HTTP API Reference

Docs > Agent Observability > Agent Observability Instrumentation > HTTP API Reference

Overview

The Agent Observability HTTP API provides an interface for developers to send LLM-related traces and spans to Datadog. If your application is written in Python, Node.js, or Java, you can use the Agent Observability SDKs.

The API accepts spans with timestamps no more than 24 hours old, allowing limited backfill of delayed data.

Spans API

Use this endpoint to send spans to Datadog. For details on the available kinds of spans, see Span Kinds.

Endpoint: https://api./api/intake/llm-obs/v1/trace/spans
Method: POST

Request

Headers (required)

DD-API-KEY=<YOUR_DATADOG_API_KEY>
Content-Type="application/json"

Body data (required)

Field	Type	Description
data [required]	SpansRequestData	Entry point into the request body.

{
  "data": {
    "type": "span",
    "attributes": {
      "ml_app": "weather-bot",
      "session_id": "1",
      "tags": [
        "service:weather-bot",
        "env:staging",
        "user_handle:example-user@example.com",
        "user_id:1234"
      ],
      "spans": [
        {
          "parent_id": "undefined",
          "trace_id": "<TEST_TRACE_ID>",
          "span_id": "<AGENT_SPAN_ID>",
          "name": "health_coach_agent",
          "meta": {
            "kind": "agent",
            "input": {
              "value": "What is the weather like today and do i wear a jacket?"
            },
            "output": {
              "value": "It's very hot and sunny, there is no need for a jacket"
            }
          },
          "start_ns": 1713889389104152000,
          "duration": 10000000000
        },
        {
          "parent_id": "<AGENT_SPAN_ID>",
          "trace_id": "<TEST_TRACE_ID>",
          "span_id": "<WORKFLOW_ID>",
          "name": "qa_workflow",
          "meta": {
            "kind": "workflow",
            "input": {
              "value": "What is the weather like today and do i wear a jacket?"
            },
            "output": {
              "value":  "It's very hot and sunny, there is no need for a jacket"
            }
          },
          "start_ns": 1713889389104152000,
          "duration": 5000000000
        },
        {
          "parent_id": "<WORKFLOW_SPAN_ID>",
          "trace_id": "<TEST_TRACE_ID>",
          "span_id": "<LLM_SPAN_ID>",
          "name": "generate_response",
          "meta": {
            "kind": "llm",
            "input": {
              "messages": [
                {
                  "role": "system",
                  "content": "Your role is to ..."
                },
                {
                  "role": "user",
                  "content": "What is the weather like today and do i wear a jacket?"
                }
              ]
            },
            "output": {
              "messages": [
                {
                  "content": "It's very hot and sunny, there is no need for a jacket",
                  "role": "assistant"
                }
              ]
            }
          },
          "start_ns": 1713889389104152000,
          "duration": 2000000000
        }
      ]
    }
  }
}

Response

If the request is successful, the API responds with a 202 network code and an empty body.

API standards

Error

Field	Type	Description
message	string	The error message.
stack	string	The stack trace.
type	string	The error type.

IO

Field	Type	Description
value	string	Input or output value. If not set, this value is inferred from messages or documents.
messages	[Message]	List of messages. Use only for LLM spans.
documents	[Document]	List of documents. Use only as output for retrieval spans.
prompt	Prompt	Structured prompt metadata that includes the template and variables used for the LLM input. This should only be used for input IO on LLM spans.
embedding	[float]	List of embedding values.
parameters	Dict[key (string), value]	Additional parameters for the input or output.

Note: When only input.messages is set for an LLM span, Datadog infers input.value from input.messages and uses the following inference logic:

If a message with role=user exists, the content of the last message is used as input.value.
If a user role message is not present, input.value is inferred by concatenating the content fields of all messages, regardless of their roles.

Message

Field	Type	Description
content [required]	string	The body of the message.
role	string	The role of the entity.
tool_calls	[ToolCall]	List of tool calls made in this message.
tool_results	[ToolResult]	List of tool execution results in this message.

Document

Field	Type	Description
text	string	The text of the document.
name	string	The name of the document.
score	float	The score associated with this document.
id	string	The id of this document.
ranking	integer	The ranking of this document.
metadata	Dict[key (string), value]	Additional metadata for this document.

ToolCall

Field	Type	Description
name	string	The name of the tool being called.
arguments	Dict[key (string), value]	The arguments passed to the tool.
tool_id	string	Unique identifier for this tool call.
type	string	The type of tool call.

