.request.temperature`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The temperature setting for the GenAI request. | `0.0` | | [`gen_ai.request.top_p`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The top_p sampling setting for the GenAI request. | `1.0` | | [`gen_ai.response.finish_reasons`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | string[] | Array of reasons the model stopped generating tokens, corresponding to each generation received. | `["stop"]`; `["stop", "length"]` | | [`gen_ai.usage.cache_creation.input_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of input tokens written to a provider-managed cache. [11] | `25` | | [`gen_ai.usage.cache_read.input_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of input tokens served from a provider-managed cache. [12] | `50` | | [`gen_ai.usage.input_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of tokens used in the GenAI input (prompt). [13] | `100` | | [`gen_ai.usage.output_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of tokens used in the GenAI response (completion). [14] | `180` | | [`server.address`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) |  | `Recommended` | string | GenAI server address. [15] | `example.com`; `10.1.2.80`; `/tmp/my.sock` | | [`gen_ai.input.messages`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The chat history provided to the model as an input. [16] | [
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Weather in Paris?"
      }
    ]
  },
  {
    "role": "assistant",
    "parts": [
      {
        "type": "tool_call",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "name": "get_weather",
        "arguments": {
          "location": "Paris"
        }
      }
    ]
  },
  {
    "role": "tool",
    "parts": [
      {
        "type": "tool_call_response",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "response": "rainy, 57°F"
      }
    ]
  }
] | | [`gen_ai.output.messages`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | Messages returned by the model where each message represents a specific model response (choice, candidate). [17] | [
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": "The weather in Paris is currently rainy with a temperature of 57°F."
      }
    ],
    "finish_reason": "stop"
  }
] | | [`gen_ai.system_instructions`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The system message or instructions provided to the GenAI model separately from the chat history. [18] | [
  {
    "type": "text",
    "content": "You are an Agent that greet users, always use greetings tool to respond"
  }
]; [
  {
    "type": "text",
    "content": "You are a language translator."
  },
  {
    "type": "text",
    "content": "Your mission is to translate text in English to French."
  }
] | | [`gen_ai.tool.definitions`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The list of tool definitions available to the GenAI agent or model. [19] | [
  {
    "type": "function",
    "name": "get_current_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "The city and state, e.g. San Francisco, CA"
        },
        "unit": {
          "type": "string",
          "enum": [
            "celsius",
            "fahrenheit"
          ]
        }
      },
      "required": [
        "location",
        "unit"
      ]
    }
  }
] | **[1] `gen_ai.operation.name`:** If one of the predefined values applies, but specific system uses a different name it's RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value. **[2] `gen_ai.provider.name`:** Semantic conventions for individual GenAI operations SHOULD clarify which kinds of providers (e.g. inference, embeddings, retrieval, memory, hosted agent providers) apply when it is not clear from context. The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual upstream provider. For example, a client SDK may be configured against a proxy or hosting platform that transparently relays requests to a different provider. The `gen_ai.provider.name` attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the `gen_ai.provider.name` set to `aws.bedrock` and include applicable `aws.bedrock.*` attributes and are not expected to include `openai.*` attributes. **[3] `error.type`:** The `error.type` SHOULD match the error code returned by the Generative AI provider or the client library, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report. **[4] `gen_ai.agent.id`:** For hosted agents, this SHOULD be the provider-assigned stable identifier of the agent resource such as [AWS Bedrock agent ARN](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_Agent.html) or [GCP Agent Registry identifier](https://docs.cloud.google.com/agent-registry/concepts#agent-identifier). It's NOT RECOMMENDED to record in-memory agent instance ids on this attribute due to their transient nature. **[5] `gen_ai.conversation.id`:** Instrumentations SHOULD populate conversation id when they have an identifier for the conversation readily available for a given operation, for example: - when client framework being instrumented manages conversation history (see [LlamaIndex chat