Core no longer assembles a whole trace locally. Every workflow node is uploaded
the moment it completes as its own `execution_node` record, and each execution
closes with exactly one `execution_chain` record; all records of one execution
carry the same `event_id`, so Space groups them back into a single chain.
- every execution starts at an inbound event: event-less Pipeline lanes and
WebUI/API debug runs synthesize their virtual inbound event at the boundary
and are marked debug=true, so a Runner is never a trace origin
- sampling is decided once per event from a stable hash of the event id (the
previous `int(id[:8], 16)` raised on every `platform:`-prefixed id and so
dropped every successful inbound-event chain) and is reported on each record
as sample{mode,denominator}; the default mode is now `all`
- the local trace buffer, the 120s TTL, the per-process trace cap and the shared
open-node mirror are gone, which also removes the truncated-chain upload and
the re-buffered-closed-trace leak they caused
- a broken chain is always uploaded, and its reason falls back to the first
failed node so the chain explains itself
- flush keeps records until the sender accepts them and counts what it drops;
telemetry health replaces the unused send-task queue
- heartbeat reports bound Agent / plugin-processor / event-binding counts
- bound Pipeline and Agent resources (plugins, MCP servers, knowledge bases,
tools with their Host source, skills, models and the Runner schema selectors
that picked them) are reported on the chain record
- failure text is bounded and redacted instead of passing exception text through
- the dead per-query feature counters and their call sites are removed
Wire contract: schema 2 (`execution_node` / `execution_chain`).
Every host step now opens a stage scope, so observations recorded inside it (including cross-task plugin/RPC calls resolved through the execution registry) carry the owning step as parent; the pipeline lane records its own pipeline/run node so acks and replies issued outside the runner still hang off a node.
close_trace() called the coroutine TelemetryManager.start_send_task without
awaiting or scheduling it, so every payload closed on that path was silently
dropped and the runtime logged "coroutine ... was never awaited". The only
other caller awaits it correctly, so both call styles stay supported: call the
manager, schedule the returned coroutine on the running loop, and close it with
a debug log when no loop is available.
Regression case added in tests/unit_tests/telemetry/test_trace.py: it fails
before this change (payload never delivered) and passes after.
Execution telemetry already reported window counters. This adds one bounded,
content-free chain per inbound platform event, so a single event can be
followed from its source platform through routing and processing to the
platform API calls it caused.
- telemetry/trace.py: ContextVar trace identity with route and run scopes, and
a 32-stage bound per chain.
- telemetry/execution.py: keeps at most 64 chains for 120s, decides sampling
from the observed outcome, and sends one payload per chain keyed by
query_id = trace_id, so Space fetches a chain by primary identity. Window
counters are unchanged and remain the source of coverage statistics.
- botmgr / pipelinemgr / orchestrator: bind the trace at the ingress boundary,
scope routing identity per dispatch, and attach the run identity, so nested
lanes (event -> route -> pipeline/runner -> platform API) reuse one chain. A
run reached without an ingress (WebUI debug, service API) owns its own chain.
- space.execution_trace selects off | failures | sampled | all (default
sampled: failures, WebUI debug runs and every N-th success).
Chains carry only code-defined identifiers (adapter and runner types, route
identity as type:uuid, run id); never message content, tool arguments or
platform user identifiers.
* feat: report independent instance and workspace identities
* test: include workspace in OAuth callback fixture
* ci: pin production cloud adapter to Space release
* fix: preserve authenticated Workspace telemetry attribution
* ci: pin production cloud adapter to final Space release
---------
Co-authored-by: dadachann <[email protected]>
Load the instance creation timestamp from data/labels/instance_id.json
(backfilling+persisting it for instances created before the field existed),
expose it as constants.instance_create_ts, and include it in the heartbeat
payload so Space can anchor Time-To-Value / onboarding analytics on real
install time rather than first-heartbeat.
Verified: py_compile, ruff, pytest tests/unit_tests/telemetry/ (37 passed).
Per-query events now carry event_type='query' and a features JSON object:
- tool_calls by source (native/plugin/mcp/skill) via ToolManager
- tool_call_rounds, kb usage (count/engine plugins/retrieved entries) via local-agent
- sandbox execs/errors via BoxService
- activated_skills and bound mcp_servers snapshots
New instance_heartbeat event (startup + daily) reports anonymous instance
profile: deploy platform, database/vdb kind, box backend/availability,
adapter type names, and resource counts. Respects space.disable_telemetry.
All collection helpers are defensive and never break the pipeline.
Verified: ruff, 37 telemetry unit tests (13 new), 504 box/provider/pipeline tests.
* feat(chat): add runner_url to payload for telemetry tracking
* feat(telemetry): add runner_url to sanitized fields in telemetry payload
* feat(telemetry): replace runner_url with runner_category in telemetry payload and add runner utility functions
* fix:ruff
* feat: add telemetry support for query execution tracking and configuration
* feat: integrate telemetry manager and enable telemetry data sending
* feat: integrate telemetry manager and enhance error handling for telemetry sending
* feat: update telemetry configuration to use 'space' instead of 'telemetry' and adjust related parameters
* feat: integrate telemetry manager and enable telemetry data sending
* feat: integrate telemetry manager and enhance error handling for telemetry sending
* feat: add instance id
* feat: enhance telemetry management with asynchronous task handling and improve model retrieval caching
---------
Co-authored-by: Junyan Qin <[email protected]>
Hyu