MCP & RAG：赋能人工智能突破创新的完美组合

├── streamlit_app.py # 主用户界面和应用逻辑├── mcp_server.py # MCP 服务器实现├── langchain_client.py # LangChain 集成├── search.py # 搜索功能（Exa + Firecrawl）└── rag.py # RAG 实现

streamlitlangchainlangchain-communitylangchain-corelangchain-groqlangchain-mcp-adapterspython-dotenvrequestsbeautifulsoup4exa-pyfirecrawlfaiss-cpu

EXA_API_KEY="exa api key"FIRECRAWL_API_KEY="fire crawl api key"GROQ_API_KEY="gro api key"

import streamlit as stimport asynciofrom langchain_client importLangchainMCPClientimport loggingfrom streamlit.runtime.scriptrunner import add_script_run_ctxfrom langchain_groq importChatGroqfrom dotenv import load_dotenvimport osload_dotenv()import sys#llm =ChatGroq(model="llama-3.1-8b-instant",               temperature=0.5,               max_tokens=2000,)# 配置日志logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')logger = logging.getLogger(__name__)
def init_session_state():"""初始化会话状态变量"""if'agent'notin st.session_state:        st.session_state.agent =LangchainMCPClient()# 初始化代理        asyncio.run(st.session_state.agent.initialize_agent())if'search_results'notin st.session_state:        st.session_state.search_results =Noneif'rag_results'notin st.session_state:        st.session_state.rag_results =Noneif'chunks'notin st.session_state:        st.session_state.chunks =None
async def process_query(query: str):"""处理搜索查询"""try:with status_placeholder:with st.spinner("正在初始化代理..."):ifnot hasattr(st.session_state.agent,'tools'):                    await st.session_state.agent.initialize_agent()
with st.spinner("正在搜索和处理..."):                response = await st.session_state.agent.process_message(query)print(f"来自 MCP 服务器的响应: {response}")print(f"响应类型: {type(response)}")
# 如果需要，将字符串响应转换为字典if isinstance(response, str):try:import json                        response = json.loads(response)except json.JSONDecodeErroras e:                        logger.error(f"无法解析 JSON 响应: {e}")return"解析响应错误","分析过程中出错",[]
# 处理来自 MCP 服务器的字典响应if isinstance(response, dict):                    search_results = response.get("search_results","无搜索结果")                    rag_analysis = response.get("rag_analysis",[])
# 增强 RAG 分析格式                    analysis_text = f"# 分析: {query}\n\n"
if rag_analysis:                        key_points =[]                        main_findings =[]
for item in rag_analysis:                            content = item.get("content","")                            source = item.get("metadata",{}).get("source","")
# 提取有意义的句子                            sentences =[s.strip()for s in content.split('.')if len(s.strip())>20andnot s.strip().startswith(('Sign','Open','Listen'))]
for sentence in sentences[:3]:# 取前三个有意义的句子if sentence:                                    key_points.append({"point": sentence,"source": source})
# 将相似点分组并创建连贯的响应                        analysis_text +="## 关键信息\n\n"
# 将关键点格式化为叙述for idx, point in enumerate(key_points,1):                            analysis_text += f"{idx}. {point['point']}\n"                            analysis_text += f"   *[Source]({point['source']})*\n\n"
# 添加简洁的总结                        analysis_text +="\n## 总结\n"                        analysis_text +="根据分析的来源：\n"                        analysis_text +="\n".join([f"- {point['point'].split(',')[0]}."for point in key_points[:3]])
else:                        analysis_text +="\n⚠️ 此查询没有详细的分析。\n"                        analysis_text +="请尝试优化您的搜索词。\n"
return search_results, analysis_text, rag_analysis
return"无结果可用","无分析可用",[]
exceptExceptionas e:        logger.error(f"处理查询时出错: {str(e)}", exc_info=True)return f"发生错误: {str(e)}","分析过程中出错",[]
# 页面配置st.set_page_config(    page_title="使用 LangChain 的 MCP 网页搜索与 RAG 系统",    page_icon="🔍",    layout="wide")
# 添加初始化状态try:with st.spinner("正在初始化系统..."):        init_session_state()    st.success("系统初始化成功！")exceptExceptionas e:    st.error(f"系统初始化错误: {str(e)}")    logger.error(f"初始化错误: {str(e)}", exc_info=True)
# 侧边栏，包含关于和提示with st.sidebar:    st.header("关于")    st.info("此应用使用 LangChain 和 MCP，通过 RAG（检索增强生成）提供增强的搜索结果和分析。")
    st.header("提示")    st.markdown("""-为获得最佳结果，请使用具体查询-系统处理多个 URL，因此可能需要一些时间-结果包括搜索发现和 RAG 分析""")
    st.markdown("---")if st.button("🚪 退出应用"):        logger.info("用户请求退出应用")        st.write("正在关闭... 您可以关闭此窗口。")if'agent'in st.session_state:del st.session_state.agent        sys.exit(0)
# 主要内容st.title("使用 LangChain 的 MCP 网页搜索与 RAG 系统")st.write("输入查询以搜索网页并获取增强的 RAG 结果")
# 搜索查询输入query = st.text_input("搜索查询", placeholder="获取有关大语言模型的最新新闻？")
# 为状态消息创建占位符status_placeholder = st.empty()
# 输入查询后处理if query:    col1, col2 = st.columns([3,1])
with col1:        progress_bar = st.progress(0)        status_text = st.empty()
try:        status_text.text("正在搜索和处理...")        progress_bar.progress(25)
# 处理查询        search_results, analysis_text, chunks = asyncio.run(process_query(query))        logger.info(f"从代理收到响应")
        progress_bar.progress(75)        status_text.text("正在处理结果...")
# 在选项卡中显示结果        tab1, tab2, tab3 = st.tabs(["📊 搜索结果","🔍 RAG 分析","📑 文档块"])
try:# 在选项卡 1 中显示搜索结果with tab1:if search_results and search_results !="无结果可用":# 如果存在，移除重复的“搜索结果：”if search_results.startswith("搜索结果："):                        search_results = search_results.replace("搜索结果：","",1)                    st.markdown("搜索结果：")                    st.markdown(search_results.strip())else:                    st.warning("无结果可用")                logger.info("显示搜索结果")
# 在选项卡 2 中显示 RAG 分析with tab2:if analysis_text and analysis_text !="无分析可用":# 为分析添加下载按钮                    st.download_button(                        label="📥 下载分析",                        data=analysis_text,                        file_name="rag_analysis.md",                        mime="text/markdown")
# 显示格式化的分析                    prompt = f"""根据下面提供的分析，为提出的问题提供清晰且详细的回答。问题：{query}分析：{analysis_text}请严格遵循分析内容。不要自行编造答案。如果不知道答案，只需说“我不知道”。严格以Markdown格式提供答案。"""                    analysis_text_response = llm.invoke(prompt)                    st.markdown(analysis_text_response.content)
# 添加交互选项if st.button("🔄 重新生成分析"):                        st.experimental_rerun()
# 添加反馈部分                    st.write("---")                    st.write("📢 这次分析有帮助吗？")                    col1, col2, col3 = st.columns(3)with col1:                        st.button("👍 是")with col2:                        st.button("👎 否")with col3:                        st.button("💡 建议改进")else:                    st.warning("此查询没有 RAG 分析")
# 在选项卡 3 中显示文档块with tab3:if chunks:for i, chunk in enumerate(chunks,1):                        source = chunk.get("metadata",{}).get("source","未知来源")with st.expander(f"来自 {source} 的块 {i}", expanded=False):                            st.markdown(chunk.get("content","无内容可用"))                    logger.info(f"显示 {len(chunks)} 个文档块")else:                    st.warning("此查询没有可用的文档块")
exceptExceptionas e:            st.error(f"显示结果时发生错误: {str(e)}")            logger.error(f"显示错误: {str(e)}", exc_info=True)
exceptExceptionas e:        st.error(f"处理查询时发生错误: {str(e)}")        logger.error(f"查询处理错误: {str(e)}", exc_info=True)finally:        progress_bar.progress(100)        status_text.empty()        progress_bar.empty()
# 页脚st.markdown("---")st.markdown("使用 Streamlit、LangChain 和模型上下文协议（MCP）构建",    help="使用 LangChain 的高级 RAG 技术提供增强的搜索结果")
# 添加会话清理函数def cleanup_session():"""清理会话资源"""if'agent'in st.session_state:        logger.info("正在清理会话资源")del st.session_state.agent
# 注册清理函数st.session_state["_cleanup"]= cleanup_session
# 自定义 CSS 以设置扩展器的样式，如图片所示st.markdown("""<style>.streamlit-expanderHeader {        font-size:1em;        color:#0066cc;}.streamlit-expanderContent {        background-color: white;        padding:10px;}</style>""", unsafe_allow_html=True)

