Building a Robust RAG Pipeline Architecture for Production
Answer up front: A RAG pipeline architecture is a set of connected services that ingest raw documents, turn them into embeddings, store them in a vect…
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RAG from Scratch with ChromaDB (No LangChain Required)
Frameworks like LangChain are great for moving fast, but they also hide a lot of what's actually happening under the hood. If you want to understand R…
We deployed a LangChain agent for a client and it silently failed for two weeks. Here's what we built to make sure it never happens again.
Six weeks ago, a LangChain agent we'd deployed for a B2B client started failing on roughly 30% of its sessions. No exceptions. No 500s. Nothing in the…
Your AI Agent Is Leaking Data Right Now — And Every Tool Call Looks Safe
How I built the first open-source tool to catch the attacks that no guardrail sees coming We talk a lot about AI safety. Prompt injections. Jailbreaks…
Tell your AI agent if the ground is sinking: measured ground-motion with SibFly + LangChain
Most "is this property risky?" data your agent can reach is modeled — a prediction from soil maps and flood zones. There's a different, underused sign…
Why Prompt Engineering Isn't Enough for Production AI Agents
TL;DR: Autonomous Agents frequently get trapped in execution loops, burning through API tokens and compute. Prompt engineering can't guarantee executi…
Running a Whole RAG Agent Offline: LangGraph + Ollama + Embedded Qdrant (Zero API Keys)
Most RAG tutorials open with "set your OPENAI_API_KEY ." This one doesn't need it. In Part 1 I claimed the LLM and embeddings are behind a swappable b…
Design Trade-offs: Why Hermes (and Many Popular Agents) Don't Use LangChain / LangGraph
Note: The Hermes repo contains no explicit statement saying "we don't use LangChain because…". This article works on two levels: the industry-wide com…
RAG Pipeline Chunking Strategies: Split Documents for Better Retrieval
Key Takeaways RAG pipeline chunking strategies determine retrieval quality more than the embedding model or vector store — most recall failures trace …
Running a LangGraph ReAct Agent in Production: OpenAI-Compatible API + Multi-Model Gateway + One-Line Tracing
Most LangGraph content stops at the notebook. You build a cute ReAct loop, it answers one question, and the article ends before the hard part: how do …
Building a Cross-Border Price-Comparison Agent: A Live Build Log
Why a build log (and not another tutorial) Every AI shopping tutorial shows the same thing: install the SDK, call a tool, ship. None of them show what…
How an AI Agent Pays $0.01 for Live SEC Data — Without an API Key
How an AI Agent Pays $0.01 for Live SEC Data — Without an API Key AI agents need real-world data to be useful. The problem: most data providers requir…
LangGraph Multi-Agent Tutorial: Build AI Agent Workflows with Real Examples
🚀 LangGraph Multi-Agent Tutorial: Build AI Agent Workflows with Real Examples 🧠 Introduction Most AI agent systems fail not because the model is weak …
Orchestrating AI: LangChain Framework Abstraction vs. Pure Native Code
When building prototypes with Generative AI, velocity is everything. Developers want to stitch together prompts, text splitters, vector stores, and mo…
Building AI agents with LangChain
LangChain agents are built on LangGraph : the model calls tools in a loop until it returns a final answer. The high-level entry point is createAgent -…
Agent Series (20): Harness in Production — From Single File to Reusable Package
From Demo Code to a Reusable Package Article 19 used a 900-line harness_full_demo.py to demonstrate eight defense layers. That file is good for explai…
LangChain + Chroma: Multi-turn RAG Memory and Automated Testing That Turned 2-Hour Bugs Into 5-Minute Fixes
At 1 a.m., the customer group chat exploded: “Does your customer service bot have only a 7-second memory? I just gave it the order number, and the nex…
Build Your Own "Longevity Scientist": A Paper-to-Action Agent using LangGraph & Mistral-7B
We live in an era where scientific breakthroughs are published faster than we can read them. For the biohacking community, the gap between a new PubMe…
RAG with OpenAI Embeddings, pgvector and LangChain
Retrieval-Augmented Generation (RAG) is a practical pattern: store knowledge as embeddings, retrieve the most relevant chunks with semantic search, th…
How to Build an AI Resume Builder with LangChain and Node.js
A few months back, my friend Marcus was applying for a senior backend role at a fintech company. He had five years of solid experience — distributed s…
Agent Series (10): MCP Protocol — Standardizing the Tool Ecosystem
More Tools, More Chaos After you build an Agent, the first thing you usually do is give it tools: search, code execution, database queries, API calls……
How to Add Memory Security to Your LangChain Agent in 5 Minutes
Why Your Agent's Memory Needs Security If you're building LangChain agents with persistent memory (ConversationBufferMemory, RedisChatMessageHistory, …
LangChain Interrupt: Agents Moved Into the Runtime | Focused
Interrupt felt different this year. Less model worship. More runtime. Instead of another round of model worship, the more useful conversations at the …
Agent Series (3): Plan-and-Solve — Think First, Then Act
Where Does ReAct Hit a Wall? The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the next…
How to Build a Human-in-the-Loop AI Agent with LangChain & LangGraph
AI agents are powerful — but sometimes too powerful. What if your agent is about to send an email on your behalf and you want a chance to say "wait, h…
Three Design Decisions That Shaped the Enterprise RAG Retrieval Pipeline
Enterprise RAG — A practitioner's build log | Post 3 of 6 A retrieval pipeline has more design surface than it appears. The technology choices — vecto…
5 silent failure patterns which I found analyzing 50+ real agent traces
After analyzing over 50 real production agent traces from developers building with LangChain, AutoGen, and custom agents, I found out that most agent …
How to Make Your AI Agent Earn Real Money — Mercatai Integration Guide
Your AI agent can write code, research topics, translate documents, and analyze data. But can it pay its own API bills? Now it can. Mercatai is a B2B …
5 Reasons Your RAG System Will Fail in Production (And the Patterns I Use to Fix Each One)
The 80% Problem Most RAG demos look magical. You drop in 10 PDFs, ask 3 questions, get clean answers. Ship it. Then production hits. The document corp…
RAG Series (17): Agentic RAG — Giving the Agent Control Over Retrieval
The Silent Failure of Pipeline RAG Every article in this series has been trying to answer the same question: how do we make retrieval better? Better c…