107 practical tutorials covering real-world problems with AI tools and agent frameworks.
Model Context Protocol (MCP) is the standard for connecting LLMs and AI agents to external tools and data sources. Activepieces has native support for running and calling MCP servers inside workflows ...
n8n is the dominant open-source automation platform. Activepieces is the fastest-growing alternative. Both are self-hostable, both have visual workflow builders, and both support AI. The differences m...
Agno agents forget everything by default. Here is how to add session memory, user memory, and persistent storage.
Agno's team pattern lets agents delegate, collaborate, and specialise. Here is the setup most tutorials skip.
Most Anthropic SDK tutorials show a single tool call. They don't show what happens when Claude calls three tools in sequence, one tool fails, or the agent needs to decide whether to keep looping. That...
A single Claude agent with 20 tools and a 10,000-token task description works — until it doesn't. Long contexts degrade instruction-following. Too many tools dilutes selection quality. Tasks with clea...
Microsoft's agent ecosystem split into four branches. Here is the plain-English guide to what each one is and which one to build on.
Group chat is AutoGen's most powerful and most opaque feature. Here is a toolkit for when it goes wrong.
Base44 gets you to a working MVP fast. Here is what changes -- and what breaks -- when real users start using it.
Vague prompts get vague apps. Here are the prompting patterns that produce clean, reliable Base44 builds first time.
Most AI browser agents fail in production because websites detect and block them. Here is what triggers detection and what actually works.
Malicious websites can hijack your AI agent by injecting instructions into the page. Here is what it looks like and how to defend against it.
Running a headless browser inside an AI agent sounds straightforward until you hit it in production: Playwright works locally but fails in a container due to missing dependencies. CAPTCHA blocks your ...
Traditional browser automation breaks when the page changes. A button moves, a class name updates, and your CSS selector stops working. Stagehand uses a vision model to interpret the page and translat...
Composio's default setup assumes one user. Here is how to scale it to hundreds of users each with their own connected accounts.
Composio abstracts away tool internals -- great for getting started, painful when something goes wrong. Here is how to debug it.
Static pages are easy. React apps, login-gated content, and aggressive rate limiters are where most crawlers break. Here is how Crawl4AI handles them.
Raw web pages are full of noise that degrades RAG quality. Here is how to configure Crawl4AI to extract the content that actually matters.
GitHub Issue #3154 -- agents simulate tool usage instead of actually calling tools. Here's why it happens and how to stop it.
Downstream agents ignoring upstream results is CrewAI's most-reported production bug. Here's what's happening and how to fix it.
A Crew is good at open-ended, role-based collaboration where agents decide how to divide and complete work. A Flow is good at structured pipelines where you need deterministic control over execution o...
A CrewAI script that works perfectly in a Jupyter notebook will often fail in production in three predictable ways: rate limit errors from concurrent requests, untracked LLM costs that quietly burn th...
When Dify's built-in nodes aren't enough, Code nodes let you write Python or JavaScript logic directly in your workflow. Here's how.
Dify's default knowledge base setup works for demos. Here's what you need to change before it's production-ready.
How to integrate any Dify app — chatbot, workflow, or agent — into external applications
Stop guessing which mode to use — here is a clear decision guide
Hand-written prompts are brittle. Change the model version, change the task slightly, or add a new requirement, and your carefully tuned prompt produces worse results. You tweak it. You test it. You t...
DSPy compilation (optimization) finds the best prompts, instructions, and few-shot examples for your program by running it repeatedly against a training dataset and scoring the outputs with your metri...
AG2 (the community fork of AutoGen) is a powerful multi-agent framework. But taking an AG2 workflow from a Python script to a production application requires work: you need a web interface for users t...
Fully autonomous agents make mistakes. In consequential workflows (sending emails, modifying databases, submitting orders), an approval gate between agent reasoning and action execution is not just a ...
Every request builds a graph that never gets freed. Here is why Flowise leaks memory, and the configuration changes that stop it.
Horizontal scaling with Flowise is possible but barely documented. Here is the full setup: Redis, load balancing, and shared storage.
Flowise has two canvas modes and the docs do not always make the distinction clear. Many builders start with Chatflow, hit a wall trying to add agent behaviour, then discover Agentflow — or vice versa...
Flowise ships with a useful set of built-in tool nodes — web search, calculator, Wikipedia, weather, and more. But most real-world agent use cases require connecting to your own systems: internal APIs...