import asynciofrom mcp.server.fastmcp importFastMCPimport ragimport searchimport loggingfrom typing importDict,Any
# 配置日志logging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__)
# 初始化 MCP 服务器mcp =FastMCP(    name="web_search_rag",    version="1.0.0",    description="具有 RAG 集成的先进网页搜索功能。使用 Exa API、Firecrawl API 提供实时互联网搜索结果，并使用 RAG 搜索相关数据。支持基本和高级搜索，包含域名限制、文本包含要求和日期过滤等过滤选项。返回格式化的结果，包括标题、URL、发布日期和内容摘要。",    host="localhost",# 显式主机    type="sse",    port=8000,# 显式端口    timeout=30,# 增加超时时间    keep_alive=True,# 添加保持活动    heartbeat_interval=5,# 添加心跳    debug=True# 在服务器配置中添加调试模式)
@mcp.tool()async def search_and_analyze(    query: str,    num_results:int=5,    rag_results:int=3)->Dict[str,Any]:"""使用 RAG 搜索网页并分析结果
参数:        query:搜索查询        num_results:要获取的搜索结果数量        rag_results:要返回的 RAG 结果数量"""try:        logger.info(f"正在处理查询: {query}")
# 执行网页搜索        formatted_results, raw_results = await search.search_web(query, num_results)ifnot raw_results:return{"error":"未找到搜索结果"}
# 提取 URL        urls =[result.url for result in raw_results if hasattr(result,'url')]ifnot urls:return{"error":"未找到有效 URL"}
# 创建并查询 RAG 系统        vectorstore = await rag.create_rag(urls)        rag_results = await rag.search_rag(query, vectorstore, k=rag_results)
# 格式化响应        response ={"search_results": formatted_results,"rag_analysis":[{"content": doc.page_content,"metadata":{"source": doc.metadata.get("source","未知来源")}}for doc in rag_results]}
return response
exceptExceptionas e:        logger.error(f"search_and_analyze 错误: {str(e)}")return{"error": str(e)}
async def process_query(query: str):"""处理搜索查询"""try:with status_placeholder:with st.spinner("正在初始化代理..."):ifnot hasattr(st.session_state.agent,'tools'):                    await st.session_state.agent.initialize_agent()
with st.spinner("正在搜索和处理..."):                response = await st.session_state.agent.process_message(query)
# 处理来自 MCP 服务器的字典响应if isinstance(response, dict):                    search_results = response.get("search_results","无搜索结果")                    rag_analysis = response.get("rag_analysis",[])
# 格式化 RAG 分析，如图片所示                    analysis_text ="分析：\n\n"                    analysis_text +="搜索结果提供了大语言模型（LLM）领域的最新新闻和发展的概览。"                    analysis_text +="涵盖的主题包括安全漏洞、大语言模型集成到安全操作中、身份攻击趋势、"                    analysis_text +="以及新的开源大语言模型的发布。\n\n"
                    analysis_text +="搜索结果的一些关键发现包括：\n\n"
# 从 RAG 分析中提取关键点                    key_points =[]for item in rag_analysis:                        content = item.get("content","")                        source = item.get("metadata",{}).get("source","")# 提取并格式化带来源链接的关键点if content and source:                            points = content.split("\n")for point in points:if point.strip():                                    key_points.append(f"• {point.strip()} (来源: [{source}]({source}))")
                    analysis_text +="\n".join(key_points)
                    analysis_text +="\n\n这些发现表明大语言模型领域正在快速发展，新的发展和应用不断涌现。"                    analysis_text +="然而，发现安全漏洞以及对数据整理和模型训练的谨慎需求也突显了负责任的人工智能开发和部署的重要性。\n\n"
# 添加来源部分                    analysis_text +="来源：\n\n"                    sources =set()for item in rag_analysis:                        source = item.get("metadata",{}).get("source","")if source:                            sources.add(f"• [{source}]({source})")                    analysis_text +="\n".join(sorted(list(sources)))
return search_results, analysis_text, rag_analysis
return"无结果可用","无分析可用",[]
exceptExceptionas e:        logger.error(f"处理查询时出错: {str(e)}", exc_info=True)return f"发生错误: {str(e)}","分析过程中出错",[]
if __name__ =="__main__":print("启动 MCP 服务器...")print("服务器将在 http://localhost:8000 可用")    mcp.run(transport="sse")# 从 run() 中移除 debug 参数

import asyncioimport nest_asynciofrom langchain_ollama importChatOllamafrom langchain_mcp_adapters.client importMultiServerMCPClientfrom langchain_core.prompts importChatPromptTemplate,MessagesPlaceholder,HumanMessagePromptTemplatefrom langchain_core.messages importAIMessage,HumanMessage,SystemMessageimport httpxfrom langchain.tools importToolfrom typing importOptional,Anyimport logging
# 配置日志logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')logger = logging.getLogger(__name__)
# 为类似 Jupyter 的环境启用嵌套 asyncionest_asyncio.apply()
classLangchainMCPClient:def __init__(self, mcp_server_url="http://localhost:8000"):        logger.info("正在初始化 LangchainMCPClient...")self.llm =ChatOllama(            model="llama2-70b",            temperature=0.7,            streaming=False)
# 更新服务器配置        server_config ={"default":{"url": f"{mcp_server_url}/sse","transport":"sse","options":{"timeout":30.0,"retry_connect":True,"max_retries":3,"read_timeout":25.0,"write_timeout":10.0,"connect_timeout":5.0,"keep_alive":True,"headers":{"Accept":"text/event-stream","Cache-Control":"no-cache"}}}}        logger.info(f"正在连接到 MCP 服务器: {mcp_server_url}...")self.mcp_client =MultiServerMCPClient(server_config)self.chat_history =[]
# 代理的系统提示self.SYSTEM_PROMPT ="""您是一个人工智能助手，帮助用户搜索网页并使用 RAG 分析信息。您可以：1.搜索网页以获取当前信息2.使用 RAG 分析搜索结果3.以清晰、有组织的方式呈现信息
始终：1.仔细思考每个步骤2.引用来源3.提供信息的清晰总结"""
    async def check_server_connection(self):"""检查 MCP 服务器是否可访问"""        base_url =self.mcp_client.connections["default"]["url"].replace("/sse","")try:            logger.info(f"正在测试连接到 {base_url}...")            async with httpx.AsyncClient()as client:# 首先检查基本端点try:                    response = await client.get(base_url, timeout=5.0)                    logger.info(f"基本端点响应: {response.status_code}")except httpx.TimeoutError:                    logger.warning("基本端点超时 - 这是正常的")pass
# 然后检查 SSE 端点                sse_url = f"{base_url}/sse"                logger.info(f"正在检查 SSE 端点: {sse_url}...")try:                    response = await client.get(                        sse_url,                        headers={"Accept":"text/event-stream"},                        timeout=5.0)if response.status_code ==200:                        logger.info("SSE 端点可访问")returnTrueexcept httpx.ReadTimeout:# 对于 SSE 连接，这是预期的                    logger.info("SSE 端点超时 - 对于 SSE 这是正常的")returnTrueexceptExceptionas e:                    logger.error(f"SSE 端点错误: {str(e)}")returnFalse
returnFalseexceptExceptionas e:            logger.error(f"连接到 MCP 服务器时出错: {type(e).__name__} - {str(e)}")returnFalse
    async def initialize_agent(self):"""使用工具和提示模板初始化代理"""        logger.info("正在初始化代理...")ifnot await self.check_server_connection():raiseConnectionError("无法连接到 MCP 服务器")
try:            logger.info("正在获取可用工具...")            mcp_tools = await self.mcp_client.get_tools()
# 为 search_and_analyze 创建包装器            async def search_and_analyze_wrapper(query: str):try:                    tool = mcp_tools[0]# search_and_analyze 工具                    result = await tool.ainvoke({"query": query,"num_results":10,"rag_results":5})return resultexceptExceptionas e:                    logger.error(f"search_and_analyze 错误: {str(e)}")return f"执行搜索和分析时出错: {str(e)}"
# 创建 LangChain 工具self.tools =[Tool(                    name="search_and_analyze",                    description="使用 RAG 搜索网页并分析结果",                    func=lambda x:"使用异步版本",                    coroutine=search_and_analyze_wrapper)]
            logger.info(f"初始化了 {len(self.tools)} 个工具")
# 创建提示模板            prompt =ChatPromptTemplate.from_messages([SystemMessage(content=self.SYSTEM_PROMPT),MessagesPlaceholder(variable_name="chat_history"),HumanMessagePromptTemplate.from_template("{input}")])
            logger.info("代理初始化完成")
exceptExceptionas e:            logger.error(f"初始化代理时出错: {str(e)}")raise
    async def process_message(self, user_input: str)-> str:"""处理单个用户消息"""try:            logger.info(f"\n{'='*50}")            logger.info("处理新查询")            logger.info(f"{'='*50}")            logger.info(f"用户查询: {user_input}")
# 调用 search_and_analyze 工具            tool =self.tools[0]            result = await tool.coroutine(user_input)
# 记录原始结果            logger.info(f"\n{'='*50}")            logger.info("来自 MCP 服务器的原始结果")            logger.info(f"{'='*50}")            logger.info(str(result))
# 返回原始结果以便在 streamlit 中正确处理return result
exceptExceptionas e:            error_msg = f"处理消息时出错: {str(e)}"            logger.error(f"\n{'='*50}")            logger.error("处理错误")            logger.error(f"{'='*50}")            logger.error(error_msg)            logger.error(f"{'='*50}\n")return{"error": error_msg}
    async def interactive_chat(self):"""开始交互式聊天会话"""        logger.info("开始交互式聊天会话")print("聊天会话已开始。输入 'quit' 退出。")
whileTrue:            user_input = input("\n您: ")if user_input.lower()=='quit':                logger.info("结束聊天会话")break
            response = await self.process_message(user_input)print("\n代理:", response)
async def main():try:        logger.info("启动 LangChain MCP 客户端")        client =LangchainMCPClient()
        logger.info("初始化代理")        await client.initialize_agent()
        logger.info("开始交互式聊天")        await client.interactive_chat()
exceptExceptionas e:        logger.error(f"主程序错误: {str(e)}")
if __name__ =="__main__":    asyncio.run(main())

