SQL reference - Braintrust

SQL queries in Braintrust provide a precise, standard syntax for querying Braintrust logs, experiments, and datasets. Use SQL to:

Filter and search for relevant logs and experiments. Use WHERE clauses to filter individual records and HAVING clauses to filter aggregated results after grouping.
Create consistent, reusable queries for monitoring.
Build automated reporting and analysis pipelines.
Write complex queries to analyze model performance.

For tips on query performance and common pitfalls, see SQL best practices. Braintrust supports two syntax styles: standard SQL syntax, and the legacy BTQL syntax with pipe-delimited clauses. The parser automatically detects which style you’re using. SQL syntax is recommended for all new queries.

Self-hosted deployments: SQL syntax support requires data plane version v1.1.29 or later.

Run SQL queries

Run SQL from the SQL sandbox, the bt sql CLI, or the API.

SQL sandbox

To test SQL with autocomplete, validation, and a table of results, use the SQL sandbox in your project. In the sandbox, you can use Loop to generate and optimize queries from natural language: Example queries:

“Find the most common errors in logs over the last week”
“What are the highest scoring rows in my experiment”
“Show me error distribution over time”
“List all traces where latency exceeded 60 seconds”

Loop automatically populates the sandbox with the generated query, runs it, and provides a text summary of the results along with suggestions for additional queries. Once you have a query in the sandbox, ask Loop to refine it:

“Update the query to show error distribution over time”
“Add a filter to only show errors from specific models”
“Group by user instead”

When your query has errors, Loop can help fix them. Select Fix with Loop next to the error message in the sandbox. Loop analyzes the issue type and context to provide targeted fixes for:

Syntax errors
Schema validation issues
Field name corrections

If a project_logs() query is missing a range filter on created, _xact_id, _pagination_key, or a specific root_span_id/id, the sandbox proactively warns you so you don’t have to wait for a timeout to discover the issue.

bt CLI

Run SQL from your terminal with bt sql. It opens an interactive editor, accepts an inline query, or reads from stdin for scripting.

bt sql                                                       # Interactive editor
bt sql "SELECT * FROM project_logs('my-project') LIMIT 10"   # Inline query
cat query.sql | bt sql                                       # Pipe from a file
bt sql --non-interactive "SELECT count(*) FROM project_logs('my-project')"

API

Access SQL programmatically with the Braintrust API:

curl -X POST https://api.braintrust.dev/btql \
  -H "Authorization: Bearer <YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"query": "select: * | from: project_logs('"'<YOUR_PROJECT_ID>'"') | filter: tags INCLUDES '"'triage'"' AND created > now() - interval 7 day"}'

The API accepts these parameters:

query (required): your SQL query string.
fmt: response format (json or parquet, defaults to json).
lint_mode: root-level lint handling mode (default or strict, defaults to default). In strict mode, lint warnings fail the query.
tz_offset: timezone offset in minutes for time-based operations.
audit_log: include audit log data.
version: an _xact_id string to query data as it existed at a specific point in time (useful for recovering deleted rows). Supported for experiment and dataset sources; not supported for project_logs.

For correct day boundaries, set tz_offset to match your timezone. For example, use 480 for US Pacific Standard Time.

Query structure

SQL queries follow a familiar structure that lets you define what data you want, how you want it returned, and how to analyze it. This example returns logs from the last 7 days from a project where Factuality is greater than 0.8, sorts by created date descending, and limits the results to 100.

SELECT *
FROM project_logs('<PROJECT_ID>', shape => 'spans')
WHERE scores.Factuality > 0.8
  AND created > now() - interval 7 day
ORDER BY created DESC
LIMIT 100

SELECT / select:: Choose which fields to retrieve.
FROM / from:: Specify the data source — a table function or a subquery.
WHERE / filter:: Define conditions to filter the data.
GROUP BY / dimensions:: Group rows for aggregation.
HAVING / final_filter:: Filter aggregated results.
TABLESAMPLE / sample:: Randomly sample a subset of the filtered data (rate or count-based).
ORDER BY / sort:: Set the order of results (ASC/DESC or asc/desc).
LIMIT / limit:: Control result size.
OFFSET '<CURSOR_TOKEN>' / cursor:: Pagination token from the previous query response (SQL OFFSET must be a string literal cursor token; use with cursor-compatible sorts such as _pagination_key or _xact_id).
SETTINGS / settings:: Pass per-query hints to the execution engine.

Always include a range filter (created, _xact_id, or _pagination_key) or scope to a specific root_span_id/id in every project_logs() query. Without one, queries scan your entire project history and will be slow or time out on large datasets.

BTQL syntax

Braintrust also supports BTQL, an alternative pipe-delimited clause syntax. The parser automatically detects whether your query is SQL or BTQL:

SQL queries start with SELECT, WITH, etc. followed by whitespace
BTQL queries use clause syntax like select:, filter:, etc.

SQL Clause	BTQL Clause
`SELECT ...`	`select: ...`
`FROM table('id', shape => 'traces')`	`from: table('id') traces`
`WHERE ...`	`filter: ...`
`GROUP BY ...`	`dimensions: ...`
`GROUP BY ROLLUP(...)`	`rollup: ...`
`GROUP BY GROUPING SETS (...)`	`grouping_sets: (...)`
`HAVING ...`	`final_filter: ...`
`TABLESAMPLE n PERCENT [SEED (seed)]`	`sample: n%`
`TABLESAMPLE n ROWS [SEED (seed)]`	`sample: n`
`ORDER BY ...`	`sort: ...`
`LIMIT n`	`limit: n`
`OFFSET '<CURSOR_TOKEN>'`	`cursor: '<CURSOR_TOKEN>'`
`SETTINGS key = value, ...`	`settings: key = value, ...`

SQL syntax specifies the shape with a named parameter (e.g., FROM experiment('id', shape => 'traces')), while BTQL uses a trailing token (e.g., from: experiment('id') traces). Table aliases on top-level table functions (e.g., FROM project_logs('id') AS t) are reserved for future use. Aliases on subquery sources (e.g., FROM (...) AS sub) are required.

Full-text search: Use the MATCH infix operator for full-text search:

WHERE input MATCH 'search term' → filter: input MATCH 'search term'
Multiple columns require OR: WHERE input MATCH 'x' OR output MATCH 'x' → filter: input MATCH 'x' OR output MATCH 'x'

Unsupported SQL features: The SQL parser does not support the following. For queries that require them, use BTQL’s native syntax.

JOIN, UNION/INTERSECT/EXCEPT, and window functions.
Subqueries in WHERE, HAVING, SELECT, or PIVOT IN (they are supported only in FROM).
PIVOT with explicit value lists, subqueries, or ORDER BY (only IN (ANY) is supported).
INCLUDES and CONTAINS operators. For exact array membership in SQL mode, use IN/NOT IN (for example, tags IN ('value')), or switch to BTQL’s filter: tags INCLUDES 'value' syntax. MATCH is a fuzzy approximation.

`SELECT`

SELECT lets you choose specific fields, compute values, or use * to retrieve every field. Use SELECT alone to retrieve individual records, or combine it with GROUP BY to aggregate results. Both work with all data shapes (spans, traces, and summary).

-- Get specific fields
SELECT
  metadata.model AS model,
  scores.Factuality AS score,
  created AS timestamp
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

Implicit aliasing: Multi-part identifiers like metadata.model automatically create implicit aliases using their last component (e.g., model), which you can use in WHERE, ORDER BY, and GROUP BY clauses when unambiguous. See Field access for details.

SQL allows you to transform data directly in the SELECT clause. This query returns metadata.model, whether metrics.tokens is greater than 1000, and a quality indicator of either “high” or “low” depending on whether or not the Factuality score is greater than 0.8.

SELECT
  -- Simple field access
  metadata.model,

  -- Computed values
  metrics.tokens > 1000 AS is_long_response,

  -- Conditional logic
  (CASE WHEN scores.Factuality > 0.8 THEN 'high' ELSE 'low' END) AS quality
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

You can also use functions in the SELECT clause to transform values and create meaningful aliases for your results. This query extracts the day the log was created, the hour, and a Factuality score rounded to 2 decimal places.

