+==================================================================================+
| |
| G I O V A N N I R O M E R O |
| |
| FULL STACK DEVELOPER // AI ENGINEER |
| |
| LANGGRAPH | MCP | LANGCHAIN | RAG | AI AGENTS | AUTOMATION | FULL-STACK AI |
| |
+==================================================================================+
giovanniromero@dev:~$ whoami
Full Stack Developer & AI Engineer · Building AI Agents & Agentic Systems ·
Cybersecurity Enthusiast.
giovanniromero@dev:~$ current_focus
> LangGraph state machines
> MCP servers and tool integrations
> LangChain / LCEL workflows
> RAG and knowledge assistants
> Full-stack AI products with APIs, dashboards, auth, databases, and deployment
> Security-aware boundaries for tools, data, permissions, logs, and secretsflowchart LR
U[User / Business Workflow] --> UI[Next.js Interface]
UI --> API[Backend API]
API --> LG[LangGraph Orchestrator]
LG --> PLAN[Planner Node]
PLAN --> TOOLS[Tool Router]
TOOLS --> MCP[MCP Server]
MCP --> EXT[External APIs / Files / DB]
LG --> RAG[RAG Retriever]
RAG --> VDB[(Vector Store)]
RAG --> DOCS[Knowledge Sources]
LG --> REVIEW[Human Review]
REVIEW --> OUT[Structured Output]
OUT --> UI
LG --> LOGS[Logs / Traces / Evaluations]
{
"profile": "Full Stack Developer & AI Engineer",
"positioning": "Building AI Agents & Agentic Systems",
"core_protocols": ["MCP", "LangGraph", "LangChain", "RAG"],
"agent_patterns": [
"tool calling",
"stateful workflows",
"human-in-the-loop review",
"retrieval-augmented generation",
"workflow automation",
"structured outputs"
],
"delivery": [
"frontend interface",
"backend API",
"database model",
"agent orchestration",
"tool boundary design",
"deployment notes"
],
"security_mindset": [
"auth boundaries",
"least privilege tools",
"secret handling",
"audit logs",
"safe API integration"
]
}| Layer | Tools | What I Build |
|---|---|---|
| Agent Orchestration | LangGraph, LangChain, LCEL | Multi-step agents, state graphs, routers, retries, review loops |
| Tool Protocol | MCP | Tool schemas, MCP servers, connector boundaries, safe tool execution |
| Knowledge | RAG, embeddings, vector stores | Knowledge assistants, grounded answers, source-aware workflows |
| Product | Next.js, React, TypeScript | Interfaces, dashboards, admin panels, user workflows |
| Backend | Node.js, Express, FastAPI | APIs, integrations, auth flows, webhooks, automation services |
| Data | MongoDB, PostgreSQL, SQL | Persistence, run history, structured outputs, reporting |
| Automation | Playwright, APIs, scheduled jobs | Research agents, content workflows, business process automation |
| Security-Aware Engineering | Auth, logs, secrets, validation | Safer boundaries around data, users, tools, and external APIs |
ai_agent_development -> scoped agent workflows, tools, memory/state, handoff
mcp_server_tool_integration -> MCP servers, tool schemas, permissions, API connectors
langgraph_workflow_builds -> graph nodes, routing, retries, human review, observability
langchain_lcel_automation -> chains, structured outputs, model/tool pipelines
rag_knowledge_assistants -> ingestion, retrieval, grounded answers, source traceability
full_stack_ai_features -> UI, API, database, auth, deployment, documentation
business_automation -> repeatable workflows, integrations, dashboards, reports
security_aware_implementation -> auth boundaries, secrets, logs, validation, safe APIs
giovanniromero@dev:~$ ls ~/repos --focus agentic-ai| Priority | Repository | Why it matters for this profile |
|---|---|---|
| 01 | osint-agent | Research automation agent with Python, LangGraph, DeepSeek, Playwright, tool calling, public web data collection, and structured reporting workflows. |
| 02 | clinisight-ai | AI medical insight prototype using FastAPI and MCP-style architecture for symptom extraction, PubMed retrieval, structured summaries, and API-driven workflows. |
| 03 | langgraph-foundations | LangGraph foundations for AI agents: ReAct patterns, tool orchestration, state graphs, streaming, debugging, and agentic workflow control. |
| 04 | lcel-llm-translator | LangChain LCEL and LangServe workflow for structured LLM pipelines, translation automation, API delivery, and full-stack AI integration patterns. |
| 05 | agentic-chatbot | Agentic chatbot scaffold for LangGraph/LangChain tool orchestration, RAG retrieval, Tavily search, FAISS memory, and full-stack AI assistant experiments. |
| 06 | airplane-tracking-with-yolov8 | Computer vision AI prototype for YOLOv8 object detection, tracking, video analysis, and visual intelligence workflows. |
1. define_scope
- user goal
- tool permissions
- data boundaries
- expected output
2. design_graph
- nodes
- edges
- state
- retry paths
- review checkpoints
3. connect_tools
- MCP server or direct API connector
- schema validation
- error handling
- logs and traces
4. build_product_layer
- UI
- backend API
- database
- auth
- admin controls
5. ship_iteration
- test cases
- usage docs
- deployment notes
- next-step roadmap
[COURSE] Complete MCP Bootcamp: Build Next-Gen AI Agents with MCP
[COURSE] Complete Agentic AI Bootcamp with LangGraph and LangChain
[FOCUS] MCP server architecture, tool schemas, agent orchestration, RAG, evaluations
[GOAL] Build useful AI systems that connect tools, data, APIs, and product interfaces
giovanniromero@dev:~$ ./connect --target professional
site : https://giovanniromero.dev
linkedin : https://www.linkedin.com/in/giovannideveloper/
x : https://x.com/giovanni_dev_
email : contact@giovanniromero.devgiovanniromero@dev:~$ _