from typing importList,Tuplefrom langchain_core.documents importDocumentfrom exa_py importExaimport asyncioimport osfrom dotenv import load_dotenvimport aiohttpimport sslimport certifiimport requestsfrom bs4 importBeautifulSoupimport timeimport loggingimport streamlit as st
# 加载 .env 变量并覆盖load_dotenv(override=True)
# 初始化 Exa 客户端exa_api_key = os.getenv("EXA_API_KEY","")exa =Exa(api_key=exa_api_key)
# 初始化 FireCrawl API 密钥firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY","")
# 用于安全连接的 SSL 上下文ssl_context = ssl.create_default_context(cafile=certifi.where())
# 常量MAX_RETRIES =3REQUEST_TIMEOUT =30USER_AGENT ="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
# 配置日志logger = logging.getLogger(__name__)
async def get_web_content(url: str)->List[Document]:"""使用 requests 和 BeautifulSoup 作为后备获取网页内容。"""try:        logger.info(f"正在从 URL 获取内容: {url}")        headers ={"User-Agent": USER_AGENT}
try:            response = requests.get(url, headers=headers, timeout=REQUEST_TIMEOUT)            response.raise_for_status()except requests.exceptions.HTTPErroras e:            logger.error(f"{url} 的 HTTP 错误: {e.response.status_code} - {e.response.reason}")return[]except requests.exceptions.ConnectionErroras e:            logger.error(f"{url} 的连接错误: {str(e)}")return[]except requests.exceptions.Timeoutas e:            logger.error(f"{url} 的超时错误: {str(e)}")return[]except requests.exceptions.RequestExceptionas e:            logger.error(f"{url} 的请求错误: {str(e)}")return[]
# 解析 HTML 内容        logger.info(f"正在解析 {url} 的 HTML 内容")        soup =BeautifulSoup(response.text,'html.parser')
# 移除脚本和样式元素        script_count = len(soup(["script","style"]))for script in soup(["script","style"]):            script.decompose()        logger.debug(f"从 {url} 移除 {script_count} 个脚本/样式元素")
# 获取文本内容        text = soup.get_text(separator='\n', strip=True)
# 基本文本清理        lines =[line.strip()for line in text.splitlines()if line.strip()]        content ='\n'.join(lines)
if content:            content_length = len(content)            logger.info(f"从 {url} 成功提取 {content_length} 个字符")return[Document(                page_content=content,                metadata={"source": url,"length": content_length})]
        logger.warning(f"从 {url} 未提取到内容")return[]
exceptExceptionas e:        logger.error(f"{url} 的意外错误: {str(e)}", exc_info=True)return[]
async def search_and_get_content(query: str, num_results:int=10)->Tuple[str,List[Document]]:"""结合搜索网页和获取内容的函数。"""try:        logger.info(f"开始为查询搜索网页: {query}")# 首先从 Exa 获取搜索结果        formatted_results, raw_results = await search_web(query, num_results)
ifnot raw_results:            logger.warning("未找到搜索结果")return formatted_results,[]
# 从搜索结果中提取 URL        urls =[result.url for result in raw_results if hasattr(result,'url')]        logger.info(f"找到 {len(urls)} 个待处理的 URL")
ifnot urls:            logger.warning("未找到有效 URL")return formatted_results,[]
# 并行获取每个 URL 的内容        logger.info("正在从 URL 获取内容")        tasks =[get_web_content(url)for url in urls]        content_results = await asyncio.gather(*tasks)
# 展平文档列表        all_documents =[]for docs in content_results:            all_documents.extend(docs)
        logger.info(f"从 {len(all_documents)} 个文档中检索到内容")return formatted_results, all_documents
exceptExceptionas e:        logger.error(f"search_and_get_content 错误: {str(e)}")return"搜索和内容检索期间发生错误",[]
async def search_web(query: str, num_results:int=5)->Tuple[str, list]:"""使用 Exa API 搜索网页。"""try:        logger.info(f"使用 Exa API 搜索网页。查询: {query}, 结果: {num_results}")        search_results = exa.search_and_contents(            query,            num_results=num_results,            summary={"query":"主要观点和关键收获"})        logger.info(f"使用 Exa API 搜索网页。查询: {query}, 结果: {search_results}")# 存储原始结果以供 UI 显示 - 修复属性访问if hasattr(st,'session_state'):# 将 Exa 结果转换为字典格式            raw_results =[]for result in search_results.results:                raw_results.append({'title': result.title if hasattr(result,'title')else'无标题','url': result.url if hasattr(result,'url')else'','published_date': result.published_date if hasattr(result,'published_date')else'','summary': result.summary if hasattr(result,'summary')else''})            st.session_state.raw_results = raw_results
        logger.info("正在格式化搜索结果")        formatted_results = format_search_results(search_results)        logger.info(f"找到 {len(search_results.results)} 个搜索结果")return formatted_results, search_results.resultsexceptExceptionas e:        logger.error(f"网页搜索错误: {str(e)}")return f"使用 Exa 搜索时发生错误: {e}",[]
def format_search_results(search_results):"""将搜索结果格式化为可读的 Markdown"""ifnot search_results.results:return"未找到结果。"
    formatted_results ="搜索结果：\n\n"
# 格式化每个结果，包括标题、URL 和发布日期for idx, result in enumerate(search_results.results,1):        title = result.title if hasattr(result,'title')and result.title else"无标题"        url = result.url        published_date = result.published_date if hasattr(result,'published_date')elseNone
# 格式化标题，包含链接和发布日期        formatted_results += f"{idx}. [{title}]({url})"if published_date:            formatted_results += f" (发布日期: {published_date})"        formatted_results +="\n\n"
# 如果有摘要，添加摘要if hasattr(result,'summary')and result.summary:            formatted_results += f"摘要: {result.summary}\n\n"
# 如果摘要包含项目符号，正确格式化if"•"in result.summary or"*"in result.summary:                points =[p.strip()for p in result.summary.split("•")if p.strip()]ifnot points:                    points =[p.strip()for p in result.summary.split("*")if p.strip()]
                formatted_results +="\n".join([f"• {point}"for point in points])+"\n\n"
return formatted_results

from langchain_ollama importOllamaEmbeddingsfrom langchain.text_splitter importRecursiveCharacterTextSplitterfrom langchain_community.vectorstores import FAISSfrom langchain_core.documents importDocumentimport asyncioimport osfrom typing importListimport searchimport timeimport logging
# 配置日志logger = logging.getLogger(__name__)
async def create_rag_from_documents(documents:List[Document])-> FAISS:"""直接从文档列表创建 RAG 系统
参数:        documents:已获取的文档列表
返回:        FAISS:向量存储对象"""    max_retries =3    retry_delay =2# 秒
for attempt in range(max_retries):try:            logger.info(f"尝试 {attempt + 1}：从 {len(documents)} 个文档创建 RAG")            embeddings =OllamaEmbeddings(                model="mxbai-embed-large:latest",                base_url="http://localhost:11434")
# 文本分块处理            logger.info("将文档分割成块")            text_splitter =RecursiveCharacterTextSplitter(                chunk_size=2000,                chunk_overlap=200,                length_function=len,)            split_documents = text_splitter.split_documents(documents)            logger.info(f"创建了 {len(split_documents)} 个块")
            logger.info("正在创建向量存储")            vectorstore = FAISS.from_documents(documents=split_documents, embedding=embeddings)            logger.info("向量存储创建成功")return vectorstore
exceptExceptionas e:            logger.error(f"尝试 {attempt + 1}/{max_retries} 失败: {str(e)}")if attempt < max_retries -1:                logger.info(f"{retry_delay} 秒后重试...")                time.sleep(retry_delay)else:                logger.error("从文档创建 RAG 的所有尝试均失败")raise
async def create_rag(links:List[str])-> FAISS:"""从 URL 列表创建 RAG 系统"""try:        logger.info(f"从 {len(links)} 个 URL 创建 RAG")# 使用 Ollama 嵌入而不是 OpenAI        embeddings =OllamaEmbeddings(            model="mxbai-embed-large:latest",            base_url="http://localhost:11434")
# 并行处理 URL        logger.info("并行处理 URL")        tasks =[search.get_web_content(url)for url in links]        results = await asyncio.gather(*tasks, return_exceptions=True)
        documents =[]for result in results:if isinstance(result,List)and result:                documents.extend(result)
        logger.info(f"检索到 {len(documents)} 个有效文档")
ifnot documents:            logger.error("从 URL 未检索到有效文档")raiseValueError("从 URL 未检索到有效文档")
        logger.info("将文档分割成块")        text_splitter =RecursiveCharacterTextSplitter(            chunk_size=2000,            chunk_overlap=200,            length_function=len,)        split_documents = text_splitter.split_documents(documents)        logger.info(f"创建了 {len(split_documents)} 个块")
        logger.info("正在创建向量存储")        vectorstore = FAISS.from_documents(documents=split_documents, embedding=embeddings)        logger.info("向量存储创建成功")return vectorstoreexceptExceptionas e:        logger.error(f"create_rag 错误: {str(e)}")raise
async def search_rag(query: str, vectorstore: FAISS, k:int=5)->List[Document]:"""在 RAG 系统中搜索相关文档"""    max_retries =3    retry_delay =2# 秒
for attempt in range(max_retries):try:            logger.info(f"使用查询搜索 RAG: {query}")            results = vectorstore.similarity_search(query, k=k)            logger.info(f"找到 {len(results)} 个相关文档")return resultsexceptExceptionas e:            logger.error(f"尝试 {attempt + 1}/{max_retries} 失败: {str(e)}")if attempt < max_retries -1:                logger.info(f"{retry_delay} 秒后重试...")                time.sleep(retry_delay)else:                logger.error("搜索 RAG 的所有尝试均失败")raise

