Export Logs

Once your application is instrumented and logs are flowing into the Monitor pillar, you can export that data programmatically. Use the export endpoints to feed traces and spans into data warehouses, build custom dashboards, power alerting pipelines, or run offline analysis that goes beyond what the Monitor UI provides. Adaline exposes three REST endpoints for reading log data. All three use cursor-based pagination and support the same filtering and response conventions used across the Adaline API.

Available endpoints

Endpoint	Method	When to use
List traces	`GET /v2/logs`	Simple queries with query-string filters.
Search traces	`POST /v2/logs/traces`	Complex queries with body filters, range comparisons, and multi-condition filtering.
Search spans	`POST /v2/logs/spans`	Export model-level span data for a specific prompt — cost, tokens, latency per LLM call.

Quick start

Fetch the most recent traces from a project:

cURL
TypeScript
Python

curl "https://app.adaline.ai/api/v2/logs?projectId=$PROJECT_ID&limit=50" \
  -H "Authorization: Bearer $ADALINE_API_KEY"

const response = await fetch(
  `https://app.adaline.ai/api/v2/logs?projectId=${projectId}&limit=50`,
  { headers: { Authorization: `Bearer ${process.env.ADALINE_API_KEY}` } }
);

const { data, pagination } = await response.json();
console.log(`Fetched ${data.length} traces, hasMore: ${pagination.hasMore}`);

import httpx

response = httpx.get(
    "https://app.adaline.ai/api/v2/logs",
    params={"projectId": project_id, "limit": 50},
    headers={"Authorization": f"Bearer {os.environ['ADALINE_API_KEY']}"},
)

result = response.json()
print(f"Fetched {len(result['data'])} traces, hasMore: {result['pagination']['hasMore']}")

The response envelope is the same for all three endpoints:

{
  "data": [ ... ],
  "pagination": {
    "limit": 50,
    "returned": 50,
    "hasMore": true,
    "nextCursor": "eyJ2IjoxLCJzdGFydGVkQXQiOi..."
  }
}

Pagination

All export endpoints use cursor-based pagination. Pass the nextCursor value from the previous response as cursor in the next request. When hasMore is false, you have reached the end.

Parameter	Type	Default	Description
`limit`	integer	50	Items per page (max 200).
`cursor`	string	—	Opaque cursor from a previous response. Omit for the first page.

The cursor is opaque — do not parse or construct it. Always use the value returned by the API.

Pagination loop

To export all matching data, loop until hasMore is false:

cURL
TypeScript
Python

CURSOR=""
while true; do
  BODY='{"projectId":"'"$PROJECT_ID"'","limit":200'
  if [ -n "$CURSOR" ]; then
    BODY="$BODY"',"cursor":"'"$CURSOR"'"'
  fi
  BODY="$BODY}"

  RESPONSE=$(curl -s -X POST "https://app.adaline.ai/api/v2/logs/traces" \
    -H "Authorization: Bearer $ADALINE_API_KEY" \
    -H "Content-Type: application/json" \
    -d "$BODY")

  echo "$RESPONSE" | jq '.data[]'

  HAS_MORE=$(echo "$RESPONSE" | jq -r '.pagination.hasMore')
  CURSOR=$(echo "$RESPONSE" | jq -r '.pagination.nextCursor')

  if [ "$HAS_MORE" != "true" ]; then break; fi
done

let cursor: string | undefined;

do {
  const body: Record<string, unknown> = { projectId, limit: 200 };
  if (cursor) body.cursor = cursor;

  const response = await fetch("https://app.adaline.ai/api/v2/logs/traces", {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.ADALINE_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify(body),
  });

  const { data, pagination } = await response.json();

  for (const trace of data) {
    // Process each trace
    console.log(trace.id, trace.name, trace.status);
  }

  cursor = pagination.hasMore ? pagination.nextCursor : undefined;
} while (cursor);

import httpx

cursor = None

while True:
    body = {"projectId": project_id, "limit": 200}
    if cursor:
        body["cursor"] = cursor

    response = httpx.post(
        "https://app.adaline.ai/api/v2/logs/traces",
        json=body,
        headers={"Authorization": f"Bearer {os.environ['ADALINE_API_KEY']}"},
    )
    result = response.json()

    for trace in result["data"]:
        # Process each trace
        print(trace["id"], trace["name"], trace["status"])

    if not result["pagination"]["hasMore"]:
        break
    cursor = result["pagination"]["nextCursor"]

Filtering

All endpoints support two complementary filtering mechanisms that are ANDed together: flat params for simple queries and a filters array for advanced comparisons.

