30 resources · 29 free
AI Agents & Automation
Build autonomous agents and automation — workflows, tool use, and agentic systems.
Hugging Face AI Agents Course
A free course from Hugging Face that takes you from beginner to expert in understanding, using, and building AI agents. You study agent theory and practice with libraries like smolagents, LlamaIndex, and LangGraph, complete hands-on assignments in pre-configured Spaces, and can earn a certificate. Includes challenges with leaderboards where your agent competes against other students'. One of the most current and community-backed agent courses available.
OpenAI Cookbook
OpenAI's official collection of practical guides and code examples, including a large Agents section covering the Agents SDK: building agent loops, tool use and function calling, RAG with File Search, evals, and deployment. Each guide is a worked example you can read and run, making it a strong study-and-practice resource for building LLM agents. Continuously updated with the latest API capabilities and free to read.
LangChain Tutorials
The official step-by-step tutorials for the LangChain and LangGraph ecosystems, starting with creating your first agent with custom tools and progressing to orchestration, memory, and evaluation. Each tutorial pairs concept explanations with runnable code, so you learn by building. Suited to developers building LLM applications and agents in Python. Free, authoritative, and maintained by the LangChain team.
Claude Agent SDK Documentation
Anthropic's official documentation for the Claude Agent SDK, which lets you build production AI agents programmatically in Python and TypeScript. Guides cover the agent loop, tools, hooks, subagents, MCP connections, permissions, and sessions, plus a quickstart that builds a working bug-fixing agent. Ideal for developers who want the same agent capabilities that power Claude Code inside their own applications. Free to read with open example projects.
AutoGen Documentation
The official documentation for AutoGen, Microsoft's open-source framework for building multi-agent AI applications. It teaches core concepts and patterns such as conversable agents, group chats, tool use, and human-in-the-loop workflows, with tutorials and full API references. Aimed at developers and researchers building orchestrated agent systems in Python. Free, with an active community and ongoing releases.
n8n AI Workflow Templates
A library of 11,000+ community-submitted workflow templates, including a large collection of AI agent workflows that connect LLMs, tools, and apps. You can study how ready-made automation and agent chains are structured, copy a template, and run it in your own n8n instance to practice building AI automations. Free to browse and reuse, and n8n itself is open source. Suited to automation-minded builders who learn by example.
DeepLearning.AI Short Courses
DeepLearning.AI's library of short, focused courses on AI agents and automation, such as AI agents in LangGraph, multi-agent systems with CrewAI, function calling, and retrieval workflows, taught by experts from OpenAI, Anthropic, LangChain, and others. Most courses are one to two hours and free to access with a free account; certificates and some newer content require a subscription. Ideal for hands-on practitioners who learn fastest by coding along with experts. Free with limits.
Building Effective Agents (Anthropic)
Anthropic's landmark engineering essay on when and how to build agents: it compares workflows and autonomous agents, then details reusable patterns including prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, and autonomous loops, with production code examples. Free to read and widely referenced across the industry. Essential study material for any engineer designing agentic systems, from startups to enterprise teams.
LangChain Academy
Free course platform from LangChain with structured video courses on building LLM applications and agents, including introduction to LangChain for Python, LangChain essentials, LangGraph, and deep agents. Each course pairs lessons with code you can run, and progress is tracked with a free account. Aimed at developers moving from chat applications to production agent systems. Maintained by the team behind one of the most widely used LLM application ecosystems.
LangGraph Documentation
Official documentation for LangGraph, the orchestration framework for building stateful, multi-agent LLM applications. It includes conceptual guides, API references, and tutorials covering graph design, persistence, human-in-the-loop workflows, and multi-agent patterns. Completely free to read. Essential study material for engineers who want to move beyond simple chains to robust, production-grade agent architectures with checkpointing and controlled execution flow.
