Buckets:
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 identifiercategory: Category (agent_architecture, framework, mcp_tools, anti_pattern, qa)subcategory: Specific subcategoryquestion: Question in Englishanswer: Detailed answer in Englishmetadata: JSON metadata (components, use cases, pros/cons, etc.)source_url: Reference URLlanguage: 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
- French version dataset: AYI-NEDJIMI/ai-agents-fr
- Complete collection: AYI-NEDJIMI on HuggingFace
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.
- Website: ayinedjimi-consultants.fr
- Expertise: Generative AI, RAG, LangChain, Cybersecurity, AI Act Compliance
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.
Xet Storage Details
- Size:
- 4.73 kB
- Xet hash:
- fbdd977e49b4c1c1d4cf45b465bcb099617966068e148ee72452b1305a108a9b
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