Agentic-AI-Engineer

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Agentic-AI-Engineer

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An open, community-built learning hub for engineers who want to design, build, evaluate, and ship real agentic AI systems — continually being updated 🧭.

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# Agentic AI Engineer

> An open, community-built learning hub for engineers who design, build, evaluate, and ship real agentic AI systems &mdash; not just prompts.

---

## 🚀 At a glance

<table>
<tr>
<td align="center">9 Learning paths (all substantial)</td>
<td align="center">63 Hands-on labs (notebooks)</td>
<td align="center">8 Capstone projects</td>
<td align="center">18 Math foundations (with code)</td>
</tr>
<tr>
<td align="center">12 Architecture patterns</td>
<td align="center">9 Frameworks compared</td>
<td align="center">65+ Glossary entries</td>
<td align="center">71+ Concept pages</td>
</tr>
</table>

**Stack covered:** LangGraph 1.0 &middot; LangChain 1.0 &middot; MCP 2025-11-25 &middot; A2A v1.0 &middot; CrewAI &middot; AutoGen &middot; Pydantic AI &middot; OpenAI Agents SDK &middot; LlamaIndex &middot; Haystack &middot; LangSmith &middot; Google ADK

---

## 🎯 Why this exists

Three problems this repository fixes.

> **Tutorials go stale fast.** A LangGraph blog post from a year ago is mostly wrong today. Every tool page here carries a *verified-as-of* date and a link to the official changelog.

> **Concepts and tools get tangled.** Most courses teach &quot;LangGraph&quot; when they mean &quot;agent state machines.&quot; This repo keeps them apart &mdash; concepts and math live in their own folders and don't change when the framework does.

> **The math is usually skipped or overdone.** Agentic AI rests on real math &mdash; autoregressive generation, embeddings, MDPs, policies, retrieval theory &mdash; but most engineers don't need a textbook. The math pages here include the equations that change how you debug a system, with executable Python next to each. Nothing else.

The result: a practical, technically deep, community-maintained reference that separates **stable fundamentals** (still right in five years) from **fast-moving tooling** (LangGraph, MCP, A2A, vector DBs, eval platforms). Updates to fast-moving tools don't break the curriculum. Six months from now, the concept pages still work.

---

## 👤 Who this is for

- 🛠️ **Software engineers** moving from chatbots to real agentic systems with tools, memory, and orchestration.
- 🧪 **ML/AI practitioners** who know the modeling side and want a structured map of agent patterns, protocols, and production concerns.
- 🎓 **Advanced learners** who already understand transformers, embeddings, and Python at a working level and want to go from notebook prototypes to evaluated, observable, deployable agents.

> **New to Python or LLM APIs?** Start with [AI Engineering Preparation](AI-Engineering-Preparation/README.md) first, then return here.
---

## 🧰 What you'll build

Working through the labs and projects, you'll end up with code worth keeping:

- 🤖 A **ReAct-style agent from scratch**, no framework, so you understand the loop before you abstract it away.
- 🔍 **RAG systems** including agentic RAG where retrieval is a tool, not a fixed pipeline.
- 👥 **Multi-agent topologies** &mdash; supervisor, hierarchical, swarm &mdash; and a clear sense of when each one is wrong for the job.
- 🔌 **MCP servers and clients** wired to real data sources.
- 🤝 **A2A agents** that discover and delegate to each other.
- 📊 **Evaluated agents** with traces, golden datasets, and judge-based scorers.
- 🚢 **Production-shaped systems** with cost engineering, latency budgets, streaming, human-in-the-loop, and safety guardrails.