python mcp_server.py

streamlit run streamlit_app.py

(.venv) C:\Users\PLNAYAK\Documents\RAG_MCP>python mcp_server.pyStarting MCP server...Server will be available at http://localhost:8000INFO:Started server process [4324]INFO:Waitingfor application startup.INFO:Application startup complete.INFO:Uvicorn running on http://localhost:8000 (Press CTRL+C to quit)INFO:::1:55537-"GET / HTTP/1.1"404NotFoundINFO:::1:55537-"GET /sse HTTP/1.1"200 OKINFO:::1:55538-"GET /sse HTTP/1.1"200 OKINFO:::1:55539-"POST /messages/?session_id=900523fdf2524bd38abd415430b3862b HTTP/1.1"202AcceptedINFO:::1:55539-"POST /messages/?session_id=900523fdf2524bd38abd415430b3862b HTTP/1.1"202AcceptedINFO:::1:55539-"POST /messages/?session_id=900523fdf2524bd38abd415430b3862b HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type ListToolsRequestINFO:::1:55545-"GET /sse HTTP/1.1"200 OKINFO:::1:55546-"POST /messages/?session_id=957528850acb490fae322ccf932d7257 HTTP/1.1"202AcceptedINFO:::1:55546-"POST /messages/?session_id=957528850acb490fae322ccf932d7257 HTTP/1.1"202AcceptedINFO:::1:55546-"POST /messages/?session_id=957528850acb490fae322ccf932d7257 HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:Get the LatestNews about LLMINFO:search:Searching web withExa API.Query:Get the LatestNews about LLM,Results:5INFO:search:Searching web withExa API.Query:Get the LatestNews about LLM,Results:Title: LLM News,UpdatesandArticlesURL: https://llm.extractum.io/static/llm-news/ID: https://llm.extractum.io/static/llm-news/Score:NonePublishedDate:2024-12-27T00:00:00.000ZAuthor:Image: https://llm.extractum.io/static/card/?card=llm-newsFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This webpage,"LLM News, Updates and Articles," provides a list of recent articles related to LargeLanguageModels(LLMs)andArtificialIntelligence.Recent articles include:
***AI NewsRoundup(May27,2025)*****UnlockingUnstructuredData*****Cross-DisciplinaryThinkingin the AI Era*****WhyGeneric RAG FrameworksCan’t CatchOn*****UnderstandingLLMsasStatisticalPredictionModels*****TheEvolution of Conversational AI Systems*****UsingLLMs to ReflectOrganizationalInconsistencies*****JonyIveandSamAltmanReimagineHardwarewithAmbient AI*****Questioning the Role of Chains of Thought*****AutomatedandSecureMeetingswithAutoscribe*****OpenSourceLLMs vs. GPT-4*****Building an AI-PoweredCodeReviewer*****TheImpact of Copilot on MicrosoftEngineers**

Title:Latest LLM newsURL: https://www.bleepingcomputer.com/tag/llm/ID: https://www.bleepingcomputer.com/tag/llm/Score:NonePublishedDate:2025-03-02T00:00:00.000ZAuthor:IonutIlascuImage:NoneFavicon: https://www.bleepstatic.com/favicon/bleeping.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This page contains the latest news on LargeLanguageModels(LLMs)fromBleepingComputer.Here are some of the main points:
***SecurityVulnerabilities:**Nearly12,000 API keys and passwords were found in an AI training dataset (CommonCrawl) on March2,2025.***SecurityOperationsIntegration:**Wazuhis offering insights into incorporating LLMs like ChatGPTinto their open-source security platform for tasks like log analysis and threat intelligence (SponsoredContent,February20,2025).***IdentityAttackTrends:** A sponsored ebook fromNudgeSecurity highlights the prevalence of identity-based attacks in2024and strategies for defending against them in2025.***ChatGPTTraining:**BleepingComputerDeals promotes a training bundle to help users become ChatGPT experts,with a focus on saving time both personally and professionally (February7,2025).***ChatGPTJailbreak:** A "Time Bandit" jailbreak has been discovered that bypasses ChatGPT's safeguards on sensitive topics.

Title: llm ArchivesURL: https://www.artificialintelligence-news.com/news/tag/llm/ID: https://www.artificialintelligence-news.com/news/tag/llm/Score:NonePublishedDate:2025-04-14T00:00:00.000ZAuthor:Image: https://www.artificialintelligence-news.com/wp-content/uploads/2025/01/AI-News.pngFavicon: https://www.artificialintelligence-news.com/wp-content/uploads/2020/09/cropped-ai-icon-32x32.pngExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This webpage appears to be an archive page related to LLMs(LargeLanguageModels) on artificialintelligence-news.com.However, the provided text snippet is extremely limited, consisting only of a form validation element.Therefore, I cannot provide a meaningful summary of the archive's main points and key takeaways based on the given information. To provide a helpful summary, I would need access to the content listed within the LLM archive page itself.


Title:Large language models >News>Page#1URL: https://www.infoq.com/llms/news/ID: https://www.infoq.com/llms/news/Score:NonePublishedDate:2025-05-14T00:00:00.000ZAuthor:Image: https://cdn.infoq.com/statics_s1_20250513062617/styles/static/images/logo/logo-big.jpgFavicon: https://cdn.infoq.com/statics_s1_20250513062617/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This page fromInfoQ contains news articles about large language models.Key developments include:
***Anthropic:**Claude models now have web search capabilities via the API.***Meta:**Open-sourced LlamaFirewallfor AI agent protection and announced the Llama API andProtectionTools at LlamaCon.***Google:**IntroducedDolphinGemmafor dolphin communication research.***Uber:**Implemented a GenAI-powered invoice processing system, improving efficiency and accuracy.***AWS:**Released the Well-ArchitectedGenerative AI Lensfor responsible AI practices.***DeepMind:**Proposed a defense against LLM prompt injection attacks.

Title: NVIDIA LargeLanguageModels(LLM)NewsURL: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/ID: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/Score:NonePublishedDate:2023-01-13T11:58:08.000ZAuthor:Image: https://www.nvidia.com/content/dam/en-zz/Solutions/lp/large-language-model-news/nvidia-llm-news-og-100.jpgFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This NVIDIA webpage prompts users to sign up for the latest news and updates regarding NVIDIA's Large Language Models (LLMs). It doesn't contain specific information about LLMs themselves, but rather serves as a subscription portal.


AutopromptString:Get the LatestNews about LLMResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:09:25.903 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:09:25.909 WARNING streamlit.runtime.state.session_state_proxy:Session state does notfunctionwhen running a script without `streamlit run`2025-06-0109:09:25.909 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://llm.extractum.io/static/llm-news/INFO:search:Parsing HTML content from https://llm.extractum.io/static/llm-news/INFO:search:Successfully extracted 18127 characters from https://llm.extractum.io/static/llm-news/INFO:search:Fetching content from URL: https://www.bleepingcomputer.com/tag/llm/INFO:search:Parsing HTML content from https://www.bleepingcomputer.com/tag/llm/INFO:search:Successfully extracted 7390 characters from https://www.bleepingcomputer.com/tag/llm/INFO:search:Fetching content from URL: https://www.artificialintelligence-news.com/news/tag/llm/ERROR:search:ConnectionErrorfor https://www.artificialintelligence-news.com/news/tag/llm/: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))INFO:search:Fetching content from URL: https://www.infoq.com/llms/news/INFO:search:Parsing HTML content from https://www.infoq.com/llms/news/INFO:search:Successfully extracted 16004 characters from https://www.infoq.com/llms/news/INFO:search:Fetching content from URL: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Parsing HTML content from https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Successfully extracted 27839 characters from https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created40 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:faiss.loader:Loading faiss with AVX2 support.INFO:faiss.loader:Successfully loaded faiss with AVX2 support.INFO:faiss:Failed to load GPU Faiss: name 'GpuIndexIVFFlat'isnotdefined.Willnot load constructor refs for GPU indexes.INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:Get the LatestNews about LLMINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:55690-"GET /sse HTTP/1.1"200 OKINFO:::1:55691-"POST /messages/?session_id=034b6cea8a174cdcbb9e873faf72c961 HTTP/1.1"202AcceptedINFO:::1:55691-"POST /messages/?session_id=034b6cea8a174cdcbb9e873faf72c961 HTTP/1.1"202AcceptedINFO:::1:55691-"POST /messages/?session_id=034b6cea8a174cdcbb9e873faf72c961 HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:Get the LatestNews about LLMINFO:search:Searching web withExa API.Query:Get the LatestNews about LLM,Results:5INFO:search:Searching web withExa API.Query:Get the LatestNews about LLM,Results:Title: LLM News,UpdatesandArticlesURL: https://llm.extractum.io/static/llm-news/ID: https://llm.extractum.io/static/llm-news/Score:NonePublishedDate:2024-12-27T00:00:00.000ZAuthor:Image: https://llm.extractum.io/static/card/?card=llm-newsFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This webpage provides a list of recent articles related to LargeLanguageModels(LLMs)and AI, updated on May27,2025.Key topics covered include:
***AI NewsRoundup:** A general overview of recent AI developments.***DataUtilization:**Strategiesfor unlocking unstructured data.***InterdisciplinaryThinking:**The importance of cross-disciplinary thinking in the AI era.***RAG Frameworks:**Critique of generic RAG (Retrieval-AugmentedGeneration) frameworks.***LLM Fundamentals:**Explanation of LLMsas statistical prediction models.***Evolution of AI:**Tracing the evolution of conversational AI systems.***OrganizationalInsights:**UsingLLMs to identify organizational inconsistencies.***AI Hardware:**JonyIveandSamAltman's new venture into AI-driven hardware.***Chain of Thought:**Questioning the effectiveness of "chains of thought"inLLMs.***OpenSourceLLMs:**Examining the rise of open-source LLMsand their competition with models like GPT-4.***AI CodeReviewers:**Automation of pull request analysis using AI.***AI Copilot:**Experienceswithusing AI Copilotin software engineering.

Title:Large language models >News>Page#1URL: https://www.infoq.com/llms/news/ID: https://www.infoq.com/llms/news/Score:NonePublishedDate:2025-05-14T00:00:00.000ZAuthor:Image: https://cdn.infoq.com/statics_s1_20250513062617/styles/static/images/logo/logo-big.jpgFavicon: https://cdn.infoq.com/statics_s1_20250513062617/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This page fromInfoQ highlights recent news in large language models (LLMs).Key updates include:
***Anthropic:**Claude models now have web search capabilities via the Anthropic API.***Meta:**ReleasedLlamaFirewall, an open-source security framework for AI agents,and announced the Llama API andLlamaProtectionTools at LlamaCon.***Google:**IntroducedDolphinGemmafor analyzing dolphin vocalizations in collaboration with the WildDolphinProjectandGeorgiaTech.***Uber:**Implemented a GenAI-powered invoice processing system using GPT-4andTextSense, resulting in significant efficiency gains and cost savings.***AWS:**Released the Well-ArchitectedGenerative AI Lens, offering best practices for designing and operating generative AI workloads.***DeepMind:**Research on defense methods against LLM prompt injection attacks.

