Open Source Tools for Developers

🧠 12 open-source GenAI tools that actually deliver (and scale) Not every tool with a GitHub repo deserves your trust. These ones do. 👉 If you're building real GenAI systems—not just demos—save this list. I grouped them into Build, Orchestrate, and Monitor so you know when to use what. GenAI AgentOS: (NEW) 📎 Agent registry → memory handoff → orchestration layer → HITL toggle ✅ Focused on production reliability and audit trails ⭐ https://lnkd.in/gyzMnnjw 🔧 BUILD – For devs building GenAI-powered apps LangChain – The Swiss army knife for chains, RAG, agents, and tools. ⭐ 70k+ stars | https://lnkd.in/gun-rmdj LlamaIndex – Clean integration layer between LLMs and your data. Great for structured docs + flexible vector backends ⭐ 30k+ stars | https://lnkd.in/gW-iBKR2 Flowise – Drag-and-drop LLM orchestration (perfect for demos & MVPs) UI-first, deploy fast, iterate even faster ⭐ 19k+ stars | https://lnkd.in/gA8J3Tr5 Embedchain – Minimalist RAG framework that just works Perfect if you’re tired of config overkill ⭐ 8.5k+ stars | https://lnkd.in/g8DnHQg2 RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. 🔁 ORCHESTRATE – For managing agents, workflows & system logic LangGraph – Declarative, stateful agent workflows built on top of LangChain Role-based agents + memory + edge control ⭐ 2.5k+ stars | https://lnkd.in/gveKVfE4 Superagent – Plug-and-play LLM agent framework API + UI, works with OpenAI, Claude, Mistral ⭐ 5.5k+ stars | https://lnkd.in/gtsy5CQ3 CrewAI – Multi-agent task planning + collaboration Gives each agent purpose, tool access, and autonomy ⭐ 9k+ stars | https://lnkd.in/gUpwvbn9 📊 MONITOR – For logging, debugging, and scaling safely Langfuse – Logging, tracing, and evals for GenAI pipelines Inspect every token and decision ⭐ 4.5k+ stars | https://lnkd.in/g6BEnVyA Phoenix – Open-source observability for LLM workflows Error tracking, token usage, monitoring ⭐ 3k+ stars | https://lnkd.in/gT3ERHgm PromptLayer – Prompt logging + analytics Simple but powerful tracking for prompt performance ⭐ 4k+ stars | https://lnkd.in/gGSRRBrH Helicone – Open-source alternative to OpenAI’s usage dashboard Understand cost, latency, and user behavior ⭐ 6k+ stars | https://lnkd.in/gCgcy7Kd 🔍 Why these matter: Too many GenAI teams waste time gluing together 20 tools, only to discover they can’t scale. These 12 tools are: ✅ Well-maintained ✅ Actively used in production ✅ Community-supported ✅ Actually helpful when you go beyond a chatbot Don’t just play with LLMs. Build systems that can grow. 🔖 Save this. ♻️ Repost this.

The open-source AI ecosystem for agents developers has exploded in the past few months. I've been testing dozens of new libraries, and honestly, it's becoming increasingly difficult to keep track of what actually works and what the state of the art is. So, I built an updated map of the tools that matter, the ones I'd actually reach for when building a new agent. The interesting pattern I'm seeing: we're moving past the "ChatGPT wrapper" phase into genuine infrastructure. The overview includes 40+ open-source packages across: → Agent orchestration frameworks that go beyond basic LLM wrappers: CrewAI for role-playing agents, AutoGPT for autonomous workflows, Langflow for visual agent building. → Tools for computer control and browser automation: Browser Use and Stagehand for LLM-friendly web navigation, Open Interpreter for local machine control, and Cua to control Mac environments. → Voice interaction capabilities beyond basic speech-to-text: Ultravox for real-time voice, Dia for natural TTS, Pipecat for complete voice agent stacks. → Memory systems that enable truly personalized experiences: Mem0 for self-improving memory, Letta for long-term context across sessions, LangMem for shared knowledge bases. → Testing and monitoring solutions for production-grade agents: AgentOps for benchmarking, Langfuse for LLM observability, VoiceLab for voice agent evaluation. Full breakdown with GitHub repos links https://lnkd.in/g3fntJVc

LLMOps is becoming the new DevOps for AI engineers. Getting a prompt to work is the easy part. The real challenge is making your LLM applications repeatable, scalable, and reliable in production. That’s where LLMOps comes in. Think of it as the operating system for LLM-driven applications, from data prep to responsible deployment. Here are the core components of an LLMOps pipeline (see diagram 👇): ➡️ Model Customization: data preparation, supervised fine-tuning, evaluation ➡️ Behind the Scenes: foundation + fine-tuned models, pre-processing, grounding with external knowledge, post-processing with responsible AI filters ➡️ LLM Response Layer: prompting, user interaction, and outputs ➡️ Pipelines: orchestration (data versioning, configs, workflow design) and automation (deployment, execution, monitoring) As engineers, the craft isn’t just in building the model, it’s in building the system around the model. 💡 Here are some excellent repos/resources to explore: 👉 Prompt orchestration & pipelines → Haystack, LangGraph 👉 Evaluation & Responsible AI → Ragas, LlamaIndex evals 👉 Data prep & tuning → OpenPipe, Axolotl 👉 Deployment → vLLM, Ray Serve, Fireworks AI If you’re building production-grade AI, don’t stop at the model. Learn to think in terms of LLMOps pipelines- orchestration, automation, and continuous improvement. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