SELECT
  -- Date/time functions
  day(created) AS date,
  hour(created) AS hour,

  -- Numeric calculations
  round(scores.Factuality, 2) AS rounded_score
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

`GROUP BY` for aggregations

Instead of a simple SELECT, you can use SELECT ... GROUP BY to group and aggregate data. This query returns a row for each distinct model with the day it was created, the total number of calls, the average Factuality score, and the latency percentile.

-- Analyze model performance over time
SELECT
  metadata.model AS model,
  day(created) AS date,
  count(1) AS total_calls,
  avg(scores.Factuality) AS avg_score,
  percentile(latency, 0.95) AS p95_latency
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY metadata.model, day(created)

The available aggregate functions are:

count(expr): number of rows
count_distinct(expr): number of distinct values
count_if(expr): number of rows where expr is true
sum(expr): sum of numeric values
avg(expr): mean (average) of numeric values
min(expr): minimum value
max(expr): maximum value
any_value(expr): an arbitrary non-null value from the group for the given expression
percentile(expr, p): a percentile where p is between 0 and 1
GROUPING(expr): returns 1 if expr is a rolled-up dimension in the current ROLLUP/GROUPING SETS row, 0 if it is a real value

LIMIT works with GROUP BY queries to restrict the number of grouped results returned. When combined with ORDER BY, rows are sorted before limiting. See LIMIT for examples.

`ROLLUP` and `GROUPING SETS`

ROLLUP and GROUPING SETS extend GROUP BY to produce multiple levels of aggregation in a single query, eliminating the need for several GROUP BY queries. ROLLUP produces a row for each grouping level: individual groups, subtotals for each prefix of the column list, and a grand total. GROUP BY ROLLUP(model, date) generates groups for (model, date), (model), and () (the grand total). Rolled-up dimensions appear as NULL in the output.

-- Per-model-per-day counts, per-model totals, and a grand total — one query
SELECT
  metadata.model AS model,
  day(created) AS date,
  count(1) AS total_calls
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY ROLLUP(metadata.model, day(created))
ORDER BY model, date

GROUPING SETS lets you specify exactly which grouping combinations to compute. Each set is one aggregation level. The example below is equivalent to the ROLLUP query above, but makes the groupings explicit.

SELECT
  metadata.model AS model,
  day(created) AS date,
  count(1) AS total_calls
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY GROUPING SETS (
  (metadata.model, day(created)),
  (metadata.model),
  ()
)

GROUPING(expr) returns 1 when expr is a rolled-up dimension in the current row (meaning this row is a subtotal or grand total that aggregated over that dimension), and 0 when it holds a real group key value. Use it to label or filter subtotal rows.

SELECT
  metadata.model AS model,
  day(created) AS date,
  count(1) AS total_calls,
  CASE
    WHEN GROUPING(metadata.model) = 1 THEN 'Grand total'
    WHEN GROUPING(day(created)) = 1 THEN 'Model subtotal'
    ELSE 'Day detail'
  END AS row_type
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY ROLLUP(metadata.model, day(created))

`HAVING` for filtering aggregations

HAVING filters the results after aggregation, letting you narrow down grouped data based on aggregate values. Use HAVING with GROUP BY when you need to filter by aggregated metrics like counts, averages, or sums. This query returns models with high average scores:

-- Find models with strong performance
SELECT
  metadata.model AS model,
  avg(scores.Factuality) AS avg_score,
  count(1) AS total_calls
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY metadata.model
HAVING avg(scores.Factuality) > 0.8
  AND count(1) > 100
ORDER BY avg_score DESC

You can combine WHERE and HAVING to filter both before and after aggregation. This query filters individual logs before grouping, then filters the aggregated results:

-- Track high-performing production models
SELECT
  metadata.model AS model,
  day(created) AS date,
  avg(scores.Factuality) AS avg_score,
  count(1) AS call_count
FROM project_logs('my-project-id')
WHERE metadata.is_production = true
  AND created > now() - interval 7 day
GROUP BY metadata.model, day(created)
HAVING avg(scores.Factuality) > 0.7
  AND count(1) >= 10
ORDER BY date DESC, avg_score DESC

HAVING supports the same operators and aggregate functions as other clauses. You can reference aggregated values by their alias or by repeating the aggregate expression.

`FROM`

The FROM clause identifies where the records are coming from.

Data sources

The FROM clause accepts these table functions. Each accepts one or more IDs. Most also accept one or more object names, which Braintrust resolves to IDs before running the query.

Data source	Query by ID	Query by name
`experiment`	`experiment('<id1>', '<id2>')`	`experiment('<name1>', '<name2>')`
`dataset`	`dataset('<id1>', '<id2>')`	`dataset('<name1>', '<name2>')`
`prompt`	`prompt('<id1>', '<id2>')`	Not supported
`function`	`function('<id1>', '<id2>')`	Not supported
`view`	`view('<id1>', '<id2>')`	Not supported
`project_logs`	`project_logs('<id1>', '<id2>')`	`project_logs('<name1>', '<name2>')`
`project_prompts`	`project_prompts('<id1>', '<id2>')`	`project_prompts('<name1>', '<name2>')`
`project_functions`	`project_functions('<id1>', '<id2>')`	`project_functions('<name1>', '<name2>')`
`org_prompts`	`org_prompts('<id1>', '<id2>')`	`org_prompts('<name1>', '<name2>')`
`org_functions`	`org_functions('<id1>', '<id2>')`	`org_functions('<name1>', '<name2>')`
`audit_logs`	`audit_logs('<org_id>')`	`audit_logs('<org_name>')`

The project_* and org_* functions return all objects of that type for a project or organization. audit_logs returns all audit logs for an organization. See Audit logging for the available fields and events.

Querying by name

When you pass a name instead of a UUID, Braintrust looks up the matching object and substitutes its ID before running the query. The Query by name column above shows which functions support this.

-- Query an experiment by name
SELECT * FROM experiment('my-experiment-name') LIMIT 10

-- Query project logs by project name
SELECT * FROM project_logs('my-project') WHERE created > now() - interval 7 day

If the same name matches more than one object across different projects, the query returns an error listing the candidates with their IDs. Use a two-element array to disambiguate by including the parent object name:

-- Disambiguate by passing ['project-name', 'experiment-name']
SELECT * FROM experiment(['my-project', 'my-experiment-name']) LIMIT 10

-- Disambiguate project logs by passing ['org-name', 'project-name']
SELECT * FROM project_logs(['my-org', 'my-project']) WHERE created > now() - interval 7 day

To query several objects at once, pass multiple names separated by commas, the same way you pass multiple IDs.

-- Multiple names
SELECT * FROM experiment('experiment-a', 'experiment-b') LIMIT 10

-- Multiple qualified names
SELECT * FROM experiment(['project-a', 'shared-name'], ['project-b', 'shared-name']) LIMIT 10

If you pass an ID, Braintrust skips name lookup and uses the ID directly. You can mix names, qualified names, and IDs in a single call.

Subqueries

A subquery uses the result of an inner query as the data source for an outer query:

FROM (<inner query>) AS <alias>

The alias is required. Subqueries can be nested to multiple levels. Constraints: Outer queries use standard SQL only. Custom Braintrust syntax (span_filter, trace_filter, ANY_SPAN(), FILTER_SPANS()) is only supported in the innermost query, where it runs against raw data.

span_filter and trace_filter are not supported on subquery sources.
ANY_SPAN() and FILTER_SPANS() are not supported in the outer WHERE clause when the source is a subquery.
HAVING (final_filter) is supported.

Example:

-- Aggregate per model in the inner query,
-- then filter on the aggregated result in the outer query
SELECT model, avg_score
FROM (
  SELECT
    metadata.model AS model,
    AVG(scores.Factuality) AS avg_score
  FROM project_logs('my-project-id')
  WHERE created > now() - interval 7 day
  GROUP BY metadata.model
) AS model_scores
WHERE avg_score > 0.8
ORDER BY avg_score DESC

Data shapes

You can add an optional parameter to the FROM clause that defines how the data is returned. The options are spans (default), traces, and summary.

`spans`

spans returns individual spans that match the filter criteria. This example returns 10 LLM call spans that took more than 0.2 seconds to use the first token.

SELECT *
FROM project_logs('my-project-id', shape => 'spans')
WHERE span_attributes.type = 'llm' AND metrics.time_to_first_token > 0.1
  AND created > now() - interval 7 day
LIMIT 10

The response is an array of spans. Check out the Extend traces page for more details on span structure.

