Designing explainable graph + multi-agent systems with SDKs and APIs

🚀 𝐅𝐫𝐨𝐦 𝐑𝐚𝐰 𝐋𝐨𝐠𝐬 𝐭𝐨 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐥𝐞𝐫𝐭𝐢𝐧𝐠, 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐛𝐲 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧! Just finished wiring up a small but powerful 𝐧8𝐧 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 that connects real-world 𝐉𝐚𝐯𝐚 𝐦𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐥𝐨𝐠𝐬 to a 𝐎𝐩𝐞𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 for contextual analysis and then routes insights automatically to the right people: 🧩 𝐋1 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: Gets a clear SOP & quick-fix steps 👨💻 𝐋2 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬: Receive detailed code-level rectification 🧠 𝐋3 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬: See architectural recommendations & preventive design notes The flow: Webhook → Extract from File → Open AI Message Model → Gmail nodes (L1/L2/L3) Now every production log that hits the workflow is not just a line of text — it’s an actionable insight routed to the right role. No more triaging chaos. 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐦𝐨𝐭𝐢𝐨𝐧.

𝑳𝒂𝒖𝒏𝒄𝒉𝒊𝒏𝒈 𝒎𝒚 𝒎𝒐𝒅𝒖𝒍𝒂𝒓 𝑨𝒈𝒆𝒏𝒕𝒊𝒄 𝑨𝑰 𝒑𝒓𝒐𝒋𝒆𝒄𝒕: 𝒂 𝒈𝒓𝒂𝒑𝒉-𝒃𝒂𝒔𝒆𝒅, 𝒔𝒕𝒂𝒕𝒆𝒇𝒖𝒍 𝒘𝒐𝒓𝒌𝒇𝒍𝒐𝒘 𝒘𝒊𝒕𝒉 𝒄𝒍𝒆𝒂𝒏, 𝑹𝑨𝑮-𝒔𝒕𝒚𝒍𝒆 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 𝒕𝒉𝒊𝒏𝒌𝒊𝒏𝒈 𝒂𝒏𝒅 𝒇𝒖𝒍𝒍𝒚 𝒅𝒆𝒄𝒐𝒖𝒑𝒍𝒆𝒅 𝒄𝒐𝒎𝒑𝒐𝒏𝒆𝒏𝒕𝒔 What’s inside 𝐌𝐨𝐝𝐮𝐥𝐚𝐫 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 : LLM config, graph builder, state schema, and nodes are independently swappable for maintainability and scale. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 : Directed-graph workflow with shared state enables branching, tool-calls, loops, and multi-step reasoning. 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐭 𝐔𝐈: Sidebar controls for selecting LLMs/models, use cases, and API keys; main pane for chat and actionable outputs. LLM_OPTIONS: Groq, ChatGPT, Gemini Pro, Llama 3, Claude-3, DeepSeek. GROQ_MODEL_OPTIONS: llama-3.1-8b-instant, gemma-7b-i, groq-llama3-70b-instant, groq-claude3-70b-instant. Use cases (3 nodes) 👇 ➡️ 𝐁𝐚𝐬𝐢𝐜𝐂𝐡𝐚𝐭𝐛𝐨𝐭𝐍𝐨𝐝𝐞 — pure LLM chat ◾ F͟u͟n͟c͟t͟i͟o͟n͟i͟n͟g͟ : Reads messages from shared state, invokes the selected LLM, and appends the model’s reply back into state. ◾ O͟u͟t͟p͟u͟t͟ : Fast, context-aware conversational responses. ➡️ 𝐂𝐡𝐚𝐭𝐛𝐨𝐭𝐖𝐢𝐭𝐡𝐓𝐨𝐨𝐥𝐍𝐨𝐝𝐞 — LLM + tool-calling ◾ F͟u͟n͟c͟t͟i͟o͟n͟i͟n͟g͟: Uses conditional routing to call a tool when the query requires external data; merges tool output into state, then generates the final answer. ◾O͟u͟t͟p͟u͟t͟ : Answers grounded with live/tool data when needed, otherwise direct LLM replies. ➡️ 𝐀𝐈 𝐍𝐞𝐰𝐬 𝐍𝐨𝐝𝐞 — daily/weekly/monthly summaries ◾ F͟u͟n͟c͟t͟i͟o͟n͟i͟n͟g͟ : 🔶 𝙛𝙚𝙩𝙘𝙝_𝙣𝙚𝙬𝙨 : Fetches top AI tech news for the selected timeframe and stores results in state. 🔶 𝙨𝒖𝙢𝒎𝙖𝒓𝙞𝒛𝙚_𝙣𝒆𝙬𝒔 : Uses the LLM to produce markdown summaries grouped by date (latest first) with source links. 🔶 𝒔𝙖𝒗𝙚_𝙧𝒆𝙨𝒖𝙡𝒕 : Exports a ready-to-share markdown file (e.g., ./AINews/weekly_summary.md) for download. ◾ O͟u͟t͟p͟u͟t͟ : A polished, date-sorted AI news digest with links that can be downloaded directly from the app. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 Plug-and-play: Add or swap nodes without touching others, thanks to a stable state schema and dependency-injected LLMs. Production patterns: Explicit graph flow, error isolation per node, and human-in-the-loop readiness. Extensible: Drop in RAG retrieval, web search, or new tools and routes without refactoring the core graph. Built with a Streamlit front end for fast iteration and a great demo experience, including one-click downloads of AI News summaries GITHUB REPO- https://lnkd.in/gUxY9dNC