store](https://docs.llamaindex.ai/en/stable/module_guides/storing/chat_stores/)) - when instrumenting GenAI client libraries that maintain conversation on the backend side (see [AWS Bedrock agent sessions](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-session-state.html), [OpenAI Assistant threads](https://platform.openai.com/docs/api-reference/threads)) When no identifier for the conversation is available, instrumentations SHOULD NOT populate conversation id. For example, a new UUID, a trace identifier, or a hash of request content SHOULD NOT be used as a fallback value. Application developers that manage conversation history MAY add conversation id to GenAI and other spans or logs using custom span or log record processors or hooks provided by instrumentation libraries. **[6] `gen_ai.data_source.id`:** Data sources are used by AI agents and RAG applications to store grounding data. A data source may be an external database, object store, document collection, website, or any other storage system used by the GenAI agent or application. The `gen_ai.data_source.id` SHOULD match the identifier used by the GenAI system rather than a name specific to the external storage, such as a database or object store. Semantic conventions referencing `gen_ai.data_source.id` MAY also leverage additional attributes, such as `db.*`, to further identify and describe the data source. **[7] `gen_ai.output.type`:** When applicable and if the request includes an output format. **[8] `gen_ai.output.type`:** This attribute SHOULD be used when the client requests output of a specific type. The model may return zero or more outputs of this type. This attribute specifies the output modality and not the actual output format. For example, if an image is requested, the actual output could be a URL pointing to an image file. Additional output format details may be recorded in the future in the `gen_ai.output.{type}.*` attributes. **[9] `server.port`:** When observed from the client side, and when communicating through an intermediary, `server.port` SHOULD represent the server port behind any intermediaries, for example proxies, if it's available. **[10] `gen_ai.request.model`:** This attribute SHOULD be populated if and only if the instrumented library allows to set only a single model per agent. It SHOULD NOT be populated for agents that support multiple models or dynamic selection. **[11] `gen_ai.usage.cache_creation.input_tokens`:** The value SHOULD be included in `gen_ai.usage.input_tokens`. **[12] `gen_ai.usage.cache_read.input_tokens`:** The value SHOULD be included in `gen_ai.usage.input_tokens`. **[13] `gen_ai.usage.input_tokens`:** This value SHOULD include all types of input tokens, including cached tokens. Instrumentations SHOULD make a best effort to populate this value, using a total provided by the provider when available or, depending on the provider API, by summing different token types parsed from the provider output. When the provider reports both billed token counts and model-consumed token counts (for example, Cohere exposes both `usage.billed_units` and `usage.tokens`), instrumentations SHOULD report the billed count so the value matches the units the customer is charged for. **[14] `gen_ai.usage.output_tokens`:** When the provider reports both billed token counts and model-consumed token counts (for example, Cohere exposes both `usage.billed_units` and `usage.tokens`), instrumentations SHOULD report the billed count so the value matches the units the customer is charged for. **[15] `server.address`:** When observed from the client side, and when communicating through an intermediary, `server.address` SHOULD represent the server address behind any intermediaries, for example proxies, if it's available. **[16] `gen_ai.input.messages`:** Messages MUST be provided in the order they were sent to the model. Instrumentations MAY provide a way for users to filter or truncate input messages. > [!Warning] > This attribute is likely to contain sensitive information including user/PII data. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-input-messages.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[17] `gen_ai.output.messages`:** Each message represents a single output choice/candidate generated by the model. Each message corresponds to exactly one generation (choice/candidate) and vice versa - one choice cannot be split across multiple messages or one message cannot contain parts from multiple choices. Instrumentations MAY provide a way for users to filter or truncate output messages. > [!Warning] > This attribute is likely to contain sensitive information including user/PII data. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-output-messages.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[18] `gen_ai.system_instructions`:** This attribute SHOULD be used when the corresponding provider or API allows to provide system instructions or messages separately from the chat history. Instructions that are part of the chat history SHOULD be recorded in `gen_ai.input.messages` attribute instead. Instrumentations MAY provide a way for users to filter or truncate system instructions. > [!Warning] > This attribute may contain sensitive information. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-system-instructions.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[19] `gen_ai.tool.definitions`:** Since this attribute could be large, it's NOT RECOMMENDED to populate non-required properties by default. Instrumentations MAY provide a way to enable populating optional properties. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-tool-definitions.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. The following attributes can be important for making sampling decisions and SHOULD be provided **at span creation time** (if provided at all): * [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.provider.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.request.model`](/docs/registry/attributes/gen-ai.md) * [`server.address`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) * [`server.port`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/server.md) --- `error.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `_OTHER` | A fallback error value to be used when the instrumentation doesn't define a custom value. |  | --- `gen_ai.operation.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `chat` | Chat completion operation such as [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) |  | | `create_agent` | Create GenAI agent |  | | `create_memory` | Create new memory records |  | | `create_memory_store` | Create or initialize a memory store |  | | `delete_memory` | Delete memory records |  | | `delete_memory_store` | Delete or deprovision a memory store |  | | `embeddings` | Embeddings operation such as [OpenAI Create embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create) |  | | `execute_tool` | Execute a tool |  | | `generate_content` | Multimodal content generation operation such as [Gemini Generate Content](https://ai.google.dev/api/generate-content) |  | | `invoke_agent` | Invoke GenAI agent |  | | `invoke_workflow` | Invoke GenAI workflow |  | | `plan` | Agent planning or task decomposition phase |  | | `retrieval` | Retrieval operation such as [OpenAI Search Vector Store API](https://platform.openai.com/docs/api-reference/vector-stores/search) |  | | `search_memory` | Search/query memories from a memory store |  | | `text_completion` | Text completions operation such as [OpenAI Completions API (Legacy)](https://platform.openai.com/docs/api-reference/completions) |  | | `update_memory` | Update existing memory records |  | | `upsert_memory` | Create or update memory records without the caller choosing which |  | --- `gen_ai.output.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `image` | Image |  | | `json` | JSON object with known or unknown schema |  | | `speech` | Speech |  | | `text` | Plain text |  | --- `gen_ai.provider.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `anthropic` | [Anthropic](https://www.anthropic.com/) |  | | `aws.bedrock` | [AWS Bedrock](https://aws.amazon.com/bedrock) |  | | `azure.ai.inference` | Azure AI Inference |  | | `azure.ai.openai` | [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) |  | | `cohere` | [Cohere](https://cohere.com/) |  | | `deepseek` | [DeepSeek](https://www.deepseek.com/) |  | | `gcp.gemini` | [Gemini](https://cloud.google.com/products/gemini) [20] |  | | `gcp.gen_ai` | Any Google generative AI endpoint [21] |  | | `gcp.vertex_ai` | [Vertex AI](https://cloud.google.com/vertex-ai) [22] |  | | `groq` | [Groq](https://groq.com/) |  | | `ibm.watsonx.ai` | [IBM Watsonx AI](https://www.ibm.com/products/watsonx-ai) |  | | `mistral_ai` | [Mistral AI](https://mistral.ai/) |  | | `moonshot_ai` | [Moonshot AI](https://www.moonshot.ai/) |  | | `openai` | [OpenAI](https://openai.com/) |  | | `perplexity` | [Perplexity](https://www.perplexity.ai/) |  | | `x_ai` | [xAI](https://x.ai/) |  | **[20]:** Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API. **[21]:** May be used when specific backend is unknown. **[22]:** Used when accessing the 'aiplatform.googleapis.com' endpoint. ### Invoke agent internal span **Status:**  Describes GenAI agent invocation within the same process. The `gen_ai.operation.name` SHOULD be `invoke_agent`. **Span name** SHOULD be `invoke_agent {gen_ai.agent.name}` if `gen_ai.agent.name` is readily available. When `gen_ai.agent.name` is not available, it SHOULD be `invoke_agent`. Semantic conventions for individual GenAI systems and frameworks MAY specify different span name format. Examples: LangChain agents, CrewAI agents. **Span kind** SHOULD be `INTERNAL`. **Span status** SHOULD follow the [Recording Errors](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/general/recording-errors.md) document. **Requirement level:** [Recommended](https://github.com/open-telemetry/semantic-conventions/blob/main/docs/general/signal-requirement-level.md). **Attributes:** | Key | Stability | [Requirement Level](https://opentelemetry.io/docs/specs/semconv/general/attribute-requirement-level/) | Value Type | Description | Example Values | | --- | --- | --- | --- | --- | --- | | [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The name of the operation being