Flat params

Top-level fields for common filters. Use these for straightforward queries:

Field	Type	Description
`projectId`	string	Required. Project to query.
`status`	string	`success`, `failure`, `aborted`, `cancelled`, `pending`, `unknown`.
`startedAfter`	number	Unix ms. Traces started at or after this time.
`startedBefore`	number	Unix ms. Traces started at or before this time.
`name`	string	Case-insensitive substring match on trace or span name.
`referenceId`	string	Exact match.
`sessionId`	string	Exact match.

The span endpoint (POST /v2/logs/spans) additionally supports model and provider for case-insensitive substring matching.

Filters array

For range queries, numeric comparisons, and multi-condition filtering on the same column, use the filters array:

{
  "projectId": "project-abc12345678901",
  "filters": [
    { "type": "number", "column": "latency", "operator": "gt", "value": 1000 },
    { "type": "number", "column": "totalCost", "operator": "lt", "value": 0.05 },
    { "type": "arrayContains", "column": "tags", "operator": "contains", "value": "production" }
  ]
}

Each filter object is a discriminated union on type:

Type	Operators	Value type	Example columns
`string`	`eq`, `contains`	string	name, referenceId, sessionId, model, provider
`number`	`eq`, `gt`, `gte`, `lt`, `lte`	number	latency, totalCost, cost, totalInputTokens, totalOutputTokens
`datetime`	`gt`, `gte`, `lt`, `lte`	number (Unix ms)	startedAt, endedAt
`arrayContains`	`contains`	string	tags

Both flat params and the filters array can be used together in the same request. Unsupported columns are silently ignored.

Available columns

Trace endpoints (GET /v2/logs, POST /v2/logs/traces): startedAt, endedAt, status, name, referenceId, sessionId, latency, tags, totalCost, totalInputTokens, totalOutputTokens, traceId Span endpoint (POST /v2/logs/spans) — all trace columns plus: model, provider, cost, promptTokens, completionTokens, totalTokens, logTraceId

Common export patterns

Goal	Approach
Nightly data sync	Paginate through all traces with `startedAfter` / `startedBefore` set to the previous day’s window.
Cost monitoring	Use the filters array with `totalCost > threshold` to surface expensive traces for alerting.
Model comparison	Export spans for two different models using the `model` filter, then compare latency and cost offline.
Session replay	Filter by `sessionId` to reconstruct a full multi-turn conversation for debugging or QA.
Compliance audit	Export all traces for a time window and pipe them into your data warehouse for retention.

Search traces

POST /v2/logs/traces is the most flexible trace export endpoint. Use it when you need the filters array or complex multi-condition queries:

cURL
TypeScript
Python

curl -X POST "https://app.adaline.ai/api/v2/logs/traces" \
  -H "Authorization: Bearer $ADALINE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "projectId": "'"$PROJECT_ID"'",
    "status": "success",
    "startedAfter": 1742860800000,
    "filters": [
      { "type": "number", "column": "latency", "operator": "gt", "value": 1000 },
      { "type": "number", "column": "totalCost", "operator": "gte", "value": 0.01 }
    ],
    "sort": "startedAt:desc",
    "limit": 100
  }'

const response = await fetch("https://app.adaline.ai/api/v2/logs/traces", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.ADALINE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    projectId,
    status: "success",
    startedAfter: 1742860800000,
    filters: [
      { type: "number", column: "latency", operator: "gt", value: 1000 },
      { type: "number", column: "totalCost", operator: "gte", value: 0.01 },
    ],
    sort: "startedAt:desc",
    limit: 100,
  }),
});