AI has transformed how we approach software engineering today. What has been missing from this technological shift is a comprehensive way to evaluate emerging AI coding assistants. My team has been working on a project that’s going to help give customers the data they need to choose the right AI agent for their business needs. This is one that I’m personally invested in: introducing SWE-PolyBench. 🚀 SWE-PolyBench is the first industry benchmark to evaluate AI coding agents' ability to navigate and understand complex codebases, introducing rich metrics to advance AI performance in real-world scenarios. These metrics, file retrieval and node retrieval, evaluate how well AI coding assistants can identify which files need changes and pinpoint specific functions or classes requiring modification. It’s designed to provide much deeper insights than just task completion. Beyond that, it is multilingual and supports Java, JavaScript, TypeScript, and Python with an extensive dataset and task diversity. What I’m really excited about is that we’ve made SWE-PolyBench open-source. Advancing AI-assisted software engineering is a collective effort, and SWE-PolyBench can serve as a foundation for future work. I invite you all to explore it, use it, and help shape its future. This new benchmark will bring us closer to understanding and improving how AI coding assistants perform with complexity. All the details about our launch are in the blog, check it out ➡️ https://lnkd.in/g5YkXUY2

AI Tools That Genuinely Boosted My Productivity as a Software Engineer After trying dozens of AI tools over the past few months, I’ve narrowed down the list to a few that truly made a difference in my workflow. These tools have helped me code faster, understand complex systems better, and reduce repetitive tasks. Here are the top ones that stuck with me: 1. GitHub Copilot – For coding assistance  Suggests lines, functions, even entire files.  I use it daily in VS Code to autocomplete logic, generate test cases, and eliminate boilerplate code. 2. CodeWhisperer by AWS – Secure code generation  An AWS-native alternative to Copilot, focused on security and privacy.  It’s extremely helpful when integrating AWS SDKs and working on backend services. 3. Phind – Dev-specific AI search  This replaced Google for me when it comes to technical questions.  Phind gives concise, accurate answers for framework issues, error debugging, and best practices. 4. Tabnine – Secure and private code completion  Great when you’re working with sensitive or proprietary code.  Runs on-prem and supports a wide range of languages and IDEs. 5. Codeium – Lightweight code autocomplete  A fast and free alternative to Copilot.  I use it for side projects, and it performs well with multiple languages and frameworks. 6. Cody by Sourcegraph – Chat with your codebase  Lets me ask questions like “What does this function do?” or “Where is this used?”  It’s a major help when exploring large or legacy codebases. These tools helped me: Debug faster Refactor smarter Document better Ship cleaner code If you're a developer and haven’t explored these yet, start with GitHub Copilot or Phind. They’re game changers. What AI tools are you currently using in your dev stack? Always open to trying more. Follow Abhay Singh for more such reads.

Ever wondered what robots 🤖 could achieve if they could not just see – but also feel and hear? We introduce FuSe: a recipe for finetuning large vision-language-action (VLA) models with heterogeneous sensory data, such as vision, touch, sound, and more. We use language instructions to ground all sensing modalities by introducing two auxiliary losses. In fact, we find that naively finetuning on a small-scale multimodal dataset results in the VLA over-relying on vision, ignoring much sparser tactile and auditory signals. By using FuSe, pretrained generalist robot policies finetuned on multimodal data consistently outperform baselines finetuned only on vision data. This is particularly evident in tasks with partial visual observability, such as grabbing objects from a shopping bag. FuSe policies reason jointly over vision, touch, and sound, enabling tasks such as multimodal disambiguation, generation of object descriptions upon interaction, and compositional cross-modal prompting (e.g., “press the button with the same color as the soft object”). Moreover, we find that the same general recipe is applicable to generalist policies with diverse architectures, including a large 3B VLA with a PaliGemma vision-language-model backbone. We open source the code and the models, as well as the dataset, which comprises 27k (!) action-labeled robot trajectories with visual, inertial, tactile, and auditory observations. This work is the result of an amazing collaboration at Berkeley Artificial Intelligence Research with the other co-leads Joshua Jones and Oier Mees, as well as Kyle Stachowicz, Pieter Abbeel, and Sergey Levine! Paper: https://lnkd.in/dDU-HZz9 Website: https://lnkd.in/d7A76t8e Code: https://lnkd.in/d_96t3Du Models and dataset: https://lnkd.in/d9Er5Jsx 

