Mastering RAG Architecture with LangChain and LlamaIndex

The leap from "Chatbot" to "Agent" starts with a single primitive: Tool Calling. Nyalalabs is excited to host a technical workshop featuring Abel Chin, an indie AI engineer and ex-ASEAN scholar. Abel’s journey is proof that it’s never too late to start building—having truly kicked off his builder journey in mid-2025, he’s now deep in the trenches of Agentic AI. In this session, we aren’t just talking about theory. We are writing code. 💻 Key Takeaways: Master the definition of strict JSON schemas to ensure model reliability. Learn how to bridge the gap between LLM reasoning and API execution. Walk away with a functional, runnable Python script that actually does things. If you’re ready to break LLMs out of their text-only boxes, this is for you. 📅 When: April 17th, 8:00 PM - 10:00 PM 🔗 Register via Luma: https://luma.com/2z5ak2pj #ArtificialIntelligence #LLMOps #Python #AgenticAI #TechCommunity #SingaporeAI #SoftwareDevelopment

Hot take: If your AI evaluation tool requires you to use a specific framework like LangChain or LlamaIndex to get deep traces, you don't have an observability platform. You have ecosystem lock-in. Most enterprise teams are running 2+ orchestration frameworks in production right now. Tying your deployment safety net to a single library is an architectural trap. Evaluation should be about the agent's decisions, tool-calling sequences, and outputs—not the python wrapper you used to build it. We need universal, framework-agnostic instrumentation. Build with whatever you want. Evaluate everything on one standard. #qualLoop #AI_Evaluation #orchestration #CICD_gating #harness

Hot take: If your AI evaluation tool requires you to use a specific framework to get deep traces, you don't have an observability platform. You have ecosystem lock-in. Most enterprise AI teams are running two or more orchestration frameworks in production right now. Tying your deployment safety net to a single library is an architectural trap. Evaluation should be about the agent's outputs, decisions, and tool-calling sequences—not the Python wrapper you used to build it. We need universal, framework-agnostic instrumentation. Build with whatever you want. Evaluate everything on one standard. #LLMOps #AIagents #BuildInPublic #machinelearning #SoftwareArchitecture

23,602 stars and countingOpenAI's new 'openai-agents-python' framework exploded by 752 stars today, and it's only been live for a week. This lightweight framework is pure Python, tailored for building multi-agent workflows. Unlike bloated architectures, it keeps things lean while enabling complex, coordinated tasks across agentic AI systems. Whether you're working on distributed decision-making or collaborative LLMs, the design is meant to get out of your way and let you prototype fast. This feels like OpenAI signaling more focus on scalable, accessible multi-agent AI. Multi-agent systems aren't just a cool ideatheyre a necessary evolution for AI to tackle real-world challenges where single-agent models hit their limits. Expect to see this framework pop up in research papers and production stacks soon. Whats the first multi-agent workflow you'd try to build with it? Read more: https://lnkd.in/d4_jBcCx #AIAgents #LLM #PythonFramework #MultiAgentSystems #OpenSourceAI

We've been told the path to better AI is more. More parameters. More context. More tokens. More cost. Flagship models cost more and more.   What if that's the wrong question entirely? I built Kodah. An autonomous framework specialized in bug fixing, where smarter beats bigger.   Tested on SWE-bench Lite, 300 real-world bugs across Django, scikit-learn, SymPy and others: - 81% of the #1 score (Claude Opus 4.6) - $13.59 total cost for all 300 issues - $0.045 per issue, far below flagship model costs   Smarter architecture, not brute force. Links in the comments.   #AI #ArtificialIntelligence #SWEbench #BugFixing #LLM #Python #SoftwareEngineering