ToolResult

Field	Type	Description
name	string	The name of the tool that was called.
result	string	The result returned by the tool.
tool_id	string	Unique identifier matching the corresponding tool call.
type	string	The type of tool result.

ToolDefinition

Field	Type	Description
name	string	The name of the tool.
description	string	A description of what the tool does.
schema	Dict[key (string), value]	The schema defining the tool’s parameters.

SpanField

Field	Type	Description
kind	string	The kind of span field.

Prompt

Agent Observability registers new versions of templates when the template or chat_template value is updated. If the input is expected to change between invocations, extract the dynamic parts into a variable.

Field	Type	Description
id	string	Logical identifier for this prompt template. Should be unique per `ml_app`.
name	string	Human-readable name for the prompt.
version	string	Version tag for the prompt (for example, “1.0.0”). If not provided, Agent Observability automatically generates a version by computing a hash of the template content.
template	string	Single string template form. Use placeholder syntax (like `{{variable_name}}`) to embed variables. This should not be set with `chat_template`.
chat_template	[Message]	Multi-message template form. Use placeholder syntax (like `{{variable_name}}`) to embed variables in message content. This should not be set with `template`.
variables	Dict[key (string), string]	Variables used to render the template. Keys correspond to placeholder names in the template.
query_variable_keys	[string]	Variable keys that contain the user query. Used for hallucination detection.
context_variable_keys	[string]	Variable keys that contain ground-truth or context content. Used for hallucination detection.
tags	Dict[key (string), string]	Tags to attach to the prompt run.

{
  "id": "translation-prompt",
  "chat_template": [
    {
      "role": "system",
      "content": "You are a translation service. You translate to {{language}}."
    }, {
      "role": "user",
      "content": "{{user_input}}"
    }
  ],
  "variables": {
    "language": "french",
    "user_input": "<USER_INPUT_TEXT>"
  }
}

Field	Type	Description
kind [required]	string	The span kind: `"agent"`, `"workflow"`, `"llm"`, `"tool"`, `"task"`, `"embedding"`, or `"retrieval"`.
error	Error	Error information on the span.
input	IO	The span’s input information.
output	IO	The span’s output information.
metadata	Dict[key (string), value] where the value is a float, bool, or string	Data about the span that is not input or output related. For example, you can pass `temperature` and `max_tokens` for LLM spans.
model_name	string	The name of the model used for LLM spans.
model_provider	string	The provider of the model used for LLM spans.
model_version	string	The version of the model used for LLM spans.
embedding_for_prompt_idx	integer	The prompt index for which embeddings were computed.
span	SpanField	Span field information.
tool_definitions	[ToolDefinition]	List of available tool definitions.
expected_output	IO	The expected output information.
intent	string	The intent of the span.

Metrics

A dictionary of metrics to collect for the span. The keys are metric names (strings) and values are metric values (float64 pointers). Common metrics include:

input_tokens - The number of input tokens (LLM spans)
output_tokens - The number of output tokens (LLM spans)
total_tokens - The total number of tokens (LLM spans)
non_cached_input_tokens - The number of non-cached input tokens (LLM spans)
cache_read_input_tokens - The number of cache read input tokens (LLM spans)
cache_write_input_tokens - The number of cache write input tokens (LLM spans)
reasoning_output_tokens - The number of reasoning tokens (LLM spans)
time_to_first_token - Time in seconds for first output token (streaming LLM, root spans)
time_per_output_token - Time in seconds per output token (streaming LLM, root spans)
input_cost - Input cost in dollars (LLM and embedding spans)
output_cost - Output cost in dollars (LLM spans)
total_cost - Total cost in dollars (LLM spans)
non_cached_input_cost - Non-cached input cost in dollars (LLM spans)
cache_read_input_cost - Cache read input cost in dollars (LLM spans)
cache_write_input_cost - Cache write input cost in dollars (LLM spans)
reasoning_output_cost- Reasoning output cost in dollars (LLM spans)

Type: Dict[key (string), float64]

Span

Field	Type	Description
name [required]	string	The name of the span.
span_id [required]	string	An ID unique to the span.
trace_id [required]	string	A unique ID shared by all spans in the same trace.
parent_id [required]	string	ID of the span’s direct parent. If the span is a root span, the `parent_id` must be `undefined`.
start_ns [required]	uint64	The span’s start time in nanoseconds.
duration [required]	float64	The span’s duration in nanoseconds.
meta [required]	Meta	The core content relative to the span.
status	string	Error status (`"ok"` or `"error"`). Defaults to `"ok"`.
apm_trace_id	string	The ID of the associated APM trace. Defaults to match the `trace_id` field.
metrics	Dict[key (string), float64]	Datadog metrics to collect. See Metrics for common metric names.
session_id	string	The span’s `session_id`. Overrides the top-level `session_id` field.
tags	[Tag]	A list of tags to apply to this particular span.
service	string	The service name.
ml_app	string	The LLM application name for this span. Overrides the top-level `ml_app` field.