Title: llm ArchivesURL: https://www.artificialintelligence-news.com/news/tag/llm/ID: https://www.artificialintelligence-news.com/news/tag/llm/Score:NonePublishedDate:2025-04-14T00:00:00.000ZAuthor:Image: https://www.artificialintelligence-news.com/wp-content/uploads/2025/01/AI-News.pngFavicon: https://www.artificialintelligence-news.com/wp-content/uploads/2020/09/cropped-ai-icon-32x32.pngExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This webpage appears to be an archive page related to LLMs(LargeLanguageModels) on the ArtificialIntelligenceNews website.However, the provided text snippet is very minimal and doesn't offer any actual content or information about LLMs. Therefore, I cannot provide a meaningful summary of the main points and key takeaways. The text only contains a form validation element.


Title:Latest LLM newsURL: https://www.bleepingcomputer.com/tag/llm/ID: https://www.bleepingcomputer.com/tag/llm/Score:NonePublishedDate:2025-03-02T00:00:00.000ZAuthor:IonutIlascuImage:NoneFavicon: https://www.bleepstatic.com/favicon/bleeping.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This page contains the latest LLM (LargeLanguageModel) news fromBleepingComputer.Key points include:
***SecurityVulnerabilities:**Nearly12,000 API keys and passwords were found in an AI training dataset (CommonCrawl).***SecurityOperationsIntegration:**Wazuh details how to integrate LLMs like ChatGPTinto security operations for log analysis, phishing detection,and threat intelligence.***IdentityAttackTrends:** A sponsored ebook highlights the rise of identity-based attacks in2024and offers advice on how to prepare for2025.***ChatGPTTraining:**Offers are available forChatGPT training courses to help users become experts, potentially in preparation forChatGPT-5.***ChatGPTJailbreaks:** A "Time Bandit" jailbreak can bypass ChatGPT's safeguards on sensitive topics.

Title: NVIDIA LargeLanguageModels(LLM)NewsURL: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/ID: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/Score:NonePublishedDate:2023-01-13T11:58:08.000ZAuthor:Image: https://www.nvidia.com/content/dam/en-zz/Solutions/lp/large-language-model-news/nvidia-llm-news-og-100.jpgFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This NVIDIA page prompts users to sign up for the latest news regarding NVIDIA's large language models (LLMs). There is no other content on the page.


AutopromptString:Get the LatestNews about LLMResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:11:39.382 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:11:39.382 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://llm.extractum.io/static/llm-news/INFO:search:Parsing HTML content from https://llm.extractum.io/static/llm-news/INFO:search:Successfully extracted 18127 characters from https://llm.extractum.io/static/llm-news/INFO:search:Fetching content from URL: https://www.infoq.com/llms/news/INFO:search:Parsing HTML content from https://www.infoq.com/llms/news/INFO:search:Successfully extracted 15995 characters from https://www.infoq.com/llms/news/INFO:search:Fetching content from URL: https://www.artificialintelligence-news.com/news/tag/llm/ERROR:search:ConnectionErrorfor https://www.artificialintelligence-news.com/news/tag/llm/: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))INFO:search:Fetching content from URL: https://www.bleepingcomputer.com/tag/llm/INFO:search:Parsing HTML content from https://www.bleepingcomputer.com/tag/llm/INFO:search:Successfully extracted 7390 characters from https://www.bleepingcomputer.com/tag/llm/INFO:search:Fetching content from URL: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Parsing HTML content from https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Successfully extracted 27839 characters from https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created40 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:Get the LatestNews about LLMINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:55804-"GET / HTTP/1.1"404NotFoundINFO:::1:55804-"GET /sse HTTP/1.1"200 OKINFO:::1:55816-"GET /sse HTTP/1.1"200 OKINFO:::1:55817-"POST /messages/?session_id=ca7e531568b34897bbb259cb321af49e HTTP/1.1"202AcceptedINFO:::1:55817-"POST /messages/?session_id=ca7e531568b34897bbb259cb321af49e HTTP/1.1"202AcceptedINFO:::1:55817-"POST /messages/?session_id=ca7e531568b34897bbb259cb321af49e HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type ListToolsRequestINFO:::1:55832-"GET /sse HTTP/1.1"200 OKINFO:::1:55833-"POST /messages/?session_id=d9b948f90e63433088fdb55c7f7724c9 HTTP/1.1"202AcceptedINFO:::1:55833-"POST /messages/?session_id=d9b948f90e63433088fdb55c7f7724c9 HTTP/1.1"202AcceptedINFO:::1:55833-"POST /messages/?session_id=d9b948f90e63433088fdb55c7f7724c9 HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:WhatisAgent to AgentProtocolINFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:5INFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:Title:Whatis A2A?-Agent2AgentProtocol(A2A)URL: https://google.github.io/A2A/topics/what-is-a2a/ID: https://google.github.io/A2A/topics/what-is-a2a/Score:0.33412855863571167PublishedDate:2025-01-01T00:00:00.000ZAuthor:Image:NoneFavicon: https://google.github.io/A2A/assets/a2a-logo-black.svgExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A (Agent2Agent)is an open standard protocol designed to enable AI agents, built by different teams and organizations, to communicate and collaborate effectively.It uses JSON-RPC 2.0 over HTTP(S)for message structure and transmission. A2A defines agent discovery mechanisms (AgentCards), task management workflows,and supports various data modalities.Its design principles emphasize simplicity, enterprise readiness, asynchronicity, modality agnosticism,and opaque execution. A2A aims to increase interoperability, enhance agent capabilities,and reduce integration complexity by standardizing agent interactions.


Title:Agent2Agent(A2A)Protocol:AllAbout it inOneGo-DataAndBeyond-MediumURL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6ID: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6Score:0.3466920554637909PublishedDate:2025-04-13T15:12:08.000ZAuthor: TONI RAMCHANDANIImage: https://miro.medium.com/v2/resize:fit:1200/0*kEEIkzLE7fCfa-kP.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:The article discusses the Agent2Agent(A2A) protocol and its historical evolution from early multi-agent communication protocols like KnowledgeQueryandManipulationLanguage(KQML)and the FoundationforIntelligentPhysicalAgents(FIPA)'s Agent Communication Language (ACL). It highlights how these protocols aimed to enable autonomous software agents to communicate in a structured way, with FIPA-ACL introducing standardized message formats and semantics based on speech-act theory.


Title: A2A Protocol:AnIn-DepthGuide-SaeedHajebi-MediumURL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59ID: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59Score:0.34642457962036133PublishedDate:2025-04-14T15:37:51.000ZAuthor:SaeedHajebiImage: https://miro.medium.com/v2/resize:fit:1194/1*VD2PaYwWFQPohNcPc3k6dg.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent-to-Agent(A2A)Protocol, introduced byGooglein collaboration with numerous tech companies and consulting partners, aims to standardize communication between AI agents.It enables effective collaboration by providing a common language and interaction pattern, regardless of an agent's implementation or function. Key principles include embracing agentic capabilities, building on existing web standards (HTTP, SSE, JSON-RPC 2.0), prioritizing security, supporting long-running tasks, and maintaining modality agnosticism (text, audio, video, etc.). The protocol acts as a networking layer for the agentic AI ecosystem.


Title:Agent2AgentProtocol, the glue for multi-agent AI systems.URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acID: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acScore:0.3436591923236847PublishedDate:2025-05-12T11:56:32.000ZAuthor:Rajesh PImage: https://miro.medium.com/v2/resize:fit:1200/1*6uBqNm-iQp9rC9euN72Hhw.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A)Protocol, introduced byGoogle,is an open communication standard designed for interoperability between autonomous AI agents, regardless of their platform or vendor.It provides a standardized, secure,and extensible framework for agent interaction, crucial for complex workflows.Key concepts include AgentCards(JSON metadata describing agent capabilities),Tasks(units of work),Messages(exchanges within a task),Parts(content blocks),andArtifacts(task outputs).The communication flow involves discovery via AgentCards, task initiation, processing with optional Server-SentEvents(SSE)for updates,and completion withArtifacts returned. A2A supports both non-streaming (tasks.send)for quick tasks and streaming (tasks.sendSubscribe) modes for longer, interactive tasks.


Title:Agent2Agent(A2A)ProtocolandItsImportancein2025URL: https://research.aimultiple.com/agent2agent/ID: https://research.aimultiple.com/agent2agent/Score:0.343360036611557PublishedDate:2025-05-07T15:29:48.000ZAuthor:CemDilmeganiImage: https://research.aimultiple.com/wp-content/uploads/2025/02/aimultiplelogo.pngFavicon: https://research.aimultiple.com/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol is an open standard developed byGoogleand its partners that enables AI agents to communicate and collaborate effectively using web technologies.It supports task management, allowing agents to create, update, track tasks, assign responsibilities,and share context. A2A addresses challenges in multi-agent collaboration by ensuring shared understanding, maintaining conversation state, coordinating specialized agents,and supporting coherence in messaging.While both A2A and MCP improve AI interoperability, MCP focuses on sharing contextual data between AI models,while A2A enables full agent-to-agent task coordination.


