tma1

name	tma1
description	Query TMA1 observability data (Claude Code, Codex, GitHub Copilot CLI, OpenClaw, any OTel SDK). Use when the user asks: how much did I spend, token usage, what has my agent been doing, agent cost, show me traces, show me events, check for errors, model comparison, tool usage.
context	fork
allowed-tools	Bash

TMA1 Observability Query

You are helping the user query their local TMA1 observability data.

TMA1 stores data from five kinds of sources:

• Claude Code sends OTel metrics (cumulative counters) + logs (event stream) + hooks + JSONL transcripts
• Codex sends OTel logs + metrics + session JSONL (auto-parsed from ~/.codex/sessions/)
• GitHub Copilot CLI — session JSONL only (auto-parsed from ~/.copilot/session-state/<sessionId>/events.jsonl, no OTel). Data lives in tma1_hook_events (agent_source='copilot_cli') and tma1_messages (session_id LIKE 'cp:%').
• OpenClaw sends OTel traces (spans with openclaw.* attributes) + metrics (openclaw_* tables) + session JSONL (auto-parsed from ~/.openclaw/agents/*/sessions/)
• Other agents (standard GenAI SDK) send OTel traces (spans with gen_ai.* semantic conventions)

Step 1: Check TMA1 is running

curl -sf http://localhost:14318/health

If this fails, tell the user to run /tma1-setup first.

Step 2: Detect available data sources

curl -s -X POST http://localhost:14318/api/query \
  -H 'Content-Type: application/json' \
  -d '{"sql": "SHOW TABLES"}'

Check which tables exist to determine what queries to use:

• If claude_code_cost_usage_USD_total exists → use Claude Code metrics queries
• If codex_turn_token_usage_sum or codex_* tables exist → use Codex queries
• If openclaw_tokens_total exists → use OpenClaw queries
• If opentelemetry_traces exists → use traces-based queries (check column names to distinguish OpenClaw vs GenAI)
• If opentelemetry_logs exists → use logs queries for event details
• If tma1_hook_events has rows with agent_source = 'copilot_cli' → use Copilot CLI queries
• If tma1_hook_events or tma1_messages exists → use session/conversation queries

Step 3: Choose and run query

All queries go through:

curl -s -X POST http://localhost:14318/api/query \
  -H 'Content-Type: application/json' \
  -d '{"sql": "<SQL>"}'

Important: The underlying database (GreptimeDB) uses json_get_string(), json_get_int(), json_get_float() for JSON column access. The -> / ->> operators are NOT supported. Keys containing dots (like session.id) are interpreted as nested paths and cannot be accessed via json_get_*. All timestamps are stored and returned in UTC by default. To use the user's local timezone, add -H 'X-Greptime-Timezone: <tz>' (e.g. +8:00, -5:00, Asia/Shanghai, America/New_York) — this affects both date parsing in WHERE clauses and timestamp rendering in results.

---

Claude Code Queries (logs + metrics)

Note: Claude Code resets OTel cumulative counters on each new session. Use logs (opentelemetry_logs WHERE body = 'claude_code.api_request') for accurate cost/token totals. The _total metric tables only reflect the last session's counter value.

Cost summary (from logs — accurate across sessions)

SELECT json_get_string(log_attributes, 'model') AS model,
       ROUND(SUM(json_get_float(log_attributes, 'cost_usd')), 4) AS cost_usd,
       COUNT(*) AS requests
FROM opentelemetry_logs
WHERE body = 'claude_code.api_request'
  AND timestamp >= DATE_TRUNC('day', NOW())
GROUP BY model
ORDER BY cost_usd DESC

Token usage (from logs — accurate across sessions)

SELECT json_get_string(log_attributes, 'model') AS model,
       SUM(json_get_int(log_attributes, 'input_tokens')) AS input_tok,
       SUM(json_get_int(log_attributes, 'output_tokens')) AS output_tok,
       SUM(json_get_int(log_attributes, 'cache_read_tokens')) AS cache_read,
       SUM(json_get_int(log_attributes, 'cache_creation_tokens')) AS cache_write
FROM opentelemetry_logs
WHERE body = 'claude_code.api_request'
  AND timestamp >= DATE_TRUNC('day', NOW())
GROUP BY model
ORDER BY input_tok DESC

Recent API requests (from logs)

