Curated AI Agents, Automation & Engineering resources — each with a concrete first step on how to use it
4
5 commits
updated Aug 24, 2026
README
Awesome AI Engineering 🤖⚙️
A curated collection of free & open-source resources for AI Agents, AI Automation, and AI Engineering — with practical "how to use" guidance for every resource, not just links.
What makes this list different? Every resource comes with a "How to use it" column — a concrete first step, command, or workflow so you can start in minutes instead of bookmarking and forgetting.
📚 Table of Contents
- 🚀 Quick Start
- 🤖 AI Agents
- ⚙️ AI Automation
- 🛠️ AI Engineering
- 🎓 Courses & Structured Learning
- 📄 Must-Read Papers
- 👥 Communities
- 🗺️ Learning Paths
- 🤝 Contributing
- ✨ Contributors
🚀 Quick Start
Completely new? Do these 4 things this week:
| Step | Resource | Time |
|---|---|---|
| 1️⃣ Understand what agents are | Hugging Face Agents Course – Unit 0 & 1 | 2 hours |
| 2️⃣ Build your first agent | Microsoft AI Agents for Beginners – Lesson 1–3 | 3 hours |
| 3️⃣ Run a real automation | Install n8n locally: npx n8n → build a webhook → LLM → email workflow | 1 hour |
| 4️⃣ Run a model locally | Install Ollama: ollama run llama3.2 | 15 min |
🤖 AI Agents
Agent Frameworks
| Resource | What it is | How to use it |
|---|---|---|
| LangChain | The most widely-adopted framework for LLM apps — chains, tools, RAG, and agent primitives | pip install langchain → follow the agents tutorial. Start with a single-tool ReAct agent before anything complex |
| LangGraph | Graph-based framework for stateful, controllable agent workflows (used in production by LangChain team) | pip install langgraph → build a 3-node graph: plan → act → reflect. The quickstart has copy-paste examples |
| CrewAI | Role-based multi-agent framework — define agents with roles, goals, and backstories that collaborate on tasks | pip install crewai → crewai create crew my_crew scaffolds a full project. Great first framework because the mental model (a "crew" of coworkers) is intuitive |
| Microsoft AutoGen / AG2 | Conversation-driven multi-agent framework — agents talk to each other to solve tasks, with human-in-the-loop support | pip install autogen-agentchat → run the two-agent coder + critic example from the docs. Best for research-style experimentation |
| OpenAI Agents SDK | OpenAI's lightweight, production-minded agent framework — handoffs, guardrails, tracing built in | pip install openai-agents → define an agent with tools as plain Python functions. Cleanest API if you're already on OpenAI models |
| Pydantic AI | Type-safe agent framework from the Pydantic team — structured outputs validated at runtime | pip install pydantic-ai → define your output as a Pydantic model and let the agent fill it. Best choice when you need reliable structured data |
| smolagents | Hugging Face's minimal agent library — agents that write and execute Python code as their action mechanism | pip install smolagents → CodeAgent(tools=[...], model=...). Read the source — it's small enough to fully understand in one sitting |
| Semantic Kernel | Microsoft's enterprise SDK for integrating LLMs into .NET, Python, and Java apps | Best for C#/.NET teams: dotnet add package Microsoft.SemanticKernel → build a "plugin" (tool) and let the planner call it |
| LlamaIndex | Data-centric framework — connect LLMs to your documents, databases, and APIs; strong agentic RAG support | pip install llama-index → 5 lines to index a folder of PDFs and query it. Add ReActAgent on top once retrieval works |
| Claude Agent SDK | Anthropic's SDK for building agents with the same harness that powers Claude Code | pip install claude-agent-sdk → give it a folder and tools, and it handles the agentic loop, permissions, and context management for you |
Multi-Agent Systems
| Resource | What it is | How to use it |
|---|---|---|
| MetaGPT | Simulates a software company — PM, architect, engineer agents produce specs, designs, and code from one prompt | pip install metagpt → metagpt "build a CLI todo app" and read the generated artifacts to learn how roles decompose work |
| CAMEL | Research framework for studying agent societies and role-playing communication | Run the role-playing example: two agents (user + assistant persona) solve a task via dialogue. Great for understanding emergent agent behavior |
| OpenHands | Open-source autonomous software development agent (ex-OpenDevin) — codes, debugs, browses | Run via Docker (one command in their README) → give it a GitHub issue and watch how a production-grade coding agent plans and executes |
| AutoGPT | The classic autonomous agent — goal in, self-directed loop of plan/act/reflect out | Study it more than use it: read the loop architecture to understand why unconstrained autonomy fails, then apply those lessons |
Memory & State
| Resource | What it is | How to use it |
|---|---|---|
| Letta (MemGPT) | Agents with long-term memory — self-editing memory blocks that persist across sessions | pip install letta → create an agent, chat, restart the process, chat again — memory survives. Study the memory-hierarchy paper alongside |
| Mem0 | Memory layer you can add to any agent framework — extracts and retrieves user facts automatically | pip install mem0ai → wrap your existing chat loop with memory.add() / memory.search(). Works with LangChain, CrewAI, etc. |
| Zep | Temporal knowledge-graph memory for agents — tracks facts and how they change over time | Self-host the community edition with Docker → point your agent's memory reads/writes at Zep's API |
