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* docs: Fix major memory system documentation issues - Remove misleading deprecation warnings, fix confusing comments, clearly separate three memory approaches, provide accurate examples that match implementation * fix: Correct broken image paths in README - Update crewai_logo.png and asset.png paths to point to docs/images/ directory instead of docs/ directly * docs: Add system prompt transparency and customization guide - Add 'Understanding Default System Instructions' section to address black-box concerns - Document what CrewAI automatically injects into prompts - Provide code examples to inspect complete system prompts - Show 3 methods to override default instructions - Include observability integration examples with Langfuse - Add best practices for production prompt management * docs: Fix implementation accuracy issues in memory documentation - Fix Ollama embedding URL parameter and remove unsupported Cohere input_type parameter * docs: Reference observability docs instead of showing specific tool examples * docs: Reorganize knowledge documentation for better developer experience - Move quickstart examples right after overview for immediate hands-on experience - Create logical learning progression: basics → configuration → advanced → troubleshooting - Add comprehensive agent vs crew knowledge guide with working examples - Consolidate debugging and troubleshooting in dedicated section - Organize best practices by topic in accordion format - Improve content flow from simple concepts to advanced features - Ensure all examples are grounded in actual codebase implementation * docs: enhance custom LLM documentation with comprehensive examples and accurate imports * docs: reorganize observability tools into dedicated section with comprehensive overview and improved navigation * docs: rename how-to section to learn and add comprehensive overview page * docs: finalize documentation reorganization and update navigation labels * docs: enhance README with comprehensive badges, navigation links, and getting started video * Add Common Room tracking to documentation - Script will track all documentation page views - Follows Mintlify custom JS implementation pattern - Enables comprehensive docs usage insights * docs: move human-in-the-loop guide to enterprise section and update navigation - Move human-in-the-loop.mdx from learn to enterprise/guides - Update docs.json navigation to reflect new organization
78 lines
3.2 KiB
Plaintext
78 lines
3.2 KiB
Plaintext
---
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title: "HITL Workflows"
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description: "Learn how to implement Human-In-The-Loop workflows in CrewAI for enhanced decision-making"
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icon: "user-check"
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---
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Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
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## Setting Up HITL Workflows
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<Steps>
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<Step title="Configure Your Task">
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Set up your task with human input enabled:
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<Frame>
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<img src="/images/enterprise/crew-human-input.png" alt="Crew Human Input" />
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</Frame>
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</Step>
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<Step title="Provide Webhook URL">
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When kicking off your crew, include a webhook URL for human input:
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<Frame>
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<img src="/images/enterprise/crew-webhook-url.png" alt="Crew Webhook URL" />
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</Frame>
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</Step>
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<Step title="Receive Webhook Notification">
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Once the crew completes the task requiring human input, you'll receive a webhook notification containing:
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- **Execution ID**
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- **Task ID**
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- **Task output**
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</Step>
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<Step title="Review Task Output">
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The system will pause in the `Pending Human Input` state. Review the task output carefully.
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</Step>
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<Step title="Submit Human Feedback">
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Call the resume endpoint of your crew with the following information:
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<Frame>
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<img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
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</Frame>
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<Warning>
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**Feedback Impact on Task Execution**:
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It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.
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</Warning>
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This means:
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- All information in your feedback becomes part of the task's context.
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- Irrelevant details may negatively influence it.
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- Concise, relevant feedback helps maintain task focus and efficiency.
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- Always review your feedback carefully before submission to ensure it contains only pertinent information that will positively guide the task's execution.
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</Step>
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<Step title="Handle Negative Feedback">
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If you provide negative feedback:
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- The crew will retry the task with added context from your feedback.
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- You'll receive another webhook notification for further review.
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- Repeat steps 4-6 until satisfied.
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</Step>
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<Step title="Execution Continuation">
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When you submit positive feedback, the execution will proceed to the next steps.
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</Step>
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</Steps>
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## Best Practices
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- **Be Specific**: Provide clear, actionable feedback that directly addresses the task at hand
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- **Stay Relevant**: Only include information that will help improve the task execution
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- **Be Timely**: Respond to HITL prompts promptly to avoid workflow delays
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- **Review Carefully**: Double-check your feedback before submitting to ensure accuracy
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## Common Use Cases
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HITL workflows are particularly valuable for:
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- Quality assurance and validation
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- Complex decision-making scenarios
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- Sensitive or high-stakes operations
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- Creative tasks requiring human judgment
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- Compliance and regulatory reviews |