OpenAI Cookbook
OpenAI's free collection of example code and guides for building on their platform: agents, function calling, retrieval-augmented generation, prompt engineering, and production patterns, including the Agents SDK quickstart. Notebooks and scripts are runnable and open source on GitHub. Free to read and reuse. The de facto starting point for developers building applications on OpenAI models, trusted by a large community of practitioners.
n8n Courses
Free official courses from n8n covering workflow automation end to end: connecting APIs, building multi-step automations, using AI nodes and agents, error handling, and deployment. Video lessons come with downloadable workflows you can import into n8n's free community edition. Aimed at no-code and low-code builders as well as developers. n8n is one of the leading open-source automation platforms, and the courses are regularly updated.
AI Agents for Beginners (Microsoft)
Free 12-lesson curriculum from Microsoft covering agent fundamentals: reasoning, function calling, tool use, memory, multi-agent patterns, and frameworks like Semantic Kernel and Azure AI Agent Service. Each lesson combines theory with hands-on notebooks and labs you can run locally. Free with no paid content. Ideal for developers making the leap from single chatbots to autonomous, goal-driven agent systems, with a companion site at microsoft.github.io.
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Semantic Kernel Documentation
Official Microsoft documentation for Semantic Kernel, the SDK for building AI agents and orchestrating plugins, planners, and memory in .NET, Python, and Java. It includes quickstarts, step-by-step tutorials, and deep conceptual guides with runnable code samples. Free to use and open source. Suited to enterprise developers who want to integrate LLM agents into existing applications with enterprise-grade patterns, security, and governance.
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CrewAI Documentation
Official documentation for CrewAI, the popular Python framework for role-based multi-agent systems. Tutorials cover creating agents, tasks, crews, flows, memory, and tool integration with runnable examples, and the docs include concept explainers for collaboration patterns. Free to read, with the framework itself open source. A practical starting point for developers who want to build collaborative agent teams instead of single-prompt chatbots.
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n8n Community Forum
Active official forum of the n8n automation community where users share workflow templates, ask and answer questions about AI agent nodes, webhooks, API integrations, and error handling, and showcase real automations. Free to read and join. An excellent place to learn from real-world automation problems and get answers from staff and thousands of practitioners building AI-powered workflows.
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Google Developers Learning Pathways
Google's free learning pathways hub, curating structured sequences of lessons, codelabs, and hands-on exercises across machine learning, generative AI, Gemini, and cloud topics relevant to building AI-powered products. Each pathway is a guided curriculum you follow in the browser with a free Google account. Suitable for developers who want official Google material with practical coding exercises, covering beginner through advanced tracks.
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LLM Agents MOOC (Berkeley)
Free Berkeley graduate course on LLM agents with lectures from leading researchers covering agent design, planning, tool use, memory, multi-agent systems, and open research problems. Recorded lectures, slides, and reading lists are all freely available online. Advanced material suited to graduate students, researchers, and experienced engineers who want rigorous academic grounding in agentic AI rather than a tool tutorial.
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smolagents Documentation (Hugging Face)
Official documentation for Hugging Face's smolagents framework, a lightweight library for building AI agents in a few lines of code. Guides cover code agents, tool use, multi-agent hierarchies, and integration with the Hugging Face Hub, with runnable examples throughout. Free and open source. Ideal for developers who want to understand agent internals without heavy abstraction layers and iterate quickly on experiments.
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Kaggle 5-Day AI Agents Intensive with Google
Free self-paced course by Google's ML researchers covering the full AI agent stack across five days: agent architectures, tools and MCP interoperability, context engineering and memory, agent quality evaluation, and prototype-to-production deployment. Each day pairs a technical whitepaper with hands-on codelabs built on Gemini and the Agent Development Kit. Includes a capstone that produces a real deployable agent. Requires a free Kaggle account and a Google AI Studio API key.
