Python LLM Framework with 13 Providers and 540 Tests

📣 SynapseKit v0.6.8 is live. Your agents can now search PubMed, GitHub, and YouTube. Send emails. Query your own vector store. All with zero new dependencies for most of it. That last one matters more than it sounds- every tool you add to an agent is a potential point of failure. We built these to be stdlib-first wherever possible. Also in this release: WebSocket streaming for graph workflows and structured execution tracing with timestamps. So when something breaks in production, you know exactly where and how long each node took. What SynapseKit looks like today: ⚡ 743 tests 🔌 15 LLM providers 🛠️ 29 built-in tools 🔍 18 retrieval strategies 🧠 8 memory backends 📄 14 document loaders 💾 4 cache backends 🔗 2 hard dependencies Async-native from day one. Not retrofitted. No hidden chains. No magic. Just Python you can actually read. pip install synapsekit 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #OpenSource #AI #MachineLearning #Agents #SynapseKit

agentpad is live. A multi-language runtime for AI agents that actually runs your code — bash, Python, JavaScript, SQL - against a real project directory. Not a sandbox pretending to be one. Most agent runtimes fake the environment. agentpad doesn't. You get full control over what the agent can touch, what it can write, and exactly what happened when it's done. > Overlay mode to stage changes before they stick. > Read-only mode when writes aren't allowed. > Glob allowlists and timeouts so nothing runs wild. > Structured output every time - stdout, stderr, exitCode, files written. > A full session run log so nothing is a black box. > OpenAI tool helpers so you're not rewriting the function-calling loop from scratch. Just run npm install agentpad Or pip install agentpad Website/Docs: https://slaps.dev/agentpad #openSource #aiAgents #developerTools #typescript #python

When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python

FastAPI has become the standard for building high-performance Python backends. It successfully combines the developer experience of a lightweight framework with the speed required for modern, asynchronous applications. Here is why it is a go-to for production-ready systems: • Performance: Built on Starlette and Pydantic, it is one of the fastest Python frameworks available. • Efficiency: Features like automatic OpenAPI (Swagger) docs and built-in data validation reduce boilerplate significantly. • Async Support: Native support for asynchronous programming makes it ideal for real-time apps and ML model deployment. • Reliability: Leveraging Python type hints ensures fewer bugs and better editor support during development. Whether you are architecting microservices or a simple REST API, FastAPI provides the scalability and speed that modern software demands. Are you still using Flask for your initial prototypes, or have you made the full switch to FastAPI? . . . #Python #FastAPI #Backend #WebDev #Microservices #Coding

A hands-on collection of real-world implementations to build LLM-powered applications using Python, LangChain, and Hugging Face. What’s inside: • RAG pipelines (Chroma / FAISS) • Custom/opensource LLM integrations • Memory & prompt engineering • Autonomous agents (ReAct style) Built with 2026 best practices to help you get started with production-ready #AI apps. #LLM #LangChain #HuggingFace #Python #MachineLearning #RAG #GenerativeAI https://lnkd.in/gqQ5sZVc

Amazon, YouTube, Google — all have bugs in production right now. 🐛 And that's completely fine. The difference between a server that crashes and one that keeps running isn't perfect code — it's knowing how to catch failures before they become disasters. I just published a deep-dive on the Python concept that powers it all: 🔥 try / except / else / finally — the full anatomy 🔥 Every built-in exception you'll hit in production 🔥 Logging that actually alerts you in real-time 🔥 Custom exceptions & real-world patterns used at scale Small concept. Massive impact. If you're building anything in Python — this one's for you. 👇 https://lnkd.in/dxDqRyNv #Python #SoftwareEngineering #BackendDevelopment #Programming

As I prepare for my upcoming lab performance on FastAPI with Python, I wanted to break down something simple but important: how it actually works and why we even need it. How FastAPI works: FastAPI is a modern Python framework used to build APIs (Application Programming Interfaces). In simple terms, it acts as a bridge between the frontend and the database. - You define routes (URLs) where users or systems send requests - FastAPI processes those requests using Python functions - It validates data automatically (which saves a lot of time) - Then it sends back a response (like data, status, or results) It’s built on top of powerful standards like async programming, which makes it super fast and efficient. Why we need FastAPI: Here’s the real point— - Modern apps (web, mobile) need fast and reliable backends - APIs are the core way different systems communicate - FastAPI helps build these APIs quickly with less code and fewer errors - It’s perfect for scalable systems, AI apps, and real-time services What this really means is: learning FastAPI is not just about passing a lab—it’s about understanding how real-world applications handle data and communication behind the scenes. Still learning, still improving—but getting closer to building something real. #FastAPI #Python #BackendDevelopment #APIs #CSE #LearningJourney

There's been an explosion of open-source Python type checkers and language servers recently. Now you're not locked into Pylance! I wrote about the state of the LSP world today, and why you should still care about them even if AI writes all your code: https://lnkd.in/gusUZybX

🚀 Day 1 of My Machine Learning Journey — Built My First Web App! Today wasn’t about watching tutorials. It was about execution. I built a Student Marks Analyzer Web App using: Python 🐍 Flask 🌐 HTML + CSS 🎨 💡 What it does: Takes student name & marks Calculates total, average Assigns grade Shows pass/fail result ⚡ What I learned today: Basics of Python (loops, functions, logic) How backend connects with frontend Real debugging (and yes… errors taught me a lot 😄) How to turn an idea into a working system 🔥 Biggest realization: “Learning doesn’t count. Building does.” This is just Day 1. No perfection. Just consistency + execution. 📈 Goal: Become a Machine Learning Engineer by building real projects. If you're also on a similar journey, let’s connect 🤝 #Python #Flask #WebDevelopment #LearningInPublic #100DaysOfCode #BuildInPublic #AIJourney