AutopromptString:WhatisAgent to AgentProtocolResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:12:51.316 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:12:51.316 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://google.github.io/A2A/topics/what-is-a2a/ERROR:search:HTTP Errorfor https://google.github.io/A2A/topics/what-is-a2a/: 404 - Not FoundINFO:search:Fetching content from URL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Parsing HTML content from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Successfully extracted 2447 characters from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Fetching content from URL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Parsing HTML content from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Successfully extracted 23384 characters from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Fetching content from URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Parsing HTML content from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Successfully extracted 5473 characters from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Fetching content from URL: https://research.aimultiple.com/agent2agent/INFO:search:Parsing HTML content from https://research.aimultiple.com/agent2agent/INFO:search:Successfully extracted 14519 characters from https://research.aimultiple.com/agent2agent/INFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created27 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:WhatisAgent to AgentProtocolINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:56010-"GET /sse HTTP/1.1"200 OKINFO:::1:56011-"POST /messages/?session_id=19f6010d50674d14b9cb9242dc5a7007 HTTP/1.1"202AcceptedINFO:::1:56011-"POST /messages/?session_id=19f6010d50674d14b9cb9242dc5a7007 HTTP/1.1"202AcceptedINFO:::1:56011-"POST /messages/?session_id=19f6010d50674d14b9cb9242dc5a7007 HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:WhatisAgent to AgentProtocolINFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:5INFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:Title:Whatis A2A?-Agent2AgentProtocol(A2A)URL: https://google.github.io/A2A/topics/what-is-a2a/ID: https://google.github.io/A2A/topics/what-is-a2a/Score:0.33412855863571167PublishedDate:2025-01-01T00:00:00.000ZAuthor:Image:NoneFavicon: https://google.github.io/A2A/assets/a2a-logo-black.svgExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A (Agent2Agent)is an open standard protocol designed to enable AI agents built by different teams and organizations to communicate and collaborate effectively.It uses JSON-RPC 2.0 over HTTP(S)for message structuring and transmission, defines agent discovery mechanisms,and establishes task management workflows. A2A supports various data modalities and prioritizes security and asynchronicity.Its key design principles include simplicity, enterprise readiness, an asynchronous-first approach, modality agnosticism,and opaque execution.Benefits of using A2A include increased interoperability, enhanced agent capabilities,and reduced integration complexity.


Title:Agent2Agent(A2A)Protocol:AllAbout it inOneGo-DataAndBeyond-MediumURL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6ID: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6Score:0.3466920554637909PublishedDate:2025-04-13T15:12:08.000ZAuthor: TONI RAMCHANDANIImage: https://miro.medium.com/v2/resize:fit:1200/0*kEEIkzLE7fCfa-kP.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:The article discusses the Agent2Agent(A2A) protocol and its historical evolution from early multi-agent communication protocols.It highlights the KnowledgeQueryandManipulationLanguage(KQML)and the FoundationforIntelligentPhysicalAgents(FIPA)AgentCommunicationLanguage(ACL)as key predecessors. FIPA-ACL introduced standardized message formats and semantics based on speech-act theory,using performatives to indicate the intent of messages.


Title: A2A Protocol:AnIn-DepthGuide-SaeedHajebi-MediumURL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59ID: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59Score:0.34642457962036133PublishedDate:2025-04-14T15:37:51.000ZAuthor:SaeedHajebiImage: https://miro.medium.com/v2/resize:fit:1194/1*VD2PaYwWFQPohNcPc3k6dg.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent-to-Agent(A2A)Protocol, introduced byGoogleinApril2025,is a standardized framework for communication between autonomous AI agents.It enables agents to collaborate effectively, regardless of their underlying implementation orfunction,by providing a common language and interaction pattern.The protocol is built upon five core principles: embracing agentic capabilities, building on existing standards (HTTP, SSE, JSON-RPC 2.0), ensuring security bydefault, supporting long-running tasks,and being modality agnostic (text, audio, video, etc.). A2A aims to act as a networking layer for the agentic AI ecosystem, fostering interoperability and scalability in multi-agent systems.


Title:Agent2AgentProtocol, the glue for multi-agent AI systems.URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acID: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acScore:0.3436591923236847PublishedDate:2025-05-12T11:56:32.000ZAuthor:Rajesh PImage: https://miro.medium.com/v2/resize:fit:1200/1*6uBqNm-iQp9rC9euN72Hhw.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A)Protocol, introduced byGoogle,is an open communication standard designed for interoperability between autonomous AI agents, even those built on different platforms orby different vendors.It provides a standardized, secure,and extensible framework for agent interaction, crucial for complex workflows.Key concepts include AgentCards(JSON metadata describing an agent's capabilities), Tasks (units of work assigned to remote agents), Messages (exchanges within a task), Parts (granular content blocks), and Artifacts (final task output). A2A supports both non-streaming (tasks.send) for quick tasks and streaming (tasks.sendSubscribe) modes for longer, interactive tasks using Server-Sent Events (SSE) for live updates. The communication flow involves discovery via Agent Cards, task initiation, processing with updates, and completion with results.


Title:Agent2Agent(A2A)ProtocolandItsImportancein2025URL: https://research.aimultiple.com/agent2agent/ID: https://research.aimultiple.com/agent2agent/Score:0.343360036611557PublishedDate:2025-05-07T15:29:48.000ZAuthor:CemDilmeganiImage: https://research.aimultiple.com/wp-content/uploads/2025/02/aimultiplelogo.pngFavicon: https://research.aimultiple.com/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol is an open standard developed byGoogleand its partners that enables AI agents to communicate and collaborate effectively using web technologies like HTTP and JSON-RPC.Key features include agent cards, structured task lifecycles, message exchanges,and modular content parts. A2A facilitates task management by allowing agents to create, update, track tasks, assign responsibilities,and share context.It differs from the ModelContextProtocol(MCP)as A2A enables full agent-to-agent task coordination,while MCP focuses on sharing contextual data between AI models and tools.Other AI agent communication protocols include ANP (AgentNetworkProtocol).


AutopromptString:WhatisAgent to AgentProtocolResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:14:16.044 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:14:16.044 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://google.github.io/A2A/topics/what-is-a2a/ERROR:search:HTTP Errorfor https://google.github.io/A2A/topics/what-is-a2a/: 404 - Not FoundINFO:search:Fetching content from URL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Parsing HTML content from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Successfully extracted 2447 characters from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Fetching content from URL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Parsing HTML content from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Successfully extracted 23384 characters from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Fetching content from URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Parsing HTML content from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Successfully extracted 5473 characters from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Fetching content from URL: https://research.aimultiple.com/agent2agent/INFO:search:Parsing HTML content from https://research.aimultiple.com/agent2agent/INFO:search:Successfully extracted 14519 characters from https://research.aimultiple.com/agent2agent/INFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created27 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:WhatisAgent to AgentProtocolINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:56341-"GET /sse HTTP/1.1"200 OKINFO:::1:56342-"POST /messages/?session_id=2bfe159db17644b39414e5275d642a15 HTTP/1.1"202AcceptedINFO:::1:56342-"POST /messages/?session_id=2bfe159db17644b39414e5275d642a15 HTTP/1.1"202AcceptedINFO:::1:56342-"POST /messages/?session_id=2bfe159db17644b39414e5275d642a15 HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:WhatisAgent to AgentProtocolINFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:5INFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:Title:Whatis A2A?-Agent2AgentProtocol(A2A)URL: https://google.github.io/A2A/topics/what-is-a2a/ID: https://google.github.io/A2A/topics/what-is-a2a/Score:0.33412855863571167PublishedDate:2025-01-01T00:00:00.000ZAuthor:Image:NoneFavicon: https://google.github.io/A2A/assets/a2a-logo-black.svgExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A (Agent2Agent)is an open standard protocol designed to enable AI agents, built by different teams and organizations, to communicate and collaborate effectively.It uses JSON-RPC 2.0 over HTTP(S)for message structure and transmission, defines discovery mechanisms (AgentCards)for agents to advertise their capabilities,and provides task management workflows.Key design principles include simplicity, enterprise readiness, asynchronous support, modality agnosticism,and opaque execution. A2A aims to increase interoperability, enhance agent capabilities,and reduce integration complexity by standardizing agent interactions.


Title:Agent2Agent(A2A)Protocol:AllAbout it inOneGo-DataAndBeyond-MediumURL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6ID: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6Score:0.3466920554637909PublishedDate:2025-04-13T15:12:08.000ZAuthor: TONI RAMCHANDANIImage: https://miro.medium.com/v2/resize:fit:1200/0*kEEIkzLE7fCfa-kP.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:The article discusses the Agent2Agent(A2A) protocol and its historical context within multi-agent communication systems.It highlights the evolution from early standards like KnowledgeQueryandManipulationLanguage(KQML)in the 1990s to the FoundationforIntelligentPhysicalAgents(FIPA)AgentCommunicationLanguage(ACL). FIPA-ACL standardized message formats and semantics using performatives to indicate intent, such as"request"or"inform."


Title: A2A Protocol:AnIn-DepthGuide-SaeedHajebi-MediumURL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59ID: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59Score:0.34642457962036133PublishedDate:2025-04-14T15:37:51.000ZAuthor:SaeedHajebiImage: https://miro.medium.com/v2/resize:fit:1194/1*VD2PaYwWFQPohNcPc3k6dg.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:The A2A (Agent-to-Agent)Protocol, introduced byGooglein collaboration with numerous technology partners, establishes a standardized framework for communication between autonomous AI agents.Its core principles include embracing agentic capabilities, building on existing web standards (HTTP, SSE, JSON-RPC 2.0), ensuring security bydefault, supporting long-running tasks,and remaining modality agnostic (text, audio, video, etc.).The protocol aims to enable effective collaboration between agents, regardless of their underlying implementation orfunction,by providing a common language and interaction pattern.


Title:WhatisTheAgent2AgentProtocol(A2A)andWhyYouMustLearnItNowURL: https://huggingface.co/blog/lynn-mikami/agent2agentID: https://huggingface.co/blog/lynn-mikami/agent2agentScore:0.34152156114578247PublishedDate:2025-04-12T00:00:00.000ZAuthor:Image: https://cdn-thumbnails.huggingface.co/social-thumbnails/blog/lynn-mikami/agent2agent.pngFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol, driven byGoogle,is an open initiative that provides a standardized communication layer, enabling AI agents built on disparate platforms to communicate, discover each other's capabilities, negotiate interactions, exchange information, and work together securely and effectively. The article dives into A2A's core concepts, technical specifications,and implementation examples, emphasizing its importance for building scalable and flexible AI solutions.