---

## ⚡ Quickstart

Get a working agent running locally in a few minutes.

```bash
# 1. Clone
git clone https://github.com/MHHamdan/Agentic-AI-Engineer.git
cd Agentic-AI-Engineer

# 2. Set up the environment (uv recommended; pip works too)
uv sync                  # or: python -m venv .venv && pip install -r requirements.txt

# 3. Add your API keys
cp .env.example .env     # then edit .env

# 4. Run the first lab
uv run jupyter lab labs/01-first-agent-from-scratch/lab.ipynb
```

Full setup details &mdash; Docker, local-model fallbacks via Ollama, troubleshooting &mdash; live in [`setup/`](./setup/).

---

## 🧭 Start here

New to the repo? Open these in order.

1. 🗺️ [`docs/start-here.md`](./docs/start-here.md) &mdash; 5-minute tour of the repo and how to use it.
2. 🤖 [`labs/01-first-agent-from-scratch/`](./labs/01-first-agent-from-scratch/) &mdash; build a working agent before reading any theory.
3. 📖 [`concepts/agents/what-is-an-agent.md`](./concepts/agents/what-is-an-agent.md) &mdash; the vocabulary the rest of the repo uses.
4. 🎯 [`learning-paths/`](./learning-paths/) &mdash; pick a curated path based on your goal.

---

## 🎯 Choose your path

Skip the linear reading order and jump to what you actually need.

| If you want to&hellip; | Start here | Status |
|---|---|---|
| 🤖 Build your first real agent | [01 &mdash; Foundations](./learning-paths/01-foundations/) | ✅ Complete |
| 🔍 Build retrieval-augmented agents | [02 &mdash; Agentic RAG](./learning-paths/02-agentic-rag/) | ✅ v1 + v2 |
| 👥 Orchestrate multiple cooperating agents | [03 &mdash; Multi-Agent Systems](./learning-paths/03-multi-agent-systems/) | ✅ v1 + v2 + v3 + frameworks page |
| 🔌 Wire agents to tools, data, and other agents | [04 &mdash; Tool Protocols (MCP + A2A)](./learning-paths/04-tool-protocols-mcp-a2a/) | ✅ Complete &mdash; all 7 modules |
| 🪟 Get more out of the context window | [05 &mdash; Context Engineering](./learning-paths/05-context-engineering/) | ✅ v1 complete &mdash; 6/6 modules |
| 📊 Add tracing, evals, and observability | [06 &mdash; Evaluation &amp; Observability](./learning-paths/06-evaluation-observability/) | ✅ v1 + v2 |
| 🚢 Ship to production safely | [07 &mdash; Production &amp; Safety](./learning-paths/07-production-and-safety/) | ✅ v1 complete &mdash; 8/8 modules |
| 🧮 Understand the math behind it all | [08 &mdash; Mathematical Foundations](./learning-paths/08-mathematical-foundations/) | ✅ v1 complete &mdash; 13/13 pages (refreshed with code examples) |
| 🏆 Build something portfolio-worthy | [09 &mdash; Capstones](./learning-paths/09-capstones/) | ✅ v1 complete &mdash; 8 project briefs |

Each path is a curated reading list across the rest of the repo &mdash; concepts, labs, recipes, patterns &mdash; not a duplicate folder of content. Every link in this table resolves to a real, authored README.

---

## 🗂️ Repository structure

```
agentic-ai-engineer/
├── 📚 docs/              Start-here pages, FAQ, community pages
├── 🎯 learning-paths/    Curated journeys (links into the rest of the repo)
├── 📖 concepts/          Short explainers - what something is and when to use it
├── 🧮 math-foundations/  Engineer-useful math with citations and Python code
├── 🧪 labs/              Hands-on guided exercises (notebooks + READMEs)
├── 🧰 recipes/           Copy-paste solutions to common problems
├── 🏛 patterns/          Architecture patterns with diagrams and tradeoffs
├── 🚀 projects/          Build Challenges and Capstone Projects
├── 💎 examples/          Minimal reference implementations
├── 🔧 tools/             Versioned snapshots of fast-moving frameworks
├── 📊 evaluation/        Eval frameworks, datasets, scorers
├── 🚢 production/        Deployment, cost, latency, streaming, concurrency
├── 🛡 security/          Threats, defenses, red-teaming
├── 🎨 diagrams/          Mermaid sources + rendered images
├── 📚 references/        Papers, books, talks, community resources
├── 🔤 glossary/          A-to-Z terminology
├── ⚙️ setup/             Environment setup
└── 📁 assets/            Working artifacts (not user-facing curriculum)
```

A more detailed walkthrough of every folder lives in [`docs/how-to-use-this-repo.md`](./docs/how-to-use-this-repo.md).

