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* feat(flow): report flow outcome and human-in-the-loop signals A flow reported only that it started. FlowFinishedEvent, FlowFailedEvent, MethodExecutionFailedEvent, MethodExecutionPausedEvent and FlowPausedEvent all reached the console formatter and stopped there, and FlowInputRequestedEvent, FlowInputReceivedEvent and ConversationTurnFailedEvent had no listener at all - so success rate, failure rate and every HITL pause were unmeasurable. Adds flow:completed, flow:failed, flow:method_failed, flow:paused, flow:hitl_paused, flow:input_requested, flow:input_received and flow:conversation_turn_failed as feature-usage spans, which the existing feature-usage aggregation already reads. Deliberately does not hold the Flow Execution span open to measure duration: flow_executions_daily_target counts those spans at start, so a run that never finishes would disappear from the count entirely. Duration needs its own span. Counts only - flow names, method names, error text and flow state are never recorded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * feat(flow): record how long a flow ran Adds a Flow Completed span carrying flow_name, duration_ms and outcome, emitted when a flow finishes or fails. Elapsed time comes from a monotonic stamp taken at flow start and cleared on use. Kept separate from the Flow Execution span rather than holding that one open: it is emitted and closed at start and the daily aggregate counts it, so holding it would drop every run that is killed or crashes from the execution count. A killed run now simply has no Flow Completed row, and the count is unaffected. Elapsed time is an explicit duration_ms attribute rather than the span's own duration, which the ingestion pipeline stores as a suffixed string ("0.0000184s") that downstream aggregation parses to zero. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * feat(flow): tag flow origin and report resumed runs Two gaps found while testing the pause/resume path end to end. Resumed runs were invisible. There is no resume event: a restored run re-enters through kickoff(), so it looked identical to a fresh start. flow:resumed is derived from _is_execution_resuming at flow start, which makes flow:paused - flow:resumed the abandonment rate. Flow counts are dominated by CrewAI's own AgentExecutor, which is itself a Flow and runs once per agent execution - it is the top flow in the warehouse by a wide margin. Nothing distinguished it from a user's flows except guessing at the name. Both Flow Execution and Flow Completed now carry origin: "internal" when the flow class is defined under crewai.*, "user" otherwise. Tagging only the new span would have left the existing daily count unsplittable. Both span methods take origin with a default, so their signatures stay backward compatible. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * fix(flow): scope outcome and resume signals to user flows Two findings from review, both confirmed against the code. Outcome features counted CrewAI's own flows. The agent executor, memory encoding and memory recall are all Flows and all set suppress_flow_events; they run far more often than anything a user wrote, so flow:completed, flow:failed and flow:method_failed were mostly bookkeeping. Those three are now emitted only for flows the caller wrote. Internal outcomes are still recorded on the Flow Completed span, which carries origin. flow:resumed counted checkpoint restores. _is_execution_resuming is set both by from_pending (a human pause) and by a checkpoint restore that never paused for anyone, so resumes could exceed pauses and the abandonment rate was unusable. Keyed off _pending_feedback_context instead, which only from_pending sets. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * fix(flow): declare internal flows instead of inferring them Three findings from review, all confirmed against the code. Gating on suppress_flow_events was wrong. That flag asks for console quiet and is a public field, so a caller who set it on their own flow silently lost flow:completed, flow:failed and flow:method_failed. Deciding origin from the defining module was also wrong. Flow.from_declaration() returns a Flow typed in crewai.flow.flow, so a caller's declarative flow was reported as one of CrewAI's own - the inversion this split exists to prevent. Both had the same root cause: the discriminator was inferred. Flow now declares is_crewai_internal, set on the agent executor and the memory encoding/recall flows, and one helper serves both origin and the outcome gate. A failed conversational session was reported as completed. Its session closes with FlowFinishedEvent whatever happened, so a failed turn produced flow:conversation_turn_failed and flow:completed together. The turn failure is now recorded on the flow and read back when the session finishes. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * refactor(flow): report flow lifecycle as spans, not feature usage Flow start, completion, pause and method failure are lifecycle facts, and the lifecycle is reported as spans everywhere else. Reporting them through feature usage put them in a table that aggregates on the feature string alone - it cannot carry origin, duration or outcome, so those signals could never be split between a user's flows and the ones CrewAI runs for itself. Adds Flow Paused and Flow Method Failed spans, and a resumed marker on Flow Execution so a run restored from a pause is not counted as a second fresh start. Removes the duplicate feature rows for completed, failed, method_failed, paused and resumed - every one of those facts is now on a span, with more attached to it than the feature row ever carried. Feature usage keeps only genuine adoption signals: flow:hitl_paused, flow:input_requested, flow:input_received and flow:conversation_turn_failed. Also clears the conversational turn-failure flag on every terminal path. A turn that failed without deferred finalization ends via FlowFailedEvent, and the flag left set there marked the next run on that instance as failed. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * test(flow): update the flow_execution_span caller for the resumed argument Adding the resumed marker changed a signature that tests/utilities/test_events.py asserts on exactly, and that assertion was not re-run before pushing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * test(flow): make the checkpoint-restore guard actually guard The test asserted that flow:resumed was absent from feature usage, but that signal moved onto the Flow Execution span. The assertion could no longer fail, so a regression that mis-tagged checkpoint restores as resumes would have gone unnoticed. Now asserts the resumed attribute, and waits for the handlers: the manual emit dispatches asynchronously, so the previous shape also read its result before the listener had run. Confirmed it discriminates - keying resumed off _is_execution_resuming again fails it with [('RestoredFlow', True)] == [('RestoredFlow', False)]. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH * fix(telemetry): record the resumed marker as a string Verified end to end against the live collector and ClickHouse: the pipeline encodes a boolean attribute as the presence of a vBool key, so false arrives as the key simply being absent. That is invisible in the schema and easy to read wrongly - crew_memory is extracted as "the attribute exists" and consequently reports 1 for 99.8% of crews against a field that defaults to False. A string leaves nothing to infer. Confirmed in the warehouse: the emitted span reads resumed = "false". Adds direct coverage for the attributes each flow span records, including both resumed values, and resets the Telemetry singleton in the helper so more than one span method can be exercised per session. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ASfWmW3RGy4qAQm6s8U9jH --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
85 lines
7.4 KiB
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85 lines
7.4 KiB
Plaintext
---
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title: Telemetry
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description: Understanding the telemetry data collected by CrewAI and how it contributes to the enhancement of the library.
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icon: signal-stream
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mode: "wide"
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---
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## Telemetry
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<Note>
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By default, we collect no data that would be considered personal information under GDPR and other privacy regulations.
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We do collect Tool's names and Agent's roles, so be advised not to include any personal information in the tool's names or the Agent's roles.
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Because no personal information is collected, it's not necessary to worry about data residency.
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When `share_crew` is enabled, additional data is collected which may contain personal information if included by the user.
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Users should exercise caution when enabling this feature to ensure compliance with privacy regulations.
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</Note>
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CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library.
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Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
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It's pivotal to understand that by default, **NO personal data is collected** concerning prompts, task descriptions, agents' backstories or goals,
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usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables.
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When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected
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to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks.
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Users should carefully consider the content of their crews and tasks before enabling `share_crew`.
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Users can disable telemetry by setting the environment variable `CREWAI_DISABLE_TELEMETRY` to `true` or by setting `OTEL_SDK_DISABLED` to `true` (note that the latter disables all OpenTelemetry instrumentation globally).
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### Examples:
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```python
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# Disable CrewAI telemetry only
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os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
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# Disable all OpenTelemetry (including CrewAI)
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os.environ['OTEL_SDK_DISABLED'] = 'true'
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```
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### Isolation from your own OpenTelemetry setup
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CrewAI's telemetry runs on its own private `TracerProvider` and never registers
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itself as the global one. This keeps the two directions separate:
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- Spans from other instrumented libraries in your process — web frameworks,
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database clients, HTTP clients — are never sent to CrewAI.