Learning Resources (Agents)
| Resource | What it is | How to use it |
|---|---|---|
| Microsoft AI Agents for Beginners | Free 12-lesson course — fundamentals, tool use, memory, planning, multi-agent, production | Clone it, do 2 lessons per week, and actually run every code sample. Lessons 1–6 = fundamentals, 7–12 = production patterns |
| NirDiamant/GenAI_Agents | 45+ runnable Jupyter notebooks — every major agent pattern implemented across frameworks | Don't read linearly — pick the pattern you need (e.g. "self-improving agent"), run that notebook, then port it to your own project |
| Anthropic: Building Effective Agents | The most-cited practical guide on when to use workflows vs. agents, and the core composable patterns | Read before choosing any framework. Implement the 5 workflow patterns (chaining, routing, parallelization, orchestrator, evaluator) in raw API calls first |
| OpenAI: A Practical Guide to Building Agents | Free PDF covering agent foundations, guardrails, and orchestration design | Read the guardrails chapter twice — it's the part most tutorials skip and the part production actually needs |
⚙️ AI Automation
Workflow Automation Platforms
| Resource | What it is | How to use it |
|---|---|---|
| n8n | Open-source workflow automation with 400+ integrations and first-class AI/LLM nodes — the de-facto tool for AI automation | npx n8n → open localhost:5678 → build: Webhook → AI Agent node → Gmail. Then explore the built-in agent templates |
| Activepieces | Open-source Zapier alternative with AI pieces, MCP support, and a friendlier license for self-hosting | One-command Docker deploy → recreate a Zapier workflow you'd otherwise pay for |
| Windmill | Turn Python/TypeScript scripts into workflows, UIs, and scheduled jobs — developer-first automation | Self-host with Docker → write a Python function → it becomes an app + API + cron job automatically |
| Huginn | Self-hosted agents that monitor the web and act on your behalf (the original "automation agents" project) | Deploy with Docker → create a "Website Agent" that watches a page for changes and emails you |
| Make (free tier) | Visual automation platform — 1,000 free operations/month, strong AI app integrations | Use the free tier to prototype visually, then rebuild high-volume workflows in n8n to avoid per-operation costs |
No-Code / Low-Code Agent Builders
| Resource | What it is | How to use it |
|---|---|---|
| Dify | Open-source LLM app platform — visual builder for agents, RAG pipelines, and workflows with built-in observability | Docker compose up → build a customer-support agent with a knowledge base in under 30 minutes, no code |
| Flowise | Drag-and-drop LLM flow builder on LangChain — quickest way to prototype agent logic visually | npx flowise start → drag Chat Model + Memory + Tool nodes together → export as API endpoint |
| Langflow | Visual IDE for building and prototyping LangChain flows and agents | pip install langflow → langflow run → use it to design agent architectures, then export to code |
Model Context Protocol (MCP)
| Resource | What it is | How to use it |
|---|---|---|
| MCP Specification & Docs | The open standard for connecting AI models to tools and data sources — adopted by Anthropic, OpenAI, Google | Read the concepts page, then connect one existing server (e.g. filesystem) to Claude Desktop or your IDE before building your own |
| MCP Servers (official repo) | Reference MCP server implementations — filesystem, GitHub, Postgres, and more | Pick the server closest to what you need, read its ~200 lines of source, then fork it for your own tool |
| FastMCP | Pythonic framework for building MCP servers with decorators — the fastest way to expose your tools to any AI | pip install fastmcp → decorate a function with @mcp.tool() → your Python function is now callable from Claude, Cursor, etc. |
Browser & Computer Use
| Resource | What it is | How to use it |
|---|---|---|
| Browser Use | Let agents control a real browser — navigate, fill forms, extract data | pip install browser-use → give it a task like "find the 3 cheapest flights BLR→DXB" and watch the browser run |
| Playwright | The browser automation engine most AI browser-agents build on | Learn it directly: pip install playwright → record a script with playwright codegen. Understanding it makes every browser-agent debuggable |
| Skyvern | Automate browser workflows with LLMs + computer vision — resilient to UI changes | Self-host via Docker → replace one brittle Selenium/XPath script with a natural-language task |
🛠️ AI Engineering
RAG & Retrieval
| Resource | What it is | How to use it |
|---|---|---|
| NirDiamant/RAG_Techniques | 30+ notebook implementations of RAG patterns — from naive RAG to GraphRAG, RAPTOR, and self-RAG | Start with "simple RAG", then jump straight to "reranking" and "query transformation" — those two give the biggest quality wins |
| LlamaIndex | The most complete toolkit for ingestion, chunking, indexing, and retrieval strategies | Use VectorStoreIndex defaults first, measure quality, then tune chunking — never the other way around |
| Haystack | Production-oriented framework for RAG pipelines with strong evaluation support | pip install haystack-ai → build the indexing + query pipeline from the tutorial → swap components (retriever, ranker) without rewriting |
| GraphRAG | Microsoft's knowledge-graph-based RAG — answers "global" questions plain vector RAG can't | Run the CLI on a small document set first (it's token-hungry) → compare its answers vs. vector RAG on summary-style questions |