performed. [1] | `chat`; `generate_content`; `text_completion` | | [`error.type`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/error.md) |  | `Conditionally Required` If the operation ended in an error. | string | Describes a class of error the operation ended with. [2] | `timeout`; `java.net.UnknownHostException`; `server_certificate_invalid`; `500` | | [`gen_ai.agent.description`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The free-form description of the invoked GenAI agent. | `Helps with math problems`; `Generates fiction stories` | | [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The human-readable name of the invoked GenAI agent. | `Math Tutor`; `Fiction Writer` | | [`gen_ai.conversation.id`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The unique identifier for a conversation (session, thread), used to store and correlate messages within this conversation. [3] | `conv_5j66UpCpwteGg4YSxUnt7lPY` | | [`gen_ai.data_source.id`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If applicable. | string | The data source identifier. [4] | `H7STPQYOND` | | [`gen_ai.output.type`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` [5] | string | Represents the content type requested by the client. [6] | `text`; `json`; `image` | | [`gen_ai.request.choice.count`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If available, in the request, and !=1. | int | The target number of candidate completions to return. | `3` | | [`gen_ai.request.seed`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` If applicable and if the request includes a seed. | int | Requests with same seed value more likely to return same result. | `100` | | [`gen_ai.request.frequency_penalty`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The frequency penalty setting for the GenAI request. | `0.1` | | [`gen_ai.request.max_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The maximum number of tokens the model generates for a request. | `100` | | [`gen_ai.request.model`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` If applicable. | string | The name of the GenAI model configured for the agent. [7] | `gpt-4` | | [`gen_ai.request.presence_penalty`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The presence penalty setting for the GenAI request. | `0.1` | | [`gen_ai.request.stop_sequences`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | string[] | List of sequences that the model will use to stop generating further tokens. | `["forest", "lived"]` | | [`gen_ai.request.temperature`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The temperature setting for the GenAI request. | `0.0` | | [`gen_ai.request.top_p`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | double | The top_p sampling setting for the GenAI request. | `1.0` | | [`gen_ai.response.finish_reasons`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | string[] | Array of reasons the model stopped generating tokens, corresponding to each generation received. | `["stop"]`; `["stop", "length"]` | | [`gen_ai.usage.cache_creation.input_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of input tokens written to a provider-managed cache. [8] | `25` | | [`gen_ai.usage.cache_read.input_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of input tokens served from a provider-managed cache. [9] | `50` | | [`gen_ai.usage.input_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of tokens used in the GenAI input (prompt). [10] | `100` | | [`gen_ai.usage.output_tokens`](/docs/registry/attributes/gen-ai.md) |  | `Recommended` | int | The number of tokens used in the GenAI response (completion). [11] | `180` | | [`gen_ai.input.messages`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The chat history provided to the model as an input. [12] | [
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Weather in Paris?"
      }
    ]
  },
  {
    "role": "assistant",
    "parts": [
      {
        "type": "tool_call",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "name": "get_weather",
        "arguments": {
          "location": "Paris"
        }
      }
    ]
  },
  {
    "role": "tool",
    "parts": [
      {
        "type": "tool_call_response",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "response": "rainy, 57°F"
      }
    ]
  }
] | | [`gen_ai.output.messages`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | Messages returned by the model where each message represents a specific model response (choice, candidate). [13] | [
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": "The weather in Paris is currently rainy with a temperature of 57°F."
      }
    ],
    "finish_reason": "stop"
  }
] | | [`gen_ai.system_instructions`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The system message or instructions provided to the GenAI model separately from the chat history. [14] | [
  {
    "type": "text",
    "content": "You are an Agent that greet users, always use greetings tool to respond"
  }
]; [
  {
    "type": "text",
    "content": "You are a language translator."
  },
  {
    "type": "text",
    "content": "Your mission is to translate text in English to French."
