Tools & Frameworks
Created 2 May 2025
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Tools & Frameworks
What is it?
The ecosystem of libraries, platforms, and developer tools used to build, train, deploy, and interact with AI systems.
Development Frameworks
PyTorch
- What: Deep learning framework (Meta)
- Use: Research, training, model development
- Why it dominates: Pythonic, flexible, great debugging, massive community
- Website: pytorch.org
Hugging Face Transformers
- What: Library of pre-trained models + tools to use/fine-tune them
- Use: Load any model in 3 lines of code, fine-tune, evaluate
- Why it matters: Democratised access to AI models. The “npm of AI.”
- Website: huggingface.co
LangChain
- What: Framework for building LLM-powered applications
- Use: Chains, agents, RAG, tool use, memory
- Note: Widely used but sometimes criticised for complexity/abstraction
- Website: langchain.com
LlamaIndex
- What: Data framework for LLM applications
- Use: RAG, document indexing, retrieval, knowledge bases
- Website: llamaindex.ai
Ollama
- What: Run LLMs locally with one command
- Use: Local inference, privacy, experimentation
- Why it matters: Makes running open models trivially easy
- Website: ollama.com
AI-Assisted Coding
| Tool | Type | Notes |
|---|---|---|
| Claude Code | Terminal agent | Reads/writes code, runs tests, autonomous |
| GitHub Copilot | IDE autocomplete | Inline suggestions, chat |
| Cursor | AI-native IDE | Fork of VS Code with deep AI integration |
| Windsurf (Codeium) | IDE | AI-native development environment |
| Aider | Terminal tool | AI pair programming in terminal |
| Continue | IDE extension | Open-source AI coding assistant |
Vector Databases
| Tool | Type | Best for |
|---|---|---|
| Pinecone | Managed SaaS | Production, no-ops |
| Weaviate | Open-source | Hybrid search, flexibility |
| ChromaDB | Open-source | Simplicity, Python-native |
| pgvector | PostgreSQL ext. | Already using Postgres |
| Qdrant | Open-source | Performance, filtering |
| Milvus | Open-source | Large-scale production |
Orchestration & Deployment
| Tool | Purpose |
|---|---|
| LangSmith | LLM observability and debugging |
| Weights & Biases | Experiment tracking |
| vLLM | Fast LLM inference server |
| TGI (HuggingFace) | Text generation inference |
| Vercel AI SDK | Build AI apps with Next.js/SvelteKit |
| Replicate | Run models via API |
Model Context Protocol (MCP)
Anthropic’s open standard for connecting AI to tools:
- Universal interface for tool/data access
- Servers expose capabilities, clients (Claude, etc.) use them
- Growing ecosystem of integrations
- Relevant to agent development
What to Learn First
If starting from scratch:
- Python basics → essential for AI/ML
- Hugging Face → use pre-trained models immediately
- Ollama → run models locally, experiment freely
- LangChain or LlamaIndex → build RAG apps
- An AI coding tool → accelerate your own development
Resources
- Hugging Face course (free)
- LangChain documentation & tutorials
- Ollama model library
- RAG & Retrieval — Core use case for these tools
- AI Agents — Where orchestration frameworks shine