SAP Devs: Time to Use AI, Now. Let's be blunt, SAP developers. If you're still not using AI tools for your code... you're putting yourself at a disadvantage. Waiting for your employer to buy Joule is a mistake. The tools you need to be dramatically more productive are available right now, off the shelf. I've tried several of them. For CAP and Fiori developers, options like Cursor, Windsurf, CoPilot, ClaudCode, and Gemini Code Assist are game-changers. They can instantly grasp your project's context, generate complex code snippets, help debug efficiently, and speed up routine tasks like creating PRs. ABAP developers, Eclipse with CoPilot is already making a difference. Yes, it's initial days and evolving, but it will enhance your coding workflow. And here's the key: Don't rely on free tiers or hope for a corporate license. Pay the modest fee – typically around $20/month. Think about the value you get back in saved hours and higher quality work. It's a no-brainer investment in your own skillset and output. Get started. #sap #sapcommunity #fiori #abap #saptech #developer

I just tested Firebase Studio against bolt.new, V0, and Lovable to build the same video summarizer app powered by n8n. The results were eye-opening and could save you hours of development time. In my test, I gave each tool the exact same prompt: create a video summarizer that takes a URL input, sends it to n8n via webhook, and displays the summary and key takeaways in a sleek dark mode interface. Here's what I discovered: - Firebase Studio couldn't complete the task at all. Despite its interesting features like in-editor design capabilities and Gemini integration, it failed to handle the webhook response structure properly. The UI was basic, and there was minimal explanation of what changes were being made. - Bolt completed the task after four messages. It offered better documentation of changes but spent too much time implementing error handling instead of fixing core functionality issues. The design was decent but not outstanding. - V Zero succeeded in three messages with good explanations of its approach. It handled errors well and offered numerous integrations (Supabase, Neon Database, Vercel deployment). The UI was clean and functional. - Lovable was the clear winner, completing the task in just two messages. It proactively implemented error handling, provided clear explanations, and produced the best design with loading indicators and skeleton screens. The edit mode allowed direct manipulation of UI elements without using message credits. The comparison highlights a crucial point: The efficiency of your development tool directly impacts your productivity and results. While Firebase Studio is free and has promising features, Lovable's superior usability and results justify its pricing model for serious developers. What's your experience with these AI development tools? Comment "APPS" below to receive the workflow for the video summarizer and the code for the complete app. Watch the full video: https://lnkd.in/du9Q5pDC for detailed comparisons of message counts, designs, features, and pricing to help you choose the right tool for your next project.

We have apparently reached “peak data” in training LLMs. But it's the opposite story in Physical AI - researchers still scramble to collect high-quality data on robots. To address this gap, we just released Physical AI Dataset initiative on HuggingFace at #GTC this year. We released a suite of commercial-grade, pre-validated datasets which we hope helps the research community to build next-gen models in robotics - from AV development, robotic manipulation to dexterity. We also open-sourced a massive dataset for robotic grasping- over 57 million grasps, computed for the Objaverse XL (LVIS) dataset. These grasps are specific to three common grippers: Franka Panda, the Robotiq-2f-140 for industrial picking and a suction gripper. Blog: https://lnkd.in/g_5-8iV5 Dataset: https://lnkd.in/gj98gXxT

Meet 𝐃𝐑𝐎𝐈𝐃, a large-scale, in-the-wild robot manipulation dataset with input from numerous universities and R&D organizations. Creating large, diverse, high-quality robot manipulation datasets represents a crucial milestone in advancing more capable and robust robotic manipulation policies. However, generating such datasets presents significant challenges. Collecting robot manipulation data across varied environments entails logistical and safety hurdles and substantial hardware and human resources investments. Consequently, contemporary robot manipulation policies primarily rely on data from a limited number of environments, resulting in constrained scene and task diversity. In their collaborative endeavor, the authors introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset comprising 76k demonstration trajectories or 350 hours of interaction data. This dataset was meticulously amassed across 564 scenes and 86 tasks, with contributions from 50 data collectors across North America, Asia, and Europe over 12 months. Their collective efforts illustrate that training with DROID yields policies characterized by enhanced performance, increased robustness, and superior generalization capabilities. The authors also make the entire dataset, along with the code for policy training and a comprehensive guide for replicating their robot hardware setup, available as open-source resources. Universities and Organizations that made up the DROID dataset team include: Stanford University University of California, Berkeley Toyota Research Institute Carnegie Mellon University The University of Texas at Austin Université de Montréal The University of Edinburgh Princeton University Columbia University University of Washington KAIST UC San Diego Google DeepMind University of California, Davis University of Pennsylvania 📝 Research Paper: https://lnkd.in/gGFFsKYK 📊 Project Page: https://lnkd.in/gbH8kqfv 🖥️ Dataset: https://lnkd.in/g5akx89p