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DDG Tine-to-OpenTelemetry GenAI Span Exporter
x402
DDG Tine-to-OpenTelemetry GenAI Span Exporter is a paid API for AI agents from agents.daedalusdevelopmentgroup.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Converts a .tine run into a complete OTLP/JSON document of OpenTelemetry GenAI spans (gen_ai.* attributes, deterministic trace/span IDs) for ingestion by Langfuse, Phoenix, or any OTLP collector.
Price / call$0.01
Success—
StatusUnknown
How this service works
Convert a .tine run into OpenTelemetry GenAI spans as a complete OTLP/JSON document (gen_ai.* attributes, deterministic trace/span ids) for Langfuse, Phoenix or any OTLP collector.
Output
A complete OTLP/JSON document containing OpenTelemetry GenAI spans derived from the provided tine run, with gen_ai.* attributes on each span and deterministic trace and span IDs, ready for direct ingestion by Langfuse, Arize Phoenix, or any standard OTLP collector.
Example use cases
Trace LLM pipeline in Langfuse
“I just finished a tine run for my LLM pipeline — can you export it as an OTLP/JSON document with gen_ai.* spans and deterministic trace IDs so I can push it into Langfuse?”
Debug GenAI spans in Phoenix
“Convert this tine run output into OpenTelemetry GenAI spans formatted as OTLP JSON so I can load it into Arize Phoenix and inspect each step.”
Feed tine traces into custom OTLP collector
“I have a tine run I need to ship to our internal OTLP collector — export it as a full OTLP/JSON document with gen_ai.* attributes and deterministic span IDs please.”
Technical details
Endpoint
POSThttps://agents.daedalusdevelopmentgroup.com/v1/tine/otel-export?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a .tine run artifact and need to export it as standards-compliant OpenTelemetry GenAI telemetry (OTLP/JSON) with deterministic trace and span IDs. It is purpose-built for tine-format AI pipeline executions and produces gen_ai.* attributes natively, making it the right choice for teams using Langfuse, Arize Phoenix, or any OTLP-compatible observability backend to monitor LLM workloads.
Side effects
- Missing required 'input' or 'output' fields returns a validation error
- Invalid HTTP method (not POST/PUT/PATCH) causes schema rejection
- Malformed tine run body may produce incomplete or empty span sets
- Payment failure via x402 protocol blocks the request before processing
- Unsupported bodyType value causes deserialization error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| outputrequired | object |
Tags
x402
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