LangChain Practical Labs
LangChain is a framework for building LLM applications. Learn to connect LLMs to tools, memory, and custom data sources.
Open AI & LLM Engineering Hub
Build confidence with AI & Open Data.
Practical paths through Large Language Models, Model Context Protocol (MCP), Vector Databases, and the open-source tools that power modern artificial intelligence platforms.
500+
Curated AI & MCP Tools
3+
Verifiable Labs
1,000
Roadmap Milestones
AI Architecture
Live Ecosystem
Quick Navigation
Curated paths for AI engineers
Core Pillars
Structured Learning Tracks
From your first local embedding model to production-tested multi-agent workflows, each track is crafted with reproducible code and verified dependencies.
Track 01 · Practice
Hands-on Labs
Step-by-step technical guides with complete terminal instructions, code blocks, and verification commands.
Browse all labs → Track 02 · DirectoryAI Tools Index
Instant search and filter across 500+ curated AI tools, MCP servers, vector databases, and evaluation frameworks.
Search directory → Track 03 · LLM ExplorerModels & VRAM Calculator
Interactive GPU memory sizing tool, context length boundaries, MMLU benchmarks, and curated AI agent frameworks.
Launch LLM Explorer → Track 04 · Curriculum1,000-Item Roadmap
Comprehensive engineering syllabus covering foundational math, LLM architecture, agentic orchestration, and MLOps.
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Interactive Workshops
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Featured Hands-on Labs
AI-Native Vector Database with Weaviate & Ollama
Install Weaviate locally with Docker and Ollama, generate embeddings, and build semantic search workflows.
Data Quality Metrics for Large Language Models
A comprehensive framework for assessing and improving the quality of training data for large language models.
Engineering Journal
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Latest Research & Architectural Notes
Qwen Coder Models: Breakthrough AI at Your Fingertips
By Sangam Biradar
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Model Context Protocol (MCP) Impact on Software Development Life Cycle
By AI Data Foundation
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Curated AI and Cloud-Native Tools Collection
By AI Data Foundation
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Open Weights
Full leaderboard →
Featured Model Benchmarks
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Next Useful Steps
Continue Your AI Journey
Whether you are preparing a local test environment or contributing your own model evaluation, here is where to go next.
Community Contribution
Share an AI Tool or Lab
AI Data Foundation is 100% community-driven. Submit a pull request to add your open-source model, MCP integration, or notebook.
View repository on GitHub →
Engineering Curriculum
Explore the 1,000-Item Roadmap
Structured tracks through linear algebra, transformers, inference optimization, vector indexing, and agent safety.
Open roadmap track viewer →
Vector Databases
Connect Weaviate Locally
Learn hybrid dense/sparse search, multi-tenant vector clustering, and real-time schema generation with our hands-on workshop.
Start Weaviate lab →