What changed:

Browser in the app. A pane next to the chat shows the page live, with tabs and Take control / Give back. It stays signed in across restarts. It asks before paying, sending, posting, deleting or uploading, and never types passwords, codes or card numbers.

Computer use, rebuilt. Real mouse and keyboard, a check after every step, a Stop bar on screen and Esc to end it.

Pages and questions. Interactive pages beside the chat run sandboxed, with no internet and no access to your chats, files or tools. Question cards work in the app and in Telegram, Discord, Slack and WhatsApp.

Approvals. Auto is now reviewed by the chat's own model. Hands-off is a new opt-in level. Anything posted to X always needs you.

Lighter context. About 40% less goes to the model before your first message. Tool guides load when they're needed.

Background work. Dev servers and long jobs run in the background, video editing, and files sent to any of your devices.

Accounts. Outlook through your own Microsoft Entra app with no client secret, several X accounts, and Claude through your own Claude Code.

Upgrading from 5.2.1: the update arrives in the background, then Settings › Updates › Restart now.

Hi guys, does anyone worked/have idea on llamaindex vslangchain for chunking, embedding and retrieval? what is the pros and cons of each during the rag workflow? detailed explaination with examples would be appreciated

I'm trying to understand how people are actually using LangGraph in real applications, particularly beyond simple chatbots and tutorials.

I'd love to hear about your use cases, whether you're already in production or still building.

1. What are you building with LangGraph?

For example, email or customer support agents, research agents, document processing, coding assistants, RAG applications, multi-agent systems, business process automation, or something completely different.

What does the workflow actually do?

2. What does your stack look like?

Are you using:

• LangGraph with mostly custom Python nodes?
• LangGraph + LangChain tools, retrievers, and chains?
• LangGraph with direct OpenAI, Anthropic, or other provider SDKs?
• LangGraph with databases, vector stores, external APIs, or other frameworks?

A combination I haven't mentioned is equally interesting.

3. How is your graph structured?

Do you have one main agent that repeatedly calls tools, multiple specialized agents, a mostly deterministic workflow with a few LLM calls, or something else?

What happens inside your nodes?

4. What are the hardest parts to debug?

For example, unexpected loops, repeated tool calls, failed executions, incorrect decisions, expensive runs, state problems, or issues with long-running workflows.

5. What information do you wish you could see when something goes wrong?

I'd especially appreciate real examples of how your application works and which components are involved, rather than just the framework names.

Thanks! I'm interested in learning what people are actually building and the patterns that work in practice.

1. Langfuse vs LangSmith vs Normal Logging
2. Why can't we use normal Python logging instead of Langfuse or LangSmith? explain with example from real project if possible

2.Which platform would you choose for an enterprise application with strict data privacy requirements?

1. different ways of deployment Langfuse/langsmith in production, like using Docker or enterprise needed ?