AI Agent Workflow Platforms - Sim AI is a user-friendly, open-source platform for building AI agent workflows, supporting major LLMs, MCP servers, and vectorDBs [1] - Transformer Lab offers tools like RAGFlow for deep document understanding and AutoAgent, a zero-code framework for building and deploying Agents [2] - Anything LLM is an all-in-one AI app for chatting with documents and using AI Agents, designed for multi-user environments and local operation [6] Open-Source LLM Tools - Llama Factory allows training and fine-tuning of open-source LLMs and VLMs without coding, supporting over 100 models [6] - RAGFlow is a RAG engine for building enterprise-grade RAG workflows on complex documents with citations, supporting multimodal data [2][4] - AutoAgent is a zero-code framework for building and deploying Agents using natural language, with universal LLM support and a native Vector DB [2][5] Key Features & Technologies - Sim AI's Finance Agent uses Firecrawl for web searches and Alpha Vantage's API for stock data via MCP servers [1] - RAGFlow supports multimodal data and deep research capabilities [2] - AutoAgent features function-calling and ReAct interaction modes [5] Community & Popularity - Sim AI is 100% open-source with 18 thousand stars [1] - Transformer Lab is 100% open-source with over 68 thousand stars [2] - LLaMA-Factory is 100% open-source with 62 thousand stars [6] - Anything LLM is 100% open-source with 48 thousand stars [6] - One project is 100% open-source with 8 thousand stars [3]

6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

RT Avi Chawla (@_avichawla)5 Agentic AI design patterns, explained visually!Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!The visual depicts the 5 most popular design patterns for building AI Agents.1️⃣ Reflection patternThe AI reviews its own work to spot mistakes and iterate until it produces the final response.2️⃣ Tool use patternTools allow LLMs to gather more information by:- Querying a vector database- Executing Python scripts- Invoki ...

AI Agent Design Patterns - Agentic AI 通过整合自我评估、规划和协作来优化输出 [1] - 报告描述了构建 AI Agents 的 5 种最流行的设计模式 [1] Key Design Patterns - 反思模式：AI 审查自身工作以发现错误并迭代，直到产生最终响应 [1] - 工具使用模式：工具允许 LLM 通过查询向量数据库、执行 Python 脚本、调用 API 等来收集更多信息 [1][3] - ReAct (Reason and Act) 模式：ReAct 结合了反思模式和工具使用模式 [2] - 规划模式：AI 通过细分任务和大纲目标来创建路线图，从而更有效地解决任务 [2] - 多代理模式：有多个代理，每个代理都有特定的角色和任务，每个代理也可以访问工具 [3] Frameworks and Implementation - 像 CrewAI 这样的框架主要默认使用 ReAct 模式 [2] - 在 CrewAI 中，指定 `planning=True` 以使用规划模式 [2]

AI Agent Infrastructure - The industry is focused on building a real-time context layer for AI Agents across numerous data sources [2] - Airweave offers an open-source context retrieval layer as a solution [2] Technical Challenges & Solutions - Companies face the challenge of querying data spread across multiple sources like Gmail and Drive [2] - A common but potentially insufficient solution is embedding everything in a vector database and using RAG (Retrieval-Augmented Generation) [2]

AI Engineering & RAG - The document discusses building a unified query engine over data spread across several sources using vector DB and RAG (Retrieval-Augmented Generation) [1] - It presents a scenario of an AI engineer interview at Google, focusing on querying data from sources like Gmail and Drive [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs [1]

Airweave GitHub repo: https://t.co/iU6P0KoaRf(don't forget to star it ⭐) ...

You are in an AI engineer interview at Google.The interviewer asks:"Our data is spread across several sources (Gmail, Drive, etc.)How would you build a unified query engine over it?"You: "I'll embed everything in a vector DB and do RAG."Interview over!Here's what you missed:Devs treat context retrieval like a weekend project.Their mental model is simple: "Just embed the data, store them in vector DB, and call it a day."This works beautifully for static sources.But the problem is that no real-world workflow ...

Agents forget everything after each task!Graphiti builds a temporal knowledge graph for Agents that provides a memory layer to all interactions.Fully open-source with 20k+ stars!Learn how to use Graphiti MCP to connect all AI apps via a common memory layer (100% local): https://t.co/cpAZFJcrufAvi Chawla (@_avichawla):Big update for Claude Desktop and Cursor users!Now you can connect all AI apps via a common memory layer in a minute.I used the Graphiti MCP server that runs 100% locally to cross-operate acros ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/Op1RqVm3agAvi Chawla (@_avichawla):Big update for Claude Desktop and Cursor users!Now you can connect all AI apps via a common memory layer in a minute.I used the Graphiti MCP server that runs 100% locally to cross-operate across AI apps like Claude Desktop and Cursor without losing context.(setup below) https://t.co/AZrxcS2cR5 ...