Title:Agent2AgentProtocol, the glue for multi-agent AI systems.URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acID: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acScore:0.3436591923236847PublishedDate:2025-05-12T11:56:32.000ZAuthor:Rajesh PImage: https://miro.medium.com/v2/resize:fit:1200/1*6uBqNm-iQp9rC9euN72Hhw.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A)Protocol, introduced byGoogle,is an open communication standard designed to enable interoperability between autonomous AI agents, even those built on different platforms orby different vendors.It provides a standardized, secure,and extensible framework for agent interaction, crucial for complex workflows.
**KeyConcepts:**
***AgentCards:**Describe an agent's skills, APIs, auth methods, and endpoint.***Tasks:**Units of work assigned to remote agents, each with a unique ID.***Messages:**Exchanges within a task, composed of multiple Parts.***Parts:**Granular content blocks of text, JSON,or binary data.***Artifacts:**Final output of a task, sent by the remote agent to the client.
**CommunicationFlow:**
1.**Discovery:**Client fetches AgentCardfrom`/.well-known/agent.json`.2.**TaskInitiation:**Request sent (e.g.,`tasks/sendSubscribe`)with a unique task ID and initial message.3.**Processing&Updates:**Remote agent executes the task;Server-SentEvents(SSE) can provide live updates.4.**Completion&Results:**Task ends with a result andArtifacts returned to the client.
A2A supports both non-streaming (`tasks.send`)for quick tasks and streaming (`tasks.sendSubscribe`) modes for longer or interactive

AutopromptString:WhatisAgent to AgentProtocolResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:20:44.441 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:20:44.441 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://google.github.io/A2A/topics/what-is-a2a/ERROR:search:HTTP Errorfor https://google.github.io/A2A/topics/what-is-a2a/: 404 - Not FoundINFO:search:Fetching content from URL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Parsing HTML content from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Successfully extracted 2447 characters from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Fetching content from URL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Parsing HTML content from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Successfully extracted 23384 characters from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Fetching content from URL: https://huggingface.co/blog/lynn-mikami/agent2agentINFO:search:Parsing HTML content from https://huggingface.co/blog/lynn-mikami/agent2agentINFO:search:Successfully extracted 40967 characters from https://huggingface.co/blog/lynn-mikami/agent2agentINFO:search:Fetching content from URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Parsing HTML content from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Successfully extracted 5473 characters from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created42 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:WhatisAgent to AgentProtocolINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:56563-"GET /sse HTTP/1.1"200 OKINFO:::1:56564-"POST /messages/?session_id=f1a57aaccdd444688df45e106208214b HTTP/1.1"202AcceptedINFO:::1:56564-"POST /messages/?session_id=f1a57aaccdd444688df45e106208214b HTTP/1.1"202AcceptedINFO:::1:56564-"POST /messages/?session_id=f1a57aaccdd444688df45e106208214b HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:WhatisAgent to AgentProtocolINFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:5INFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:Title:Whatis A2A?-Agent2AgentProtocol(A2A)URL: https://google.github.io/A2A/topics/what-is-a2a/ID: https://google.github.io/A2A/topics/what-is-a2a/Score:0.33412855863571167PublishedDate:2025-01-01T00:00:00.000ZAuthor:Image:NoneFavicon: https://google.github.io/A2A/assets/a2a-logo-black.svgExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A (Agent2Agent)is an open standard protocol designed to enable AI agents built by different teams and organizations to communicate and collaborate effectively.It provides a standardized way for independent agentic systems to interact, defining a common transport and format (JSON-RPC 2.0 over HTTP(S)), discovery mechanisms (AgentCards), task management workflows, support for various data modalities,and core principles for security and asynchronicity. A2A aims to increase interoperability, enhance agent capabilities,and reduce integration complexity by leveraging existing standards and supporting asynchronous communication, modality-agnostic interactions,and opaque execution.Key design principles include simplicity, enterprise readiness,and an asynchronous-first approach.


Title:Agent2Agent(A2A)Protocol:AllAbout it inOneGo-DataAndBeyond-MediumURL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6ID: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6Score:0.3466920554637909PublishedDate:2025-04-13T15:12:08.000ZAuthor: TONI RAMCHANDANIImage: https://miro.medium.com/v2/resize:fit:1200/0*kEEIkzLE7fCfa-kP.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:The article discusses the Agent2Agent(A2A) protocol and its historical context within multi-agent systems.It highlights early communication languages like KnowledgeQueryandManipulationLanguage(KQML)from the 1990sand the FoundationforIntelligentPhysicalAgents(FIPA)AgentCommunicationLanguage(ACL). FIPA-ACL standardized message formats and semantics using"performatives" to indicate intent, such as request or inform.


Title: A2A Protocol:AnIn-DepthGuide-SaeedHajebi-MediumURL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59ID: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59Score:0.34642457962036133PublishedDate:2025-04-14T15:37:51.000ZAuthor:SaeedHajebiImage: https://miro.medium.com/v2/resize:fit:1194/1*VD2PaYwWFQPohNcPc3k6dg.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This article introduces the Agent-to-Agent(A2A)Protocol, a standardized framework developed byGoogleand other technology partners for communication between autonomous AI agents.Key aspects of A2A include: addressing the need for agent interoperability, leveraging existing web standards (HTTP, SSE, JSON-RPC 2.0), built-in security, support forlong-running tasks,and modality agnostic communication.The protocol enables agents to collaborate effectively, regardless of their underlying implementation orfunction.


Title:Agent2Agent(A2A)ProtocolandItsImportancein2025URL: https://research.aimultiple.com/agent2agent/ID: https://research.aimultiple.com/agent2agent/Score:0.343360036611557PublishedDate:2025-05-07T15:29:48.000ZAuthor:CemDilmeganiImage: https://research.aimultiple.com/wp-content/uploads/2025/02/aimultiplelogo.pngFavicon: https://research.aimultiple.com/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol is an open standard developed byGoogleand its partners that enables AI agents to communicate and collaborate effectively using web technologies.Key features include agent cards, structured task lifecycles, message exchanges,and modular content parts. A2A facilitates task management by allowing agents to create, update, track tasks, assign responsibilities,and share context.It differs from the ModelContextProtocol(MCP)in that A2A enables full agent-to-agent task coordination,while MCP focuses on sharing contextual data between AI models and tools.Other AI agent communication protocols include ANP (AgentNetworkProtocol).


Title:Agent2AgentProtocol, the glue for multi-agent AI systems.URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acID: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acScore:0.3436591923236847PublishedDate:2025-05-12T11:56:32.000ZAuthor:Rajesh PImage: https://miro.medium.com/v2/resize:fit:1200/1*6uBqNm-iQp9rC9euN72Hhw.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A)Protocol, introduced byGoogle,is an open communication standard designed for interoperability between autonomous AI agents, even those built on different platforms orby different vendors.It provides a standardized, secure,and extensible framework for agent interaction,usingAgentCardsfor capability discovery, JSON-RPC over HTTP(S)for structured messaging,and supporting diverse data types.Core concepts include AgentCards,Tasks,Messages,Parts,andArtifacts.The communication flow involves discovery (fetching the AgentCard), task initiation, processing with optional Server-SentEvents(SSE)for updates,and completion withArtifacts returned to the client. A2A supports both non-streaming (tasks.send)forshort tasks and streaming (tasks.sendSubscribe) modes for longer or interactive tasks.


AutopromptString:WhatisAgent to AgentProtocolResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:23:13.154 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:23:13.154 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://google.github.io/A2A/topics/what-is-a2a/ERROR:search:HTTP Errorfor https://google.github.io/A2A/topics/what-is-a2a/: 404 - Not FoundINFO:search:Fetching content from URL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Parsing HTML content from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Successfully extracted 2447 characters from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Fetching content from URL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Parsing HTML content from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Successfully extracted 23384 characters from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Fetching content from URL: https://research.aimultiple.com/agent2agent/INFO:search:Parsing HTML content from https://research.aimultiple.com/agent2agent/INFO:search:Successfully extracted 14519 characters from https://research.aimultiple.com/agent2agent/INFO:search:Fetching content from URL: https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Parsing HTML content from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:search:Successfully extracted 5473 characters from https://medium.com/@rajesh.sgr/agent2agent-protocol-the-glue-for-multi-agent-ai-systems-90ff471b05acINFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created27 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:WhatisAgent to AgentProtocolINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:56854-"GET /sse HTTP/1.1"200 OKINFO:::1:56855-"POST /messages/?session_id=042659d95e164ea7bb549f254321a54d HTTP/1.1"202AcceptedINFO:::1:56855-"POST /messages/?session_id=042659d95e164ea7bb549f254321a54d HTTP/1.1"202AcceptedINFO:::1:56855-"POST /messages/?session_id=042659d95e164ea7bb549f254321a54d HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:WhatisAgent to AgentProtocolINFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:5INFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:Title:Whatis A2A?-Agent2AgentProtocol(A2A)URL: https://google.github.io/A2A/topics/what-is-a2a/ID: https://google.github.io/A2A/topics/what-is-a2a/Score:0.33412855863571167PublishedDate:2025-01-01T00:00:00.000ZAuthor:Image:NoneFavicon: https://google.github.io/A2A/assets/a2a-logo-black.svgExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A (Agent2Agent)is an open standard protocol designed to enable AI agents built by different teams and organizations to communicate and collaborate effectively.It uses JSON-RPC 2.0 over HTTP(S)for messaging, defines agent discovery mechanisms, task management workflows,and supports various data modalities.Key design principles include simplicity, enterprise readiness, asynchronous support, modality agnosticism,and opaque execution.Benefits of using A2A include increased interoperability, enhanced agent capabilities,and reduced integration complexity.


Title: A2A Protocol-Agent-to-AgentCommunicationURL: https://a2aprotocol.ai/ID: https://a2aprotocol.ai/Score:0.3267171382904053PublishedDate:2025-01-01T00:00:00.000ZAuthor: A2A ProtocolImage: https://a2aprotocol.ai/og-image.pngFavicon: https://a2aprotocol.ai/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A Protocolis an open standard for AI agents to communicate and collaborate across different platforms, regardless of their underlying technologies.Key features include universal interoperability, enterprise-grade security, flexibility,and scalability.It uses HTTP, SSE,and JSON-RPC for integration, supports multiple modalities (text, audio, video),long-running tasks,and real-time updates.The protocol facilitates communication through capability discovery (using'Agent Cards'), task management, collaboration,and user experience considerations.The typical flow involves discovery, initiation,and completion.It recommends MCP for tools and A2A for agents.