`traces`

traces returns all spans from traces that contain at least one matching span. This is useful when you want to see the full context of a specific event or behavior, for example if you want to see all spans in traces where an error occurred. This example returns all spans for a specific trace where one span in the trace had an error.

SELECT *
FROM project_logs('my-project-id', shape => 'traces')
WHERE root_span_id = 'trace-id' AND error IS NOT NULL

The response is an array of spans. Check out the Extend traces page for more details on span structure.

`summary`

summary provides trace-level views of your data by aggregating metrics across all spans in a trace. This shape is useful for analyzing overall performance and comparing results across experiments. The summary shape can be used in two ways:

Individual trace summaries (using SELECT): Returns one row per trace with aggregated span metrics. Use this to see trace-level details. Example: “What are the details of traces with errors?”
Aggregated trace analytics (using GROUP BY): Groups multiple traces and computes statistics. Use this to analyze patterns across many traces. Example: “What’s the average cost per model per day?”

Individual trace summaries
Aggregated trace analytics

Use SELECT with the summary shape to retrieve individual traces with aggregated metrics. This is useful for inspecting specific trace details, debugging issues, or exporting trace-level data.This example returns 10 summary rows from the project logs for ‘my-project-id’:

SELECT *
FROM project_logs('my-project-id', shape => 'summary')
WHERE created > now() - interval 7 day
LIMIT 10

Summary rows include some aggregated metrics and some preview fields that show data from the root span of the trace.The following fields are aggregated metrics across all spans in the trace. Nested fields are accessed using dot notation (for example, metrics.prompt_tokens, not prompt_tokens).

scores: object of all scores averaged across all spans. Access individual scores as scores.<score_name> (for example, scores.Factuality).
metrics: object of aggregated metrics across all spans. Fields below are accessed as metrics.<field_name>.
- metrics.prompt_tokens: total number of prompt tokens used.
- metrics.completion_tokens: total number of completion tokens used.
- metrics.prompt_cached_tokens: total number of cached prompt tokens used.
- metrics.prompt_cache_creation_tokens: total number of tokens used to create cache entries.
- metrics.prompt_cache_creation_5m_tokens: total number of tokens used to create cache entries with a 5-minute time to live.
- metrics.prompt_cache_creation_1h_tokens: total number of tokens used to create cache entries with a 1-hour time to live.
- metrics.total_tokens: total number of tokens used (prompt + completion).
- metrics.estimated_cost: total estimated cost of the trace in US dollars (prompt + completion costs).
- metrics.llm_calls: total number of LLM calls.
- metrics.tool_calls: total number of tool calls.
- metrics.errors: total number of errors (LLM + tool errors).
- metrics.llm_errors: total number of LLM errors.
- metrics.tool_errors: total number of tool errors.
- metrics.start: Unix timestamp of the first span start time.
- metrics.end: Unix timestamp of the last span end time.
- metrics.duration: wall-clock elapsed time of the trace in seconds, from the earliest span start to the latest span end.
- metrics.llm_duration: sum of all durations across LLM spans in seconds.
- metrics.time_to_first_token: the average time to first token across LLM spans in seconds.
span_type_info: object with span type info. Some fields are aggregated across all spans, others reflect attributes from the root span. Fields below are accessed as span_type_info.<field_name>.
- span_type_info.cached: true only if all LLM spans were cached.
- span_type_info.has_error: true if any span had an error.

Root span preview fields are top-level (not nested under another object): input, output, expected, error, and metadata.For example, to select nested metric fields alongside top-level preview fields:

SELECT
  input,
  metrics.prompt_tokens,
  metrics.estimated_cost,
  span_type_info.has_error
FROM project_logs('my-project-id', shape => 'summary')
WHERE created > now() - interval 7 day
LIMIT 10

To compute statistics across many traces (average cost per trace, trace counts over time), use a GROUP BY on the default spans shape with count_distinct(root_span_id) as the per-trace denominator. Do not add GROUP BY on top of the summary shape: the summary shape already pre-aggregates each trace server-side, so stacking another GROUP BY is rejected under strict lint mode.This example groups by day to track average cost per trace and trace volume over time:

SELECT
  day(created) AS date,
  count_distinct(root_span_id) AS trace_count,
  sum(estimated_cost()) / count_distinct(root_span_id) AS avg_cost_per_trace
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY 1
ORDER BY date DESC

GROUP BY over the summary shape (for example, FROM project_logs('id', shape => 'summary') ... GROUP BY ...) fails under strict lint mode with “Group by over summary queries is inefficient; consider using a ‘traces’ or ‘spans’ shaped query.” The SQL sandbox and Loop run in strict mode. Use the spans-shape pattern above instead. See Aggregate span data across a trace for the two-step form when you need to combine fields that live on different spans.

In the summary shape, WHERE scores.foo evaluates at the span level, returning traces where at least one span matches the condition. This is usually correct, since scorers typically run once per span. To filter by the averaged score across all spans in a trace, use HAVING avg(scores.foo). For example, HAVING avg(scores.Factuality) > 0.8 returns traces where the average Factuality score exceeds 0.8.

`WHERE`

The WHERE clause lets you specify conditions to narrow down results. It supports a wide range of operators and functions, including complex conditions. This example WHERE clause only retrieves data where:

Factuality score is greater than 0.8
model is “gpt-4”
tag list contains “triage” (exact array membership with IN)
input contains the word “question” (case-insensitive)
created date is later than January 1, 2024
more than 1000 tokens were used or the data being traced was made in production

SELECT *
FROM project_logs('my-project-id')
WHERE
  -- Simple comparisons
  scores.Factuality > 0.8 AND
  metadata.model = 'gpt-4' AND

  -- Array membership (exact)
  tags IN ('triage') AND

  -- Text search (case-insensitive)
  input ILIKE '%question%' AND

  -- Date ranges
  created > '2024-01-01' AND

  -- Complex conditions
  (
    metrics.tokens > 1000 OR
    metadata.is_production = true
  )

Single span filters

By default, each returned trace includes at least one span that matches all filter conditions. Use ANY_SPAN() to wrap any filter expression and find traces where at least one span matches the specified condition. Single span filters work with the traces and summary data shapes.

-- Traces where one span has an error and another is an LLM call
WHERE
  ANY_SPAN(error IS NOT NULL) AND
  ANY_SPAN(span_attributes.type = 'llm')

ANY_SPAN() can be combined with GROUP BY to aggregate traces based on span-level conditions. This is useful for analyzing patterns across traces that contain specific types of spans. See Analyze based on tags and scores, Analyze based on tags, and Analyze traces with span filters for examples.

By default, ANY_SPAN() matches against all spans in a trace. To restrict matching to only root spans, add is_root to the condition: ANY_SPAN(is_root AND error IS NOT NULL).

ANY_SPAN() supports one level of nesting. Nested calls are flattened, which allows query builders and the Braintrust UI to compose filters by wrapping conditions in ANY_SPAN(). For example:

-- Exclude traces that contain a score span named "null_score"
WHERE NOT ANY_SPAN(span_attributes.type = 'score' AND ANY_SPAN(span_attributes.name = 'null_score'))

Triple or deeper nesting (ANY_SPAN(ANY_SPAN(ANY_SPAN(...)))) is not supported. NOT ANY_SPAN() does not support multiple span filter clauses — for example, NOT ANY_SPAN(ANY_SPAN(a) AND ANY_SPAN(b)) is not supported.

Matching spans filters

While ANY_SPAN() helps you find traces you care about, matching spans filters let you filter spans within the traces you’ve already found. Use FILTER_SPANS() to return only the matching spans from those traces, rather than entire traces. This is analogous to trace_filter in BTQL. Matching spans filters work with the traces and summary data shapes. On the spans shape, FILTER_SPANS() acts as a no-op wrapper.

-- Return only score spans from traces
WHERE FILTER_SPANS(span_attributes.type = 'score')

-- Return only spans where Factuality equals 1
WHERE FILTER_SPANS(scores.Factuality = 1)

You can combine FILTER_SPANS() with other filter conditions:

-- Return score spans from traces with duration over 1 second
WHERE
  FILTER_SPANS(span_attributes.type = 'score') AND
  metrics.duration > 1

Full-text search

Use MATCH to search a specific field for exact word matches, or search() to search across all text fields at once.