Running ADK agents locally is straightforward. Getting them into production -- with scaling, auth, and monitoring -- requires a few extra steps.
ADK has five built-in agent types. Most tutorials only show LlmAgent. Here is when each one is the right choice.
Everything you need to equip your ADK agents with the right capabilities
How to write fast, reliable tests for ADK agents using InMemoryRunner and mocks
When a Haystack pipeline fails silently or returns bad results, here is how to find exactly which component is the problem.
Haystack's component protocol is powerful but poorly explained. Here is how to write custom components that work first time.
Simple field extraction — name, email, amount — is the entry point. Production systems need more: streaming partial results to reduce perceived latency, classification with confidence scores, step-by-...
Getting an LLM to return structured data — a JSON object with specific fields and types — is one of the most common tasks in AI applications. The naive approach is to ask nicely in the prompt and then...
Your LLM app is in production. Users report that responses are 'off' sometimes. You have no idea which calls are failing, what the actual prompts look like after templating, whether the retrieval step...
Tracing tells you what your LLM app did. Evaluations tell you how well it did it. Prompt management tells you which version of your instructions produced which results. Datasets let you run regression...
Loops, stuck states, and invisible failures are LangGraph's hardest debugging problems. Here's a toolkit to solve them.
LangGraph has three distinct memory concepts that confuse almost every builder. Here's the plain-English guide.
Most agent frameworks treat human review as an afterthought — a wrapper you bolt on. LangGraph builds it into the graph execution model. You can pause at any edge, inspect the full agent state, modify...
A LangGraph agent making three tool calls can easily take 15-30 seconds to complete. Without streaming, users stare at a spinner and have no idea if anything is happening. With streaming, they see the...
How to give your agents memory that survives across conversations using LangGraph's Store API
How to implement the supervisor pattern in LangGraph — routing, subgraphs, and shared state
Tracing, evals, prompt management, and cost dashboards in one place
An honest comparison of features, pricing, self-hosting, and framework compatibility
Most serious AI applications end up using more than one LLM. Claude for reasoning-heavy tasks. GPT-4o for speed. Gemini Flash when cost matters. A local Llama model for sensitive data that cannot leav...
The LiteLLM Python library is useful when you control the calling code. The LiteLLM proxy server is useful when you have multiple services, multiple developers, or multiple agents all needing LLM acce...
LlamaIndex has at least five ways to query your data. Most tutorials only show one. Here is when to use each.
Default LlamaIndex settings are great for demos. Here are the five changes that make retrieval good enough for production.
How to build complex, stateful AI pipelines using the Workflow API
How to build retrieval pipelines that understand charts, diagrams, and images alongside text
Make.com's AI modules let you add LLM-powered steps to any automation scenario: classify an incoming email, summarise a document, extract structured data from free text, or route a ticket to the right...
The automation platform you choose becomes infrastructure. Migrating 200 workflows from Zapier to n8n is painful. Getting it right the first time — or at least choosing the platform that matches your ...
Three deployment paths, three very different trade-offs. Here is a plain-English guide to which one fits your situation.
Mastra does not persist workflow execution state by default. Here is what breaks, why, and four patterns to fix it.
Mem0 integrates at two points in any agent interaction: before the agent runs (retrieve relevant memories and inject into context), and after the agent runs (add the exchange to memory). The exact hoo...
Every time a user starts a new conversation with your AI agent, it has forgotten everything. Their name, their preferences, the problem they were trying to solve last Tuesday, the fact that they told ...
How to process thousands of documents, images, or API calls in parallel without managing workers
How to deploy embedding models, LLMs, and batch jobs on serverless GPU with Modal
A decision guide to sub-workflows, AI Agent Tool nodes, and Execute Workflow -- and when each one makes sense.
The Simple Vector Store is fine for prototypes. This guide covers what you actually need: persistent stores, dynamic updates, and hybrid search.
n8n already has 400+ native integrations. MCP (Model Context Protocol, introduced by Anthropic in late 2024) adds a different kind of integration: a standardised protocol that lets AI agents discover ...
By default, an n8n webhook URL is publicly accessible to anyone who knows the path. If you are triggering workflows that send emails, update databases, or call APIs on behalf of users, an unauthentica...
n8n Cloud starts at $20/month for 2,500 workflow executions. The Starter plan limits you in both executions and active workflows. At scale, cloud pricing reaches $50-120/month for growing teams. Again...
