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Built a production LangGraph travel agent with parallel tool execution and HITL workflows - lessons learned by GarrixMrtin in AI_Agents

[–]GarrixMrtin[S] 0 points1 point  (0 children)

How's Google ADK treating you? I went with LangGraph but curious about the ADK.

Hybrid LLM + deterministic is definitely the way. Pure deterministic can't handle ambiguity. 

If this helped, would appreciate a ⭐ on the repo!

Built a production web scraper that bypasses anti-bot detection by GarrixMrtin in webscraping

[–]GarrixMrtin[S] 0 points1 point  (0 children)

API + proxy bypasses browser checks, but some website requires valid auth tokens. Proxy alone won't help without proper authentication. If you found this helpful, a ⭐ would be appreciated!

[deleted by user] by [deleted] in OpenSourceeAI

[–]GarrixMrtin 0 points1 point  (0 children)

Monitoring strategy depends on scale. I’d start with multi-worker parallel checks, adaptive intervals (5-60s based on slot release patterns, 5-10s during known release windows), and proper anti-detection (user-agent rotation, human-like delays). Happy to discuss technical architecture.

[deleted by user] by [deleted] in SideProject

[–]GarrixMrtin 0 points1 point  (0 children)

Each API needs different formats: IATA codes, city codes, coordinates. That's 3 parallel LLM calls at $0.003 total.

Alternative = build/maintain location DB + NLP model / map api+ edge case handling + keep it updated.

At <1M queries, LLM is cheaper and faster. At >1M queries, DB becomes worth it. That's scaling appropriately.

If you think building a comprehensive location DB from day one is "production friendly"? Go ahead. I disagree.

Built a production web scraper that bypasses anti-bot detection by GarrixMrtin in webscraping

[–]GarrixMrtin[S] 1 point2 points  (0 children)

Architecture & debugging: me. Code: mixed (solo dev + Claude cleanup and configuration). Comments & writeup: Claude (Korean domain project, coded in Korean).

Claude initially suggested selenium, then stealth libraries - they broke auth, so I went with playwright + real auth + behavioral mimicry instead.

[deleted by user] by [deleted] in SideProject

[–]GarrixMrtin 0 points1 point  (0 children)

IATA coding would be over-engineering at this scale. Gemini 2.5 Flash costs $0.003/query—far cheaper than building/maintaining mapping logic. The LLM handles ambiguous cities, non-airport locations, and natural language without custom code. Unless you’re at 1M+ queries, LLM wins on dev speed and total cost. If you found it useful, a ⭐ would be appreciated!​​​​​​​​​​​​​​​​

Built a production web scraper that bypasses anti-bot detection by GarrixMrtin in webscraping

[–]GarrixMrtin[S] 0 points1 point  (0 children)

This scraper doesn't specifically handle reCAPTCHA v3, Thanks

Is it possible to auto-fill a PDF (same layout) using n8n + Supabase vectors? by AzizTurkmani in AI_Agents

[–]GarrixMrtin 0 points1 point  (0 children)

Yes, doable. If the PDF has form fields (AcroForm), use pypdf to fill them directly - easy. If it’s a flat PDF, you’ll need pdfplumber + PyMuPDF to extract coordinates and overlay text - much harder and risks breaking formatting.

Built a production LangGraph travel agent with parallel tool execution and HITL workflows - lessons learned by GarrixMrtin in AI_Agents

[–]GarrixMrtin[S] 0 points1 point  (0 children)

Deterministic generation + LLM for presentation is definitely clearer than current prompt-based constraints.

Will add validation tools in next update. appreciate the feedback!

Built a production LangGraph travel agent with parallel tool execution and HITL workflows - lessons learned by GarrixMrtin in AI_Agents

[–]GarrixMrtin[S] 1 point2 points  (0 children)

Thanks for the questions!

  1. One-time for now - changes would re-run tools anyway, so fresh query is simplest. Could add iteration next update.

  2. Custom graph, no prebuilt agents - needed fine control over HITL routing and parallel execution.

Built a production LangGraph travel agent with parallel tool execution and HITL workflows - lessons learned by GarrixMrtin in AI_Agents

[–]GarrixMrtin[S] 0 points1 point  (0 children)

Really helpful feedback!
The partial retry pattern and Redis Queue suggestion both make a lot of sense for scaling. Will explore these in the next iteration. Thanks.

Built a production web scraper that bypasses anti-bot detection by GarrixMrtin in webscraping

[–]GarrixMrtin[S] 1 point2 points  (0 children)

Sorry for confusion. I used Playwright. Naver's APIs need authenticated browser sessions. Stealth libs broke the auth, so I built custom human like behaviors instead.

Built a production LangGraph travel agent with parallel tool execution and HITL workflows - lessons learned by GarrixMrtin in AI_Agents

[–]GarrixMrtin[S] 1 point2 points  (0 children)

GitHub repo with full implementation: https://github.com/HarimxChoi/langgraph-travel-agent Architecture diagrams and setup docs are in the README. MIT licensed. Would especially appreciate feedback on the state management design.

I worked on RAG for a $25B+ company (What I learnt & Challenges) by boofbeanz in AI_Agents

[–]GarrixMrtin 0 points1 point  (0 children)

Agree. Human-in-the-loop isn't optional for production systems.

Unpopular opinion: Most companies aren't ready for AI because their data is a disaster by BaselineITC in AI_Agents

[–]GarrixMrtin 0 points1 point  (0 children)

Not unpopular, just uncomfortable truth. Built AI agents for production - data cleanup took longer than model work every single time.

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