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joaomdmoura merged 12 commits into from
Dec 26, 2025
Merged

Adding HITL for Flows #4143

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3e10e44
feat: introduce human feedback events and decorator for flow methods
joaomdmoura Dec 22, 2025
858e6ee
adding deployment docs
joaomdmoura Dec 22, 2025
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New docs
joaomdmoura Dec 22, 2025
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fix printer
joaomdmoura Dec 22, 2025
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wrong change
joaomdmoura Dec 23, 2025
eb2849e
Adding Async Support
joaomdmoura Dec 24, 2025
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linter
joaomdmoura Dec 24, 2025
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fix
joaomdmoura Dec 24, 2025
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joaomdmoura Dec 25, 2025
c7aa925
updating docs
joaomdmoura Dec 25, 2025
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joaomdmoura Dec 25, 2025
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joaomdmoura Dec 25, 2025
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3 changes: 3 additions & 0 deletions docs/docs.json
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Original file line number Diff line number Diff line change
Expand Up @@ -308,6 +308,7 @@
"en/learn/hierarchical-process",
"en/learn/human-input-on-execution",
"en/learn/human-in-the-loop",
"en/learn/human-feedback-in-flows",
"en/learn/kickoff-async",
"en/learn/kickoff-for-each",
"en/learn/llm-connections",
Expand Down Expand Up @@ -735,6 +736,7 @@
"pt-BR/learn/hierarchical-process",
"pt-BR/learn/human-input-on-execution",
"pt-BR/learn/human-in-the-loop",
"pt-BR/learn/human-feedback-in-flows",
"pt-BR/learn/kickoff-async",
"pt-BR/learn/kickoff-for-each",
"pt-BR/learn/llm-connections",
Expand Down Expand Up @@ -1171,6 +1173,7 @@
"ko/learn/hierarchical-process",
"ko/learn/human-input-on-execution",
"ko/learn/human-in-the-loop",
"ko/learn/human-feedback-in-flows",
"ko/learn/kickoff-async",
"ko/learn/kickoff-for-each",
"ko/learn/llm-connections",
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49 changes: 49 additions & 0 deletions docs/en/concepts/flows.mdx
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Expand Up @@ -572,6 +572,55 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho

When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.

### Human in the Loop (human feedback)

The `@human_feedback` decorator enables human-in-the-loop workflows by pausing flow execution to collect feedback from a human. This is useful for approval gates, quality review, and decision points that require human judgment.

```python Code
from crewai.flow.flow import Flow, start, listen
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult

class ReviewFlow(Flow):
@start()
@human_feedback(
message="Do you approve this content?",
emit=["approved", "rejected", "needs_revision"],
llm="gpt-4o-mini",
default_outcome="needs_revision",
)
def generate_content(self):
return "Content to be reviewed..."

@listen("approved")
def on_approval(self, result: HumanFeedbackResult):
print(f"Approved! Feedback: {result.feedback}")

@listen("rejected")
def on_rejection(self, result: HumanFeedbackResult):
print(f"Rejected. Reason: {result.feedback}")
```

When `emit` is specified, the human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes, which then triggers the corresponding `@listen` decorator.

You can also use `@human_feedback` without routing to simply collect feedback:

```python Code
@start()
@human_feedback(message="Any comments on this output?")
def my_method(self):
return "Output for review"

@listen(my_method)
def next_step(self, result: HumanFeedbackResult):
# Access feedback via result.feedback
# Access original output via result.output
pass
```

Access all feedback collected during a flow via `self.last_human_feedback` (most recent) or `self.human_feedback_history` (all feedback as a list).

For a complete guide on human feedback in flows, including **async/non-blocking feedback** with custom providers (Slack, webhooks, etc.), see [Human Feedback in Flows](/en/learn/human-feedback-in-flows).

## Adding Agents to Flows

Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
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