---

## 🛤️ Learning paths

Nine paths, each curating content across the rest of the repo. They overlap deliberately: the **Multi-Agent** path reuses concept pages from **Foundations**, the **Production** path leans on **Evaluation**, and so on.

```mermaid
flowchart TD
 F[🤖 01 &middot; Foundations Agent loop, ReAct, tools, memory]
 R[🔍 02 &middot; Agentic RAG Retrieval as a tool]
 M[👥 03 &middot; Multi-Agent Systems Supervisor, hierarchical, swarm]
 T[🔌 04 &middot; Tool Protocols MCP + A2A]
 C[🪟 05 &middot; Context Engineering Budgets, compression, selection]
 E[📊 06 &middot; Evaluation &amp; Observability Tracing, judge ensembles, drift]
 P[🚢 07 &middot; Production &amp; Safety Cost, latency, guardrails, red-team]
 X[🧮 08 &middot; Mathematical Foundations Probability, embeddings, MDPs]
 CAP[🏆 09 &middot; Capstones End-to-end portfolio builds]

F --> R
    F --> M
    F --> T
    R --> C
    M --> C
    T --> C
    R --> E
    M --> E
    C --> E
    E --> P
    F -.-> X
    R -.-> X
    M -.-> X
    P --> CAP
    E --> CAP

classDef beginner fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px,color:#1b5e20
    classDef rag fill:#e3f2fd,stroke:#1565c0,stroke-width:2px,color:#0d47a1
    classDef multi fill:#e0f7fa,stroke:#00838f,stroke-width:2px,color:#006064
    classDef tool fill:#fff8e1,stroke:#f57f17,stroke-width:2px,color:#e65100
    classDef context fill:#fff3e0,stroke:#e65100,stroke-width:2px,color:#bf360c
    classDef eval fill:#f3e5f5,stroke:#6a1b9a,stroke-width:2px,color:#4a148c
    classDef prod fill:#ffebee,stroke:#c62828,stroke-width:2px,color:#b71c1c
    classDef math fill:#e8eaf6,stroke:#283593,stroke-width:2px,color:#1a237e
    classDef capstone fill:#fce4ec,stroke:#c2185b,stroke-width:3px,color:#880e4f

class F beginner
    class R rag
    class M multi
    class T tool
    class C context
    class E eval
    class P prod
    class X math
    class CAP capstone
```

| # | Path | Focus | Difficulty |
|---|---|---|---|
| 01 | Foundations | Agent loop, ReAct, tools, memory, first frameworks | 🟢 Beginner-friendly |
| 02 | Agentic RAG | Retrieval as a tool, hybrid search, RAG failure modes | 🟡 Intermediate |
| 03 | Multi-Agent Systems | Supervisor, hierarchical, swarm topologies | 🟡 Intermediate |
| 04 | Tool Protocols (MCP + A2A) | Standardized integration with tools and other agents | 🟡 Intermediate |
| 05 | Context Engineering | Token budgets, compression, selection strategies | 🟡 Intermediate |
| 06 | Evaluation &amp; Observability | Tracing, golden datasets, LLM-as-judge, RAG eval | 🔴 Advanced |
| 07 | Production &amp; Safety | Cost, latency, guardrails, deployment, red-teaming | 🔴 Advanced |
| 08 | Mathematical Foundations | LM probability, embeddings, MDPs, policies, eval metrics | 🟢 → 🔴 |
| 09 | Capstones | End-to-end build challenges that combine everything | 🔴 Advanced |