-- Search a specific field for exact word matches
WHERE output MATCH 'timeout'

-- Search across all text fields (input, output, expected, metadata, span_attributes)
WHERE search('timeout')

-- Combine with other filters
WHERE search('error') AND metadata.environment = 'production'

search() is equivalent to writing input MATCH query OR output MATCH query OR ... for each text field. When log search optimization is enabled, search() also benefits from bloom filter acceleration to skip irrelevant segments. Enabling shingled search optimization extends this acceleration to multi-word and phrase queries.

Pattern matching

SQL supports the % wildcard for pattern matching with LIKE (case-sensitive) and ILIKE (case-insensitive). The % wildcard matches any sequence of zero or more characters.

-- Match any input containing "question"
WHERE input ILIKE '%question%'

-- Match inputs starting with "How"
WHERE input LIKE 'How%'

-- Match emails ending with specific domains
WHERE metadata.email ILIKE '%@braintrust.com'

-- Escape literal wildcards with backslash
WHERE metadata.description LIKE '%50\% off%'  -- Matches "50% off"

LIKE and ILIKE do substring matching for string values under 65KB. To search any strings larger than 65KB, use the MATCH operator. MATCH is indexed and more efficient, but it matches whole terms rather than substrings. For example, output MATCH 'time' does not match timeout.

Time intervals

SQL supports intervals for time-based operations. This query returns all project logs from ‘my-project-id’ that were created in the last day.

SELECT *
FROM project_logs('my-project-id')
WHERE created > now() - interval 1 day

This query returns all project logs from ‘my-project-id’ that were created up to an hour ago.

SELECT *
FROM project_logs('my-project-id')
WHERE created > now() - interval 1 hour
  AND created < now()

This query returns all project logs from ‘my-project-id’ that were created within the last month but not within the last week

-- Examples with different units
SELECT *
FROM project_logs('my-project-id')
WHERE created < now() - interval 7 day    -- Last week
  AND created > now() - interval 1 month  -- Last month

`ORDER BY`

The ORDER BY clause determines the order of results. The options are DESC (descending) and ASC (ascending) on a numerical field. You can sort by a single field, multiple fields, or computed values.

-- Sort by single field
ORDER BY created DESC

-- Sort by multiple fields
ORDER BY scores.Factuality DESC, created ASC

-- Sort by computed values
ORDER BY len(tags) DESC

`SAMPLE`

TABLESAMPLE (or the shorter SAMPLE alias) randomly samples a subset of the filtered data. Use it to work with a representative slice of a large dataset without scanning every row. Two forms are supported:

Percent-based: TABLESAMPLE n PERCENT — samples approximately n% of rows (0–100).
Row-based: TABLESAMPLE n ROWS or TABLESAMPLE n — samples approximately n rows.

An optional SEED (seed) clause makes sampling deterministic. Without a seed, each query returns a different random sample.

-- Sample ~10% of rows (non-deterministic)
SELECT *
FROM project_logs('my-project-id') TABLESAMPLE 10 PERCENT
WHERE created > now() - interval 7 day

-- Sample ~10% of rows (deterministic, same result every run)
SELECT *
FROM project_logs('my-project-id') TABLESAMPLE 10 PERCENT SEED (42)
WHERE created > now() - interval 7 day

-- Sample approximately 500 rows
SELECT *
FROM project_logs('my-project-id') TABLESAMPLE 500 ROWS
WHERE created > now() - interval 7 day

-- Short SAMPLE alias works the same way
SELECT *
FROM project_logs('my-project-id') SAMPLE 10 PERCENT SEED (42)
WHERE created > now() - interval 7 day

SAMPLE is part of the table reference in the FROM clause, not a post-filter clause. The percentage or row count applies to the full table before WHERE filtering. TABLESAMPLE BERNOULLI, TABLESAMPLE SYSTEM, and other named sampling methods are not supported.

`PIVOT` and `UNPIVOT`

PIVOT and UNPIVOT are advanced operations that transform your results for easier analysis and comparison. Both SQL and BTQL syntax support these operations.

`PIVOT`

PIVOT transforms rows into columns, which makes comparisons easier by creating a column for each distinct value. This is useful when comparing metrics across different categories, models, or time periods. Structure:

SELECT <non-pivoted columns>, <pivoted columns>
FROM <table>
PIVOT(
  <AggregateFunction>(<ColumnToBeAggregated>)
  FOR <PivotColumn>
  IN (ANY)
)

Requirements:

The pivot column must be a single identifier (e.g., metadata.model)
Must include at least one aggregate measure (e.g., SUM(value), AVG(score))
Only IN (ANY) is supported (explicit value lists, subqueries, ORDER BY, and DEFAULT ON NULL are not supported)
SELECT list must include the pivot column, all measures, and all GROUP BY columns (or use SELECT *)

Pivot columns are automatically named by combining the pivot value and measure name. For example, if you pivot metadata.model with a model named “gpt-4” for measure avg_score, the column becomes gpt-4_avg_score. When using aliases, the alias replaces the measure name in the output column. Single aggregate - pivot one metric across categories:

-- Compare total score values across all scores
SELECT score, SUM(value)
FROM project_logs('my-project-id')
UNPIVOT (value FOR score IN (scores))
PIVOT(SUM(value) FOR score IN (ANY))
WHERE created > now() - interval 7 day

Multiple aggregates - pivot multiple metrics at once:

-- Compare model performance metrics across models
SELECT day(created) AS date, metadata.model, AVG(scores.Factuality), COUNT(1)
FROM project_logs('my-project-id')
WHERE metadata.model IN ('gpt-4', 'gpt-3.5-turbo')
  AND created > now() - interval 7 day
GROUP BY day(created), metadata.model
PIVOT(
  AVG(scores.Factuality),
  COUNT(1)
  FOR metadata.model IN (ANY)
)

With aliases - name your pivoted columns:

-- Use custom column names
SELECT score, total
FROM project_logs('my-project-id')
UNPIVOT (value FOR score IN (scores))
PIVOT(SUM(value) AS total FOR score IN (ANY))
WHERE created > now() - interval 7 day

With grouping - combine PIVOT with GROUP BY for multi-dimensional analysis:

-- Analyze scores by both model and date
SELECT day(created) AS date, metadata.model, AVG(scores.Factuality)
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY day(created), metadata.model
PIVOT(AVG(scores.Factuality) FOR metadata.model IN (ANY))

Using SELECT * - automatically includes all required columns:

-- SELECT * includes pivot column, measures, and GROUP BY columns automatically
SELECT *
FROM project_logs('my-project-id')
UNPIVOT (value FOR score IN (scores))
PIVOT(SUM(value) FOR score IN (ANY))
WHERE created > now() - interval 7 day

`UNPIVOT`

UNPIVOT transforms columns into rows, which is useful when you need to analyze arbitrary scores and metrics without specifying each field name in advance. This is helpful when working with dynamic sets of metrics or when you want to normalize data for aggregation. Key-value unpivot - transforms an object into rows with key-value pairs:

-- Convert scores object into rows with score names and values
SELECT id, score, value
FROM project_logs('my-project-id')
UNPIVOT (value FOR score IN (scores))
WHERE created > now() - interval 7 day

When using key-value unpivot, the source column must be an object (e.g., scores). When using array unpivot with _, the source column must be an array (e.g., tags).

Array unpivot - expands arrays by using _ as the name column:

-- Expand tags array into individual rows
SELECT id, tag
FROM project_logs('my-project-id')
UNPIVOT (tag FOR _ IN (tags))
WHERE created > now() - interval 7 day

Array of objects unpivot - expands arrays of objects and allows accessing nested fields:

-- Expand classifications array and group by nested field
SELECT classification.id, COUNT(1) AS count
FROM project_logs('my-project-id')
UNPIVOT (classification FOR _ IN (classifications.Issues))
WHERE created >= NOW() - INTERVAL 3 DAY
GROUP BY classification.id

This pattern is useful for analyzing classifications where each log may have multiple topic classifications, and you want to aggregate by specific properties of those classifications. Multiple unpivots - chain multiple UNPIVOT operations to expand multiple columns:

-- Expand both scores and tags
SELECT score, tag
FROM project_logs('my-project-id')
UNPIVOT (value FOR score IN (scores))
UNPIVOT (tag FOR _ IN (tags))
WHERE created > now() - interval 7 day

With aggregations - use UNPIVOT with GROUP BY to aggregate across unpivoted rows:

-- Calculate average value for each score across all logs
SELECT score, AVG(value) AS avg_value, COUNT(1) AS count
FROM project_logs('my-project-id')
UNPIVOT (value FOR score IN (scores))
WHERE created > now() - interval 7 day
GROUP BY score
ORDER BY avg_value DESC

`LIMIT` and cursors

`LIMIT`

The LIMIT clause controls the size of the result in number of records.