| docling | IBM's document parser — converts PDF, DOCX, PPTX to clean structured Markdown for RAG ingestion | pip install docling → run it on your ugliest PDF. If your RAG answers are bad, parsing is usually why |
Vector Databases
| Resource | What it is | How to use it |
|---|---|---|
| Chroma | Embedded, zero-config vector DB — the SQLite of vector search | pip install chromadb → 4 lines to store and query embeddings. Default choice for prototypes and small apps |
| Qdrant | Rust-based vector DB with excellent filtering and hybrid search | Run with Docker → use payload filtering (metadata + vector search together) — the feature that separates demos from products |
| pgvector | Vector search inside PostgreSQL — no new infrastructure | CREATE EXTENSION vector; → if you already run Postgres, start here before adding any dedicated vector DB |
| FAISS | Meta's similarity-search library — the raw engine many vector DBs use internally | Use when you need pure speed and control: pip install faiss-cpu → benchmark IndexFlatL2 vs. IndexHNSW on your data |
| Milvus | Distributed vector DB built for billion-scale workloads | Start with Milvus Lite (pip install pymilvus) locally; only move to the clustered deployment when you outgrow a single node |
Local Model Serving
| Resource | What it is | How to use it |
|---|---|---|
| Ollama | Run open models locally with one command — the standard for local development | ollama run llama3.2 → then point any OpenAI-compatible client at localhost:11434/v1. Free, private, offline agent development |
| vLLM | High-throughput inference server for production GPU serving | pip install vllm → vllm serve <model> gives you an OpenAI-compatible API with continuous batching. The production upgrade from Ollama |
| llama.cpp | CPU/GPU inference in C++ — runs quantized models on almost any hardware | Use via Ollama first; drop to llama.cpp directly when you need fine control over quantization and memory |
| FreeToken | Edge-native MoE serving engine (UC Berkeley Sky Lab + MIT, paper) — runs frontier open-weight MoE models on consumer NVIDIA GPUs 2–4× faster than Ollama via bandwidth-adaptive CPU–GPU co-execution | Install from the repo (NVIDIA RTX 30/40/50 only — no Apple Silicon/AMD yet) → serve an MoE model like Qwen3.6-35B-A3B (only ~3B params active per token — that's why it fits in 8GB VRAM; dense models see no such gains) → point Claude Code or any OpenAI/Anthropic-compatible client at it for fully local agentic coding |
| OpenRouter (free models) | One API for hundreds of models, including genuinely free ones | Create a key → filter models by price = $0 → prototype agents without spending anything |
Evaluation & Testing
| Resource | What it is | How to use it |
|---|---|---|
| promptfoo | Test prompts and agents like code — YAML test cases, CI integration, side-by-side model comparison | npx promptfoo init → write 10 test cases for your prompt → run on every change. The unit-test workflow for LLM work |
| Ragas | Evaluation framework for RAG — faithfulness, answer relevancy, context precision metrics | pip install ragas → score your RAG pipeline on 20 hand-written Q&A pairs before tuning anything, so you can prove improvements |
| DeepEval | Pytest-style unit testing for LLM outputs — 14+ metrics including hallucination and bias | pip install deepeval → write assert_test() cases that run in CI, failing the build when quality regresses |
| OpenAI Evals | Registry and framework for benchmarking LLM tasks | Browse the registry for an eval close to your task and adapt it, rather than inventing metrics from scratch |
Observability & Monitoring
| Resource | What it is | How to use it |
|---|---|---|
| Langfuse | Open-source LLM observability — traces, costs, evals, prompt management. Self-hostable | Docker compose up → add 3 lines of SDK to your agent → every LLM call, tool call, and cost appears in the UI. Do this before debugging anything |
| Arize Phoenix | Open-source tracing and evaluation, strong OpenTelemetry support | pip install arize-phoenix → phoenix serve → auto-instrument LangChain/LlamaIndex with one line to see full traces |
| OpenLLMetry | OpenTelemetry-based instrumentation for LLM apps — vendor-neutral | Add it when you want traces in your existing observability stack (Grafana, Datadog) instead of a new tool |
Prompt Engineering
| Resource | What it is | How to use it |
|---|---|---|
| Anthropic Prompt Engineering Docs | The most systematic free guide — role prompting, XML structure, chain-of-thought, prefilling | Work through it with the interactive tutorial notebooks. The "give Claude a role + XML tags" combo fixes most weak prompts |
| OpenAI Cookbook | Hundreds of runnable examples — function calling, structured outputs, agent patterns | Search the cookbook before writing any integration code — a working example probably exists |
| Prompt Engineering Guide | Community-maintained guide covering techniques, papers, and model-specific tips | Use as a reference, not a course — look up techniques (e.g. self-consistency) when a benchmark paper mentions them |
| DSPy | Stanford's framework for programming (not hand-writing) prompts — optimizes them automatically | Learn after manual prompting plateaus: define your task as a signature, give it 20 examples, let the optimizer beat your hand-tuned prompt |
AI Coding Agents & Workflows
| Resource | What it is | How to use it |
|---|---|---|
