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metadata
language:
  - en
license: mit
tags:
  - artificial-intelligence
  - ai-agents
  - multi-agent
  - crewai
  - langchain
  - langgraph
  - autogen
  - mcp
  - model-context-protocol
  - function-calling
  - tool-use
  - orchestration
  - ayinedjimi-consultants
pretty_name: AI Agents - English Dataset (Architectures, Frameworks, MCP)
size_categories:
  - n<1K
task_categories:
  - question-answering
  - text-generation
dataset_info:
  features:
    - name: id
      dtype: string
    - name: category
      dtype: string
    - name: subcategory
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: metadata
      dtype: string
    - name: source_url
      dtype: string
    - name: language
      dtype: string

AI Agents - English Dataset

Comprehensive bilingual dataset on AI Agents, Multi-Agent Frameworks, and the Model Context Protocol (MCP).

Dataset Contents

Category Entry Count Description
Agent Architectures 15 ReAct, Plan-and-Execute, Reflexion, Multi-Agent, Hierarchical, Swarm, etc.
Frameworks 12 CrewAI, AutoGen, LangGraph, Semantic Kernel, Haystack, Dify, etc.
MCP & Tool Use 15 MCP architecture, transports, function calling, security, ecosystem
Anti-Patterns 10 Infinite loops, privilege escalation, prompt injection, etc.
Questions & Answers 80 Q&A covering all aspects of AI agents
Total 132

Reference Articles

This dataset covers topics from the following articles:

  • Autonomous AI Agents - Architecture and patterns for autonomous agents
  • CrewAI, AutoGen, LangGraph - Multi-agent framework comparison
  • MCP (Model Context Protocol) - The standard protocol for connecting LLMs to tools
  • Function Calling and Tool Use - Function calling and tool use techniques
  • AI Agent Orchestration - Orchestration patterns and production deployment

Data Structure

Each entry contains:

  • id: Unique identifier
  • category: Category (agent_architecture, framework, mcp_tools, anti_pattern, qa)
  • subcategory: Specific subcategory
  • question: Question in English
  • answer: Detailed answer in English
  • metadata: JSON metadata (components, use cases, pros/cons, etc.)
  • source_url: Reference URL
  • language: Language (en)

Usage

from datasets import load_dataset

dataset = load_dataset("AYI-NEDJIMI/ai-agents-en")

# Filter by category
architectures = [r for r in dataset["train"] if r["category"] == "agent_architecture"]
frameworks = [r for r in dataset["train"] if r["category"] == "framework"]
mcp = [r for r in dataset["train"] if r["category"] == "mcp_tools"]
anti_patterns = [r for r in dataset["train"] if r["category"] == "anti_pattern"]
qa = [r for r in dataset["train"] if r["category"] == "qa"]

Topics Covered

  • ReAct (Reasoning + Acting) - Reasoning and action loop
  • CrewAI vs AutoGen vs LangGraph - Detailed framework comparison
  • MCP Protocol - Client-server architecture, transports, servers
  • Function Calling - OpenAI, Anthropic, Google formats
  • Tool Use - Best practices, security, validation
  • Multi-Agent Patterns - Supervisor, Swarm, Hierarchical, Conversational
  • Orchestration - Workflows, event-driven, state management
  • Security - Prompt injection, privilege escalation, sandboxing
  • Cost Optimization - Caching, model routing, token budgets
  • Production Deployment - Monitoring, observability, testing, CI/CD

Author

AYI-NEDJIMI Consultants - Expertise in Artificial Intelligence and Compliance

Free Resources

License

MIT License - Free to use, modify, and distribute.


About the Author

AYI NEDJIMI Consultants - Consulting firm specialized in Artificial Intelligence, Cybersecurity and Digital Transformation.

Free Resources

Discover our free whitepapers on AI and cybersecurity:

Complete Collection

Find all our datasets on HuggingFace: AYI-NEDJIMI


Dataset generated by AYI NEDJIMI Consultants. MIT License.

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