ai-agents
Level 1 AI Agent in n8n (Gemini + Tools + Memory)
Just built a custom Level 1 AI Agent using n8n! 🚀 Here is what's running under the hood: - LLM Model: Google Gemini Chat Model - Memory: Window Buffer Memory for conversation context - Tools: Custom HTTP Request tool fetching live web data (Wikipedia API) & Calculator tool In this demo, the agent autonomously decides to call the HTTP tool to fetch live information about Tesla and formats the output smoothly. Feedback and suggestions for Level 2 features are welcome!
Level 2 Orchestrator + Sub-Agent System in n8n
Upgraded my n8n multi-agent setup to Level 2! 🚀 This architecture features a central Orchestrator Agent that delegates complex requests to specialized Sub-Agents: Orchestrator Agent: Parses user prompt and breaks down tasks. Research Agent: Handles deep-dive research with Google Gemini & web tools. Writer Agent: Synthesizes research into structured, final reports. In this demo, a prompt requesting a detailed report on AI in healthcare gets routed, researched, and formatted autonomously. Next up: Level 3!
Shipped: An AI Job Matcher Agent — paste a job URL + your resume, get a match score, skill gaps, and a cover letter
Applying for jobs is the same loop every time: read the posting, compare it with your resume, guess your chances, then write a cover letter from scratch. I built an agent that does all of it in one request. Flow: paste a job posting URL → upload your resume PDF → get the analysis back. Output: - match score (0-100) — how well the profile fits the role - skill gaps — what the job asks for that the resume doesn't have - strengths — what actually lines up - cover letter — personalized, ready to send Stack: - FastAPI — async API, two endpoints: /analyze (JSON, local resume path) and /analyze-upload (multipart file upload). Auto Swagger docs at /docs . - OpenAI GPT — the reasoning layer. Single structured prompt, JSON-only output, validated with Pydantic before it ever reaches the response. - httpx + BeautifulSoup — scrapes the job page, pulls the title and hunts for requirement lists (looks for requirements / responsibilities / you will headings, falls back to the first bullet lists on the page). - pdfplumber — page-by-page text extraction from the resume PDF. - Jinja2 + vanilla CSS — drag-and-drop UI, no frontend framework. - Docker — python:3.12-slim , single container, runs anywhere. Two things I'd call out: 1. Graceful degradation. If there's no API key or the LLM call fails, it doesn't 500 — it falls back to a keyword-based scorer that intersects job skills with resume skills and returns a real score. The app is always usable. 2. Scraping is the hard part, not the AI. Every job board renders differently, and half of them are JS-heavy. The heuristic parser works on plain HTML postings but site-specific parsers (LinkedIn, Indeed) are the obvious next step. Next up: semantic matching with embeddings instead of keyword overlap, batch mode for multiple postings at once, and swapping temp-file handling for in-memory PDF parsing. Happy to answer anything about the scraping heuristics or the fallback design. I mostly build AI agents and automation for businesses — chatbots, workflow automation, the boring repetitive stuff that shouldn't be manual. If you're working on something similar, DMs are open. Portfolio: https://danish614.github.io/danish-portfolio/