| Claude Code Harness | A disciplined delivery loop for Claude Code — Spec → Plan → Work → Review → Ship, with evidence gates at every step so agent work doesn't drift (plans lost in chat, skipped tests, late review) | Install it into your Claude Code setup per the README, then run one small feature through the full 5-verb loop. The Spec and Review gates alone will noticeably tighten your agent workflow |
| OmniRoute | Workflow rules engine for AI agents — defines intelligent routing, branching, and orchestration logic for autonomous systems | Clone the repo and review the workflow definition format, then integrate it into your agent pipeline to add declarative control flow and conditional routing |
🎓 Courses & Structured Learning
| Course | Provider | What you'll learn | How to approach it |
|---|---|---|---|
| AI Agents Course | Hugging Face | Agent fundamentals → smolagents, LlamaIndex, LangGraph → final certified project | The certification deadline creates real accountability — enroll and finish within the cohort |
| AI Agents for Beginners | Microsoft | 12 lessons: architecture, tool use, memory, planning, multi-agent, production | Pair each lesson with the matching video playlist |
| Generative AI for Beginners | Microsoft | 21 lessons on GenAI foundations — do this first if LLM basics are shaky | Skim lessons you know; do the assignments for ones you don't |
| Kaggle 5-Day GenAI Intensive | Prompting, embeddings, agents, domain models, MLOps — with Kaggle notebooks | Do it with the live cohort when offered; the Discord discussion is half the value | |
| DeepLearning.AI Short Courses | DeepLearning.AI | 1–2 hour courses on LangGraph, CrewAI, AutoGen, evals, and more — free to audit | Pick courses by the framework you've already chosen — don't collect them all |
| Full Stack LLM Bootcamp | FSDL | Production LLM engineering: UX, monitoring, test sets, deployment | Watch the "LLMOps" and "UX for Language UIs" lectures even if you skip the rest |
📄 Must-Read Papers
| Paper | Why it matters | How to read it |
|---|---|---|
| ReAct: Synergizing Reasoning and Acting | The pattern behind nearly every tool-using agent today | Read §2, then implement the thought→action→observation loop yourself in ~50 lines |
| Reflexion | Agents that learn from verbal self-feedback across attempts | Implement it as a wrapper around any agent you already have: run → critique → retry |
| Toolformer | How models learn when and how to call tools | Skim for concepts — the self-supervised labeling idea explains modern function-calling training |
| Generative Agents (Smallville) | 25 agents with memory, reflection, and planning living in a simulated town | Read §4 (architecture) — its memory-retrieval scoring (recency × importance × relevance) is directly reusable |
| From LLM Reasoning to Autonomous AI Agents | Comprehensive 2025 survey connecting reasoning techniques to agent architectures | Use as a map: read the taxonomy, then chase citations only for areas you're building in |
👥 Communities
| Community | Platform | Best for |
|---|---|---|
| r/AI_Agents | Project showcases, framework debates, weekly discussion threads | |
| r/LocalLLaMA | Local models, quantization, hardware advice for self-hosted agents | |
| LangChain Discord | Discord | Framework help, LangGraph patterns |
| CrewAI Discord | Discord | Crew-based agent building support |
| Hugging Face Discord | Discord | Agents course support, open-source model discussion |
| n8n Community | Forum | Automation workflow help, AI node patterns |
| OpenAI Developer Forum | Forum | API, function calling, and Agents SDK discussion |
🗺️ Learning Paths
Path 1 — Beginner → First Agent (2–4 weeks)
- Foundations: HF Agents Course Units 0–1
- Guided course: Microsoft AI Agents for Beginners lessons 1–6
- Read: Anthropic – Building Effective Agents
- Build: A single-tool agent (weather, calculator, or web search) in the framework of your choice
- Ship: Push it to GitHub with a README and a demo GIF
Path 2 — Automation Builder (3–6 weeks)
- Setup: Self-host n8n with Docker
- Build 3 workflows: inbox triage → LLM summary; RSS → AI digest; form → enrichment → CRM
- Add agents: Use n8n's AI Agent node with tools and memory
- Extend: Build one custom MCP server for a tool n8n doesn't have
- Ship: Document your workflows in a public repo (export JSON + screenshots)
Path 3 — AI Engineer (8–16 weeks)
- RAG deeply: Work through RAG_Techniques — naive → reranking → query transforms → GraphRAG
- Evaluate everything: Add Ragas + promptfoo to the pipeline; keep a scores spreadsheet
- Observability: Instrument with Langfuse from day one
- Agents in production: Build a multi-step agent with LangGraph, with retries, guardrails, and human-in-the-loop
- Serve locally: Benchmark your app on Ollama vs. vLLM
- Ship: An end-to-end project — ingestion → RAG → agent → evals → dashboard — as your portfolio piece
🤝 Contributing
Contributions are what make open source amazing! Found a great free resource? Know a better "how to use it" tip?
- Read the Contribution Guidelines
- Open an issue to suggest a resource, or
- Submit a PR directly — every accepted contribution gets you featured below ⬇️
Rules of thumb: free (or genuinely useful free tier) · actively maintained · you've personally used it · comes with a real "how to use it" tip.
✨ Contributors
Thanks to everyone who helps grow this collection — every contribution is shown here automatically:
Made with contrib.rocks.
⭐ Support
If this helped you, star the repo — it helps others find these free resources.
