mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-09-21 02:16:27 +00:00
1f6e327b3c
* feat(events): report machine size as a coarse band, not a core count
`runtime_context` says where a process runs but carries no capacity axis, and
its largest bucket is a catch-all: a gunicorn worker on a VM and
`python main.py > out.log` on a MacBook both report `non_interactive`. Docker
Desktop on a laptop reports `container` via /.dockerenv, and a Remote-SSH shell
on a server reports `vscode_terminal`. So "server or laptop" is not answerable
from it today.
Adds `cpu_band` to the common span attributes, so it rides every span the way
`runtime_context` does rather than sitting on `Crew Created` alone - which would
answer nothing for Flow-only, CLI-only or standalone-agent runs.
Six bands, powers of two, top one open-ended: 1-2, 3-4, 5-8, 9-16, 17-32, 33+.
Open-ended because the exact count is the fingerprint - the observed fleet
maximum is 512, and a span reporting 512 identifies one machine. The vocabulary
is closed and asserted, like KNOWN_CODING_AGENTS and KNOWN_RUNTIME_CONTEXTS.
The share_crew-gated exact `cpus` attribute and the four platform* attributes
are untouched. That gating was a deliberate 2024 classification of machine
fingerprint as shareable content (44e38b1d5), and this does not reverse it: a
band is a range, the gated attribute remains the precise value.
Documents a trap in the docstring rather than leaving it to be rediscovered:
os.cpu_count() reports HOST cores, not the cgroup quota, so a 1-vCPU pod on a
96-core node lands in 33+. Right for "what kind of machine", wrong for "what did
this run get" - os.process_cpu_count() gives the latter but needs 3.13, above
this package's floor.
Docs updated in en/ar/ko/pt-BR, in the default-on Execution Environment row,
stating explicitly that the band is a range and the exact count stays opt-in.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* docs(telemetry): say where the cpu band comes from
The Execution Environment row ended "Detection reads only whether known
environment variables are set, never their values". True for the assistant
and runtime-context fields, and wrong for the band this PR adds:
detect_cpu_band() reads os.cpu_count() (runtime_env.py:306) and touches no
environment variable. In a privacy disclosure table that is the kind of
inaccuracy worth a line.
Names the source explicitly and scopes the env-var sentence to the two
fields it actually describes. All four locales at parity.
pt-BR also takes the reviewer's wording fix: "um de uma lista fixa" ->
"um valor de uma lista fixa", and the missing comma before o `project_id`.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
87 lines
9.5 KiB
Plaintext
87 lines
9.5 KiB
Plaintext
---
|
|
title: Telemetry
|
|
description: Understanding the telemetry data collected by CrewAI and how it contributes to the enhancement of the library.
|
|
icon: signal-stream
|
|
mode: "wide"
|
|
---
|
|
|
|
## Telemetry
|
|
|
|
<Note>
|
|
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.
|
|
</Note>
|
|
|
|
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:
|
|
```python
|
|
# 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`), the `project_id` from your `pyproject.toml` when one is configured, and a coarse size band for the machine (one of `1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+`, or `unknown`). The band is a range, never the exact core count — the exact count is opt-in only, under Environment Information below. The size band comes from the host CPU count; assistant and location 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. |
|
|
|
|
<Note>
|
|
"No" in the "Defaulted" column indicates that this data is only collected when `share_crew` is set to `true`.
|
|
</Note>
|
|
|
|
### Opt-In Further Telemetry Sharing
|
|
|
|
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.
|
|
|
|
<Warning>
|
|
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.
|
|
</Warning>
|