Ollama exposes an OpenAI-compatible REST API on localhost:11434. Most AI frameworks have an OpenAI client built in — pointing it at Ollama instead of api.openai.com routes all inference to your local ...
Ollama runs model inference on GPU (or CPU fallback). VRAM is the binding constraint. A model that does not fit in VRAM either runs on CPU (10-50x slower) or crashes. The rule: the model's weights in ...
The Assistants API shuts down August 26, 2026. Here is exactly what changes, what you need to rewrite, and what stays the same.
The Agents SDK handoff system lets agents delegate to specialists. Here is how it works and the patterns that hold up in production.
An agent that responds helpfully to valid requests can still cause serious problems if it: processes inputs it should refuse, leaks PII in its outputs, returns data in the wrong format, or gets manipu...
Runner.run() waits for the full agent response before returning anything. Runner.run_streamed() returns a StreamedRunResult immediately and yields events as they are generated — tokens, tool calls, ag...
OpenClaw went from 9k to 210k GitHub stars in January 2026. That kind of growth is almost never about the code — it is about timing, a viral demo, and the right Hacker News thread. Understanding what ...
This creates an agent with no tools — it can only reason with the model's built-in knowledge. Useful for confirming your API key works.
Pipedream's AI workflow builder is useful but has real limitations. Here is what experienced builders do when they hit the ceiling.
Pipedream Connect lets your users authenticate third-party apps inside your product. Here is everything you need to know before you ship it.
PydanticAI's DI system lets you inject databases, API clients, and config into agents without global state. Here is how it works.
PydanticAI's killer feature is type-safe, validated agent outputs. Here is how to use it properly -- and what breaks when you don't.
How to build orchestrator/subagent architectures without a separate framework
How to stream text and partial structured responses from PydanticAI agents
Classic Rasa used stories (example conversation paths) and rules (strict if-then conditions) to define dialogue management. Training required hundreds of story examples to handle variations. Edge case...
Rasa's pitch has always been: full control, on-premise deployment, no vendor lock-in. In 2026, two things have changed the competitive landscape. First, cloud builders like Voiceflow and Intercom AI h...
Relevance AI's two-part billing model confuses almost every new user. Here is how it works and how to stay in control.
Relevance AI's tool builder turns any API or data source into an agent skill. Here is how to build tools that actually work reliably.
Credits can burn faster than expected with Replit Agent. Here is how pricing works and how to make it more predictable.
Replit Agent is impressive for some tasks and unreliable for others. Here is an honest map of both, based on real community testing.
Microsoft merged both frameworks into Agent Framework in 2025. Whether you need to migrate right now depends on your situation.
SK plugins are powerful but have sharp edges. Here is everything the quickstart tutorials skip.
Smolagents was built by HuggingFace with open-source and local models as a first-class concern. Unlike frameworks that assume GPT-4 or Claude, Smolagents is designed to run on models you control — on ...
Smolagents ships two agent types with fundamentally different execution models. Most frameworks hide this choice. Smolagents makes it explicit.
Stack AI's document processing is powerful but has configuration choices that significantly affect retrieval quality. Here is what to set and why.
Stack AI is enterprise-first and over-engineered for simple use cases. Here is the honest guide to when it is worth it.
Parallel Activities, Signals for approval, and monitoring via the Temporal UI
What Temporal solves, and how to structure AI pipelines as reliable Workflows and Activities
How to build agentic loops with tools, maxSteps, and server/client execution
streamText, generateObject, useChat, and provider switching in under 30 minutes
High latency is Voiceflow's most-reported production pain point. Here are the root causes and the fixes that actually work.
Comparing agents, managing versions, and running regression tests in Voiceflow requires workarounds. Here they are.
Zapier AI Agents (formerly Zapier Copilot) is an AI-powered layer on top of Zapier's automation infrastructure. You describe a task in plain English, and the agent decides which Zapier integrations to...
Zapier charges a premium. n8n and Dify are cheaper (or free to self-host). The question is not which tool is objectively better — it is which tool is right for your team's technical level, workflow co...
Step-by-step integration patterns for the most popular Python agent frameworks
How Zep stores facts, entities, and user preferences — and how it differs from Mem0 and LangGraph memory
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