---

## 📐 How the content is organized

Five content types, each doing one job well.

| Type | What it answers | Length | Where it lives |
|---|---|---|---|
| 📖 **Concept** | *What is this and when do I use it?* | ~10-min read | [`concepts/`](./concepts/) |
| 🧪 **Lab** | *Walk me through building this hands-on.* | 30 to 120 min, notebook + README | [`labs/`](./labs/) |
| 🧰 **Recipe** | *I have this specific problem &mdash; what's the fix?* | Copy-paste, 5-min read | [`recipes/`](./recipes/) |
| 🏛 **Pattern** | *Which architecture should I use, and why?* | Diagram + tradeoffs | [`patterns/`](./patterns/) |
| 🚀 **Project** | *Let me build something substantial.* | Hours to days | [`projects/`](./projects/) |

Two conventions used in this repo for non-tutorial work: **Build Challenges** are smaller, time-boxed builds living inside paths or labs, and **Projects** are larger end-to-end builds in [`projects/`](./projects/). Neither is called &quot;homework&quot; &mdash; this is a public resource, not a classroom.

---

## 🧮 Mathematical foundations

The math is here because it makes you a better engineer, not because it's a textbook. **All 13 pages are authored, refreshed, and ready to read.** Every page follows the same template:

1. ✨ **Why this matters for agentic AI** &mdash; the engineering motivation in two or three sentences.
2. 📐 **The equation** &mdash; clean GitHub-rendered LaTeX, every symbol defined immediately below.
3. 🗣️ **How to read this equation** &mdash; a plain-language walkthrough.
4. 💡 **Mathematical intuition** &mdash; the underlying ideas.
5. 🔧 **Where this appears in agentic systems** &mdash; specific connections to repo content.
6. 🐍 **Code example** &mdash; a minimal, executable Python snippet.
7. ⚠️ **Common mistakes** &mdash; failure modes engineers actually run into.
8. 🔗 **Repo cross-references** &mdash; direct links into concepts, labs, and patterns.
9. 🧭 **Related pages** &mdash; what to read next.
10. 📚 **References** &mdash; papers and textbooks with one-sentence relevance notes.

What's covered (all 13 ✅):

| # | Page | Equation anchor |
|---|---|---|
| 01 | [Language model probability](./math-foundations/01-language-model-probability.md) | $p(x_t \mid x_{<t}; \theta)$ |
| 02 | [Embeddings and vector similarity](./math-foundations/02-embeddings-vector-similarity.md) | $\cos(\mathbf{u}, \mathbf{v})$ |
| 03 | [RAG formulation as marginalization](./math-foundations/03-rag-formulation.md) | $p(y \mid x) = \sum_z p(y \mid x, z) \, p(z \mid x)$ |
| 04 | [Agents as policies](./math-foundations/04-agents-as-policies.md) | $\pi_\theta(a_t \mid s_t)$ |
| 05 | [MDP / POMDP intuition](./math-foundations/05-mdp-pomdp.md) | $(\mathcal{S}, \mathcal{A}, P, R, \gamma)$ |
| 06 | [The ReAct loop, formalized](./math-foundations/06-react-formalization.md) | $(\tau_t, a_t, \text{stop}_t) \sim \pi_\theta$ |
| 07 | [Tool selection as function selection](./math-foundations/07-tool-selection.md) | $a_t \sim \pi_\theta$ over $\mathcal{A}$ |
| 08 | [Planning and search](./math-foundations/08-planning-search.md) | BFS / A* / MCTS |
| 09 | [Memory models](./math-foundations/09-memory-models.md) | $M = M_s \cup M_w \cup M_l$ |
| 10 | [Multi-agent coordination graphs](./math-foundations/10-multi-agent-coordination.md) | $G = (V, E)$ |
| 11 | [Evaluation metrics](./math-foundations/11-evaluation-metrics.md) | P, R, F1, faithfulness, ECE |
| 12 | [Uncertainty and safety](./math-foundations/12-uncertainty-safety.md) | $H(p)$, calibration, abstention |
| 13 | [Context-window optimization](./math-foundations/13-context-window-optimization.md) | $\max \sum v_i x_i \text{ s.t. } \sum c_i x_i \leq B$ |

