Telemetry

By default, we collect no data that would be considered personal information under GDPR and other privacy regulations. 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. Because no personal information is collected, it’s not necessary to worry about data residency. When share_crew is enabled, additional data is collected which may contain personal information if included by the user. Users should exercise caution when enabling this feature to ensure compliance with privacy regulations.

CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users. It’s pivotal to understand that by default, NO personal data is collected concerning prompts, task descriptions, agents’ backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables. When the share_crew feature is enabled, detailed data including task descriptions, agents’ backstories or goals, and other specific attributes are collected to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks. Users should carefully consider the content of their crews and tasks before enabling share_crew. 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).

Examples:

# Disable CrewAI telemetry only
os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'

# Disable all OpenTelemetry (including CrewAI)
os.environ['OTEL_SDK_DISABLED'] = 'true'

Isolation from your own OpenTelemetry setup

CrewAI’s telemetry runs on its own private TracerProvider and never registers itself as the global one. This keeps the two directions separate:

Spans from other instrumented libraries in your process — web frameworks, database clients, HTTP clients — are never sent to CrewAI.
CrewAI’s telemetry spans are never sent to your observability backend, so they will not appear in Langfuse, Braintrust, Phoenix, or any other collector you configure.

Observability integrations are unaffected: they instrument CrewAI through their own tracer provider, which is independent of the one described here.

Data Explanation:

Defaulted	Data	Reason and Specifics
Yes	CrewAI and Python Version	Tracks software versions. Example: CrewAI v1.2.3, Python 3.8.10. No personal data.
Yes	Crew Metadata	Includes: randomly generated key and ID, process type (e.g., ‘sequential’, ‘parallel’), boolean flag for memory usage (true/false), a boolean flag for whether any inputs were passed to the run (true/false — never the input keys or values, which are only collected when `share_crew` is enabled), count of tasks, count of agents. All non-personal.
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.
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.
Yes	Tool Usage Statistics	Includes: tool name (should not include personal info), number of usage attempts (integer), LLM attributes used. No personal data.
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.
Yes	Task Lifecycle Data	Includes: creation and execution start/end times, crew and task identifiers, and whether the task succeeded or failed. When a task fails, the class name of the exception is recorded (for example `TimeoutError`) so failures can be counted and diagnosed — never the error message, which can contain prompts, model output, file paths or credentials. Stored as spans with timestamps. No personal data.
Yes	LLM Attributes	Includes: name, model_name, model, top_k, temperature, and class name of the LLM. All technical, non-personal data.
Yes	Project Creation using crewAI CLI	Includes: that a new project was scaffolded by `crewai create`, which kind it was (`crew`, `json_crew` or `flow`), and the project ID minted for that new project and written into its own `pyproject.toml`. That is the new project’s own ID, recorded separately from the `project_id` of the directory the command was run from — the two can differ. No project name, no file contents, no code. No personal data.
Yes	Crew Deployment attempt using crewAI CLI	Includes: The fact a deploy is being made and crew id, whether it’s trying to pull logs, and whether the deploy was started from a CLI command or from the run TUI. No project or crew contents. No personal data.
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.
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. When a flow or one of its methods fails, the class name of the exception is recorded (for example `TimeoutError`) so that failures can be diagnosed — never the error message, which can contain prompts, model output, file paths or credentials. Method names and flow state are never recorded. No personal data.
Yes	Trace Sharing Signal	Includes: that a batch of traces was successfully shared with CrewAI AMP, and whether it was shared anonymously (before you have an account) or linked to your account. Like every span, it also carries the Execution Environment attributes described above (`project_id` when configured, the coding assistant, and the runtime). This row describes sharing telemetry only — not the trace contents or access granted by shared trace links. Trace contents, inputs, and outputs are never recorded on this signal. Before sharing traces, review secrets, personal data, and AMP redaction and retention settings.
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.
No	Detailed Task Information	Includes: task description, expected output description, context references. Users should ensure no personal info is included in these fields.
No	Environment Information	Includes: platform, release, system, version, and CPU count. Example: ‘Windows 10’, ‘x86_64’. No personal data.
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.
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.

“No” in the “Defaulted” column indicates that this data is only collected when share_crew is set to true.

Users can choose to share their complete telemetry data by enabling the share_crew attribute to True in their crew configurations. Enabling share_crew results in the collection of detailed crew and task execution data, including goal, backstory, context, and output of tasks. This enables a deeper insight into usage patterns.

If you enable share_crew, the collected data may include personal information if it has been incorporated into crew configurations, task descriptions, or outputs. Users should carefully review their data and ensure compliance with GDPR and other applicable privacy regulations before enabling this feature.

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Telemetry

Telemetry

Telemetry

Examples:

Isolation from your own OpenTelemetry setup

Data Explanation:

​Telemetry

​Examples:

​Isolation from your own OpenTelemetry setup

​Data Explanation:

​Opt-In Further Telemetry Sharing

Telemetry

Examples:

Isolation from your own OpenTelemetry setup

Data Explanation:

Opt-In Further Telemetry Sharing