🚀 Beating the Best Model Most devs default to “always use the best model.” Safe, right? But in practice… it often means paying way more than necessary. We put this to the test. 👇

@LangdbAi 📊 Results: ✅ 100% accuracy parity 💰 ~42% lower cost 🏆 @LangdbAi 's Auto Router matched or outperformed “always best” every time

🚀 New on @LangdbAi : @Zai_org's GLM-4.5 models are now available. Frontier reasoning + open-source, delivered through one simple API.

@LangdbAi @Zai_org @OpenAI @AnthropicAI @deepseek_ai @Google Try it today →

🚀 Instantly connect @crewAIInc with @LangdbAi Gateway - Just one init() call = full tracing for every agent step. 🔗 Guide: https://t.co/L8TnnMWwSS

🚀 Build smarter multi-agent AI fast: @AgnoAgi orchestrates, @LangdbAi ships 350+ LLMs ＋ end-to-end tracing—no extra config. #AI #MultiAgent #LangDB #Agno

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

Ever hit the 40-tool wall in @cursor_ai , @AnthropicAI's Claude or @windsurf_ai ? 🤔 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:

@figma @supabase Tech I used: - @windsurf_ai (AI Code Editor) - @figma MCP (Live design tokens) - @supabase MCP (Live database models) - @upstash's Context7 ( Live Docs) - @LangdbAi's Virtual MCP (to combine only the tools I needed into a single endpoint)

Bonus Tip: Using @LangdbAi '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.

Here’s the full blog post along with video showing it in action👇 https://t.co/xnEh8bo97g

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 🧵

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_ai and @LangdbAi 🧵:

@OpenAI GPT-4.1 Family is now live on @LangdbAi 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: https://t.co/PVdnqIyJKL #LLM #OpenAI #LangDB

🚀 It’s not just Gemini making noise. Now live on @LangdbAi: 🦙 @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 → https://t.co/FDFWTJNRyg #langDB #Llama4 #GROK3AI

Built a multi-agent setup using @OpenAI with models like Claude, Gemini, and Grok. @LangdbAi handled the model switching and provided trace-level visibility into agent decisions, tool usage, and execution time Blog: https://t.co/rb2SxuZ3Sb #OpenAI #AgenticAI #LangDB

@Google new Gemini 2.5 Pro is out and it's raising the bar. Gemini 2.5 Pro is already available on @LangdbAi , making it easy to explore and compare with other top models. Checkout: https://t.co/FDFWTJNRyg More details and benchmark from Google: https://t.co/yqoZRxEMk5

Thrilled to attend an eye-opening session by @MrunmayShelar at the Global AI Community Bootcamp! 🚀 Dived deep into AI Agents, Model Context Protocol, AI Disruption, and the AI Gateway — truly expanded my perspective on AI’s evolving impact. ⚡ Excited to explore more! #GAIB25

Enterprises need visibility & governance to avoid costly mistakes. How are you tackling AI reliability in your workflows? Let’s discuss in the comments! 👇 #AI #LLM #EnterpriseAI #AIGovernance #LangDB

That’s where @LangdbAi comes in—giving you full traceability & control over AI workflows. Track, compare & refine responses effortlessly.