Query Logfire Data

When to Use This Skill

Invoke this skill when:

• User wants to query traces, logs, spans, or metrics from Logfire
• User wants to search for specific events, errors, or patterns in telemetry data
• User wants to analyze OpenTelemetry data stored in Logfire
• User wants to add programmatic query capabilities to their code
• User asks to "query logfire", "search traces", "find logs", "get metrics", "count", "summarize", "compare", or "find root cause"

User-Facing Progress

Keep progress updates terse. Do not narrate route classification, local skill instructions, schema selection, or routine query setup. If an update is needed, use one short sentence focused on the action, such as "Querying recent Logfire errors."

Critical Routing: One Workflow Per Request

Before using any query tool, classify the request.

• Query route: "analyze", "query", "count", "summarize", "compare", "find root cause", "find slowest", "look up errors", "get metrics", or "add query capabilities".
• UI route: "open", "show in browser", "show in Codex", "show in Logfire", "live view", "open Explore", "open the UI", "give me a link", or GUI/browser presentation. Use logfire-ui; do not call query_run just to make a URL.
• Ambiguous route: prompts like "show recent errors", "view logs", or "show spans" do not specify whether the user wants chat analysis or the Logfire UI. Ask the user to choose query analysis or UI view.
• Combined route: if the user explicitly asks for both analysis and a link, query only for the requested analysis or to identify the requested item, then provide the relevant Logfire link. Do not add UI/browser work unless the user asked for it.

Only query before opening Logfire UI when the user asks to open a specific unknown item that must be found first, such as "find the slowest trace and open it" or "open the latest error trace".

Two Approaches

Quick Schema Reference

records table (spans and logs)

Key columns for querying:

metrics table

Full schema: references/schema.md

SQL Syntax

Logfire uses Apache DataFusion (Postgres-like). Key patterns:

-- Time filtering
WHERE start_timestamp > now() - interval '1 hour'

-- JSON attribute access
WHERE attributes->>'user_id' = '123'
SELECT attributes->>'http.url' as url FROM records

-- Nested JSON
attributes->'request'->>'method'

-- Array filtering
WHERE array_has(tags, 'production')

-- Level filtering (string comparison works)
WHERE level = 'error'

-- Case-insensitive matching
WHERE message ILIKE '%timeout%'

-- Time bucketing for aggregation
SELECT time_bucket(interval '5 minutes', start_timestamp) as bucket,
       count(*) FROM records GROUP BY bucket ORDER BY bucket

MCP Approach (Interactive)

Call the query_run MCP tool:

• query (required): SQL query string
• project (optional): target project (default: user's current project)
• min_timestamp / max_timestamp (optional): ISO timestamps for time window

Default window is last 30 min. Max range is 14 days. Always include LIMIT in SQL.

Common queries

-- Recent errors
SELECT start_timestamp, message, exception_type, exception_message
FROM records WHERE is_exception LIMIT 20

-- Slow spans
SELECT span_name, duration, start_timestamp
FROM records WHERE duration > 1.0 ORDER BY duration DESC LIMIT 20

-- Endpoint errors
SELECT start_timestamp, message, http_response_status_code
FROM records WHERE http_route = '/api/users' AND level = 'error' LIMIT 20

-- Full trace
SELECT span_name, message, duration, parent_span_id
FROM records WHERE trace_id = '<id>' ORDER BY start_timestamp

-- Error breakdown by service
SELECT service_name, count(*) as errors
FROM records WHERE is_exception GROUP BY service_name ORDER BY errors DESC

UI Links After Querying

If the user explicitly asks for both analysis and a Logfire link, finish the query analysis first, then use a Logfire link only for the known result:

• For a known trace_id, use project_logfire_link(trace_id=trace_id, project=project, handoff=True) only when the user asked to open it immediately in the browser. Use project_logfire_link(trace_id=trace_id, project=project) for a durable or shareable URL.
• For a project/filter view, use the logfire-ui routing rules.
• Do not open the browser unless the user asked to open the link.

For a span-count prompt, provide SQL like this when the user wants an aggregate query or analysis:

SELECT
  time_bucket(interval '5 minutes', start_timestamp) AS bucket,
  count(*) AS span_count
FROM records
WHERE kind = 'span'
GROUP BY bucket
ORDER BY bucket
LIMIT 200

REST API Approach (Programmatic)

Endpoint: GET https://logfire-api.pydantic.dev/v1/query

Region variants:

• US: https://logfire-us.pydantic.dev/v1/query
• EU: https://logfire-eu.pydantic.dev/v1/query

Auth: Authorization: Bearer <read_token>

Parameters:

• sql (required): SQL query
• min_timestamp / max_timestamp (optional): ISO timestamps
• limit (optional): row limit (default 500, max 10,000)

Response formats (via Accept header):

• application/json — column-oriented JSON (default)
• application/json with row_oriented=true param — row-oriented JSON
• text/csv — CSV
• application/vnd.apache.arrow.stream — Apache Arrow

Python clients: LogfireQueryClient (sync), AsyncLogfireQueryClient (async), logfire.db_api (PEP 249 / pandas).

Detailed examples: references/client-usage.md

Query Best Practices

1. Always LIMIT — start with 20, increase as needed
2. Use min_timestamp/max_timestamp params for simple time windows instead of SQL WHERE
3. Filter efficiently — service_name, span_name, trace_id, is_exception are fast filters
4. Use ->>'key' for JSON attribute access (returns text); use -> for nested JSON objects
5. Avoid SELECT * — select only the columns you need
6. Max 14-day range — queries cannot span more than 14 days