-- Basic limit
LIMIT 100

When using LIMIT with GROUP BY, it restricts the number of grouped results returned. This is useful for getting top-N results after aggregation. When combined with ORDER BY, rows are sorted before limiting.

-- Get top 5 models by usage
SELECT
  metadata.model AS model,
  count(1) AS total_calls
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY metadata.model
ORDER BY total_calls DESC
LIMIT 5

-- Limit aggregated results without sorting
SELECT
  metadata.category AS category,
  avg(metrics.duration) AS avg_duration
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY metadata.category
LIMIT 10

Cursors for pagination

Cursors are supported in both SQL and BTQL queries. Cursors are automatically returned in query responses. After an initial query, pass the returned cursor token in the follow-on query. When a cursor has reached the end of the result set, the data array will be empty, and no cursor token will be returned. In SQL syntax, pass cursor tokens using OFFSET '<CURSOR_TOKEN>'. Numeric offsets are not supported. For cursor pagination, use cursor-compatible sorts such as _pagination_key (recommended) or _xact_id.

-- Pagination with cursor token
-- Page 1 (first 100 results)
SELECT *
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 7 day
ORDER BY _pagination_key DESC
LIMIT 100

-- Page 2 (next 100 results)
SELECT *
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 7 day
ORDER BY _pagination_key DESC
LIMIT 100
OFFSET '<CURSOR_TOKEN>'  -- From previous query response

`SETTINGS`

The SETTINGS clause passes per-query hints to the Brainstore execution engine. It appears at the end of the query. Multiple options can be set in a single clause, separated by commas. The following options are supported:

Option	Type	Description
`max_bloom_terms`	Nonneg integer	Maximum number of disjunctive terms (for example, `OR` branches or `IN` list values) for which Brainstore consults a bloom filter during segment elimination. Above this threshold, the bloom filter is skipped because checking it adds overhead without filtering many segments. Set this option to override the engine default for a specific query.
`preview_length`	Integer	Truncates the preview fields (`input`, `output`, `expected`, `error`, `metadata`) to this many characters, appending `...` when truncated. Use `-1` for no truncation and `0` for maximum truncation. For the `summary` shape, an engine default is applied when this option is not set. In BTQL, `preview_length` is a top-level clause (`preview_length: N`), not a `settings:` option.

SELECT id, input
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 7 day
  AND metadata.request_id IN ('req-a', 'req-b', 'req-c')
SETTINGS max_bloom_terms = 256

Multiple options:

SELECT id
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 7 day
SETTINGS max_bloom_terms = 256

Expressions

SQL operators

You can use the following operators in your SQL queries.

-- Comparison operators
=           -- Equal to (alias for 'eq')
!=          -- Not equal to (alias for 'ne', can also use '<>')
>           -- Greater than (alias for 'gt')
<           -- Less than (alias for 'lt')
>=          -- Greater than or equal (alias for 'ge')
<=          -- Less than or equal (alias for 'le')
<=>         -- Null-safe equal: true if both sides are equal OR both are null
<!=>        -- Null-safe not-equal: true if values differ, treating null as a distinct value
IN          -- Check if value exists in a list of values
NOT IN      -- Check if value does not exist in a list of values

-- Null operators
IS NULL     -- Check if value is null
IS NOT NULL -- Check if value is not null

-- Text matching
LIKE        -- Case-sensitive pattern matching (supports % wildcard; strings under 65KB only)
NOT LIKE    -- Negated case-sensitive pattern matching
ILIKE       -- Case-insensitive pattern matching (supports % wildcard; strings under 65KB only)
NOT ILIKE   -- Negated case-insensitive pattern matching
MATCH       -- Full-word semantic search (faster but requires exact word matches, e.g. 'apple' won't match 'app')
NOT MATCH   -- Negated full-word semantic search

-- Logical operators
AND         -- Both conditions must be true
OR          -- Either condition must be true
NOT         -- Unary operator to negate condition

-- Arithmetic operators
+           -- Addition (alias: add)
-           -- Subtraction (alias: sub)
*           -- Multiplication (alias: mul)
/           -- Division (alias: div)
%           -- Modulo (alias: mod)
-x          -- Unary negation (alias: neg)

INCLUDES, NOT INCLUDES, and CONTAINS are BTQL-only operators and raise a syntax error in SQL mode. For exact array membership in SQL mode, use IN and NOT IN (e.g., tags IN ('value')). MATCH is a fuzzy approximation that tokenizes text, so tags MATCH 'value' also matches values containing the term as a token, such as value-added.

SQL functions

You can use the following functions in SELECT, WHERE, GROUP BY clauses, and aggregate measures.

-- Date/time functions
second(timestamp)                 -- Extract second from timestamp
minute(timestamp)                 -- Extract minute from timestamp
hour(timestamp)                   -- Extract hour from timestamp
day(timestamp)                    -- Extract day from timestamp
week(timestamp)                   -- Extract week from timestamp
month(timestamp)                  -- Extract month from timestamp
year(timestamp)                   -- Extract year from timestamp
date_trunc(interval, timestamp)   -- Truncate timestamp to specified interval
                                  -- Intervals: 'second', 'minute', 'hour', 'day', 'week', 'month', 'year',
                                  --   or a positive integer multiple of 'second(s)', 'minute(s)',
                                  --   'hour(s)', or 'day(s)' (e.g., '15 minutes', '2 hours')
current_timestamp()               -- Get current timestamp (alias: now())
current_date()                    -- Get current date

-- String functions
lower(text)                       -- Convert text to lowercase
upper(text)                       -- Convert text to uppercase
substring(text, start, length)    -- Extract substring; 1-based positions; start may be ≤ 0 (window clipped
                                  --   to existing characters); zero length returns empty string;
                                  --   returns NULL if any argument is NULL
concat(text1, text2, ...)         -- Concatenate strings

-- Array functions
len(expr)                         -- Get length of an array. Returns 1 for scalar
                                  -- values (string, number, or boolean), and 0 for
                                  -- null and objects.

-- JSON functions
json_extract(json_str, path)      -- Extract value from JSON string using a path expression

-- Null handling functions
coalesce(val1, val2, ...)        -- Return first non-null value
                                 -- Note: coalesce(field, 'x') != 'y' is automatically rewritten
                                 -- to field <!=> 'y' for better index performance
nullif(val1, val2)               -- Return null if val1 equals val2
least(val1, val2, ...)           -- Return smallest non-null value
greatest(val1, val2, ...)        -- Return largest non-null value

-- Conditional functions
if(cond, then_val, else_val)     -- Return then_val if cond is true, else_val otherwise.
                                 --   Equivalent to CASE WHEN cond THEN then_val ELSE else_val END.
                                 --   Use CASE WHEN for more than two branches.

-- Type conversion
round(number, precision)          -- Round to specified precision

-- Cast functions
to_string(value)                 -- Cast value to string
to_boolean(value)                -- Cast value to boolean
to_integer(value)                -- Cast value to integer
to_number(value)                 -- Cast value to number
to_date(value)                   -- Cast value to date
to_datetime(value)               -- Cast value to datetime
to_interval(value)               -- Cast value to interval

-- Search functions
search(query)                     -- Full-text search across all text fields (input, output, expected,
                                  --   metadata, span_attributes). Equivalent to writing
                                  --   input MATCH query OR output MATCH query OR ... for each field.
                                  --   When log search optimization is enabled on the project, search()
                                  --   uses bloom filters to skip irrelevant segments automatically.
                                  --   Shingled search optimization extends this to multi-word and phrase queries.