📜 License
Maintained by Fahim A R Akil · Resource descriptions and usage guidance are original work.
ai-agents
ai-automation
ai-engineering
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Curated AI Agents, Automation & Engineering resources — each with a concrete first step on how to use it
4
5 commits
updated Aug 24, 2026
README
Awesome AI Engineering 🤖⚙️
A curated collection of free & open-source resources for AI Agents, AI Automation, and AI Engineering — with practical "how to use" guidance for every resource, not just links.
What makes this list different? Every resource comes with a "How to use it" column — a concrete first step, command, or workflow so you can start in minutes instead of bookmarking and forgetting.
📚 Table of Contents
- 🚀 Quick Start
- 🤖 AI Agents
- ⚙️ AI Automation
- 🛠️ AI Engineering
- 🎓 Courses & Structured Learning
- 📄 Must-Read Papers
- 👥 Communities
- 🗺️ Learning Paths
- 🤝 Contributing
- ✨ Contributors
🚀 Quick Start
Completely new? Do these 4 things this week:
| Step | Resource | Time |
|---|---|---|
| 1️⃣ Understand what agents are | Hugging Face Agents Course – Unit 0 & 1 | 2 hours |
| 2️⃣ Build your first agent | Microsoft AI Agents for Beginners – Lesson 1–3 | 3 hours |
| 3️⃣ Run a real automation | Install n8n locally: npx n8n → build a webhook → LLM → email workflow | 1 hour |
| 4️⃣ Run a model locally | Install Ollama: ollama run llama3.2 | 15 min |
🤖 AI Agents
Agent Frameworks
| Resource | What it is | How to use it |
|---|---|---|
| LangChain | The most widely-adopted framework for LLM apps — chains, tools, RAG, and agent primitives | pip install langchain → follow the agents tutorial. Start with a single-tool ReAct agent before anything complex |
| LangGraph | Graph-based framework for stateful, controllable agent workflows (used in production by LangChain team) | pip install langgraph → build a 3-node graph: plan → act → reflect. The quickstart has copy-paste examples |
| CrewAI | Role-based multi-agent framework — define agents with roles, goals, and backstories that collaborate on tasks | pip install crewai → crewai create crew my_crew scaffolds a full project. Great first framework because the mental model (a "crew" of coworkers) is intuitive |
| Microsoft AutoGen / AG2 | Conversation-driven multi-agent framework — agents talk to each other to solve tasks, with human-in-the-loop support | pip install autogen-agentchat → run the two-agent coder + critic example from the docs. Best for research-style experimentation |
| OpenAI Agents SDK | OpenAI's lightweight, production-minded agent framework — handoffs, guardrails, tracing built in | pip install openai-agents → define an agent with tools as plain Python functions. Cleanest API if you're already on OpenAI models |
| Pydantic AI | Type-safe agent framework from the Pydantic team — structured outputs validated at runtime | pip install pydantic-ai → define your output as a Pydantic model and let the agent fill it. Best choice when you need reliable structured data |
| smolagents | Hugging Face's minimal agent library — agents that write and execute Python code as their action mechanism | pip install smolagents → CodeAgent(tools=[...], model=...). Read the source — it's small enough to fully understand in one sitting |
| Semantic Kernel | Microsoft's enterprise SDK for integrating LLMs into .NET, Python, and Java apps | Best for C#/.NET teams: dotnet add package Microsoft.SemanticKernel → build a "plugin" (tool) and let the planner call it |
| LlamaIndex | Data-centric framework — connect LLMs to your documents, databases, and APIs; strong agentic RAG support | pip install llama-index → 5 lines to index a folder of PDFs and query it. Add ReActAgent on top once retrieval works |
| Claude Agent SDK | Anthropic's SDK for building agents with the same harness that powers Claude Code | pip install claude-agent-sdk → give it a folder and tools, and it handles the agentic loop, permissions, and context management for you |
Multi-Agent Systems
| Resource | What it is | How to use it |
|---|---|---|
| MetaGPT | Simulates a software company — PM, architect, engineer agents produce specs, designs, and code from one prompt | pip install metagpt → metagpt "build a CLI todo app" and read the generated artifacts to learn how roles decompose work |
| CAMEL | Research framework for studying agent societies and role-playing communication | Run the role-playing example: two agents (user + assistant persona) solve a task via dialogue. Great for understanding emergent agent behavior |
| OpenHands | Open-source autonomous software development agent (ex-OpenDevin) — codes, debugs, browses | Run via Docker (one command in their README) → give it a GitHub issue and watch how a production-grade coding agent plans and executes |
| AutoGPT | The classic autonomous agent — goal in, self-directed loop of plan/act/reflect out | Study it more than use it: read the loop architecture to understand why unconstrained autonomy fails, then apply those lessons |
Memory & State
| Resource | What it is | How to use it |
|---|---|---|
| Letta (MemGPT) | Agents with long-term memory — self-editing memory blocks that persist across sessions | pip install letta → create an agent, chat, restart the process, chat again — memory survives. Study the memory-hierarchy paper alongside |
| Mem0 | Memory layer you can add to any agent framework — extracts and retrieves user facts automatically | pip install mem0ai → wrap your existing chat loop with memory.add() / memory.search(). Works with LangChain, CrewAI, etc. |
