LangDB at NIT Hamirpur! 🚀 Excited to share that LangDB was an in-kind sponsor at NIT Hamirpur, supporting the next wave of AI & ML innovators! 💡 Events like these help shape the future of AI, ML, and LLMs! Huge shoutout to the organizers for an amazing event! Looking forward

The first inbound lead from our PR AI Agent 😎 A few weeks ago, I created Amanda—a savvy PR agent that trolls hashtag #reddit and hashtag #hackernews for discussions about AI gateways, multi-LLM access, or competitors to LangDB . The plan? A demo to show off how LangDB makes

AI models are powerful, but without tracking, they’re a black box. Building with AI isn’t just about picking a model—it’s about knowing what happens under the hood. 🔍 Which model performs best? 📊 Where is the cost leaking? 🧩 How do multi-agent workflows play out?

Google new Gemini 2.5 Pro is out and it's raising the bar. Gemini 2.5 Pro is already available on LangDB , making it easy to explore and compare with other top models. Checkout: app.langdb.ai/models More details and benchmark from Google: blog.google/technology/goo…

Built a multi-agent setup using OpenAI with models like Claude, Gemini, and Grok. LangDB handled the model switching and provided trace-level visibility into agent decisions, tool usage, and execution time Blog: blog.langdb.ai/integrate-gemi… #OpenAI #AgenticAI #LangDB

🚀 It’s not just Gemini making noise. Now live on LangDB: 🦙 Meta LLaMA 4 variants 🧠 xAI Grok-3 & Grok-3-mini Run, trace, and compare them - all via one API. No provider lock-in. Just full control. Try them now → app.langdb.ai/models #langDB #Llama4 #GROK3AI

OpenAI GPT-4.1 Family is now live on LangDB Pick your GPT: ⚙️ 4.1 – 55% SWE-bench, 1M context ⚡ 4.1 mini – 40% faster, 3x smarter than 4o mini 🚀 4.1 nano – fastest & cheapest 📉 All traced. All switchable. 🔗 Run smarter: langdb.ai #LLM #OpenAI #LangDB

Built a todo form using a prompt like this: 𝘈𝘥𝘥 𝘴𝘦𝘳𝘷𝘦𝘳 𝘢𝘤𝘵𝘪𝘰𝘯𝘴, 𝘶𝘴𝘦𝘍𝘰𝘳𝘮𝘚𝘵𝘢𝘵𝘦, 𝘡𝘰𝘥 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘰𝘯. 𝘜𝘴𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵7 𝘢𝘯𝘥 𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘢𝘭𝘛𝘩𝘪𝘯𝘬𝘪𝘯𝘨. No stale docs. Less Hallucinations. with Windsurf and LangDB 🧵:

Here’s the full blog post along with video showing it in action👇 blog.langdb.ai/smarter-coding…

Integrating AI agents with tools like GitHub, Slack, or ClickHouse sounds simple—until you're managing multiple APIs, credentials, and weird edge cases. Here's how Virtual MCP Servers help you skip the chaos 🧵

Figma Supabase Tech I used: - Windsurf (AI Code Editor) - Figma MCP (Live design tokens) - Supabase MCP (Live database models) - Upstash's Context7 ( Live Docs) - LangDB's Virtual MCP (to combine only the tools I needed into a single endpoint)

Bonus Tip: Using LangDB 's Virtual MCP meant I didn’t have to wire multiple servers manually. Only the tools I actually needed, unified cleanly. Less setup. More focus on building.

Ever hit the 40-tool wall in Cursor , Anthropic's Claude or Windsurf ? 🤔 LangDB’s Virtual MCP Server merges multiple MCP endpoints into one—so you load only the tools your agent needs. No unused tools. One endpoint. Leaner context. 🚀 Learn more:

Built an agent using the Google ADK. Instead of hardcoding everything, we offloaded logic to LangDB AI Gateway that lets you manage prompts, MCP, and guardrails without redeploying code. Here’s how 👇