Ollama Supports Llama 3.2 Vision: Talk to ANY Image 100% Local! 📚 Step-by-step Hands-on tutorial ⚡ How to Install? ollama 🏃‍♂️ How to Run? Meta @aiatmeta 🔌 How to Integrate in your App? 🎮 How to Create UI? Chainlit 📈 Analyse charts and graphs

🌻 Chainlit + AutoGen Chainlit is an open-source Python package to build chat apps for AI. Here's how you can build a chat app that uses Chainlit for UI and AutoGen for backend. - Define any AutoGen team - Send user queries from the UI to the team - Run the team with the

🌻AutoGen Python v0.4.5 released with a few exciting features: - Streaming tokens from Agents and Teams - Native support for R1-style reasoning output - Partial functions as tools Adds new samples to demonstrate these new features: - AgentChat + Chainlit with streaming -

Let’s build a Multi-Agent System to Compare Stock Price Performance 🚀 - 100 % Local and Open Source - Chainlit as an intuitive chatbot UI - AG2 to implement the AI agents Watch until the end!

Gemma 3 100% Local using ollama and Chainlit Create an Application DEMO: 👇 🚀 27B, 12B, 4B, 1B 🌐 140+ languages supported 📊 128K context window 🖼️ Multimodal capabilities 🏆 Outperforms Llama 405B, DeepSeek V3 💻 Runs on single GPU 🤖 Enables function calling 🔍 Vision

OpenAI Agents SDK 100% Local using ollama Create an Application with DEMO: 👇 🎓 Learn to Create: 1️⃣ Single Agent 2️⃣ Custom Tool 3️⃣ Multi Agents 4️⃣ UI Chainlit

🚀 Built a chat app at the AGI House MCP hackathon! 🔹 Powered by Anthropic 🔹 Connects to Linear MCP server by Vikram Subbiah from Smithery Henry Mao 🔹 Built with Chainlit for a seamless MCP client experience 🔹 LLM can call any tool from the MCP server but

Excited to announce the integration of Linkup’s search in Chainlit's conversational AI. In a minute, add a “🌐Search web” button to your chatbot and give it access to fresh grounding content for accurate answers. cc Dan Constantini Literal AI

new mcp spec just dropped: 1. auth is fixed! at last! 2. elicitation now makes it possible for a server to ask an end-user for more info, enabling more agentic behaviors 3. structured tool outputs makes it easier to reason about tool responses 4. more security documentation