opentraces

Open schema + CLI for capturing agent traces into a private bucket, searching and slicing them locally, and publishing workflow-projected dataset rows to Hugging Face Hub.

opentraces has four separate surfaces:

1. Trace workflow: capture agent sessions into a portable bucket, then use Trace Discovery, Trace Trails, and Context Tree to inspect the retained environment.
2. Dataset workflows: skill-format row builders that use trace discovery, context, and trail evidence to project purposeful rows.
3. Datasets: local HuggingFace-shaped row stores that can be reviewed, scheduled, and published.
4. Clients: evaluation jobs, training loops, dashboards, context warmup, and manual trace-capsule patterns that consume either rows or bucket evidence.

Trace Workflow

opentraces setup                          # install capture hooks and optional integrations
opentraces init                           # enroll a repo explicitly (optional under global tracking)
opentraces init --agent pi                # enroll Pi explicitly (optional; auto under global tracking)
opentraces bucket status                  # inspect private retained trace evidence
opentraces trace query --since 7d         # search retained traces
opentraces trace skills --json            # rank observed skills by usage
opentraces trace map <trace-id> --bursts  # deterministic edit/intent map
opentraces trace get <trace-id> --run-intel    # resteer/recovery/loop signals
opentraces trace slice <trace-id> --template bursts
opentraces trail blame commit <sha>       # which traces contributed to a commit
opentraces ctx tree <trace-id>            # what the agent saw across the trace
opentraces workflow templates             # choose a row projection template
opentraces dataset new my-dataset --workflow my-workflow
opentraces dataset run my-dataset         # synthesize dataset rows from retained traces
opentraces dataset new skill-episodes --from-skill opentraces
opentraces dataset run skill-episodes --executor script --json
opentraces dataset review approve my-dataset --all
opentraces dataset publish my-dataset     # upload reviewed rows to the active remote

init writes the committable project marker at .opentraces.json. Captured traces, bucket state, and upload bookkeeping stay machine-local under ~/.opentraces/. Global tracking (the default) auto-enrolls every agent, including Pi, the first time a capture hook or the Pi extension fires in a repo, private + review-required. Capture is opt-out: opentraces config tracking-mode manual or a per-project excluded marker turns it off.

What You Get

For individual developers. A private trace bucket, deterministic local search, and dataset publishing only when you choose to project and approve rows.

For teams. Shared bucket remotes for retained evidence, per-dataset HuggingFace remotes for curated rows, and reproducible workflow templates.

For dataset consumers. Schema-valid row streams for training, evaluation, teacher/student reinforcement learning, analytics, and attribution.

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