A symbol-and-notation cheat sheet is at [`math-foundations/notation.md`](./math-foundations/notation.md) &mdash; one source of truth for $\pi$, $s$, $a$, $\theta$, $z$, and friends.

Math pages are cross-linked from the concept pages, so you can read either track first. None require more than undergraduate probability and linear algebra. The full landing page with reading approaches is at [`math-foundations/README.md`](./math-foundations/README.md).

---

## 🌳 Stable vs. fast-changing content

Agentic AI moves fast. The shape of this repo reflects that.

```mermaid
flowchart TD
    subgraph Stable["🟢 Stable - months to years"]
        direction TB
        S1[📖 concepts/]
        S2[🧮 math-foundations/]
        S3[🏛 patterns/]
    end

subgraph SlowMoving["🟡 Slow-moving - quarters"]
        direction TB
        M1[🧰 recipes/]
        M2[🚀 projects/]
        M3[📊 evaluation/]
    end

subgraph FastChanging["🔴 Fast-changing - weeks to months"]
        direction TB
        F1[🔧 tools/]
        F2[💎 examples/]
        F3[🚢 production/ deployment specifics]
    end

Stable --> SlowMoving
    SlowMoving --> FastChanging

classDef stable fill:#c8e6c9,stroke:#1b5e20,stroke-width:2px,color:#1b5e20
    classDef slow fill:#fff9c4,stroke:#f57f17,stroke-width:2px,color:#e65100
    classDef fast fill:#ffcdd2,stroke:#b71c1c,stroke-width:2px,color:#b71c1c
    classDef sgStable fill:#e8f5e9,stroke:#2e7d32,stroke-width:3px,color:#1b5e20
    classDef sgSlow fill:#fffde7,stroke:#f9a825,stroke-width:3px,color:#e65100
    classDef sgFast fill:#ffebee,stroke:#c62828,stroke-width:3px,color:#b71c1c

class S1,S2,S3 stable
    class M1,M2,M3 slow
    class F1,F2,F3 fast
    class Stable sgStable
    class SlowMoving sgSlow
    class FastChanging sgFast
```

| Tier | Update cadence | What lives here |
|---|---|---|
| 🟢 Stable | Years | The ReAct loop, RAG marginalization, supervisor vs swarm, eval metric definitions, safety theory |
| 🟡 Slow-moving | 6 to 12 months | Architecture patterns, agentic-RAG strategies, eval workflows |
| 🔴 Fast-changing | Weeks to months | LangGraph APIs, LangSmith UI, MCP spec revisions, A2A SDKs, vector-DB pricing, model names |

Anything in 🔴 territory carries a verification badge &mdash; see below.

---

## ✅ Tool-version verification policy

Every page in [`tools/`](./tools/) and every code snippet that depends on a specific library version carries a header like this:

```
> 🔴 Tool snapshot — <tool> <version>, verified <YYYY-MM-DD>
> Source: <official docs / changelog / spec link>
```