✨ Day 31/50 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 🚀🐍 i. Every API begins as a simple endpoint… ii. But transforming it into a production-grade service requires structure, performance tuning, and scalability in mind. ⚙️ iii. In this phase of my journey, I explored how to design robust FastAPI services that can handle real-world AI workloads efficiently. 🔹 Async Architecture Leveraged async/await and ThreadPoolExecutor for non-blocking I/O and concurrent processing. This allowed multiple client requests to run smoothly without blocking the main thread. 🔹 Scalability Patterns Introduced load balancing with multiple Uvicorn workers — enabling 10+ simultaneous requests. Used shared model loading for optimized memory utilization and faster inference time. 🔹 Best Practices in Action ✅ Pydantic Models for data validation and schema enforcement ✅ Background Tasks for cleanup and temporary file management ✅ Health Check endpoints for better monitoring ✅ Error handling, logging, and rate limiting for production stability 🔹 Real-World Implementation Used these principles to build an Invoice Extraction API, capable of processing uploaded PDFs asynchronously. The system handles PDF parsing, model inference, JSON structuring, and auto-cleanup — all within seconds. 💡 Performance Snapshot: ⏱ Response Time: 2.5s 💾 Memory Usage: 3GB ⚡ Throughput: 10 req/s #FastAPI #Python #BackendDevelopment #AsyncProgramming #API #Scalability #MachineLearning #AI #LLM #SoftwareEngineering #Gramosoft

Can your coding assistant modernize a 20-year-old core system? Generic AI tools speed up typing, but they don’t map dependencies or enforce architecture. Point them at a legacy application and you’ll get isolated fixes, not a production-ready migration. Legacyleap starts by building a semantic model of your entire system. From there, it generates traceable code and validated tests, preserving workflows and meeting compliance. Projects that would take 6–12 months can reach a working pilot in 2–4 months with up to 70% automation. If your own experiments with AI fell short, share your story. And if you’re ready to see what your legacy infrastructure actually looks like, start with our $0 assessment for a complete map of your system to plan the right way forward. Get yours today: https://lnkd.in/gWVX4YmB #AppModernization #ApplicationModernization #GenAIModernization #ZeroDollarAssessment #LegacyModernization

🚀 New blog: Turning a causal LLM into a text-embedding tower with a simple token trick! 📝 Blog: link in comments I built a CLIP-style dual encoder that adopts a JEPA-style “predict-in-embedding-space” approach: QLoRA-tuned Qwen3 for text + MobileNetV4 for vision, trained on COCO with CyCLIP/SigLIP losses. The twist? Wrap captions with <EMBED> ... </EMBED> and read the final hidden state at </EMBED> as the caption embedding. 🧠📸 TL;DR: 🔧 Model: Qwen3-4B (QLoRA) text tower + MobileNetV4 vision tower 🧩 Token trick: <EMBED> … </EMBED> to coerce a single caption vector from a causal LLM ⚖️ Losses: SigLIP, CyCLIP asymmetry, and in-modal structure alignment 🧪 Data & Train: COCO captions, AdamW, OneCycle, batch 64, single L40 (~2 hours) 📈 Results: Strong retrieval; even “misses” are semantically close (see examples) Why this matters: Use your favorite causal LLMs as text encoders without redesigning the whole stack Keep separate towers for efficiency and stability, but still learn a tight shared metric space 🖼️ Visuals: architecture recap + retrieval results included in the post. 🧑💻 Code: https://lnkd.in/gnbG7sVG #CLIP #JEPA #RepresentationLearning #Multimodal #Qwen #MobileNetV4 #ContrastiveLearning #SigLIP #CyCLIP #LoRA #ComputerVision #DeepLearning #ML

🚀 Simplifying AI Integration with Model Context Protocol (MCP) MCP serves as a universal bridge between AI models and external tools, enabling context-rich, real-time intelligence without the need for custom connectors. It functions like the “USB port” for AI systems—allowing users to plug in any data source, making the model operate more intelligently. MCP is built on a Client-Server architecture, where a host application (the client) can connect to multiple servers. With MCP, large language models (LLMs) can seamlessly integrate with databases, files, development tools, Web APIs, and more. #ModelContextProtocol #AIAutomation #ContextualAI #AIIntegration

LLMs can reason like engineers, if prompted right. 🛠️ Structure prompts like a mini-spec: role, context, constraints. This lets LLMs generate: - Production-ready code snippets - Architectural suggestions - Automation scripts I’ve turned prototypes into MVPs this way without wasting cycles on irrelevant outputs. 💡 Pro tip: define inputs, expected outputs, and edge cases your AI will thank you. #PromptEngineering #LLM #ArtificialIntelligence #AIForDevelopers #AgenticAI #AutomationWithAI #AIProductivity