SELECT timestamp,
       json_get_string(log_attributes, 'model') AS model,
       json_get_int(log_attributes, 'input_tokens') AS input_tok,
       json_get_int(log_attributes, 'output_tokens') AS output_tok,
       json_get_float(log_attributes, 'cost_usd') AS cost_usd,
       json_get_float(log_attributes, 'duration_ms') AS duration_ms
FROM opentelemetry_logs
WHERE body = 'claude_code.api_request'
ORDER BY timestamp DESC
LIMIT 20

API errors

SELECT timestamp,
       json_get_string(log_attributes, 'model') AS model,
       json_get_string(log_attributes, 'error') AS error,
       json_get_string(log_attributes, 'status_code') AS status_code,
       json_get_float(log_attributes, 'duration_ms') AS duration_ms
FROM opentelemetry_logs
WHERE body = 'claude_code.api_error'
ORDER BY timestamp DESC
LIMIT 20

Tool usage (from logs)

SELECT json_get_string(log_attributes, 'tool_name') AS tool,
       COUNT(*) AS uses,
       ROUND(AVG(json_get_float(log_attributes, 'duration_ms'))) AS avg_ms,
       SUM(CASE WHEN json_get_string(log_attributes, 'success') = 'true' THEN 1 ELSE 0 END) AS ok,
       SUM(CASE WHEN json_get_string(log_attributes, 'success') = 'false' THEN 1 ELSE 0 END) AS fail
FROM opentelemetry_logs
WHERE body = 'claude_code.tool_result'
GROUP BY tool
ORDER BY uses DESC

User prompts

SELECT timestamp,
       json_get_int(log_attributes, 'prompt_length') AS prompt_len
FROM opentelemetry_logs
WHERE body = 'claude_code.user_prompt'
ORDER BY timestamp DESC
LIMIT 20

Active time (last session only — counter resets per session)

SELECT type,
       ROUND(MAX(greptime_value), 1) AS seconds
FROM claude_code_active_time_seconds_total
WHERE greptime_timestamp >= DATE_TRUNC('day', NOW())
GROUP BY type

Lines of code (last session only — counter resets per session)

SELECT type,
       MAX(greptime_value) AS lines
FROM claude_code_lines_of_code_count_total
WHERE greptime_timestamp >= DATE_TRUNC('day', NOW())
GROUP BY type

Model comparison (all time)

SELECT json_get_string(log_attributes, 'model') AS model,
       ROUND(SUM(json_get_float(log_attributes, 'cost_usd')), 4) AS cost_usd,
       COUNT(*) AS requests
FROM opentelemetry_logs
WHERE body = 'claude_code.api_request'
GROUP BY model
ORDER BY cost_usd DESC

---

OpenClaw Queries (traces + metrics)

These queries work when openclaw_tokens_total or opentelemetry_traces with openclaw.* attributes exist.

Recent LLM calls

SELECT timestamp,
       "span_attributes.openclaw.model" AS model,
       "span_attributes.openclaw.channel" AS channel,
       CAST("span_attributes.openclaw.tokens.input" AS BIGINT) AS input_tok,
       CAST("span_attributes.openclaw.tokens.output" AS BIGINT) AS output_tok,
       CAST("span_attributes.openclaw.tokens.cache_read" AS BIGINT) AS cache_read,
       ROUND(duration_nano / 1000000.0, 1) AS duration_ms
FROM opentelemetry_traces
WHERE span_name = 'openclaw.model.usage'
ORDER BY timestamp DESC
LIMIT 20

Token usage by model (from metrics)

SELECT openclaw_model AS model, openclaw_token AS token_type, SUM(greptime_value) AS tokens
FROM openclaw_tokens_total
WHERE greptime_timestamp > NOW() - INTERVAL '1 day'
GROUP BY openclaw_model, openclaw_token
ORDER BY tokens DESC

Messages by channel

SELECT "span_attributes.openclaw.channel" AS channel,
       "span_attributes.openclaw.outcome" AS outcome,
       COUNT(*) AS messages
FROM opentelemetry_traces
WHERE span_name = 'openclaw.message.processed'
  AND timestamp > NOW() - INTERVAL '1 day'
GROUP BY channel, outcome
ORDER BY messages DESC

Error spans

SELECT timestamp, span_name,
       "span_attributes.openclaw.channel" AS channel,
       "span_attributes.openclaw.sessionKey" AS session
FROM opentelemetry_traces
WHERE span_name IN ('openclaw.webhook.error', 'openclaw.session.stuck')
ORDER BY timestamp DESC
LIMIT 20

Cost estimate (from traces)

SELECT "span_attributes.openclaw.model" AS model,
       COUNT(*) AS requests,
       SUM(CAST("span_attributes.openclaw.tokens.input" AS BIGINT)) AS input_tok,
       SUM(CAST("span_attributes.openclaw.tokens.output" AS BIGINT)) AS output_tok
FROM opentelemetry_traces
WHERE span_name = 'openclaw.model.usage'
  AND timestamp > NOW() - INTERVAL '1 day'
GROUP BY model
ORDER BY input_tok DESC

---

Codex Queries (logs + traces + metrics)

These queries work when Codex telemetry is flowing into opentelemetry_logs, opentelemetry_traces, or native codex_* metric tables.

