# End the AgentOps session
agentops.end_session("Success")
```

```
🖇 AgentOps: Session Stats - Duration: 7m 9.2s | Cost: $0.013102 | LLMs: 26 | Tools: 5 | Actions: 0 | Errors: 0
🖇 AgentOps: Replay: https://app.agentops.ai/drilldown?session_id=e1d4ca98-f21d-4f65-b4b1-
```

🎉 Go to the AgentOps link shown above, you will be able to see the detailed record for this running like below.

**NOTE**: The AgentOps link is private and tied to the AgentOps account. To access the link, you'll need to run the session using your own AgentOps API Key, which will then allow you to open the link with the session's running information.

## 🌟 Highlights

This notebook has guided you through setting up and running a CAMEL RAG workflow with Dappier for a multi-agent role-playing task. You can adapt and expand this example for various other scenarios requiring advanced web information retrieval and AI collaboration.

Key tools utilized in this notebook include:

* **CAMEL**: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.
* **OpenAI**: A leading provider of advanced AI models capable of natural language understanding, contextual reasoning, and content generation. It enables intelligent, human-like interactions and supports a wide range of applications across various domains.
* **Dappier**: A platform connecting LLMs to real-time, rights-cleared data from trusted sources, specializing in domains like web search, finance, and news. It delivers enriched, prompt-ready data, empowering AI with verified and up-to-date information for diverse applications.
* **AgentOps**: Track and analysis the running of CAMEL Agents.

This comprehensive setup allows you to adapt and expand the example for various scenarios requiring advanced web information retrieval, AI collaboration, and multi-source data aggregation.