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.Avi Chawla (@_avichawla):A Python decorator is all you need to trace LLM apps (open-source).Most LLM evals treat the app like an end-to-end black box.But LLM apps need component-level evals and tracing since the issue can be anywhere inside the box, like the retriever, tool call, or the LLM itself. https://t.co/dWXyJb3DNs ...

GitHub repo: https://t.co/LfM6AdsO74(don't forget to star it ⭐️) ...

Core Functionality - DeepEval provides open-source tracing for LLM applications using a Python decorator `@observe` [1] - The solution enables component-level evaluations of LLM apps, addressing issues within retrievers, tool calls, or the LLM itself [1] - It allows attaching different metrics to each component of the LLM application [1] - DeepEval offers a visual breakdown of the performance of each component [1] Open Source and Hosting - DeepEval is 100% open-source with over 8,500 stars [2] - The solution can be self-hosted, ensuring data privacy [2]

Agentic Applications - Agentic applications 需要 A2A (Agent2Agent) 和 MCP (Model Control Plane) [1] - MCP 为 agents 提供工具访问权限 [1] - A2A 允许 agents 相互连接和协作 [1] Protocols Explained - A2A 协议的详细解释和可视化说明，请参考提供的链接 [1]

Agent Technology & Protocol - Agent2Agent (A2A) protocol is explained with visuals [1] - Tutorials and insights on DS, ML, LLMs, and RAGs are shared daily [1] Resource Sharing - The author encourages readers to reshare the content with their network if they find it insightful [1] Author Information - Avi Chawla (@_avichawla) shares the content [1]

What makes A2A powerful?- Secure collaboration- Task and state management- UX negotiation- Capability discovery- Agents from different frameworks working togetherAdditionally, it can integrate with MCP. https://t.co/BJZN9Vkp2s ...

Agent Cards (ID cards for Agents)A2A-supporting Remote Agents must publish a JSON Agent Card detailing their capabilities and authentication.Clients use this to find and communicate with the best agent for a task. https://t.co/h8DuccbDlk ...

What is A2A?A2A (Agent2Agent) enables multiple AI agents to work together on tasks without directly sharing their internal memory, thoughts, or tools.Instead, they communicate by exchanging context, task updates, instructions, and data. https://t.co/a45nDM6ORR ...

Agentic Applications - Agentic applications require both Agent-to-Agent communication (A2A) and Machine Control Protocol (MCP) [1] Agent Collaboration - MCP equips agents with tool access [2] - A2A enables agents to connect and collaborate in teams [2]

MCP & A2A (Agent2Agent) protocol, clearly explained (with visuals): ...