| Zep | Temporal knowledge-graph memory for agents — tracks facts and how they change over time | Self-host the community edition with Docker → point your agent's memory reads/writes at Zep's API |
Learning Resources (Agents)
| Resource | What it is | How to use it |
|---|---|---|
| Microsoft AI Agents for Beginners | Free 12-lesson course — fundamentals, tool use, memory, planning, multi-agent, production | Clone it, do 2 lessons per week, and actually run every code sample. Lessons 1–6 = fundamentals, 7–12 = production patterns |
| NirDiamant/GenAI_Agents | 45+ runnable Jupyter notebooks — every major agent pattern implemented across frameworks | Don't read linearly — pick the pattern you need (e.g. "self-improving agent"), run that notebook, then port it to your own project |
| Anthropic: Building Effective Agents | The most-cited practical guide on when to use workflows vs. agents, and the core composable patterns | Read before choosing any framework. Implement the 5 workflow patterns (chaining, routing, parallelization, orchestrator, evaluator) in raw API calls first |
| OpenAI: A Practical Guide to Building Agents | Free PDF covering agent foundations, guardrails, and orchestration design | Read the guardrails chapter twice — it's the part most tutorials skip and the part production actually needs |
⚙️ AI Automation
Workflow Automation Platforms
| Resource | What it is | How to use it |
|---|---|---|
| n8n | Open-source workflow automation with 400+ integrations and first-class AI/LLM nodes — the de-facto tool for AI automation | npx n8n → open localhost:5678 → build: Webhook → AI Agent node → Gmail. Then explore the built-in agent templates |
| Activepieces | Open-source Zapier alternative with AI pieces, MCP support, and a friendlier license for self-hosting | One-command Docker deploy → recreate a Zapier workflow you'd otherwise pay for |
| Windmill | Turn Python/TypeScript scripts into workflows, UIs, and scheduled jobs — developer-first automation | Self-host with Docker → write a Python function → it becomes an app + API + cron job automatically |
| Huginn | Self-hosted agents that monitor the web and act on your behalf (the original "automation agents" project) | Deploy with Docker → create a "Website Agent" that watches a page for changes and emails you |
| Make (free tier) | Visual automation platform — 1,000 free operations/month, strong AI app integrations | Use the free tier to prototype visually, then rebuild high-volume workflows in n8n to avoid per-operation costs |
No-Code / Low-Code Agent Builders
| Resource | What it is | How to use it |
|---|---|---|
| Dify | Open-source LLM app platform — visual builder for agents, RAG pipelines, and workflows with built-in observability | Docker compose up → build a customer-support agent with a knowledge base in under 30 minutes, no code |
| Flowise | Drag-and-drop LLM flow builder on LangChain — quickest way to prototype agent logic visually | npx flowise start → drag Chat Model + Memory + Tool nodes together → export as API endpoint |
| Langflow | Visual IDE for building and prototyping LangChain flows and agents | pip install langflow → langflow run → use it to design agent architectures, then export to code |
Model Context Protocol (MCP)
| Resource | What it is | How to use it |
|---|---|---|
| MCP Specification & Docs | The open standard for connecting AI models to tools and data sources — adopted by Anthropic, OpenAI, Google | Read the concepts page, then connect one existing server (e.g. filesystem) to Claude Desktop or your IDE before building your own |
| MCP Servers (official repo) | Reference MCP server implementations — filesystem, GitHub, Postgres, and more | Pick the server closest to what you need, read its ~200 lines of source, then fork it for your own tool |
| FastMCP | Pythonic framework for building MCP servers with decorators — the fastest way to expose your tools to any AI | pip install fastmcp → decorate a function with @mcp.tool() → your Python function is now callable from Claude, Cursor, etc. |
Browser & Computer Use
| Resource | What it is | How to use it |
|---|---|---|
| Browser Use | Let agents control a real browser — navigate, fill forms, extract data | pip install browser-use → give it a task like "find the 3 cheapest flights BLR→DXB" and watch the browser run |
| Playwright | The browser automation engine most AI browser-agents build on | Learn it directly: pip install playwright → record a script with playwright codegen. Understanding it makes every browser-agent debuggable |
| Skyvern | Automate browser workflows with LLMs + computer vision — resilient to UI changes | Self-host via Docker → replace one brittle Selenium/XPath script with a natural-language task |
🛠️ AI Engineering
RAG & Retrieval
| Resource | What it is | How to use it |
|---|---|---|
| NirDiamant/RAG_Techniques | 30+ notebook implementations of RAG patterns — from naive RAG to GraphRAG, RAPTOR, and self-RAG | Start with "simple RAG", then jump straight to "reranking" and "query transformation" — those two give the biggest quality wins |
| LlamaIndex | The most complete toolkit for ingestion, chunking, indexing, and retrieval strategies | Use VectorStoreIndex defaults first, measure quality, then tune chunking — never the other way around |
| Haystack | Production-oriented framework for RAG pipelines with strong evaluation support | pip install haystack-ai → build the indexing + query pipeline from the tutorial → swap components (retriever, ranker) without rewriting |
| GraphRAG | Microsoft's knowledge-graph-based RAG — answers "global" questions plain vector RAG can't | Run the CLI on a small document set first (it's token-hungry) → compare its answers vs. vector RAG on summary-style questions |