Concrete examples of how this policy is applied (verified at the time of writing):

| Tool / Spec | Status | Source |
|---|---|---|
| 🔌 **MCP specification** | Current stable: **2025-11-25**. A release candidate dated **2026-07-28** was announced on May 21, 2026. | [modelcontextprotocol.io/specification/2025-11-25](https://modelcontextprotocol.io/specification/2025-11-25), [MCP blog](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/) |
| 🤝 **A2A protocol** | **v1.0** released; protocol donated to the Linux Foundation in June 2025. | [a2a-protocol.org/latest/](https://a2a-protocol.org/latest/), [announcing-1.0](https://a2a-protocol.org/latest/announcing-1.0/) |
| 🔗 **LangGraph** | **1.0 GA** (Oct 2025) &mdash; first stable major release. `langgraph.prebuilt` is deprecated in favor of `langchain.agents`. | [LangChain changelog](https://changelog.langchain.com/announcements/langgraph-1-0-is-now-generally-available) |
| 🦜 **LangChain** | **1.0 GA** (Oct 2025) &mdash; `create_agent` abstraction, middleware system. | [LangChain changelog](https://changelog.langchain.com/announcements/langchain-1-0-now-generally-available) |
| 📊 **LangSmith, Google ADK, CrewAI, AutoGen, vector DBs** | Each carries its own *verified-as-of* date in [`tools/`](./tools/). | Linked per page. |

Verified-as-of dates are refreshed during routine maintenance sweeps (tracked in [`CHANGELOG.md`](./CHANGELOG.md)). If you spot stale information, please open an issue with the `stale-tool-version` label.

---

## 📊 Current state

🎉 **Repo v1 phase is structurally complete.** All nine paths have substantial content; six are fully complete at v1; three have multiple revisions (v1, v2, v3) plus extended materials.

**Substantial-content paths (nine of nine)**:

- ✅ **Path 01 &mdash; Foundations**: complete
- ✅ **Path 02 &mdash; Agentic RAG**: v1 + v2 shipped
- ✅ **Path 03 &mdash; Multi-Agent Systems**: v1 + v2 (6 patterns) + v3 (3 capstones) + frameworks comparison page (9 frameworks)
- ✅ **Path 04 &mdash; Tool Protocols (MCP + A2A)**: all 7 modules complete
- ✅ **Path 05 &mdash; Context Engineering**: v1 complete, 6 of 6 modules
- ✅ **Path 06 &mdash; Evaluation &amp; Observability**: v1 + v2 complete
- ✅ **Path 07 &mdash; Production &amp; Safety**: v1 complete, 8 of 8 modules
- ✅ **Path 08 &mdash; Mathematical Foundations**: v1 complete, all 13 pages authored and refreshed with Python code examples
- ✅ **Path 09 &mdash; Capstones**: v1 complete, all 8 project briefs

Future work is continuous improvement (depth, breadth, examples, recipes, references), not gap-filling.

**Supporting infrastructure**:

- 🧪 50 lab notebooks (all pre-executed; outputs visible on GitHub)
- 🏛 12 of 12 top-level architecture patterns authored
- 📖 [`concepts/`](./concepts/) (~71 files): `agents/`, `context/`, `evaluation/`, `memory/`, `multi-agent/`, `rag/`, `tools/` &mdash; multiple subdirs complete
- 🚢 [`production/`](./production/) (cost engineering, latency, streaming) &mdash; 5 deep pages
- 🛡 [`security/`](./security/) (prompt injection, tool abuse, data exfiltration, safety policy, red-teaming) &mdash; 5 deep pages
- 🔤 [`glossary/terms.md`](./glossary/terms.md) &mdash; 65+ A-to-Z entries cross-linked to canonical sources

**Currently scaffold-state &mdash; community contribution welcomed**:

- 💎 `examples/` &mdash; minimal reference implementations (placeholder)
- 🧰 `recipes/` &mdash; copy-paste solutions to specific problems (placeholder)
- 📚 `references/` &mdash; curated reading lists (placeholder; concept-page inline citations cover the gap)
- 📊 `evaluation/` &mdash; eval frameworks/datasets/scorers (the conceptual side is already in [`concepts/evaluation/`](./concepts/evaluation/))
- 📝 `quizzes/` &mdash; knowledge checks across the curriculum (31 files; partial coverage)

For each scaffold-state folder, the folder's own README documents what's planned and where related content currently lives. The curriculum is usable now; the supporting folders deepen it over time.

