Standards Alignment

opentraces sits at the intersection of four public standards. It adopts what works from each, and bridges the gap between trajectory (process) and attribution (output).

ATIF / Harbor (v1.6)

github.com/laude-institute/harbor

A training trajectory serialization format for agent research. Defines the step-based TAO (Thought-Action-Observation) loop, with fields for token IDs, logprobs, and reward signals designed for RL and SFT pipelines.

Relationship: opentraces is a superset of ATIF. We adopt the step-based model, role conventions (system | user | agent), and field patterns. We add attribution blocks, per-step token breakdowns, environment metadata, dependency tracking, and security metadata. The downstream field mappings live in packages/opentraces-schema/FIELD-MAPPINGS.md; the public export workflow is still experimental.

ADP (Agent Data Protocol)

arxiv.org/abs/2410.10762

An interlingua for normalizing diverse agent trace formats into a common structure for training. Proposes a universal adapter layer so each dataset and each agent only needs one converter, O(D+A), instead of pairwise mappings, O(D*A).

Relationship: opentraces' adapter-based normalization follows the same pattern. Per-agent parsers are ADP-style adapters outputting the enriched schema.

Agent Trace (Cursor/community, v0.1.0 RFC)

github.com/cursor/agent-trace

A code attribution spec (CC BY 4.0) that records which lines of code came from which agent conversation, at file/line granularity. Backed by 10+ sponsors (Cloudflare, Vercel, Google Jules, Cognition).

Relationship: opentraces embeds Agent Trace attribution blocks directly in the trace record. Agent Trace focuses on output (code attribution), opentraces bridges that with process (trajectory).

OTel GenAI Semantic Conventions

opentelemetry.io/docs/specs/semconv/gen-ai

OpenTelemetry's GenAI semantic conventions define standardized span attributes for LLM calls in observability pipelines, covering model names, token counts, and request metadata.

Relationship: opentraces' per-step token usage and model fields align with OTel GenAI conventions, enabling cross-referencing between observability spans and training trajectories.

The Core Insight

Agent Trace preserves which lines came from AI. ATIF/ADP preserve how the agent reasoned. Neither alone tells the complete story. opentraces connects the full conversation trajectory to the specific code output at line granularity.

Message Taxonomy

opentraces adopts a training-oriented message taxonomy:

Agent steps are further classified by call_type (main, subagent, warmup) and agent_role (main, explore, plan).