  }
] | | [`gen_ai.tool.definitions`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The list of tool definitions available to the GenAI agent or model. [15] | [
  {
    "type": "function",
    "name": "get_current_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "The city and state, e.g. San Francisco, CA"
        },
        "unit": {
          "type": "string",
          "enum": [
            "celsius",
            "fahrenheit"
          ]
        }
      },
      "required": [
        "location",
        "unit"
      ]
    }
  }
] | **[1] `gen_ai.operation.name`:** If one of the predefined values applies, but specific system uses a different name it's RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value. **[2] `error.type`:** The `error.type` SHOULD match the error code returned by the Generative AI provider or the client library, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report. **[3] `gen_ai.conversation.id`:** Instrumentations SHOULD populate conversation id when they have an identifier for the conversation readily available for a given operation, for example: - when client framework being instrumented manages conversation history (see [LlamaIndex chat store](https://docs.llamaindex.ai/en/stable/module_guides/storing/chat_stores/)) - when instrumenting GenAI client libraries that maintain conversation on the backend side (see [AWS Bedrock agent sessions](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-session-state.html), [OpenAI Assistant threads](https://platform.openai.com/docs/api-reference/threads)) When no identifier for the conversation is available, instrumentations SHOULD NOT populate conversation id. For example, a new UUID, a trace identifier, or a hash of request content SHOULD NOT be used as a fallback value. Application developers that manage conversation history MAY add conversation id to GenAI and other spans or logs using custom span or log record processors or hooks provided by instrumentation libraries. **[4] `gen_ai.data_source.id`:** Data sources are used by AI agents and RAG applications to store grounding data. A data source may be an external database, object store, document collection, website, or any other storage system used by the GenAI agent or application. The `gen_ai.data_source.id` SHOULD match the identifier used by the GenAI system rather than a name specific to the external storage, such as a database or object store. Semantic conventions referencing `gen_ai.data_source.id` MAY also leverage additional attributes, such as `db.*`, to further identify and describe the data source. **[5] `gen_ai.output.type`:** When applicable and if the request includes an output format. **[6] `gen_ai.output.type`:** This attribute SHOULD be used when the client requests output of a specific type. The model may return zero or more outputs of this type. This attribute specifies the output modality and not the actual output format. For example, if an image is requested, the actual output could be a URL pointing to an image file. Additional output format details may be recorded in the future in the `gen_ai.output.{type}.*` attributes. **[7] `gen_ai.request.model`:** This attribute SHOULD be populated if and only if the instrumented library allows to set only a single model per agent. It SHOULD NOT be populated for agents that support multiple models or dynamic selection. **[8] `gen_ai.usage.cache_creation.input_tokens`:** The value SHOULD be included in `gen_ai.usage.input_tokens`. **[9] `gen_ai.usage.cache_read.input_tokens`:** The value SHOULD be included in `gen_ai.usage.input_tokens`. **[10] `gen_ai.usage.input_tokens`:** This value SHOULD include all types of input tokens, including cached tokens. Instrumentations SHOULD make a best effort to populate this value, using a total provided by the provider when available or, depending on the provider API, by summing different token types parsed from the provider output. When the provider reports both billed token counts and model-consumed token counts (for example, Cohere exposes both `usage.billed_units` and `usage.tokens`), instrumentations SHOULD report the billed count so the value matches the units the customer is charged for. **[11] `gen_ai.usage.output_tokens`:** When the provider reports both billed token counts and model-consumed token counts (for example, Cohere exposes both `usage.billed_units` and `usage.tokens`), instrumentations SHOULD report the billed count so the value matches the units the customer is charged for. **[12] `gen_ai.input.messages`:** Messages MUST be provided in the order they were sent to the model. Instrumentations MAY provide a way for users to filter or truncate input messages. > [!Warning] > This attribute is likely to contain sensitive information including user/PII data. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-input-messages.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[13] `gen_ai.output.messages`:** Each message represents a single output choice/candidate generated by the model. Each message corresponds to exactly one generation (choice/candidate) and vice versa - one choice cannot be split across multiple messages or one message cannot contain parts from multiple choices. Instrumentations MAY provide a way for users to filter or truncate output messages. > [!Warning] > This attribute is likely to contain sensitive information including user/PII data. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-output-messages.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[14] `gen_ai.system_instructions`:** This attribute SHOULD be used when the corresponding provider or API allows to provide system instructions or messages separately from the chat history. Instructions that are part of the chat history SHOULD be recorded in `gen_ai.input.messages` attribute instead. Instrumentations MAY provide a way for users to filter or truncate system instructions. > [!Warning] > This attribute may contain sensitive information. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-system-instructions.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[15] `gen_ai.tool.definitions`:** Since this attribute could be large, it's NOT RECOMMENDED to populate non-required properties by default. Instrumentations MAY provide a way to enable populating optional properties. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-tool-definitions.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. The following attributes can be important for making sampling decisions and SHOULD be provided **at span creation time** (if provided at all): * [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) * [`gen_ai.request.model`](/docs/registry/attributes/gen-ai.md) --- `error.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `_OTHER` | A fallback error value to be used when the instrumentation doesn't define a custom value. |  | --- `gen_ai.operation.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `chat` | Chat completion operation such as [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) |  | | `create_agent` | Create GenAI agent |  | | `create_memory` | Create new memory records |  | | `create_memory_store` | Create or initialize a memory store |  | | `delete_memory` | Delete memory records |  | | `delete_memory_store` | Delete or deprovision a memory store |  | | `embeddings` | Embeddings operation such as [OpenAI Create embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create) |  | | `execute_tool` | Execute a tool |  | | `generate_content` | Multimodal content generation operation such as [Gemini Generate Content](https://ai.google.dev/api/generate-content) |  | | `invoke_agent` | Invoke GenAI agent |  | | `invoke_workflow` | Invoke GenAI workflow |  | | `plan` | Agent planning or task decomposition phase |  | | `retrieval` | Retrieval operation such as [OpenAI Search Vector Store API](https://platform.openai.com/docs/api-reference/vector-stores/search) |  | | `search_memory` | Search/query memories from a memory store |  | | `text_completion` | Text completions operation such as [OpenAI Completions API (Legacy)](https://platform.openai.com/docs/api-reference/completions) |  | | `update_memory` | Update existing memory records |  | | `upsert_memory` | Create or update memory records without the caller choosing which |  | --- `gen_ai.output.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `image` | Image |  | | `json` | JSON object with known or unknown schema |  | | `speech` | Speech |  | | `text` | Plain text |  | ### Invoke workflow span **Status:**  Represents an operation that executes a coordinated process composed of multiple agents or other operations involving generative AI. The `gen_ai.operation.name` SHOULD be `invoke_workflow`. **Span name** SHOULD be `invoke_workflow {gen_ai.workflow.name}`. This span SHOULD be reported by the instrumentations when they can reliably determine that invocation is a workflow (i.e. groups several agent invocations) and SHOULD NOT be reported by instrumentations that can't distinguish it `invoke_workflow` from `invoke_agent`. eg: Some frameworks like ADK have workflow agents that orchestrate other agents and report `invoke_agent` spans, so `invoke_workflow` SHOULD NOT be reported by such instrumentations. Conversely, frameworks like CrewAI have a distinct concept of crew (similar to workflow) that is separate from individual agents, so they SHOULD report `invoke_workflow` spans. **Span kind** SHOULD be `INTERNAL`. **Span status** SHOULD follow the [Recording Errors](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/general/recording-errors.md) document. **Requirement level:** [Recommended](https://github.com/open-telemetry/semantic-conventions/blob/main/docs/general/signal-requirement-level.md). **Attributes:** | Key | Stability | [Requirement Level](https://opentelemetry.io/docs/specs/semconv/general/attribute-requirement-level/) | Value Type | Description | Example Values | | --- | --- | --- | --- | --- | --- | | [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The name of the operation being performed. [1] | `chat`; `generate_content`; `text_completion` | | [`error.type`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/error.md) |  | `Conditionally Required` If the operation ended in an error. | string | Describes a class of error the operation ended with. [2] | `timeout`; `java.net.UnknownHostException`; `server_certificate_invalid`; `500` | | [`gen_ai.workflow.name`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | Human-readable name of the GenAI workflow provided by the application. [3] | `multi_agent_rag`; `customer_support_pipeline` | | [`gen_ai.input.messages`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | The chat history provided to the model as an input. [4] | [
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Weather in Paris?"