Recent API requests

SELECT timestamp,
       COALESCE(json_get_string(log_attributes, 'model'), 'unknown') AS model,
       COALESCE(json_get_int(log_attributes, 'input_token_count'), 0) AS input_tok,
       COALESCE(json_get_int(log_attributes, 'output_token_count'), 0) AS output_tok,
       COALESCE(json_get_int(log_attributes, 'cached_token_count'), 0) AS cached_tok,
       json_get_float(log_attributes, 'duration_ms') AS duration_ms
FROM opentelemetry_logs
WHERE scope_name LIKE 'codex_%'
  AND json_get_int(log_attributes, 'input_token_count') IS NOT NULL
ORDER BY timestamp DESC
LIMIT 20

Requests by model (from native metrics)

SELECT model,
       SUM(greptime_value) AS requests
FROM codex_websocket_request_total
WHERE greptime_timestamp > NOW() - INTERVAL '1 day'
GROUP BY model
ORDER BY requests DESC

Tool performance (from native metrics)

SELECT tool,
       success,
       SUM(greptime_value) AS calls
FROM codex_tool_call_total
WHERE greptime_timestamp > NOW() - INTERVAL '1 day'
GROUP BY tool, success
ORDER BY calls DESC

Average TTFT by model

SELECT s.model,
       ROUND(SUM(s.greptime_value) / NULLIF(SUM(c.greptime_value), 0), 2) AS avg_ttft_ms
FROM codex_turn_ttft_duration_ms_milliseconds_sum s
JOIN codex_turn_ttft_duration_ms_milliseconds_count c
  ON s.model = c.model
 AND s.service_version = c.service_version
 AND s.greptime_timestamp = c.greptime_timestamp
WHERE s.greptime_timestamp > NOW() - INTERVAL '1 day'
GROUP BY s.model
ORDER BY avg_ttft_ms DESC

---

Copilot CLI Queries (JSONL auto-discovery, no OTel)

Copilot CLI has no OTel exporter. All data comes from ~/.copilot/session-state/*/events.jsonl and lands in:

• tma1_hook_events with agent_source = 'copilot_cli' — session lifecycle, tool calls, subagent lifecycle
• tma1_messages with session_id LIKE 'cp:%' — user / assistant / thinking messages

Recent sessions

SELECT session_id,
       MIN(ts) AS started,
       MAX(ts) AS last_event,
       SUM(CASE WHEN event_type = 'PreToolUse' THEN 1 ELSE 0 END) AS tool_calls,
       SUM(CASE WHEN event_type = 'PostToolUseFailure' THEN 1 ELSE 0 END) AS tool_failures
FROM tma1_hook_events
WHERE agent_source = 'copilot_cli'
  AND ts > NOW() - INTERVAL '1 day'
GROUP BY session_id
ORDER BY last_event DESC
LIMIT 20

Output tokens by model

SELECT model,
       SUM(COALESCE(output_tokens, 0)) AS output_tokens,
       COUNT(*) AS messages
FROM tma1_messages
WHERE session_id LIKE 'cp:%'
  AND model != ''
  AND ts > NOW() - INTERVAL '1 day'
GROUP BY model
ORDER BY output_tokens DESC

Tool usage

SELECT tool_name,
       COUNT(*) AS calls
FROM tma1_hook_events
WHERE agent_source = 'copilot_cli'
  AND event_type = 'PreToolUse'
  AND tool_name != ''
  AND ts > NOW() - INTERVAL '1 day'
GROUP BY tool_name
ORDER BY calls DESC
LIMIT 15

Subagent runs (model, tokens, duration in metadata)

SELECT ts, agent_type, metadata
FROM tma1_hook_events
WHERE agent_source = 'copilot_cli'
  AND event_type = 'SubagentStop'
ORDER BY ts DESC
LIMIT 20

Conversation for a session

SELECT ts, message_type, "role", model, content
FROM tma1_messages
WHERE session_id = 'cp:<sessionId>'
ORDER BY ts ASC

Replace <sessionId> with the on-disk directory name from ~/.copilot/session-state/ (the cp: prefix is the DB namespace).

