What is the Claude API and how do I get started?

The Claude API is Anthropic's programmatic interface for integrating Claude's AI capabilities into your own applications. To get started, create an account at console.anthropic.com, generate an API key, install the Anthropic SDK (pip install anthropic for Python or npm install @anthropic-ai/sdk for Node.js), and make your first API call with a messages.create() request. The free tier includes trial credits to start building immediately.

What is the difference between Claude Opus, Sonnet, and Haiku?

Claude Opus is the most capable model for complex reasoning, analysis, and tasks requiring deep intelligence — it is best for research, complex coding, and nuanced writing. Claude Sonnet balances capability and speed at a lower cost, making it ideal for most production applications. Claude Haiku is the fastest and most affordable, best suited for high-volume, latency-sensitive tasks like classification and summarisation where speed matters more than depth.

What is prompt caching and when should I use it?

Prompt caching allows you to cache a portion of your prompt (typically the system prompt or a large document) so that subsequent requests reuse the cached version without reprocessing it. This reduces latency by up to 85% and cuts costs by up to 90% for the cached portion. Use prompt caching whenever you have a long, stable system prompt or reference document that is sent with every request in a multi-turn conversation or batch processing workflow.

What is tool use (function calling) in the Claude API?

Tool use allows Claude to call functions you define — such as searching a database, fetching a URL, or running a calculation — and incorporate the results into its response. You define tools as JSON schemas describing the function name, description, and parameters. Claude decides when to call a tool based on the user's request, returns a tool_use block, you execute the function and return the result, and Claude incorporates it into its final response. Tool use is the foundation of agentic AI applications.

Claude API Complete Guide: Build AI Apps with Anthropic (2026)

Why Build with the Claude API?

Claude is Anthropic's AI model — designed from the ground up for safety, reliability, and performance on complex tasks. The Claude API gives developers direct access to Claude's capabilities: text generation, reasoning, tool use, vision, document analysis, and autonomous agent behaviour.

Unlike using Claude through a chat interface, the API lets you:

Embed Claude inside your own products — no third-party UI, no branding constraints
Control the full context — inject system prompts, tools, and structured data at every call
Build autonomous agents — Claude can use tools, run loops, and take multi-step actions
Scale to production — with rate limits, caching, and AWS Bedrock deployment options

This hub organises every tutorial on this site by phase. Whether you are making your first API call today or deploying a multi-agent pipeline to production, start at the section that matches where you are.

Quick Reference: Claude Models (2026)

Model	Best For	Context Window
Claude Opus 4	Complex reasoning, long documents, agentic tasks	200K tokens
Claude Sonnet 4	Balanced speed and quality for most production use	200K tokens
Claude Haiku 4	High-volume, latency-sensitive tasks	200K tokens

All models support tool use, vision, and structured outputs. Start with Sonnet for most projects — move to Opus when you need maximum reasoning depth.

Phase 1: Getting Started

Everything you need before you write a single line of code.

Tutorial	What You Will Learn
Getting Started with Claude AI	What Claude is, what it can do, and how the API fits in
What is Anthropic AI?	The company, the safety mission, and why it matters for developers
Claude Model Family: Opus, Sonnet & Haiku	Which model to use for your specific task and budget
Claude vs ChatGPT vs Gemini (2026)	Objective comparison across benchmarks, pricing, and developer experience
AI Tools for Developers 2026	Where Claude fits in the broader AI developer toolchain

Phase 2: API Foundations

Get connected and make your first call in under 10 minutes.

Tutorial	What You Will Learn
Get Your Anthropic API Key in 5 Minutes (2026 Setup Guide)	Create your key, set environment variables, set spend limits
First API Call: Python & JavaScript	Working code for your first successful request
The Messages API Explained	Role/content structure, system prompts, multi-turn conversations
Pricing & Tokens Explained	How tokens are counted, cost calculation, controlling spend

Test yourself: API Foundations Quiz

Phase 3: Core Capabilities

Go beyond basic text generation.

Tutorial	What You Will Learn
Claude Tool Use Explained	Define tools, parse tool calls, return results — full lifecycle
Vision & PDF Analysis	Send images, charts, and multi-page PDFs to Claude
Structured Outputs (JSON)	Force Claude to return valid JSON schemas every time
Files API Tutorial	Upload documents once, reference them across multiple API calls
Web Search Tool Tutorial	Give Claude live internet access via the built-in search tool
Claude Extended Thinking: When to Use It and What It Actually Does	Budget tokens for deeper reasoning on hard problems

Test yourself: Tools & Capabilities Quiz

Phase 4: Prompting Mastery

The quality of your prompts determines the quality of your outputs. These tutorials close the gap between what Claude can do and what your prompts actually unlock.