| docling | IBM's document parser — converts PDF, DOCX, PPTX to clean structured Markdown for RAG ingestion | pip install docling → run it on your ugliest PDF. If your RAG answers are bad, parsing is usually why |
Vector Databases
| Resource | What it is | How to use it |
|---|---|---|
| Chroma | Embedded, zero-config vector DB — the SQLite of vector search | pip install chromadb → 4 lines to store and query embeddings. Default choice for prototypes and small apps |
| Qdrant | Rust-based vector DB with excellent filtering and hybrid search | Run with Docker → use payload filtering (metadata + vector search together) — the feature that separates demos from products |
| pgvector | Vector search inside PostgreSQL — no new infrastructure | CREATE EXTENSION vector; → if you already run Postgres, start here before adding any dedicated vector DB |
| FAISS | Meta's similarity-search library — the raw engine many vector DBs use internally | Use when you need pure speed and control: pip install faiss-cpu → benchmark IndexFlatL2 vs. IndexHNSW on your data |
| Milvus | Distributed vector DB built for billion-scale workloads | Start with Milvus Lite (pip install pymilvus) locally; only move to the clustered deployment when you outgrow a single node |
Local Model Serving
| Resource | What it is | How to use it |
|---|---|---|
| Ollama | Run open models locally with one command — the standard for local development | ollama run llama3.2 → then point any OpenAI-compatible client at localhost:11434/v1. Free, private, offline agent development |
| vLLM | High-throughput inference server for production GPU serving | pip install vllm → vllm serve <model> gives you an OpenAI-compatible API with continuous batching. The production upgrade from Ollama |
| llama.cpp | CPU/GPU inference in C++ — runs quantized models on almost any hardware | Use via Ollama first; drop to llama.cpp directly when you need fine control over quantization and memory |
| FreeToken | Edge-native MoE serving engine (UC Berkeley Sky Lab + MIT, paper) — runs frontier open-weight MoE models on consumer NVIDIA GPUs 2–4× faster than Ollama via bandwidth-adaptive CPU–GPU co-execution | Install from the repo (NVIDIA RTX 30/40/50 only — no Apple Silicon/AMD yet) → serve an MoE model like Qwen3.6-35B-A3B (only ~3B params active per token — that's why it fits in 8GB VRAM; dense models see no such gains) → point Claude Code or any OpenAI/Anthropic-compatible client at it for fully local agentic coding |
| OpenRouter (free models) | One API for hundreds of models, including genuinely free ones | Create a key → filter models by price = $0 → prototype agents without spending anything |
Evaluation & Testing
| Resource | What it is | How to use it |
|---|---|---|
| promptfoo | Test prompts and agents like code — YAML test cases, CI integration, side-by-side model comparison | npx promptfoo init → write 10 test cases for your prompt → run on every change. The unit-test workflow for LLM work |
| Ragas | Evaluation framework for RAG — faithfulness, answer relevancy, context precision metrics | pip install ragas → score your RAG pipeline on 20 hand-written Q&A pairs before tuning anything, so you can prove improvements |
| DeepEval | Pytest-style unit testing for LLM outputs — 14+ metrics including hallucination and bias | pip install deepeval → write assert_test() cases that run in CI, failing the build when quality regresses |
| OpenAI Evals | Registry and framework for benchmarking LLM tasks | Browse the registry for an eval close to your task and adapt it, rather than inventing metrics from scratch |
Observability & Monitoring
| Resource | What it is | How to use it |
|---|---|---|
| Langfuse | Open-source LLM observability — traces, costs, evals, prompt management. Self-hostable | Docker compose up → add 3 lines of SDK to your agent → every LLM call, tool call, and cost appears in the UI. Do this before debugging anything |
| Arize Phoenix | Open-source tracing and evaluation, strong OpenTelemetry support | pip install arize-phoenix → phoenix serve → auto-instrument LangChain/LlamaIndex with one line to see full traces |
| OpenLLMetry | OpenTelemetry-based instrumentation for LLM apps — vendor-neutral | Add it when you want traces in your existing observability stack (Grafana, Datadog) instead of a new tool |
Prompt Engineering
| Resource | What it is | How to use it |
|---|---|---|
| Anthropic Prompt Engineering Docs | The most systematic free guide — role prompting, XML structure, chain-of-thought, prefilling | Work through it with the interactive tutorial notebooks. The "give Claude a role + XML tags" combo fixes most weak prompts |
| OpenAI Cookbook | Hundreds of runnable examples — function calling, structured outputs, agent patterns | Search the cookbook before writing any integration code — a working example probably exists |
| Prompt Engineering Guide | Community-maintained guide covering techniques, papers, and model-specific tips | Use as a reference, not a course — look up techniques (e.g. self-consistency) when a benchmark paper mentions them |
| DSPy | Stanford's framework for programming (not hand-writing) prompts — optimizes them automatically | Learn after manual prompting plateaus: define your task as a signature, give it 20 examples, let the optimizer beat your hand-tuned prompt |
AI Coding Agents & Workflows
| Resource | What it is | How to use it |
|---|---|---|
