Building a Notion x Arxiv Agent (the MCP client broke me 😅)

2025-10-30 · Source: Nicholas Renotte · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

An agent has been developed that automates the process of identifying trending machine learning research, summarizing it, and integrating it into a Notion board. The system leverages Langflow Desktop as its foundational platform, allowing users to construct workflows by dragging and dropping components. Key components include a chat input, chat output, a Large Language Model (LLM) (with Claude 3.5 Haiku as an example, though others like Ollama are supported), an agent component, and an ArXiv component for fetching research papers. A crucial part of the setup involves configuring a Notion MCP client, which requires creating a Notion integration to obtain a bearer token and granting the integration access to specific Notion pages. This setup enables the agent to process prompts such as "summarize papers on reward hacking and write it to my notion board."

Key takeaway

For AI Engineers seeking to streamline their research workflow, this agent provides a practical method to automate the discovery and summarization of trending machine learning papers directly into Notion. You should consider using no-code platforms like Langflow to rapidly prototype and deploy such integrated solutions, significantly reducing manual effort in staying current with research.

Key insights

Automate ML research discovery and summarization into Notion using a no-code agent builder.

Principles

• Integrate LLMs with external APIs.
• No-code platforms simplify agent creation.

Method

Build an agent in Langflow Desktop by connecting chat I/O, an LLM, an agent component, an ArXiv component, and a Notion MCP client configured with an integration token and page access.

In practice

• Use Langflow Desktop for agent development.
• Configure Notion integrations for API access.

Topics

• AI Agents
• Machine Learning Research
• ArXiv Integration
• Notion Automation
• Langflow

Best for: Machine Learning Engineer, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Nicholas Renotte.