-- Cost functions
-- Note: on the spans and traces shapes, estimated_cost() and metrics.estimated_cost
--   are populated on LLM spans, not the root/task span. Filtering to is_root = true
--   returns null cost. To get a trace's total cost, sum across its spans:
--   sum(estimated_cost()). The summary shape rolls this up per trace automatically.
estimated_cost()                  -- Estimated cost of a span in USD. Uses metrics.estimated_cost
                                  --   when available; otherwise computes from token metrics and
                                  --   model registry pricing. Works in spans, traces, and summary
                                  --   shapes. Can be used inside aggregates: sum(estimated_cost())
estimated_cost_breakdown()        -- Returns per-component costs for a span as a serialized JSON string
                                  --   (not a structured object). Keys: promptUncachedTokensCost,
                                  --   promptCachedTokensCost, promptCacheCreationTokensCost
                                  --   (single-tier cache creation), promptCacheCreation5mTokensCost
                                  --   and promptCacheCreation1hTokensCost (two-tier cache creation,
                                  --   e.g. Anthropic), completionTokensCost, totalCost. Returns NULL
                                  --   when cost cannot be computed. To read individual components in
                                  --   SQL, use estimated_cost_component(name) instead.
estimated_cost_component(name)    -- Returns the numeric cost for a single component of the breakdown.
                                  --   Valid names: 'promptUncachedTokensCost', 'promptCachedTokensCost',
                                  --   'promptCacheCreationTokensCost', 'promptCacheCreation5mTokensCost',
                                  --   'promptCacheCreation1hTokensCost', 'completionTokensCost', 'totalCost'.
                                  --   Returns NULL when cost cannot be computed or name is unrecognized.

-- Aggregate functions (only in measures/with GROUP BY)
count(expr)                       -- Count number of rows
count_distinct(expr)              -- Count number of distinct values
count_if(expr)                    -- Count rows where expr is true. Equivalent to
                                  --   count(CASE WHEN expr THEN 1 ELSE NULL END)
sum(expr)                         -- Sum numeric values
avg(expr)                         -- Calculate mean of numeric values
min(expr)                         -- Find minimum value
max(expr)                         -- Find maximum value
any_value(expr)                   -- An arbitrary non-null value from the group
percentile(expr, p)               -- Calculate percentile (p between 0 and 1)
GROUPING(expr)                    -- Returns 1 if expr is a rolled-up (subtotal/grand-total) dimension
                                  --   in the current ROLLUP/GROUPING SETS row, 0 if it is a real value

Field access

SQL provides flexible ways to access nested data in arrays and objects:

-- Object field access
metadata.model             -- Access nested object field  e.g. {"metadata": {"model": "value"}}
metadata."field name"      -- Access field with spaces	  e.g. {"metadata": {"field name": "value"}}
metadata."field-name"      -- Access field with hyphens   e.g. {"metadata": {"field-name": "value"}}
metadata."field.name"      -- Access field with dots	  e.g. {"metadata": {"field.name": "value"}}

-- Array access (0-based indexing)
tags[0]                    -- First element
tags[-1]                   -- Last element

-- Combined array and object access
metadata.models[0].name    -- Field in first array element
responses[-1].tokens       -- Field in last array element
spans[0].children[-1].id   -- Nested array traversal

-- Facets access
facets.task                -- Task facet value
facets.sentiment           -- Sentiment facet value
facets."custom-facet"      -- Custom facet (quotes for hyphens)

-- Classifications access (array of objects)
classifications.Task[0].label              -- Classification label
classifications.Task[0].metadata.distance  -- Distance metric
classifications.Sentiment[0].label         -- Sentiment classification

Array indices are 0-based, and negative indices count from the end (-1 is the last element).

When you have JSON data stored as a string field (rather than as native SQL objects), use the json_extract function to access values within it. The path is a JSONPath expression that supports nested object keys, array indexing, and an optional $ root prefix:

-- Extract from JSON string fields
json_extract(metadata.config, 'api_key')              -- Top-level "api_key" field
json_extract(metadata.config, 'auth.user_id')         -- Nested: config → auth → user_id
json_extract(output, 'choices[0].message.content')    -- Array index plus nested keys

Implicit aliasing

When you reference multi-part identifiers (e.g., metadata.category), SQL automatically creates an implicit alias using the last component of the path (e.g., category). This allows you to use the short form in your queries when unambiguous.

-- Use short form in WHERE clause
SELECT metadata.category, metadata.model
FROM project_logs('my-project-id')
WHERE category = 'support' AND model = 'gpt-4'
  AND created > now() - interval 7 day

-- Use short form in ORDER BY
SELECT metadata.user.name
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
ORDER BY name

-- Use short form in GROUP BY
SELECT metadata.model, COUNT(*) as cnt
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY model

Important notes about implicit aliasing:

Ambiguity prevention: If multiple fields share the same last component (e.g., metadata.name and user.name), the short form name becomes ambiguous and cannot be used. You must use the full path instead.
Top-level field priority: Top-level fields take precedence over nested fields. If you have both id and metadata.id, the short form id refers to the top-level field.
Explicit aliases override: When you provide an explicit alias (e.g., metadata.category AS cat), the implicit alias is disabled and you must use either the explicit alias or the full path.
Duplicate alias detection: SQL will detect and reject queries with duplicate aliases in the SELECT list, whether explicit or implicit. For example, SELECT id, user.number AS id will raise an error.

Examples of ambiguous references:

-- ERROR: Cannot use 'name' - ambiguous between two fields
SELECT metadata.name, user.name
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
ORDER BY name  -- Error: ambiguous

-- CORRECT: Use full path when ambiguous
SELECT metadata.name, user.name
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
ORDER BY metadata.name

Freeing up short forms with explicit aliases: When one field uses an explicit alias, its short form becomes available for other fields:

-- 'user_name' is the explicit alias, 'name' now refers to metadata.name
SELECT user.name AS user_name, metadata.name
FROM project_logs('my-project-id')
WHERE name = 'configuration'  -- Refers to metadata.name
  AND created > now() - interval 7 day

Conditional expressions

SQL supports conditional logic through CASE WHEN for multi-branch conditions, IF for two-branch conditions, and COUNT_IF for conditional aggregation.

`CASE WHEN`

Use CASE WHEN ... THEN ... ELSE ... END for one or more branches:

-- Basic conditions
SELECT
  CASE WHEN scores.Factuality > 0.8 THEN 'high' ELSE 'low' END AS quality,
  CASE WHEN error IS NOT NULL THEN -1 ELSE metrics.tokens END AS valid_tokens
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

-- Nested conditions
SELECT
  CASE
    WHEN scores.Factuality > 0.9 THEN 'excellent'
    WHEN scores.Factuality > 0.7 THEN 'good'
    WHEN scores.Factuality > 0.5 THEN 'fair'
    ELSE 'poor'
  END AS rating
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

-- Use in calculations
SELECT
  CASE WHEN metadata.model = 'gpt-4' THEN metrics.tokens * 2 ELSE metrics.tokens END AS adjusted_tokens,
  CASE WHEN error IS NULL THEN metrics.latency ELSE 0 END AS valid_latency
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

`IF` and `COUNT_IF`

For two-branch conditions, IF(condition, then_value, else_value) is a shorter alternative to CASE WHEN. Use CASE WHEN when you need more than two branches.

-- IF as a shorter alternative to a two-branch CASE WHEN
SELECT
  IF(scores.Factuality > 0.8, 'high', 'low') AS quality,
  IF(error IS NOT NULL, -1, metrics.tokens) AS valid_tokens
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

COUNT_IF(condition) counts rows where the condition is true. It is equivalent to count(CASE WHEN condition THEN 1 ELSE NULL END).

-- Count high-quality and errored spans per model
SELECT
  metadata.model AS model,
  count(1) AS total,
  COUNT_IF(scores.Factuality > 0.8) AS high_quality,
  COUNT_IF(error IS NOT NULL) AS errored
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day
GROUP BY metadata.model

IF composes with other aggregates. For example, count distinct users who hit a condition:

SELECT count(DISTINCT IF(error IS NOT NULL, metadata.user_id, NULL)) AS users_with_errors
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

Examples

Track token usage

This query helps you monitor token consumption across your application.