Full release history with verification fingerprints lives in [`CHANGELOG.md`](./CHANGELOG.md).

---

## 🎨 Diagrams

Architecture and concept diagrams are written as Mermaid in [`diagrams/`](./diagrams/) with rendered SVG/PNG committed alongside the source. Inline Mermaid blocks (like the two above) render natively on GitHub. The full list of diagrams currently in the repo, with descriptions, lives in [`diagrams/README.md`](./diagrams/README.md).

---

## 🤝 Community and contributions

This is built to be a community resource, not a one-author site. Useful contributions include:

- 🧰 **New recipes** for problems you've actually hit in production.
- 🏛 **New patterns or comparison tables.**
- 🔧 **Updating a `tools/` page** when a framework ships a breaking change.
- 🌐 **Translating a concept page.**
- 🐛 **Filing issues** when something is unclear, wrong, or stale.
- 🏆 **Adding your project** to the community showcase (planned `docs/community/showcase.md`).

The contribution workflow, templates for each content type, and the style guide are in [`CONTRIBUTING.md`](./CONTRIBUTING.md). Good first issues are labeled [`good-first-issue`](https://github.com/MHHamdan/Agentic-AI-Engineer/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22).

We follow a [Code of Conduct](./CODE_OF_CONDUCT.md) &mdash; please read it before posting.

---

## 📚 References and further reading

Curated reading lives in [`references/`](./references/), organized by type. The folder currently scaffolds four planned pages:

- 📄 `references/papers.md` &mdash; foundational papers (ReAct, RAG, Toolformer, Reflexion, and so on) with citations (planned)
- 📖 `references/books.md` &mdash; books that have aged well (planned)
- 🎤 `references/talks.md` &mdash; conference talks worth your time (planned)
- 👥 `references/community.md` &mdash; blogs, repos, and people worth following (planned)

In the interim, every concept and pattern page cites its sources inline. The `references/` folder is where those citations will be consolidated into curated reading lists.

External references cited in this README:

- 🔌 **Model Context Protocol** &mdash; [modelcontextprotocol.io](https://modelcontextprotocol.io/), [blog.modelcontextprotocol.io](https://blog.modelcontextprotocol.io/)
- 🤝 **Agent2Agent (A2A) Protocol** &mdash; [a2a-protocol.org](https://a2a-protocol.org/latest/)
- 🦜 **LangGraph &amp; LangChain changelog** &mdash; [changelog.langchain.com](https://changelog.langchain.com/)
- 📘 **LangChain docs** &mdash; [docs.langchain.com](https://docs.langchain.com/)

---

## 📝 Citation

If you use this material in research or teaching, please cite the repo via the [`CITATION.cff`](./CITATION.cff) file. A BibTeX snippet is also provided there.

---

## ⚖️ License

This repository uses a dual license.

- 💻 **Code** (Python, notebooks, scripts, configs) is licensed under [Apache License 2.0](./LICENSE). You can use it commercially, modify it, and distribute it, with attribution and a patent grant.
- 📖 **Educational prose, diagrams, and other written content** are licensed under [Creative Commons Attribution 4.0 (CC-BY-4.0)](./LICENSE-CC-BY-4.0). You can reuse and adapt them with attribution.

When in doubt, attribute. When attributing, link back to this repo.

---

> 🌱 **Built in the open. Maintained by the community.** PRs, issues, and &quot;this is wrong&quot; comments all welcome.

<a href="https://github.com/MHHamdan/Agentic-AI-Engineer/stargazers">⭐ Star the repo</a> &middot;
 <a href="https://github.com/MHHamdan/Agentic-AI-Engineer/fork">🍴 Fork &amp; adapt</a> &middot;
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Agentic-AI-Engineer

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Agentic-AI-Engineer

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