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Ragas Evaluation Framework Documentation
Official docs for Ragas, the open-source framework for evaluating LLM applications and AI agents with metrics instead of vibe checks. Covers core concepts, metric catalogs (faithfulness, answer relevancy, context precision), test-set generation, and dedicated guides for RAG evaluation and agent evaluation. Includes quickstarts, how-to guides, and LLM-as-judge patterns that teach a complete evaluation workflow. Entirely free and open source, usable with any LLM provider.
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Letta Documentation (Agent Memory Systems)
Documentation for Letta, the open-source agent memory framework (originally MemGPT) that gives agents persistent, self-editing memory. Explains memory blocks, archival memory with semantic search, shared memory between agents, and MemFS, a git-backed memory filesystem agents can inspect and update. Includes architecture guides, tutorials, and best practices for building agents that learn across conversations. Free and open source, self-hostable.
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Model Context Protocol Documentation
Official documentation for the Model Context Protocol, the open standard that connects AI agents to external tools, data sources, and systems. Walks through core concepts, architecture, transports, and the full specification, with getting-started tutorials for building MCP servers and clients in TypeScript, Python, and other SDKs. Includes reference server implementations and the MCP Inspector development tool. Free, vendor-neutral, and the definitive MCP learning resource.
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OpenAI Function Calling Guide
Official guide to connecting LLMs to external systems through function and tool calling. Covers the full tool-calling flow, JSON-schema function definitions, strict mode, parallel function calls, tool choice controls, custom tools, and streaming. Explains best practices for defining functions and handling call results, plus tool search for deferring rarely used tools. Free documentation that teaches the core mechanism behind most agent implementations.
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Writing Effective Tools for AI Agents (Anthropic)
Anthropic engineering article on designing high-quality tools and evaluations for agentic AI systems. Covers choosing which tools to implement, namespacing, returning meaningful context, optimizing tool responses for token efficiency, and prompt-engineering tool descriptions and specs. Demonstrates an evaluation-driven workflow where agents prototype, test, and improve their own tools. Free in-depth reading for anyone building production agents.
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OWASP GenAI Security Project
Community-driven security project documenting the most critical risks in LLM, GenAI, and agentic AI applications, including the OWASP Top 10 for LLM Applications. Provides practical explanations, real attack scenarios, and actionable mitigations for prompt injection, insecure output handling, excessive agency, and other agent-specific threats. Maps risks to NIST, MITRE ATLAS, and CWE frameworks. Free and open source, essential reading for securing AI agents.
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Arize Phoenix Documentation (Agent Observability)
Documentation for Phoenix, the open-source AI observability and evaluation platform built on OpenTelemetry. Teaches how to trace LLM and agent applications step by step, run LLM-as-judge evaluations, and debug failures across OpenAI Agents SDK, LangGraph, CrewAI, LlamaIndex, and other frameworks. Includes tracing quickstarts, integration guides, and experiment workflows for comparing prompt and model changes. Free, self-hostable, and vendor-agnostic.
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Chroma Documentation (Vector Databases for Agents)
Official docs for Chroma, the open-source AI data infrastructure used to give agents retrieval memory. The getting-started path teaches embedding storage, dense and sparse search, metadata filtering, and hybrid retrieval with runnable Python and TypeScript examples. Includes guides on embedding functions, deployment, and building RAG pipelines. Free, runs locally on your machine, and a practical entry point to vector databases for agents.
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CAMEL-AI Documentation (Multi-Agent Orchestration)
Documentation for CAMEL-AI, an open-source modular framework for building multi-agent systems and agent societies. Teaches agent primitives, role-playing societies, code interpreters, memory, RAG pipelines, and workforce orchestration patterns, plus MCP integration guides. Includes installation, quickstart, cookbooks, and API references with runnable examples. Free and open source for learning multi-agent orchestration end to end.
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Anthropic Cookbook
Free open-source GitHub repository of Jupyter notebooks and guides for building with AI agents. Covers tool use, function calling, RAG deep dives, agent memory, and multi-agent orchestration with working code. MIT-licensed; free to use, fork, and adapt. Actively maintained by Anthropic with practical, runnable examples.
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