SELECT
  day(created) AS time,
  sum(metrics.total_tokens) AS total_tokens,
  sum(metrics.prompt_tokens) AS input_tokens,
  sum(metrics.completion_tokens) AS output_tokens
FROM project_logs('<YOUR_PROJECT_ID>')
WHERE created > '<ISO_8601_TIME>'
GROUP BY 1
ORDER BY time ASC

-- Alternative using date_trunc function
SELECT
  date_trunc('day', created) AS time,
  sum(metrics.total_tokens) AS total_tokens,
  sum(metrics.prompt_tokens) AS input_tokens,
  sum(metrics.completion_tokens) AS output_tokens
FROM project_logs('<YOUR_PROJECT_ID>')
WHERE created > '<ISO_8601_TIME>'
GROUP BY date_trunc('day', created)
ORDER BY time ASC

-- Custom interval multiples for finer-grained buckets
SELECT
  date_trunc('15 minutes', created) AS time,
  sum(metrics.total_tokens) AS total_tokens,
  sum(metrics.prompt_tokens) AS input_tokens,
  sum(metrics.completion_tokens) AS output_tokens
FROM project_logs('<YOUR_PROJECT_ID>')
WHERE created > '<ISO_8601_TIME>'
GROUP BY date_trunc('15 minutes', created)
ORDER BY time ASC

The response shows daily token usage:

{
  "time": "2024-11-09T00:00:00Z",
  "total_tokens": 100000,
  "input_tokens": 50000,
  "output_tokens": 50000
}

Analyze cost breakdown

Use estimated_cost_component() to break total cost into its components — useful for understanding how much of your spend comes from cached reads vs. uncached prompt tokens vs. completions.

-- Cost breakdown by component, model, and day
SELECT
  metadata.model AS model,
  day(created) AS date,
  sum(estimated_cost_component('promptUncachedTokensCost'))      AS uncached_prompt_cost,
  sum(estimated_cost_component('promptCachedTokensCost'))        AS cached_read_cost,
  sum(estimated_cost_component('promptCacheCreationTokensCost')) AS cache_write_cost,
  sum(estimated_cost_component('completionTokensCost'))          AS completion_cost,
  sum(estimated_cost()) AS total_cost
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 7 day
  AND span_attributes.type = 'llm'
GROUP BY metadata.model, day(created)
ORDER BY date DESC, total_cost DESC

For models with two-tier cache creation (e.g. Anthropic), replace promptCacheCreationTokensCost with promptCacheCreation5mTokensCost and promptCacheCreation1hTokensCost to split by tier.

Use estimated_cost_breakdown() when you want all components at once as a JSON object, for example to export or inspect the full breakdown per span.

SELECT id, metadata.model, estimated_cost_breakdown() AS cost_breakdown
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 1 day
  AND span_attributes.type = 'llm'
LIMIT 100

Monitor model quality

Track model performance across different versions and configurations.

-- Compare factuality scores across models
SELECT
  metadata.model AS model,
  day(created) AS date,
  avg(scores.Factuality) AS avg_factuality,
  percentile(scores.Factuality, 0.05) AS p05_factuality,
  percentile(scores.Factuality, 0.95) AS p95_factuality,
  count(1) AS total_calls
FROM project_logs('<PROJECT_ID>')
WHERE created > '2024-01-01'
GROUP BY 1, 2
ORDER BY date DESC, model ASC

-- Find potentially problematic responses
SELECT *
FROM project_logs('<PROJECT_ID>')
WHERE scores.Factuality < 0.5
  AND metadata.is_production = true
  AND created > now() - interval 1 day
ORDER BY scores.Factuality ASC
LIMIT 100

-- Compare performance across specific models
SELECT *
FROM project_logs('<PROJECT_ID>')
WHERE metadata.model IN ('gpt-4', 'gpt-4-turbo', 'claude-3-opus')
  AND scores.Factuality IS NOT NULL
  AND created > now() - interval 7 day
ORDER BY scores.Factuality DESC
LIMIT 500

Analyze errors

Identify and investigate errors in your application.

-- Error rate by model
SELECT
  metadata.model AS model,
  hour(created) AS hour,
  count(1) AS total,
  count(error) AS errors,
  count(error) / count(1) AS error_rate
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 1 day
GROUP BY 1, 2
ORDER BY error_rate DESC

-- Find common error patterns
SELECT
  error.type AS error_type,
  metadata.model AS model,
  count(1) AS error_count,
  avg(metrics.latency) AS avg_latency
FROM project_logs('<PROJECT_ID>')
WHERE error IS NOT NULL
  AND created > now() - interval 7 day
GROUP BY 1, 2
ORDER BY error_count DESC

-- Exclude known error types from analysis
SELECT *
FROM project_logs('<PROJECT_ID>')
WHERE error IS NOT NULL
  AND error.type NOT IN ('rate_limit', 'timeout', 'network_error')
  AND metadata.is_production = true
  AND created > now() - interval 1 day
ORDER BY created DESC
LIMIT 100

Analyze latency

Monitor and optimize response times.

-- Track p95 latency by endpoint
SELECT
  metadata.endpoint AS endpoint,
  hour(created) AS hour,
  percentile(metrics.latency, 0.95) AS p95_latency,
  percentile(metrics.latency, 0.50) AS median_latency,
  count(1) AS requests
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 1 day
GROUP BY 1, 2
ORDER BY hour DESC, p95_latency DESC

-- Find slow requests
SELECT
  metadata.endpoint,
  metrics.latency,
  metrics.tokens,
  input,
  created
FROM project_logs('<PROJECT_ID>')
WHERE metrics.latency > 5000  -- Requests over 5 seconds
  AND created > now() - interval 1 hour
ORDER BY metrics.latency DESC
LIMIT 20

Analyze prompts

Analyze prompt effectiveness and patterns.

-- Track prompt token efficiency
SELECT
  metadata.prompt_template AS template,
  day(created) AS date,
  avg(metrics.prompt_tokens) AS avg_prompt_tokens,
  avg(metrics.completion_tokens) AS avg_completion_tokens,
  avg(metrics.completion_tokens) / avg(metrics.prompt_tokens) AS token_efficiency,
  avg(scores.Factuality) AS avg_factuality
FROM project_logs('<PROJECT_ID>')
WHERE created > now() - interval 7 day
GROUP BY 1, 2
ORDER BY date DESC, token_efficiency DESC

-- Find similar prompts
SELECT *
FROM project_logs('<PROJECT_ID>')
WHERE input MATCH 'explain the concept of recursion'
  AND scores.Factuality > 0.8
  AND created > now() - interval 7 day
ORDER BY created DESC
LIMIT 10

Analyze based on tags

Use tags to track and analyze specific behaviors. In SQL mode, use IN and NOT IN to filter tags by exact membership:

-- Track issue resolution: tagged needs-review but not resolved
SELECT created, tags, metadata.model, scores.Factuality, output
FROM project_logs('<PROJECT_ID>')
WHERE tags IN ('needs-review')
  AND tags NOT IN ('resolved')
  AND created > now() - interval 1 day
ORDER BY scores.Factuality ASC

tags IN ('needs-review') matches rows where needs-review is one of the tags, and tags NOT IN ('resolved') matches rows without the resolved tag. NOT IN excludes rows where tags is null or absent.

MATCH (tags MATCH 'feedback') is a fuzzy approximation. It tokenizes text, so it also matches tags that contain the term as a token, such as feedback-given.

INCLUDES is not supported in SQL mode. The examples below use BTQL syntax, which supports INCLUDES for exact array membership:

-- Monitor feedback patterns
select: tags[0] AS primary_tag, metadata.model AS model, count(1) AS feedback_count, avg(scores.Factuality > 0.8 ? 1 : 0) AS high_quality_rate
| from: project_logs('<PROJECT_ID>')
| filter: tags INCLUDES 'feedback' AND created > now() - interval 30 day
| dimensions: 1, 2
| sort: feedback_count DESC

-- Track issue resolution
select: created, tags, metadata.model, scores.Factuality, output
| from: project_logs('<PROJECT_ID>')
| filter: tags INCLUDES 'needs-review' AND NOT tags INCLUDES 'resolved' AND created > now() - interval 1 day
| sort: scores.Factuality ASC

Analyze based on tags and scores

A common pattern is filtering traces by both tags and scores when automated scorers apply scores at the span level while tags exist on root spans. Use separate ANY_SPAN() clauses to match traces where any span contains the tag AND any span contains the score. Each ANY_SPAN() clause evaluates independently across all spans in a trace. The conditions don’t need to match on the same span - the first ANY_SPAN() finds traces where at least one span has the tag, while the second finds traces where at least one span has the score.