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- CrewAI's telemetry spans are never sent to your observability backend, so they
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will not appear in Langfuse, Braintrust, Phoenix, or any other collector you
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configure.
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Observability integrations are unaffected: they instrument CrewAI through their
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own tracer provider, which is independent of the one described here.
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### Data Explanation:
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| Defaulted | Data | Reason and Specifics |
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|:----------|:------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
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| Yes | CrewAI and Python Version | Tracks software versions. Example: CrewAI v1.2.3, Python 3.8.10. No personal data. |
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| Yes | Crew Metadata | Includes: randomly generated key and ID, process type (e.g., 'sequential', 'parallel'), boolean flag for memory usage (true/false), count of tasks, count of agents. All non-personal. |
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| Yes | Agent Data | Includes: randomly generated key and ID, role name (should not include personal info), boolean settings (verbose, delegation enabled, code execution allowed), max iterations, max RPM, max retry limit, LLM info (see LLM Attributes), list of tool names (should not include personal info). No personal data. |
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| Yes | Task Metadata | Includes: randomly generated key and ID, boolean execution settings (async_execution, human_input), associated agent's role and key, list of tool names. All non-personal. |
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| Yes | Tool Usage Statistics | Includes: tool name (should not include personal info), number of usage attempts (integer), LLM attributes used. No personal data. |
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| Yes | Test Execution Data | Includes: crew's randomly generated key and ID, number of iterations, model name used, quality score (float), execution time (in seconds). All non-personal. |
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| Yes | Task Lifecycle Data | Includes: creation and execution start/end times, crew and task identifiers. Stored as spans with timestamps. No personal data. |
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| Yes | LLM Attributes | Includes: name, model_name, model, top_k, temperature, and class name of the LLM. All technical, non-personal data. |
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| Yes | Crew Deployment attempt using crewAI CLI | Includes: The fact a deploy is being made and crew id, and if it's trying to pull logs, no other data. |
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| Yes | Execution Environment | Includes: which AI coding assistant is running the process, if any (one of a fixed list such as `claude_code`, `codex`, `cursor`, or `unknown`), where the process runs (one of a fixed list such as `ci`, `container`, `serverless`, `interactive`), and the `project_id` from your `pyproject.toml` when one is configured. Detection reads only whether known environment variables are set, never their values. No personal data. |
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| Yes | Flow Lifecycle Signals | Includes: that a flow started, whether it completed or failed, whether one of its methods failed, whether it paused for human input or feedback, whether the start was a resumed run, whether a conversation turn failed, how long the flow ran, and whether the flow is one CrewAI runs internally or one you wrote. The flow name is recorded, as it already is for flow creation and execution. Method names, error messages and flow state are never recorded. No personal data. |
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| No | Agent's Expanded Data | Includes: goal description, backstory text, i18n prompt file identifier. Users should ensure no personal info is included in text fields. |
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| No | Detailed Task Information | Includes: task description, expected output description, context references. Users should ensure no personal info is included in these fields. |
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| No | Environment Information | Includes: platform, release, system, version, and CPU count. Example: 'Windows 10', 'x86_64'. No personal data. |
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| No | Crew and Task Inputs and Outputs | Includes: input parameters and output results as non-identifiable data. Users should ensure no personal info is included. |
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| No | Comprehensive Crew Execution Data | Includes: detailed logs of crew operations, all agents and tasks data, final output. All non-personal and technical in nature. |
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<Note>
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"No" in the "Defaulted" column indicates that this data is only collected when `share_crew` is set to `true`.
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</Note>
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### Opt-In Further Telemetry Sharing
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Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations.
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Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks.
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This enables a deeper insight into usage patterns.
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<Warning>
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If you enable `share_crew`, the collected data may include personal information if it has been incorporated into crew configurations, task descriptions, or outputs.
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Users should carefully review their data and ensure compliance with GDPR and other applicable privacy regulations before enabling this feature.
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</Warning>
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