Essential Tools For Working With AI Frameworks

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

After extensive research and hands-on experience, I've created this comprehensive visualization of the AI Agents ecosystem. Whether you're building, deploying, or scaling AI agents, this stack covers all essential components. Key Components: 1. Vertical Agents - Industry leaders like Anthropic, Decagon, and Perplexity showing what's possible - Specialized solutions from MultiOn, Harvey, and others 2. Observability & Memory - Tools like LangSmith and Arize for monitoring - Memory solutions: MemGPT, LangChain for context retention - Braintrust and AgentOps.ai for performance tracking 3. Framework & Hosting - Robust frameworks: Letta, LangGraph, AutogenAI - Reliable hosting: Letta, LangGraph, LiveKit - Integration tools from Semantic Kernel and Phidata 4. Model Serving & Storage - Enterprise solutions: OpenAI, Anthropic, Together.ai - Vector stores: Chroma, Pinecone, Supabase - Efficient serving with vLLM and SGL You can start with one tool from each category based on your specific use case. The ecosystem is evolving rapidly, but these foundations will remain relevant. Perfect reference for: - AI Engineers - MLOps Teams - Product Managers - Tech Architects Feel free to save and share! Let me know if you have questions about implementing any part of this stack.

The rise of Agentic AI is transforming how we build, deploy, and interact with intelligent systems Here’s a complete look at an Open Agentic AI Stack, showcasing the essential tools and frameworks across each layer: 🔹 Foundation Models: LLaMA 4, Mistral, Qwen 3 Fusion, DeepSeek — open-source giants powering intelligent reasoning and generation. 🔹 Serving & Fine-Tuning: From vLLM and Text Generation Inference to LoRA Adapters, Ollama, and BentoML — enabling efficient model deployment and adaptation. 🔹 Memory & Retrieval: LanceDB, Weaviate, Mem0, Marqo, Qdrant — robust vector databases for contextual memory and real-time retrieval. 🔹 Orchestration & Agents: LangGraph, AutoGen, CrewAI, DSPy, Flowise, OpenDevin — empowering modular, composable agent workflows. 🔹 Evaluation & Safety: AgentBench 2025, RAGAS, TruLens, PromptGuard 2, Zeno — ensuring performance, transparency, and responsible AI use. The open ecosystem is evolving fast, making it easier than ever to build production-ready AI agents from scratch. 💡 Building your agentic stack? Start modular, go open, and think long-term.

The Future of AI is Open-Source! 10 years ago when I started in ML, building out end-to-end ML applications would take you months, to say the least, but in 2025, going from idea to MVP to production happens in weeks, if not days. One of the biggest changes I am observing is "free access to the best tech", which is making the ML application development faster. You don't need to be working in the best-tech company to have access to these, now it is available to everyone, thanks to the open-source community!   I love this visual of the open-source AI stack by ByteByteGo. It lays out the tools/frameworks you can use (for free) and build these AI applications right on your laptop. If you are an AI engineer getting started, checkout the following tools: ↳ Frontend Technologies : Next.js, Vercel, Streamlit ↳ Embeddings and RAG Libraries : Nomic, Jina AI, Cognito, and LLMAware ↳ Backend and Model Access : FastAPI, LangChain, Netflix Metaflow, Ollama, Hugging Face ↳ Data and Retrieval : Postgres, Milvus, Weaviate, PGvector, FAISS ↳ Large Language Models: llama models, Qwen models, Gemma models, Phi models, DeepSeek models, Falcon models ↳ Vision Language Models: VisionLLM v2, Falcon 2 VLM, Qwen-VL Series, PaliGemma ↳ Speech-to-text & Text-to-speech models: OpenAI Whisper, Wav2Vec, DeepSpeech, Tacotron 2, Kokoro TTS, Spark-TTS, Fish Speech v1.5, StyleTTS (I added more models missing in the infographic) Plus, I would recommend checking out the following tools as well: ↳ Agent frameworks: CrewAI, AutoGen, SuperAGI, LangGraph ↳ Model Optimization & Deployment: vLLM, TensorRT, and LoRA methods for model fine-tuning PS: I had shared some ideas about portfolio projects you can build, in an earlier post, so if you are curious about that, check out my past post. Happy Learning 🚀  There is nothing stopping you to start building on your idea! ----------- If you found this useful, please do share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI educational content and insights to help you stay up-to-date in the AI space :)