---

GenAI Conversation Search (full-text)

Requires the OTel SDK to capture conversation content into opentelemetry_logs. For Python with OpenAI, use opentelemetry-instrumentation-openai-v2 and set OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true.

The openai_v2 instrumentation stores log bodies in these formats:

• User prompt: {"content":"..."}
• LLM completion: {"message":{"role":"assistant","content":"..."}}
• Tool result: {"content":"...","id":"call_..."}
• Tool call definition: {"tool_calls":[...]} (no displayable content)

Search conversations by keyword

SELECT timestamp, trace_id,
  CASE
    WHEN json_get_string(parse_json(body), 'message.role') IS NOT NULL
      THEN json_get_string(parse_json(body), 'message.role')
    WHEN json_get_string(parse_json(body), 'id') IS NOT NULL THEN 'tool'
    WHEN json_get_string(parse_json(body), 'content') IS NOT NULL THEN 'user'
    ELSE 'unknown'
  END AS role,
  COALESCE(
    json_get_string(parse_json(body), 'message.content'),
    json_get_string(parse_json(body), 'content')
  ) AS content
FROM opentelemetry_logs
WHERE matches_term(body, 'your_keyword')
ORDER BY timestamp DESC LIMIT 50

Conversation replay by trace_id

SELECT timestamp,
  CASE
    WHEN json_get_string(parse_json(body), 'message.role') IS NOT NULL
      THEN json_get_string(parse_json(body), 'message.role')
    WHEN json_get_string(parse_json(body), 'id') IS NOT NULL THEN 'tool'
    WHEN json_get_string(parse_json(body), 'content') IS NOT NULL THEN 'user'
    ELSE 'unknown'
  END AS role,
  COALESCE(
    json_get_string(parse_json(body), 'message.content'),
    json_get_string(parse_json(body), 'content')
  ) AS content
FROM opentelemetry_logs
WHERE trace_id = '<trace_id>'
ORDER BY timestamp LIMIT 100

Prompt injection scan

SELECT timestamp, trace_id,
  json_get_string(parse_json(body), 'content') AS content
FROM opentelemetry_logs
WHERE trace_id != ''
  AND json_get_string(parse_json(body), 'content') IS NOT NULL
  AND json_get_string(parse_json(body), 'message.role') IS NULL
  AND json_get_string(parse_json(body), 'id') IS NULL
  AND (
    body LIKE '%ignore%previous%instructions%'
    OR body LIKE '%ignore%above%'
    OR body LIKE '%disregard%previous%'
    OR body LIKE '%reveal%system%prompt%'
    OR body LIKE '%jailbreak%'
    OR body LIKE '%DAN%mode%'
    OR body LIKE '%bypass%safety%'
    OR body LIKE '%[system]%'
  )
  AND timestamp > NOW() - INTERVAL '24 hours'
ORDER BY timestamp DESC LIMIT 50

---

GenAI Traces Queries (other agents)

These queries only work when opentelemetry_traces exists with gen_ai.* attributes.

Recent traces

SELECT span_name,
       "span_attributes.gen_ai.request.model" AS model,
       "span_attributes.gen_ai.usage.input_tokens" AS input_tok,
       "span_attributes.gen_ai.usage.output_tokens" AS output_tok,
       duration_nano / 1000000 AS duration_ms,
       timestamp
FROM opentelemetry_traces
WHERE "span_attributes.gen_ai.system" IS NOT NULL
ORDER BY timestamp DESC
LIMIT 20

Cost by model (using TMA1's pricing table)

-- Joins against tma1_model_pricing (seeded by tma1-server on first start).
-- To see/edit pricing: SELECT * FROM tma1_model_pricing ORDER BY priority;
SELECT t.model,
       ROUND(SUM(
         CAST(t.input_tok AS DOUBLE) * p.input_price / 1e6 +
         CAST(t.output_tok AS DOUBLE) * p.output_price / 1e6
       ), 4) AS cost_usd
FROM (
  SELECT "span_attributes.gen_ai.request.model" AS model,
         "span_attributes.gen_ai.usage.input_tokens" AS input_tok,
         "span_attributes.gen_ai.usage.output_tokens" AS output_tok
  FROM opentelemetry_traces
  WHERE "span_attributes.gen_ai.system" IS NOT NULL
    AND timestamp >= DATE_TRUNC('day', NOW())
) t
JOIN tma1_model_pricing p
  ON t.model LIKE CONCAT('%', p.model_pattern, '%')
GROUP BY t.model
ORDER BY cost_usd DESC

