docs: add Langfuse guide

[jannikmaierhoefer]

This PR adds a guide on how to use Langfuse to monitor the steps of the CrewAI agent.

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for Langfuse Integration Documentation PR

Overview

The pull request introduces a new documentation file, langfuse-observability.mdx, which serves as a guide for integrating Langfuse observability with CrewAI using OpenTelemetry.

Documentation Quality Analysis

Strengths:

1. Clear Structure: The documentation maintains a logical flow with a step-by-step approach, helping users navigate through the integration process easily.
2. Comprehensive Instructions: It includes all necessary code snippets and dependencies, making it straightforward for users to replicate the integration.
3. Informative Content: There is a good explanation of what Langfuse is, providing context and relevance which enhances reader understanding.
4. Useful Visuals: The documentation includes visuals that show example traces, helping users visualize the potential outcomes from their integration efforts.
5. Proper MDX Frontmatter: The frontmatter is well-structured with necessary metadata, aiding the documentation's organization.

Issues and Suggested Improvements:

1. Typo in Dependencies Installation:

   - %pip install langfuse opnelit crewai crewai_tools
   + %pip install langfuse openlit crewai crewai_tools

   • Suggested Correction: Correct the typo to avoid confusion and installation issues.

2. Environment Variables Security:

   • Improvement Suggestion: Add a note on the security best practices regarding API keys:

   > **Security Note**: Never commit API keys directly in your code. Always use environment variables or a secure configuration management system in production environments.

3. Missing Version Requirements:

   • Improvement Suggestion: To ensure compatibility, specify version requirements:

   %pip install langfuse>=2.0.0 openlit>=1.0.0 crewai>=0.9.0 crewai_tools>=0.9.0

4. Code Example Improvement:

   • Improvement Suggestion: Enhance example code with error handling:

   try:
       openlit.init()
   except Exception as e:
       print(f"Failed to initialize OpenLit: {e}")
       raise

5. Missing Configuration Options:

   • Improvement Suggestion: Provide a section outlining optional environment configurations:

   # Optional configuration
   os.environ["OTEL_SERVICE_NAME"] = "my-crewai-app"
   os.environ["OTEL_RESOURCE_ATTRIBUTES"] = "deployment.environment=production"

6. Documentation Structure Suggestions:

   • Improvement Suggestion: Add a troubleshooting section to address potential user issues:

   ## Troubleshooting
   
   ### Common Issues
   1. **Connection Errors**: Ensure your API keys are correct and you have internet connectivity.
   2. **Missing Traces**: Verify that OpenTelemetry is properly initialized before running your CrewAI application.
   3. **Authentication Issues**: Double-check your base64 encoded authentication string.

7. Code Example Completeness:

   • Improvement Suggestion: Introduce an example output section for clarity:

   ### Example Output
   ```python
   result = crew.kickoff()
   print(result)

   Expected output:

   Multiplication haiku:
   Numbers dance in pairs
   Growing larger with each step
   Products bloom like flowers
   

8. Missing Error Handling in Environment Setup:

   • Improvement Suggestion: Implement validation for environment variables:

   def validate_environment():
       required_vars = ["LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY", "OPENAI_API_KEY"]
       missing = [var for var in required_vars if not os.getenv(var)]
       if missing:
           raise EnvironmentError(f"Missing required environment variables: {', '.join(missing)}")
   
   validate_environment()

General Recommendations:

1. Data Privacy and Compliance: Add a dedicated section about data privacy considerations.
2. Resource Links: Provide links to additional resources and community support for more guidance.
3. Trace Visualization Options: Include examples of different trace visualization options available in Langfuse.
4. Performance Impact: Highlight performance considerations when using this integration in production environments.
5. Production Deployment Best Practices: Add a section on best practices for deploying this integration safely.

Conclusion

Overall, the documentation for integrating Langfuse with CrewAI is well-structured and informative. Addressing the aforementioned improvements, particularly correcting the typo in the installation command, will enhance the quality and usability of the documentation, preparing it for a successful merge. The current draft effectively conveys critical information and instructions for users, establishing a solid foundation for working with Langfuse observability.

[jannikmaierhoefer]

FYI @tonykipkemboi :)

[tonykipkemboi]

Looks good. Thanks for the PR.