-- Find traces with a specific tag and score
select: *
| from: project_logs('<PROJECT_ID>') traces
| filter: ANY_SPAN(tags INCLUDES 'content-change') AND ANY_SPAN(scores."email sent" IS NOT NULL) AND created > now() - interval 7 day
| limit: 100

For score names with spaces or special characters, wrap the name in double quotes:

-- Filter by tag and score with spaces in name
select: *
| from: project_logs('<PROJECT_ID>') summary
| filter: ANY_SPAN(tags INCLUDES 'production') AND ANY_SPAN(scores."Email Draft vs Sent Similarity" IS NOT NULL) AND created > now() - interval 7 day
| sort: created DESC
| limit: 50

Analyze traces with span filters

Use single span filters with aggregations to analyze traces based on span-level conditions. This is useful for understanding patterns across complex, multi-step operations.

-- Find traces with both errors and LLM spans, grouped by error type
SELECT
  error.type AS error_type,
  metadata.model AS model,
  count_distinct(root_span_id) AS trace_count,
  sum(estimated_cost()) / count_distinct(root_span_id) AS avg_cost_per_trace
FROM project_logs('<PROJECT_ID>', shape => 'traces')
WHERE ANY_SPAN(error IS NOT NULL)
  AND ANY_SPAN(span_attributes.type = 'llm')
  AND created > now() - interval 7 day
GROUP BY error.type, metadata.model
ORDER BY trace_count DESC

-- Analyze average cost per trace by day for traces with errors and LLM calls
SELECT
  day(created) AS date,
  sum(estimated_cost()) / count_distinct(root_span_id) AS avg_cost_per_trace,
  count_distinct(root_span_id) AS trace_count
FROM project_logs('<PROJECT_ID>', shape => 'traces')
WHERE ANY_SPAN(error IS NOT NULL)
  AND ANY_SPAN(span_attributes.type = 'llm')
  AND created > now() - interval 7 day
GROUP BY day(created)
ORDER BY date DESC

Use FILTER_SPANS() to analyze only the spans that match specific criteria:

-- Analyze score distribution for only scorer spans
SELECT
  span_attributes.name AS scorer_name,
  avg(scores.Factuality) AS avg_score,
  count(1) AS span_count
FROM project_logs('<PROJECT_ID>', shape => 'traces')
WHERE FILTER_SPANS(span_attributes.type = 'score')
  AND created > now() - interval 7 day
GROUP BY span_attributes.name
ORDER BY avg_score DESC

Extract data from JSON strings

Use json_extract to pull values out of a field whose value is a JSON-encoded string (rather than a native SQL object). The second argument is a JSONPath expression that supports nested object keys, array indexing (including negative indices), and an optional $ root prefix.

-- Extract a top-level field
SELECT
  id,
  json_extract(metadata.config, 'api_key') AS api_key
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

-- Traverse nested keys and array elements
SELECT
  id,
  json_extract(metadata.settings, 'user.preferences.theme') AS theme,
  json_extract(output, 'choices[0].message.content') AS first_message
FROM project_logs('my-project-id')
WHERE created > now() - interval 7 day

-- Extract and filter
SELECT *
FROM project_logs('my-project-id')
WHERE json_extract(metadata.config, 'environment') = 'production'
  AND json_extract(metadata.config, 'version') > 2.0
  AND created > now() - interval 7 day

json_extract returns null for invalid JSON or missing keys rather than raising an error, making it safe to use in filters and aggregations. Because the path is a JSONPath expression, dots and brackets are interpreted as traversal: a.b descends into key a then key b, and a[0] indexes into an array. A JSON key whose name literally contains a dot or bracket can’t be addressed this way.

Query by classifications

Classifications are categorical labels generated by topics automations (for example, classifications.Task[0].label). Each classification object has these nested fields, accessed as classifications.<name>[<i>].<field>:

classifications.<name>[<i>].label: the category label.
classifications.<name>[<i>].metadata.distance: distance metric for the classification.
classifications.<name>[<i>].source: source of the classification.

Filter and analyze logs by topic classifications to understand patterns in your data.

SELECT
  classifications.Task[0].label as topic,
  count(*) as count
FROM project_logs('my-project-id')
WHERE classifications.Task IS NOT NULL
  AND created > now() - interval 7 day
GROUP BY topic
ORDER BY count DESC

To analyze all classifications in an array rather than just the first element, use UNPIVOT to expand the array. See Array of objects unpivot for examples.

Filter by specific topic and distance threshold:

SELECT id, facets.task, classifications.Task[0].label as topic
FROM project_logs('my-project-id')
WHERE classifications.Task[0].label = 'Creating datasets'
  AND classifications.Task[0].metadata.distance < 0.5
  AND created > now() - interval 7 day
ORDER BY classifications.Task[0].metadata.distance ASC
LIMIT 50

Facets are AI-extracted attributes that summarize logs (e.g., facets.task, facets.sentiment). They’re generated by topics automations. Query logs by facet values to identify patterns and issues.

SELECT id, created, facets.task, facets.sentiment
FROM project_logs('my-project-id')
WHERE facets.sentiment IN ('NEGATIVE', 'MIXED')
  AND created > now() - interval 7 day
ORDER BY created DESC
LIMIT 100

Analyze relationships between facets and classifications to gain deeper insights.

SELECT
  facets.sentiment,
  classifications.Task[0].label as topic,
  count(*) as count,
  avg(metrics.total_tokens) as avg_tokens
FROM project_logs('my-project-id', shape => 'traces')
WHERE facets.sentiment IS NOT NULL
  AND classifications.Task IS NOT NULL
  AND created > now() - interval 7 day
GROUP BY facets.sentiment, topic
ORDER BY count DESC
LIMIT 20

Analyze topic distribution with distance metrics:

SELECT
  classifications.Task[0].label as topic,
  count(*) as occurrences,
  avg(classifications.Task[0].metadata.distance) as avg_distance
FROM project_logs('my-project-id', shape => 'traces')
WHERE classifications.Task IS NOT NULL
  AND created > now() - interval 7 day
GROUP BY topic
HAVING count(*) > 10
ORDER BY occurrences DESC

​Run SQL queries

​SQL sandbox

​bt CLI

​API

​Query structure

​BTQL syntax

​SELECT

​GROUP BY for aggregations

​ROLLUP and GROUPING SETS

​HAVING for filtering aggregations

​FROM

​Data sources

​Querying by name

​Subqueries

​Data shapes

​spans

​traces

​summary

​WHERE

​Single span filters

​Matching spans filters

​Full-text search

​Pattern matching

​Time intervals

​ORDER BY

​SAMPLE

​PIVOT and UNPIVOT

​PIVOT

​UNPIVOT

​LIMIT and cursors

​LIMIT

​Cursors for pagination

​SETTINGS

​Expressions

​SQL operators

​SQL functions

​Field access

​Implicit aliasing

​Conditional expressions

​CASE WHEN

​IF and COUNT_IF

​Examples

​Track token usage

​Analyze cost breakdown

​Monitor model quality

​Analyze errors

​Analyze latency

​Analyze prompts

​Analyze based on tags

​Analyze based on tags and scores

​Analyze traces with span filters

​Extract data from JSON strings

​Query by classifications

​Analyze facet distributions

​Combine facets and classifications

Run SQL queries

SQL sandbox

bt CLI

API

Query structure

BTQL syntax

`SELECT`

`GROUP BY` for aggregations

`ROLLUP` and `GROUPING SETS`

`HAVING` for filtering aggregations

`FROM`

Data sources

Querying by name

Subqueries

Data shapes

`spans`

`traces`

`summary`

`WHERE`

Single span filters

Matching spans filters

Full-text search

Pattern matching

Time intervals

`ORDER BY`

`SAMPLE`

`PIVOT` and `UNPIVOT`

`PIVOT`

`UNPIVOT`

`LIMIT` and cursors

`LIMIT`

Cursors for pagination

`SETTINGS`

Expressions

SQL operators

SQL functions

Field access

Implicit aliasing

Conditional expressions

`CASE WHEN`

`IF` and `COUNT_IF`

Examples

Track token usage

Analyze cost breakdown

Monitor model quality

Analyze errors

Analyze latency

Analyze prompts

Analyze based on tags

Analyze based on tags and scores

Analyze traces with span filters

Extract data from JSON strings

Query by classifications

Analyze facet distributions

Combine facets and classifications