Tutorial	What You Will Learn
Prompt Engineering for Beginners	Role, task, format, and context — the four pillars of a good prompt
Advanced Claude Prompting: CoT, Few-Shot & XML Tags That Work	Before/after examples for chain-of-thought, few-shot, and XML structuring
Prompt Caching Guide	Cache static context to cut latency and cost on repetitive calls
Prompting Techniques Refresher	Quick reference card for all major prompting patterns

Phase 5: Agents and Automation

Claude's most powerful capabilities emerge when it can take actions, use tools, and operate in loops.

Tutorial	What You Will Learn
What Are AI Coding Agents?	Definitions, architectures, and the current state of the field
Cursor vs Copilot vs Devin vs Claude Code: Which Wins?	SWE-bench scores, pricing, and a verdict for each use case
AI Coding Agents in CI/CD	Automated code review and testing pipelines
The Claude Agentic Loop Explained	Reason → Act → Observe — how agents think across steps
Claude AI Agent Tutorial for Beginners	Build your first working agent with tool calls and a loop
AI Agents Refresher Guide	Core concepts and patterns, fast
Model Context Protocol (MCP)	Connect Claude to any external tool via the open MCP standard
Retrieval-Augmented Generation (RAG)	Give Claude access to your private knowledge base
Claude Computer Use Explained	Let Claude operate a desktop — clicks, typing, browser control

Phase 6: Real-World Builds

End-to-end project tutorials. Each one produces a working application you can deploy.

Build	What You Will Create
Build a Customer Support Chatbot	Conversational agent with document grounding and escalation logic
Build a Code Review Assistant	Automated PR reviews with structured, actionable feedback
Build a CV/Resume Analyser	Extract structured data from PDFs and score candidates
Build a Meeting Notes Summariser	Turn call transcripts into action items and owner assignments
Build a Multi-Language Translation Engine	Context-aware translation preserving tone and domain terminology
Build an AI Incident Report Generator	Automate post-mortems from system logs and alert data

Phase 7: Production and Scale

Move from prototype to production-grade deployment.

Tutorial	What You Will Learn
Claude on AWS Bedrock: Production Guide	IAM, VPC, cross-region inference, cost controls
Build a Claude Coding Agent (Full API)	Extensible coding assistant with tool definitions and loop logic
Build an Autonomous Bug Fixer Agent	Agent that detects, diagnoses, and patches bugs from test output
Build a GitHub PR Review Agent	Claude integrated into GitHub Actions for automated reviews
Build a Data Analyst Agent	Query databases, generate charts, and narrate insights

Agentic and Enterprise Architecture

For developers building AI-native systems rather than individual features.

Article	What You Will Learn
Agentic AI Architecture	System design patterns for multi-step AI workflows
Multi-Agent Architecture Patterns	Orchestrator/subagent patterns, parallelism, and routing
AI-Native Architecture Design	Designing systems where AI is a first-class component
RAG Architecture Patterns for Enterprise	Chunking, retrieval, and reranking at scale
AI Prompt Injection: Security and Mitigation	Defending your agents against indirect injection attacks
GitHub Copilot vs Claude: AI Productivity Compared	When to use Copilot vs Claude API for developer workflows
Anthropic AI Series: Final Knowledge Test	Cumulative quiz covering all Claude API concepts

Frequently Asked Questions

Which programming language should I use with the Claude API? Both Python and JavaScript/TypeScript have first-class Anthropic SDK support. Python is more common in the AI/ML community and has better tooling for data processing. TypeScript is the better choice if you are building a Next.js or Node.js product. The tutorials on this site cover both.

How much does the Claude API cost? Pricing is per-token and varies by model. Haiku is the cheapest, Opus the most expensive. For most applications, Sonnet offers the best price-performance ratio. Prompt caching can reduce costs by 80–90% on calls with repetitive context. See the pricing guide for worked examples.

What is the difference between Claude's tool use and function calling? They are the same concept with different names. OpenAI popularised "function calling"; Anthropic uses "tool use." The mechanics are identical: you define a schema, Claude decides when to call it, you execute it, and return the result. See Claude Tool Use Explained for implementation details.

Can Claude access the internet? Yes — via the built-in web search tool (available in some API tiers) or by connecting an external search function via tool use. See Web Search Tool Tutorial for both approaches.

What is MCP and do I need it? The Model Context Protocol is an open standard for connecting AI models to external tools and data sources. You do not need it for simple API usage, but it becomes valuable when you want Claude to connect to many different tools without writing custom integration code for each one. See MCP Explained.

Start with your API key, make your first call, and pick one real-world build to work through. Every tutorial above is self-contained with working code — copy, run, and modify.