Just finished building a custom Retrieval-Augmented Generation (RAG) Chatbot from scratch. Over the past few days, I have been diving deep into Generative AI architecture and successfully built a conversational AI engine that goes beyond the basic LLM cutoff limits by proactively reading and learning from my own offline knowledge base. Technical Stack & Implementation Details: • Python & LlamaIndex for core logic and RAG orchestration • Groq integrated to power ultra-fast inference leveraging the Llama 3 (70B) model • HuggingFace's sentence-transformers (`all-MiniLM-L6-v2`) used locally to map large document bodies into embedded semantic vectors. Key Milestones: • Vector Store Indexing: Implemented a process to automatically ingest raw textual data, logically split it, mathematically embed it, and securely cache the mathematical vectors in local storage. • Semantic Retrieval: Designed the query engine to capture conversational inputs, perform mathematical similarity searches against the cache, and inject the `Top_K` most relevant context chunks directly into the model's awareness. • Context Management: Bypassed token degradation and rate limits in long conversations by utilizing LlamaIndex's `ChatSummaryMemoryBuffer` to condense older history chunks without losing context. I am proud of the clean, modular "Separation of Concerns" codebase that powers this engine efficiently. Check out the code and architectural setup on my GitHub: https://lnkd.in/dFcYyZM2 #AI #GenerativeAI #MachineLearning #Python #LlamaIndex #Groq #RAG #SoftwareEngineering #DataScience

I built an open source tool that cuts Claude Code token usage by 99.9%. The problem I kept hitting: Claude reads your entire codebase  on every prompt. For a repo like FastAPI, that's 946,000 tokens  of dead weight per question. So I built llm-diet. It parses your repo into a call graph using AST analysis, then  injects only the most relevant functions before Claude starts  reasoning. No embeddings. No vector DB. No LLM calls in the  retrieval path. Real benchmark on FastAPI: → 946,210 tokens baseline → 186 tokens average injected → 99.9% reduction → 432ms latency Two commands to set up: pip install llm-diet context-engine install Early days. Python/JS/TS support today. More languages coming. Open source: https://lnkd.in/gwaPCdZz If you use Claude Code daily, would love your feedback. #AI #OpenSource #DeveloperTools #ClaudeCode #Python

AI can generate applications in minutes. But building maintainable, scalable systems still requires architecture thinking. Built using Claude Code, this project demonstrates a simple evolution - from basic AI-generated app to modular architecture. #AI #PromptEngineering #SoftwareArchitecture #Python #Streamlit #SolutionArchitecture #AIEngineering #TechLeadership Full details 👉 - https://lnkd.in/eb6EcRQE Git repo 👉 - https://lnkd.in/e-JEyfNa Demo video 👇

LangChain Just Released Deep Agents — And It Changes How You Build AI Systems (April 2026) "There’s a pattern I’ve watched repeat itself across almost every team that gets serious about building agents. It’s not that LangGraph is bad. It’s extremely powerful. But it’s a runtime — a low-level primitive — and most people are using it as if it’s an application framework. LangChain noticed this, and deepagents is their answer deepagents is a standalone Python library — installable with pip install deepagents — that sits on top of LangChain and LangGraph. The LangChain docs describe it as an "agent harness": it provides the same core tool-calling loop as other frameworks, but with a set of built-in capabilities baked in so you don't have to reinvent them. The Five Capabilities That Make This Different 1. Built-in Planning with write_todos 2. A Virtual Filesystem 3. Subagent Spawning 4. Automatic Context Compression and Summarization 5. Long-term Memory Across Conversations" https://lnkd.in/eZfihAmm

Been studying for some AI certs and wanted to go hands-on with something real, so I built LudBot (lol), a live AI agent that answers weather and time questions for any city in the world. - LangChain handles the agent loop and tool orchestration - Groq runs via API, the LLM inference (Meta Llama 4 Scout) - Two real tools: live weather via wttr.in and timezone resolution worldwide - Gradio for the UI with the live log panel - Deployed live on Hugging Face (Nice free hosting platform — wakes up in a few seconds if sleeping) Try it here: https://lnkd.in/eB9VmJWp Repo is public if you want to build your own or dig into the code: https://lnkd.in/efjdVupK #AgenticAI #Python #GenerativeAI