SpansRequestData

Field	Type	Description
type [required]	string	Identifier for the request. Set to `span`.
attributes [required]	SpansPayload	The body of the request.

SpansPayload

Field	Type	Description
ml_app [required]	string	The name of your LLM application. See Application naming guidelines.
spans [required]	[Span]	A list of spans.
tags	[Tag]	A list of top-level tags to apply to each span.
session_id	string	The session the list of spans belongs to. Can be overridden or set on individual spans as well.

Tag

Tags should be formatted as a list of strings (for example, ["user_handle:dog@gmail.com", "app_version:1.0.0"]). They are meant to store contextual information surrounding the span.

For more information about tags, see Getting Started with Tags.

Application naming guidelines

Your application name (the value of DD_LLMOBS_ML_APP) must be a lowercase Unicode string. It may contain the characters listed below:

Alphanumerics
Underscores
Minuses
Colons
Periods
Slashes

The name can be up to 193 characters long and may not contain contiguous or trailing underscores.

Evaluations API

For comprehensive examples and guidance on building custom evaluators, see the Evaluation Developer Guide.

Use this endpoint to send evaluations to Datadog at the span, trace, or session level.

Endpoint: https://api./api/intake/llm-obs/v2/eval-metric
Method: POST

Use the eval_scope field to set the granularity of the evaluation:

span (default): The evaluation is associated with a specific span. Use join_on to identify the target span with a tag key-value pair or a span ID and trace ID combination.
trace: The evaluation is associated with an entire trace. Use join_on to identify the root span of the trace.
session: The evaluation is associated with a session. Provide session_id instead of join_on.

Request

Headers (required)

DD-API-KEY=<YOUR_DATADOG_API_KEY>
Content-Type="application/json"

Body data (required)

Field	Type	Description
data [required]	EvalMetricsRequestData	Entry point into the request body.

{
  "data": {
    "type": "evaluation_metric",
    "attributes": {
      "metrics": [
        {
          "eval_scope": "span",
          "join_on": {
            "span": {
              "span_id": "20245611112024561111",
              "trace_id": "13932955089405749200"
            }
          },
          "ml_app": "weather-bot",
          "timestamp_ms": 1609459200,
          "metric_type": "categorical",
          "label": "Sentiment",
          "categorical_value": "Positive"
        },
        {
          "eval_scope": "trace",
          "join_on": {
            "span": {
              "span_id": "20245611112024561111",
              "trace_id": "13932955089405749200"
            }
          },
          "ml_app": "weather-bot",
          "timestamp_ms": 1609479200,
          "metric_type": "score",
          "label": "Accuracy",
          "score_value": 3,
          "assessment": "fail",
          "reasoning": "The response provided incorrect information about the weather forecast."
        },
        {
          "eval_scope": "session",
          "session_id": "abc123def456",
          "ml_app": "weather-bot",
          "timestamp_ms": 1609479200,
          "metric_type": "boolean",
          "label": "Topic Relevancy",
          "boolean_value": true
        },
        {
          "eval_scope": "span",
          "join_on": {
            "tag": {
              "key": "msg_id",
              "value": "1123132"
            }
          },
          "ml_app": "weather-bot",
          "timestamp_ms": 1609479200,
          "metric_type": "json",
          "label": "Custom Evaluation",
          "json_value": {
            "verdict": "pass",
            "confidence": 0.95,
            "is_valid": true,
            "metrics": {
              "accuracy": 0.92,
              "precision": 0.88
            },
            "passed_checks": ["coherence", "relevance", "factuality"]
          }
        }
      ]
    }
  }
}

Response

Field	Type	Description	Guaranteed
ID	string	Response UUID generated upon submission.	Yes
metrics	[EvalMetric]	A list of evaluations.	Yes

