Log Trace
log_traceRecord an agent execution trace with spans, tool calls, token usage, and cost for later evaluation and analysis.
Instructions
Persist a single agent execution trace (input, output, spans, tool calls, cost, latency, token usage).
Sibling tools — evaluate_output runs heuristic scoring on the trace; evaluate_with_llm_judge runs semantic LLM-based scoring; verify_citations checks citation grounding; get_traces queries stored traces; delete_trace removes a single trace; list_rules / deploy_rule / delete_rule manage custom evaluation rules. log_trace is the WRITE path that records executions; everything else reads, scores, or manages around it.
Behavior. Writes one row to Iris storage (SQLite by default; Postgres in Cloud tier). When IRIS_OTEL_ENDPOINT is set, ALSO fires a best-effort async export to the configured OTLP/HTTP collector (Jaeger, Tempo, Datadog OTLP, OTEL Collector). The OTel export is fire-and-forget — its success does not affect the tool response; failures are logged but the trace is still stored locally. No authentication in stdio mode; HTTP mode requires Bearer token. Rate-limited to 20 req/min on HTTP MCP, unlimited on stdio. Not idempotent: each call mints a fresh trace_id, so resubmitting the same payload creates a duplicate trace.
Output shape. Returns a JSON string: { "trace_id": "<32-hex>", "status": "stored" }. The trace_id is the key you pass to evaluate_output or get_traces afterwards.
Use when you want to record an agent execution for later evaluation, analysis, or audit. Call it AFTER the agent has produced output; call evaluate_output afterwards to score it; call get_traces to query historical traces. Store rich context: spans (span tree), tool_calls (which tools were invoked with latency/errors), token_usage, cost_usd, metadata (arbitrary key-value). All optional except agent_name.
Don't use when you only need a transient log (use console logging). Don't use to update an existing trace — there is no update path in v0.4 (traces are immutable once stored).
Parameters. agent_name is required; everything else is optional. token_usage and cost_usd are summary fields — if you ALSO pass spans with per-tool-call costs, the summary fields are treated as authoritative (no auto-aggregation). spans without an explicit start_time fall back to the trace timestamp; spans with an end_time get a duration_ms derived. metadata is opaque key-value (queryable in the dashboard, not via get_traces filters). tool_calls record per-tool latency + errors; missing latency_ms means "not reported," not "zero." Defaults: span.kind="INTERNAL", span.status_code="UNSET", timestamp=now() if omitted.
Error modes. Throws on missing agent_name. Throws on malformed span or tool_call objects (Zod rejects). Returns 500 on storage failure (disk full, DB locked). Never blocks on the agent — returns within ~50ms for typical payloads.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| agent_name | Yes | Agent name — used for filtering in get_traces (e.g., "customer-support-bot") | |
| framework | No | Agent framework identifier (e.g., langchain, autogen, custom) | |
| input | No | Agent input text — the user prompt or upstream input that produced this output | |
| output | No | Agent output text — what the agent produced (pass to evaluate_output for scoring) | |
| tool_calls | No | Tool calls made during execution (per-call latency, errors, input/output) | |
| latency_ms | No | Total execution time in milliseconds (end-to-end agent latency) | |
| token_usage | No | Token usage breakdown (prompt/completion/total — used for cost analysis) | |
| cost_usd | No | Total cost in USD — overrides per-span aggregation when provided (treated as authoritative) | |
| metadata | No | Opaque key-value tags (e.g. {requestId, userId, env}) — queryable in dashboard, not via get_traces filters | |
| spans | No | Detailed execution spans (hierarchical span tree with timings, attributes, events) | |
| timestamp | No | Trace timestamp (ISO 8601); defaults to now() when omitted |