Token usage by model

SELECT "span_attributes.gen_ai.request.model" AS model,
       SUM(CAST("span_attributes.gen_ai.usage.input_tokens" AS DOUBLE)) AS input_tok,
       SUM(CAST("span_attributes.gen_ai.usage.output_tokens" AS DOUBLE)) AS output_tok,
       COUNT(*) AS requests
FROM opentelemetry_traces
WHERE "span_attributes.gen_ai.system" IS NOT NULL
  AND timestamp >= DATE_TRUNC('day', NOW())
GROUP BY model
ORDER BY input_tok DESC

Error rate

SELECT "span_attributes.gen_ai.request.model" AS model,
       COUNT(*) AS requests,
       SUM(CASE WHEN span_status_code = 'STATUS_CODE_ERROR' THEN 1 ELSE 0 END) AS errors
FROM opentelemetry_traces
WHERE "span_attributes.gen_ai.system" IS NOT NULL
  AND timestamp > NOW() - INTERVAL '24 hours'
GROUP BY model
ORDER BY errors DESC

Latency percentiles (p50 / p95)

SELECT "span_attributes.gen_ai.request.model" AS model,
       ROUND(APPROX_PERCENTILE_CONT(duration_nano, 0.50) / 1000000.0, 0) AS p50_ms,
       ROUND(APPROX_PERCENTILE_CONT(duration_nano, 0.95) / 1000000.0, 0) AS p95_ms,
       COUNT(*) AS requests
FROM opentelemetry_traces
WHERE "span_attributes.gen_ai.system" IS NOT NULL
  AND timestamp > NOW() - INTERVAL '24 hours'
GROUP BY model
ORDER BY p95_ms DESC

Sessions (from hooks + JSONL transcripts)

The tma1_messages table includes token usage columns: input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, duration_ms (populated for assistant messages from JSONL transcripts). Both tma1_hook_events and tma1_messages include a conversation_id column linking events within the same conversation turn. Agent source is identified by agent_source in tma1_hook_events: 'claude_code', 'codex', or 'openclaw'. OpenClaw session IDs are prefixed oc:<agentId>:<sessionId>.

-- List recent sessions with tool counts
SELECT session_id, conversation_id, agent_source, MIN(ts) AS start_ts, MAX(ts) AS end_ts,
  SUM(CASE WHEN event_type = 'PreToolUse' THEN 1 ELSE 0 END) AS tool_calls,
  SUM(CASE WHEN event_type = 'SubagentStart' THEN 1 ELSE 0 END) AS subagents
FROM tma1_hook_events WHERE ts > NOW() - INTERVAL '24 hours'
GROUP BY session_id, conversation_id, agent_source ORDER BY MIN(ts) DESC

-- Search conversation content
SELECT session_id, conversation_id, ts, message_type, content FROM tma1_messages
WHERE matches_term(content, 'search keyword')
  AND ts > NOW() - INTERVAL '7 days'
ORDER BY ts DESC LIMIT 20

-- Session token usage (from JSONL transcripts)
SELECT session_id, SUM(input_tokens) AS input_tok, SUM(output_tokens) AS output_tok,
  SUM(cache_read_tokens) AS cache_read, SUM(cache_creation_tokens) AS cache_write
FROM tma1_messages WHERE message_type = 'assistant' AND ts > NOW() - INTERVAL '24 hours'
GROUP BY session_id ORDER BY input_tok DESC

-- Session tool breakdown
SELECT tool_name, COUNT(*) AS calls FROM tma1_hook_events
WHERE session_id = '<session_id>' AND event_type = 'PreToolUse'
GROUP BY tool_name ORDER BY calls DESC

---

Step 4: Execute and format

Run the chosen query via curl. Parse the JSON response and present it as a readable table or summary.

If a table does not exist (error code 4001), skip that query and try the alternative data source.

If the query returns no rows, explain that there may not be data for the requested time range.

Step 5: Offer follow-ups

After presenting results, suggest related queries the user might want:

• "Want to see the breakdown by model?"
• "Should I check for API errors?"
• "Want to see tool usage stats?"
• "Want to compare sessions?"
• "Want to scan for prompt injection attempts?"

Remind the user that the full dashboard is available at http://localhost:14318.