const { data: traces } = await response.json();

response = httpx.post(
    "https://app.adaline.ai/api/v2/logs/traces",
    json={
        "projectId": project_id,
        "status": "success",
        "startedAfter": 1742860800000,
        "filters": [
            {"type": "number", "column": "latency", "operator": "gt", "value": 1000},
            {"type": "number", "column": "totalCost", "operator": "gte", "value": 0.01},
        ],
        "sort": "startedAt:desc",
        "limit": 100,
    },
    headers={"Authorization": f"Bearer {os.environ['ADALINE_API_KEY']}"},
)

traces = response.json()["data"]

Search spans

POST /v2/logs/spans exports model-level span data for a specific prompt. Only spans with contentType of Model or ModelStream are returned — use this to analyze LLM performance at the individual-call level.

Field	Required	Description
`promptId`	Yes	Prompt to search model spans from.
`model`	No	Case-insensitive substring match on model name.
`provider`	No	Case-insensitive substring match on provider name.

cURL
TypeScript
Python

curl -X POST "https://app.adaline.ai/api/v2/logs/spans" \
  -H "Authorization: Bearer $ADALINE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "projectId": "'"$PROJECT_ID"'",
    "promptId": "'"$PROMPT_ID"'",
    "model": "gpt-4.1",
    "filters": [
      { "type": "number", "column": "cost", "operator": "lt", "value": 0.05 }
    ],
    "sort": "startedAt:desc",
    "limit": 100
  }'

const response = await fetch("https://app.adaline.ai/api/v2/logs/spans", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.ADALINE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    projectId,
    promptId,
    model: "gpt-4.1",
    filters: [
      { type: "number", column: "cost", operator: "lt", value: 0.05 },
    ],
    sort: "startedAt:desc",
    limit: 100,
  }),
});

const { data: spans } = await response.json();

response = httpx.post(
    "https://app.adaline.ai/api/v2/logs/spans",
    json={
        "projectId": project_id,
        "promptId": prompt_id,
        "model": "gpt-4.1",
        "filters": [
            {"type": "number", "column": "cost", "operator": "lt", "value": 0.05},
        ],
        "sort": "startedAt:desc",
        "limit": 100,
    },
    headers={"Authorization": f"Bearer {os.environ['ADALINE_API_KEY']}"},
)

spans = response.json()["data"]

The content, parsedContent, attributes, tags, and events fields on span objects are serialized JSON strings. Parse them in your application code to access the structured data.

Error responses

Status	Meaning
`400`	Invalid parameters or malformed cursor.
`401`	Missing or invalid API key.
`403`	API key does not have access to the requested project.
`404`	Project or prompt does not exist.
`500`	Internal server error.

Next steps

Search Traces API

Full OpenAPI reference for the trace search endpoint.

Search Spans API

Full OpenAPI reference for the span search endpoint.

Filter and Search Logs

Use the Monitor UI to filter logs visually before exporting.

Analyze Log Charts

Spot trends with aggregated analytics, then export the underlying data.

Get started

Instrument

Monitor

Iterate

Evaluate

Deploy

Admin

Others

Available endpoints

Quick start

Filtering

Flat params

Filters array

Available columns

Common export patterns

Search traces

Search spans

Error responses

Next steps

Search Traces API

Search Spans API

Filter and Search Logs

Analyze Log Charts

Get started

Instrument

Monitor

Iterate

Evaluate

Deploy

Admin

Others

​Available endpoints

​Quick start

​Pagination

​Pagination loop

​Filtering

​Flat params

​Filters array

​Available columns

​Common export patterns

​Search traces

​Search spans

​Error responses

​Next steps

Search Traces API

Search Spans API

Filter and Search Logs

Analyze Log Charts

Available endpoints

Quick start

Pagination

Pagination loop

Filtering

Flat params

Filters array

Available columns

Common export patterns

Search traces

Search spans

Error responses

Next steps