{
  "data": {
    "type": "evaluation_metric",
    "id": "456f4567-e89b-12d3-a456-426655440000",
    "attributes": {
      "metrics": [
        {
          "id": "d4f36434-f0cd-47fc-884d-6996cee26da4",
          "eval_scope": "span",
          "join_on": {
            "span": {
              "span_id": "20245611112024561111",
              "trace_id": "13932955089405749200"
            }
          },
          "ml_app": "weather-bot",
          "timestamp_ms": 1609459200,
          "metric_type": "categorical",
          "label": "Sentiment",
          "categorical_value": "Positive"
        },
        {
          "id": "cdfc4fc7-e2f6-4149-9c35-edc4bbf7b525",
          "eval_scope": "trace",
          "join_on": {
            "span": {
              "span_id": "20245611112024561111",
              "trace_id": "13932955089405749200"
            }
          },
          "ml_app": "weather-bot",
          "timestamp_ms": 1609479200,
          "metric_type": "score",
          "label": "Accuracy",
          "score_value": 3,
          "assessment": "fail",
          "reasoning": "The response provided incorrect information about the weather forecast."
        },
        {
          "id": "haz3fc7-g3p2-1s37-8m12-ndk4hbf7a522",
          "eval_scope": "session",
          "session_id": "abc123def456",
          "ml_app": "weather-bot",
          "timestamp_ms": 1609479200,
          "metric_type": "boolean",
          "label": "Topic Relevancy",
          "boolean_value": true
        },
        {
          "id": "abc1234-h4i5-6j78-9k01-lmn2opq3rst4",
          "eval_scope": "span",
          "join_on": {
            "tag": {
              "key": "msg_id",
              "value": "1123132"
            }
          },
          "ml_app": "weather-bot",
          "timestamp_ms": 1609479200,
          "metric_type": "json",
          "label": "Custom Evaluation",
          "json_value": {
            "verdict": "pass",
            "confidence": 0.95,
            "is_valid": true,
            "metrics": {
              "accuracy": 0.92,
              "precision": 0.88
            },
            "passed_checks": ["coherence", "relevance", "factuality"]
          }
        }
      ]
    }
  }
}

API standards

Attributes

Field	Type	Description
metrics [required]	[EvalMetric]	A list of evaluations each associated with a span.
tags	[Tag]	A list of tags to apply to all the evaluations in the payload.

EvalMetric

Field	Type	Description
ID	string	Evaluation metric UUID (generated upon submission).
eval_scope	string	The granularity of the evaluation: `"span"` (default), `"trace"`, or `"session"`.
join_on [required for span and trace scope]	[JoinOn]	How the evaluation is joined to a span or trace. Required when `eval_scope` is `"span"` or `"trace"`. Must be absent when `eval_scope` is `"session"`.
session_id [required for session scope]	string	The session ID the evaluation is associated with. Required when `eval_scope` is `"session"`. Must be absent when `eval_scope` is `"span"` or `"trace"`.
timestamp_ms [required]	int64	A UTC UNIX timestamp in milliseconds representing the time the request was sent.
ml_app [required]	string	The name of your LLM application. See Application naming guidelines.
metric_type [required]	string	The type of evaluation: `"categorical"`, `"score"`, `"boolean"`, or `"json"`.
label [required]	string	The unique name or label for the provided evaluation .
categorical_value [required if the metric_type is “categorical”]	string	A string representing the category that the evaluation belongs to.
score_value [required if the metric_type is “score”]	number	A score value of the evaluation.
boolean_value [required if the metric_type is “boolean”]	boolean	A boolean value of the evaluation.
json_value [required if the metric_type is “json”]	Dict[key (string), value]	A JSON object value of the evaluation.
assessment	string	An assessment of this evaluation. Accepted values are `pass` and `fail`.
reasoning	string	A text explanation of the evaluation result.
tags	[Tag]	A list of tags to apply to this particular evaluation metric.

JoinOn

Field	Type	Description
span	[SpanContext]	Uniquely identifies the span associated with this evaluation using span ID & trace ID.
tag	[TagContext]	Uniquely identifies the span associated with this evaluation using a tag key-value pair.

SpanContext

Field	Type	Description
span_id [required]	string	The span ID of the span that this evaluation is associated with. Must be a decimal string (for example, `"20245611112024561111"`). If your instrumentation produces hexadecimal span IDs (such as OpenTelemetry), convert them to decimal before submitting.
trace_id [required]	string	The trace ID of the span that this evaluation is associated with. Must be a decimal string (for example, `"13932955089405749200"`) or a 32-character lowercase hexadecimal string for 128-bit trace IDs.

TagContext

Field	Type	Description
key [required]	string	The tag key name. This must be the same key used when setting the tag on the span.
value [required]	string	The tag value. This value must match exactly one span with the specified tag key/value pair.