The missing piece in GenAI isn’t better models. It’s declarative architecture. After testing popular GenAI vibe tools, I saw why they fail at business logic: Loss of abstraction. 5 simple requirements explode into 200+ lines of procedural code. It’s opaque. Buggy. And ironically, too complex for GenAI itself to maintain. ✅ The fix: Pair GenAI with declarative DSLs + runtime engines. We’ve built it — and here’s the comparison: Procedural GenAI: → 200+ lines of generated code → Missed corner cases → Client-side logic (?!?) → Difficult to maintain Declarative GenAI: → 5 compact business rules → Error-free execution → Transparent to business users → Engine-managed enforcement The declarative runtime handles: Multi-table derivations Cascading updates Dependency ordering SQL optimization Automatically. Auditable. Extensible. Point it at your database → get an instant, MCP-ready API with business logic. It’s free and open source. 👉 Full technical breakdown: https://lnkd.in/gFAZMG9i Have you tried GenAI for real business logic? Did it work — or collapse into glue? #GenAI #Declarative #EnterpriseSoftware #SoftwareArchitecture #LowCode #BusinessLogic

🚀 𝗡𝗲𝘄 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗢𝗖𝗥 𝗶𝘀 𝗵𝗲𝗿𝗲! 👑 Excited to share a groundbreaking development in the world of document processing! 📄 A new open-source OCR model, Datalab's Chandra, is here to revolutionize how we handle document digitization. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆 𝗖𝗵𝗮𝗻𝗱𝗿𝗮 𝗶𝘀 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿: - 🌍 Supports over 40 languages. - 👨💻 Fully open-source and accessible on GitHub. - 🏆 Topped independent benchmarks, outperforming the previously leading dots-ocr. - 🧠 Handles a wide array of documents, including complex layouts, handwritten text, and intricate tables. - ⚡️ Outperforms major proprietary models like DeepSeek OCR, GPT-4o, and Gemini Flash 2 in OCR-specific evaluations. Whether you're dealing with images or PDFs, Chandra seamlessly extracts text, tables, and even mathematical formulas. The proficiency with handwritten text is a massive leap forward for OCR technology. For those interested in the technical details and benchmark results, I've dropped the GitHub repository link in the comments below. 👇 💬 Question: Do you think open‑source OCR models like this will soon rival paid enterprise solutions? #𝗔𝗜 #𝗢𝗖𝗥 #𝗢𝗽𝗲𝗻𝗦𝗼𝘂𝗿𝗰𝗲 #𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 #𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 #𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝗩𝗶𝘀𝗶𝗼𝗻 #𝗗𝗮𝘁𝗮𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 #𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 #𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 #𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗔𝗜 #𝗧𝗲𝗰𝗵𝗡𝗲𝘄𝘀 #𝗙𝘂𝘁𝘂𝗿𝗲𝗢𝗳𝗔𝗜

Most chatbots are reactive. Some remember. Few are modular, resumable, and capable of multi-agent planning with local LLMs. 🎯 The Problem: I needed to build a chatbot that could: - 🧠 Persist user context across sessions - 🧩 Separate planning from execution for auditability - 🔄 Recover from crashes mid-conversation - 🧠🧠 Use multiple locally hosted LLMs for specialized planning and execution - 📜 Maintain a full traceable log of every exchange 💡 The Solution: n8n + SQLite + FastAPI + Ollama Here’s the architecture: 🔹 Trigger: Incoming message via Telegram or web chat 🔹 Planner Agent (Ollama - Mistral): Parses message, queries SQLite for context, selects tools, and stores a response plan 🔹 Executor Agent (Ollama - Code Llama / Deepseek Coder): Reads plan, invokes tools (APIs, code generation), and logs output 🔹 Memory Layer (SQLite): Stores user history, unresolved intents, and conversation metadata 🔹 Auditor Agent: Generates Markdown transcript with timestamps, tool usage, and stores it for compliance 🧠 Why Ollama? - Local inference = full control, no latency spikes, no token limits - Model switching = use Mistral for planning, Code Llama for execution, Deepseek for code-heavy tasks - Privacy-first = no external calls unless explicitly routed 🛠️ Why n8n? - Modular orchestration: Each agent is a subflow with retry logic and checkpointing - Crash recovery: Workflows resume from last successful node - Interoperability: Connects seamlessly with SQLite, REST, GraphQL, and Ollama endpoints - Auditability: Execution logs + memory = full traceability This isn’t just a chatbot—it’s a multi-agent, memory-aware, locally inferenced communication system. If you're building LLM-native interfaces, n8n + Ollama gives you the backbone to scale with autonomy and compliance. n8n #Ollama #MultiLLM #AgenticWorkflows #AIChatbot #FastAPI #SQLite #ModularThinking #Auditability #upGradAI #JioPlatforms #AIEngineering #ConversationalAI #WorkflowDesign #AutonomousAgents