      }
    ]
  },
  {
    "role": "assistant",
    "parts": [
      {
        "type": "tool_call",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "name": "get_weather",
        "arguments": {
          "location": "Paris"
        }
      }
    ]
  },
  {
    "role": "tool",
    "parts": [
      {
        "type": "tool_call_response",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "response": "rainy, 57°F"
      }
    ]
  }
] | | [`gen_ai.output.messages`](/docs/registry/attributes/gen-ai.md) |  | `Opt-In` | any | Messages returned by the model where each message represents a specific model response (choice, candidate). [5] | [
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": "The weather in Paris is currently rainy with a temperature of 57°F."
      }
    ],
    "finish_reason": "stop"
  }
] | **[1] `gen_ai.operation.name`:** If one of the predefined values applies, but specific system uses a different name it's RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value. **[2] `error.type`:** The `error.type` SHOULD match the error code returned by the Generative AI provider or the client library, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report. **[3] `gen_ai.workflow.name`:** This attribute can be populated in different frameworks; for example, as the name of the first chain in LangChain or the name of the crew in CrewAI. The workflow name is usually provided by the application in a way that is specific to the generative AI framework or library that orchestrates the workflow. It is usually a static name that is expected to be unique within an application. `gen_ai.workflow.name` MUST have low cardinality. Semantic conventions for individual Generative AI frameworks SHOULD document what `gen_ai.workflow.name` means in the context of that framework. If there is no low-cardinality workflow name available for a given framework, this attribute MUST NOT be captured by default. **[4] `gen_ai.input.messages`:** Messages MUST be provided in the order they were sent to the model. Instrumentations MAY provide a way for users to filter or truncate input messages. > [!Warning] > This attribute is likely to contain sensitive information including user/PII data. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-input-messages.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. **[5] `gen_ai.output.messages`:** Each message represents a single output choice/candidate generated by the model. Each message corresponds to exactly one generation (choice/candidate) and vice versa - one choice cannot be split across multiple messages or one message cannot contain parts from multiple choices. Instrumentations MAY provide a way for users to filter or truncate output messages. > [!Warning] > This attribute is likely to contain sensitive information including user/PII data. See [Recording content on attributes](/docs/gen-ai/gen-ai-spans.md#recording-content-on-attributes) section for more details. Instrumentations MUST follow [JSON schema](/model/gen-ai/gen-ai-output-messages.json). When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise. The following attributes can be important for making sampling decisions and SHOULD be provided **at span creation time** (if provided at all): * [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) --- `error.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `_OTHER` | A fallback error value to be used when the instrumentation doesn't define a custom value. |  | --- `gen_ai.operation.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `chat` | Chat completion operation such as [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) |  | | `create_agent` | Create GenAI agent |  | | `create_memory` | Create new memory records |  | | `create_memory_store` | Create or initialize a memory store |  | | `delete_memory` | Delete memory records |  | | `delete_memory_store` | Delete or deprovision a memory store |  | | `embeddings` | Embeddings operation such as [OpenAI Create embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create) |  | | `execute_tool` | Execute a tool |  | | `generate_content` | Multimodal content generation operation such as [Gemini Generate Content](https://ai.google.dev/api/generate-content) |  | | `invoke_agent` | Invoke GenAI agent |  | | `invoke_workflow` | Invoke GenAI workflow |  | | `plan` | Agent planning or task decomposition phase |  | | `retrieval` | Retrieval operation such as [OpenAI Search Vector Store API](https://platform.openai.com/docs/api-reference/vector-stores/search) |  | | `search_memory` | Search/query memories from a memory store |  | | `text_completion` | Text completions operation such as [OpenAI Completions API (Legacy)](https://platform.openai.com/docs/api-reference/completions) |  | | `update_memory` | Update existing memory records |  | | `upsert_memory` | Create or update memory records without the caller choosing which |  | ### Plan span **Status:**  Represents an agent planning or task decomposition phase. The `gen_ai.operation.name` SHOULD be `plan`. **Span name** SHOULD be `plan {gen_ai.agent.name}` if `gen_ai.agent.name` is readily available. When `gen_ai.agent.name` is not available, it SHOULD be `plan`. A plan span represents the decision phase where an agent formulates a strategy before executing it. The LLM call that generates the plan SHOULD be a child of the plan span, and the tool or task spans produced from the plan are typically sibling operations under the same `invoke_agent` span. This span SHOULD be reported by instrumentations when they can reliably determine that the operation being instrumented is planning or task decomposition, and SHOULD NOT be reported when the instrumentation cannot distinguish planning from generic reasoning or normal inference. **Span kind** SHOULD be `INTERNAL`. **Span status** SHOULD follow the [Recording Errors](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/general/recording-errors.md) document. **Requirement level:** [Recommended](https://github.com/open-telemetry/semantic-conventions/blob/main/docs/general/signal-requirement-level.md). **Attributes:** | Key | Stability | [Requirement Level](https://opentelemetry.io/docs/specs/semconv/general/attribute-requirement-level/) | Value Type | Description | Example Values | | --- | --- | --- | --- | --- | --- | | [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) |  | `Required` | string | The name of the operation being performed. [1] | `chat`; `generate_content`; `text_completion` | | [`error.type`](https://github.com/open-telemetry/semantic-conventions/blob/v1.43.0/docs/registry/attributes/error.md) |  | `Conditionally Required` If the operation ended in an error. | string | Describes a class of error the operation ended with. [2] | `timeout`; `java.net.UnknownHostException`; `server_certificate_invalid`; `500` | | [`gen_ai.agent.name`](/docs/registry/attributes/gen-ai.md) |  | `Conditionally Required` When available. | string | The human-readable name of the agent performing the planning. | `Math Tutor`; `Fiction Writer` | **[1] `gen_ai.operation.name`:** If one of the predefined values applies, but specific system uses a different name it's RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value. **[2] `error.type`:** The `error.type` SHOULD match the error code returned by the Generative AI provider or the client library, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report. The following attributes can be important for making sampling decisions and SHOULD be provided **at span creation time** (if provided at all): * [`gen_ai.operation.name`](/docs/registry/attributes/gen-ai.md) --- `error.type` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `_OTHER` | A fallback error value to be used when the instrumentation doesn't define a custom value. |  | --- `gen_ai.operation.name` has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used. | Value | Description | Stability | | --- | --- | --- | | `chat` | Chat completion operation such as [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) |  | | `create_agent` | Create GenAI agent |  | | `create_memory` | Create new memory records |  | | `create_memory_store` | Create or initialize a memory store |  | | `delete_memory` | Delete memory records |  | | `delete_memory_store` | Delete or deprovision a memory store |  | | `embeddings` | Embeddings operation such as [OpenAI Create embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create) |  | | `execute_tool` | Execute a tool |  | | `generate_content` | Multimodal content generation operation such as [Gemini Generate Content](https://ai.google.dev/api/generate-content) |  | | `invoke_agent` | Invoke GenAI agent |  | | `invoke_workflow` | Invoke GenAI workflow |  | | `plan` | Agent planning or task decomposition phase |  | | `retrieval` | Retrieval operation such as [OpenAI Search Vector Store API](https://platform.openai.com/docs/api-reference/vector-stores/search) |  | | `search_memory` | Search/query memories from a memory store |  | | `text_completion` | Text completions operation such as [OpenAI Completions API (Legacy)](https://platform.openai.com/docs/api-reference/completions) |  | | `update_memory` | Update existing memory records |  | | `upsert_memory` | Create or update memory records without the caller choosing which |  | ## Execute tool span If you are using some tools in your agent, refer to [Execute Tool Span](./gen-ai-spans.md#execute-tool-span). [DocumentStatus]: https://opentelemetry.io/docs/specs/otel/document-status