| Claude Code Harness | A disciplined delivery loop for Claude Code — Spec → Plan → Work → Review → Ship, with evidence gates at every step so agent work doesn't drift (plans lost in chat, skipped tests, late review) | Install it into your Claude Code setup per the README, then run one small feature through the full 5-verb loop. The Spec and Review gates alone will noticeably tighten your agent workflow |
| OmniRoute | Workflow rules engine for AI agents — defines intelligent routing, branching, and orchestration logic for autonomous systems | Clone the repo and review the workflow definition format, then integrate it into your agent pipeline to add declarative control flow and conditional routing |
🎓 Courses & Structured Learning
| Course | Provider | What you'll learn | How to approach it |
|---|---|---|---|
| AI Agents Course | Hugging Face | Agent fundamentals → smolagents, LlamaIndex, LangGraph → final certified project | The certification deadline creates real accountability — enroll and finish within the cohort |
| AI Agents for Beginners | Microsoft | 12 lessons: architecture, tool use, memory, planning, multi-agent, production | Pair each lesson with the matching video playlist |
| Generative AI for Beginners | Microsoft | 21 lessons on GenAI foundations — do this first if LLM basics are shaky | Skim lessons you know; do the assignments for ones you don't |
| Kaggle 5-Day GenAI Intensive | Prompting, embeddings, agents, domain models, MLOps — with Kaggle notebooks | Do it with the live cohort when offered; the Discord discussion is half the value | |
| DeepLearning.AI Short Courses | DeepLearning.AI | 1–2 hour courses on LangGraph, CrewAI, AutoGen, evals, and more — free to audit | Pick courses by the framework you've already chosen — don't collect them all |
| Full Stack LLM Bootcamp | FSDL | Production LLM engineering: UX, monitoring, test sets, deployment | Watch the "LLMOps" and "UX for Language UIs" lectures even if you skip the rest |
📄 Must-Read Papers
| Paper | Why it matters | How to read it |
|---|---|---|
| ReAct: Synergizing Reasoning and Acting | The pattern behind nearly every tool-using agent today | Read §2, then implement the thought→action→observation loop yourself in ~50 lines |
| Reflexion | Agents that learn from verbal self-feedback across attempts | Implement it as a wrapper around any agent you already have: run → critique → retry |
| Toolformer | How models learn when and how to call tools | Skim for concepts — the self-supervised labeling idea explains modern function-calling training |
| Generative Agents (Smallville) | 25 agents with memory, reflection, and planning living in a simulated town | Read §4 (architecture) — its memory-retrieval scoring (recency × importance × relevance) is directly reusable |
| From LLM Reasoning to Autonomous AI Agents | Comprehensive 2025 survey connecting reasoning techniques to agent architectures | Use as a map: read the taxonomy, then chase citations only for areas you're building in |
👥 Communities
| Community | Platform | Best for |
|---|---|---|
| r/AI_Agents | Project showcases, framework debates, weekly discussion threads | |
| r/LocalLLaMA | Local models, quantization, hardware advice for self-hosted agents | |
| LangChain Discord | Discord | Framework help, LangGraph patterns |
| CrewAI Discord | Discord | Crew-based agent building support |
| Hugging Face Discord | Discord | Agents course support, open-source model discussion |
| n8n Community | Forum | Automation workflow help, AI node patterns |
| OpenAI Developer Forum | Forum | API, function calling, and Agents SDK discussion |
🗺️ Learning Paths
Path 1 — Beginner → First Agent (2–4 weeks)
- Foundations: HF Agents Course Units 0–1
- Guided course: Microsoft AI Agents for Beginners lessons 1–6
- Read: Anthropic – Building Effective Agents
- Build: A single-tool agent (weather, calculator, or web search) in the framework of your choice
- Ship: Push it to GitHub with a README and a demo GIF
Path 2 — Automation Builder (3–6 weeks)
- Setup: Self-host n8n with Docker
- Build 3 workflows: inbox triage → LLM summary; RSS → AI digest; form → enrichment → CRM
- Add agents: Use n8n's AI Agent node with tools and memory
- Extend: Build one custom MCP server for a tool n8n doesn't have
- Ship: Document your workflows in a public repo (export JSON + screenshots)
Path 3 — AI Engineer (8–16 weeks)
- RAG deeply: Work through RAG_Techniques — naive → reranking → query transforms → GraphRAG
- Evaluate everything: Add Ragas + promptfoo to the pipeline; keep a scores spreadsheet
- Observability: Instrument with Langfuse from day one
- Agents in production: Build a multi-step agent with LangGraph, with retries, guardrails, and human-in-the-loop
- Serve locally: Benchmark your app on Ollama vs. vLLM
- Ship: An end-to-end project — ingestion → RAG → agent → evals → dashboard — as your portfolio piece
🤝 Contributing
Contributions are what make open source amazing! Found a great free resource? Know a better "how to use it" tip?
- Read the Contribution Guidelines
- Open an issue to suggest a resource, or
- Submit a PR directly — every accepted contribution gets you featured below ⬇️
Rules of thumb: free (or genuinely useful free tier) · actively maintained · you've personally used it · comes with a real "how to use it" tip.
✨ Contributors
Thanks to everyone who helps grow this collection — every contribution is shown here automatically:
Made with contrib.rocks.
⭐ Support
If this helped you, star the repo — it helps others find these free resources.
📜 License
Maintained by Fahim A R Akil · Resource descriptions and usage guidance are original work.
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