LangGraph Cloud - How to Publish your AI Agents in Cloud?

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AI Summary

• Introduction to Langro Cloud from Langchain
  • Demonstrates querying for the latest AI news
  • Interaction between user, agent, and search tool
  • Clear monitoring dashboard available
  • Deployment of Langro as an API endpoint
  • Integration into applications
• Creating a Langro Application
  • Subscribe to the YouTube channel for AI content
  • Folder structure includes agent.py, requirements.txt, lang.json, and .env
  • Installation of dependencies using pip install -r requirements.txt
  • .env file for local testing with API keys
  • agent.py uses ChatGPT from Anthropic and TLY search tool
  • lang.json for configuration
• Testing Locally
  • Use Langro CLI to build and test
  • Test with curl command to check functionality
• Deploying to Langro Cloud
  • Save project to GitHub repository
  • Create new deployment on LSmith platform
  • Link GitHub repo and configure deployment settings
  • Build image and deploy
  • Test endpoints and integration options
• Integrating into Your Application
  • Use Streamlit for user interface
  • Install Streamlit and run the application
  • Post request to Langro endpoint with user query
  • Monitor requests and responses in LSmith
• Conclusion
  • Detailed visibility of the process
  • Excitement for future related videos
  • Encouragement to like, share, and subscribe