Obot Learning Center – Page 3
Building with MCP: Anthropic Guidance and Code Execution in Claude
What Is the Model Context Protocol (MCP) by Anthropic? This is part of a series of articles about the Model Context Protocol. The model context protocol (MCP) is a framework introduced by Anthropic for its language models, such as Claude. MCP improves dynamic tool use by enabling language models to interact with code execution environments […]
MCP Gateway: How It Works, Capabilities and Use Cases
Understand how MCP Gateways provide a single entry point for AI agents, enabling secure access, centralized management, auditing, and scalable MCP server connectivity.
How MCP Servers Work, Use Cases and Notable Examples
Learn what MCP servers are, how they connect AI agents to data and tools, and why they're essential for secure, scalable Model Context Protocol integrations.
GPT 4 Playground: The Basics and a Quick Tutorial
A beginner-friendly guide to the OpenAI Playground for GPT-4: covers how it works, key features, model selection and tips to test powerful prompts.
AI Agent Frameworks: Components & Top 5 Open Source Solutions
AI agent frameworks compared for 2026: components, the agent loop, MCP and A2A protocol support, and how LangGraph, CrewAI, and others actually differ.
ChatGPT Advanced Data Analysis (Code Interpreter): Practical Guide
Unlock the power of the ChatGPT Advanced Data Analysis tool — formerly Code Interpreter — to clean, analyze and visualize your data without writing Python. Get practical guidance.
Anthropic Claude API: A Practical Guide
Dive into the Claude API – learn to integrate Anthropic’s Claude models into your apps: discover how Claude and Obot can help you build agents, handle prompts, and scale real-world workflows.
AI Copilots: Enterprise Use Cases and Key Considerations
AI copilots are digital assistants using AI, often large language models (LLMs), to help with tasks like code generation, creative writing, and decision making.
Working with Gemini API: Text Gen, Doc Processing & Code Execution
What Is the Google Gemini API? Google Gemini is a multi-modal large language model (LLM). It provides natural language and image processing capabilities to enable text generation, sentiment analysis, document processing, image and video analysis, and more. Using the Gemini API, developers can integrate AI functionalities into their applications without needing deep expertise in machine […]
Code Interpreter: Traditional vs. LLM Use Cases & Top 5 Tools
Learn the differences between traditional and LLM code interpreters and find the top five tools to support analysis, automation and data tasks.
Parameter-Efficient Fine-Tuning (PEFT): The Basics and a Quick Tutorial
Explore Parameter-Efficient Fine-Tuning to reduce training time and resources while achieving high performance in neural networks.
Top 10 RAG Tutorials in 2024 + Bonus LangChain Tutorial
What Is Retrieval Augmented Generation (RAG)? Retrieval-augmented generation (RAG) combines large language models (LLMs) with external knowledge retrieval. Traditional LLMs generate responses based solely on pre-trained data. With RAG, the model can access updated and specific information at the time of inference, providing more accurate and context-rich responses. This method leverages repositories of external data, […]