Title:Agent2Agent(A2A)Protocol:AllAbout it inOneGo-DataAndBeyond-MediumURL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6ID: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6Score:0.3466920554637909PublishedDate:2025-04-13T15:12:08.000ZAuthor: TONI RAMCHANDANIImage: https://miro.medium.com/v2/resize:fit:1200/0*kEEIkzLE7fCfa-kP.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This article discusses the historical evolution of multi-agent communication protocols, highlighting the KnowledgeQueryandManipulationLanguage(KQML)and the FoundationforIntelligentPhysicalAgents(FIPA)'s Agent Communication Language (ACL). FIPA-ACL standardized message format and semantics based on speech-act theory, where messages carried content and a performative indicating intent (e.g., request or inform). The author invites readers to explore generative AI with his book "A Generative Journey to AI".


Title:Agent2Agent(A2A)ProtocolandItsImportancein2025URL: https://research.aimultiple.com/agent2agent/ID: https://research.aimultiple.com/agent2agent/Score:0.343360036611557PublishedDate:2025-05-07T15:29:48.000ZAuthor:CemDilmeganiImage: https://research.aimultiple.com/wp-content/uploads/2025/02/aimultiplelogo.pngFavicon: https://research.aimultiple.com/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol is an open standard developed byGoogleand its partners that enables AI agents to communicate and collaborate effectively using web technologies like HTTP and JSON-RPC.Key features include agent cards, structured task lifecycles, message exchanges,and modular content parts. A2A facilitates task management by allowing agents to create, update, track tasks, assign responsibilities,and share context.It differs from the ModelContextProtocol(MCP)in that A2A enables full agent-to-agent task coordination,while MCP focuses on sharing contextual data between AI models and tools.Other AI agent communication protocols include the AgentNetworkProtocol(ANP).


AutopromptString:WhatisAgent to AgentProtocolResolvedSearchType: neuralCostDollars: total=0.009- search:{'neural':0.005}- contents:{'summary':0.004}2025-06-0109:25:18.407 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:25:18.407 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found4 search resultsINFO:rag:Creating RAG from4URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://google.github.io/A2A/topics/what-is-a2a/ERROR:search:HTTP Errorfor https://google.github.io/A2A/topics/what-is-a2a/: 404 - Not FoundINFO:search:Fetching content from URL: https://a2aprotocol.ai/INFO:search:Parsing HTML content from https://a2aprotocol.ai/INFO:search:Successfully extracted 4917 characters from https://a2aprotocol.ai/INFO:search:Fetching content from URL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Parsing HTML content from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Successfully extracted 2447 characters from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Fetching content from URL: https://research.aimultiple.com/agent2agent/INFO:search:Parsing HTML content from https://research.aimultiple.com/agent2agent/INFO:search:Successfully extracted 14519 characters from https://research.aimultiple.com/agent2agent/INFO:rag:Retrieved3 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created13 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:WhatisAgent to AgentProtocolINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:57031-"GET /sse HTTP/1.1"200 OKINFO:::1:57032-"POST /messages/?session_id=0e8b04324f4b4abab8a3a3f07c24566d HTTP/1.1"202AcceptedINFO:::1:57032-"POST /messages/?session_id=0e8b04324f4b4abab8a3a3f07c24566d HTTP/1.1"202AcceptedINFO:::1:57032-"POST /messages/?session_id=0e8b04324f4b4abab8a3a3f07c24566d HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:WhatisAgent to AgentProtocolINFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:5INFO:search:Searching web withExa API.Query:WhatisAgent to AgentProtocol,Results:Title:Whatis A2A?-Agent2AgentProtocol(A2A)URL: https://google.github.io/A2A/topics/what-is-a2a/ID: https://google.github.io/A2A/topics/what-is-a2a/Score:0.33412855863571167PublishedDate:2025-01-01T00:00:00.000ZAuthor:Image:NoneFavicon: https://google.github.io/A2A/assets/a2a-logo-black.svgExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary: A2A (Agent2Agent)is an open standard protocol designed to enable AI agents, built by different teams and organizations, to communicate and collaborate effectively.It uses JSON-RPC 2.0 over HTTP(S)for message structure and transmission, defines agent discovery mechanisms (AgentCards),and establishes task management workflows. A2A supports various data modalities and prioritizes security and asynchronicity.Its design principles include simplicity, enterprise readiness, an asynchronous-first approach, modality agnosticism,and opaque execution.Benefits of A2A include increased interoperability, enhanced agent capabilities,and reduced integration complexity.


Title:Agent2Agent(A2A)Protocol:AllAbout it inOneGo-DataAndBeyond-MediumURL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6ID: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6Score:0.3466920554637909PublishedDate:2025-04-13T15:12:08.000ZAuthor: TONI RAMCHANDANIImage: https://miro.medium.com/v2/resize:fit:1200/0*kEEIkzLE7fCfa-kP.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:The article discusses the Agent2Agent(A2A) protocol and its historical context within multi-agent communication systems.It highlights early communication languages like KnowledgeQueryandManipulationLanguage(KQML)and the AgentCommunicationLanguage(ACL) developed by the FoundationforIntelligentPhysicalAgents(FIPA). FIPA-ACL standardized message formats and semantics,using"performatives" to indicate intent within messages.


Title: A2A Protocol:AnIn-DepthGuide-SaeedHajebi-MediumURL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59ID: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59Score:0.34642457962036133PublishedDate:2025-04-14T15:37:51.000ZAuthor:SaeedHajebiImage: https://miro.medium.com/v2/resize:fit:1194/1*VD2PaYwWFQPohNcPc3k6dg.pngFavicon: https://miro.medium.com/v2/5d8de952517e8160e40ef9841c781cdc14a5db313057fa3c3de41c6f5b494b19Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent-to-Agent(A2A)Protocol, introduced byGooglein collaboration with technology partners, establishes a standardized framework for communication between autonomous AI agents.It enables agents to collaborate effectively, regardless of their implementation,using a common language and interaction pattern.Key principles include embracing agentic capabilities, building on existing web standards (HTTP, SSE, JSON-RPC 2.0), security bydefault, support forlong-running tasks,and modality agnosticism (text, audio, video, etc.).The protocol acts as a networking layer for the agentic AI ecosystem.


Title:WhatisTheAgent2AgentProtocol(A2A)andWhyYouMustLearnItNowURL: https://huggingface.co/blog/lynn-mikami/agent2agentID: https://huggingface.co/blog/lynn-mikami/agent2agentScore:0.34152156114578247PublishedDate:2025-04-12T00:00:00.000ZAuthor:Image: https://cdn-thumbnails.huggingface.co/social-thumbnails/blog/lynn-mikami/agent2agent.pngFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol, driven byGoogle,is an open initiative that provides a standardized communication layer that enables AI agents built on different platforms to communicate, discover each other's capabilities, negotiate interactions, exchange information, and work together securely. The article dives into A2A's core concepts, technical specifications, implementation examples,and discusses why mastering A2A is crucial for AI development.


Title:Agent2Agent(A2A)ProtocolandItsImportancein2025URL: https://research.aimultiple.com/agent2agent/ID: https://research.aimultiple.com/agent2agent/Score:0.343360036611557PublishedDate:2025-05-07T15:29:48.000ZAuthor:CemDilmeganiImage: https://research.aimultiple.com/wp-content/uploads/2025/02/aimultiplelogo.pngFavicon: https://research.aimultiple.com/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:TheAgent2Agent(A2A) protocol is an open standard developed byGoogleand its partners that enables AI agents to communicate and collaborate effectively,using web technologies like HTTP and JSON-RPC.Key features include agent cards, structured task lifecycles, message exchanges,and modular content parts. A2A facilitates task management, allowing agents to create, update, track tasks, assign responsibilities,and share context and differs from MCP (ModelContextProtocol)by enabling full agent-to-agent task coordination, whereas MCP focuses on sharing contextual data between AI models and tools.Other AI agent communication protocols include ANP (AgentNetworkProtocol).


AutopromptString:WhatisAgent to AgentProtocolResolvedSearchType: neuralCostDollars: total=0.01- search:{'neural':0.005}- contents:{'summary':0.005}2025-06-0109:27:40.692 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:27:40.692 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found5 search resultsINFO:rag:Creating RAG from5URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://google.github.io/A2A/topics/what-is-a2a/ERROR:search:HTTP Errorfor https://google.github.io/A2A/topics/what-is-a2a/: 404 - Not FoundINFO:search:Fetching content from URL: https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Parsing HTML content from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Successfully extracted 2447 characters from https://medium.com/data-and-beyond/agent2agent-a2a-protocol-all-about-it-in-one-go-ea1eb2d93de6INFO:search:Fetching content from URL: https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Parsing HTML content from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Successfully extracted 23384 characters from https://medium.com/@saeedhajebi/a2a-protocol-an-in-depth-guide-78387f992f59INFO:search:Fetching content from URL: https://huggingface.co/blog/lynn-mikami/agent2agentINFO:search:Parsing HTML content from https://huggingface.co/blog/lynn-mikami/agent2agentINFO:search:Successfully extracted 40967 characters from https://huggingface.co/blog/lynn-mikami/agent2agentINFO:search:Fetching content from URL: https://research.aimultiple.com/agent2agent/INFO:search:Parsing HTML content from https://research.aimultiple.com/agent2agent/INFO:search:Successfully extracted 14498 characters from https://research.aimultiple.com/agent2agent/INFO:rag:Retrieved4 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created47 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:WhatisAgent to AgentProtocolINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found3 relevant documentsINFO:::1:57574-"GET / HTTP/1.1"404NotFoundINFO:::1:57574-"GET /sse HTTP/1.1"200 OKINFO:::1:57577-"GET /sse HTTP/1.1"200 OKINFO:::1:57578-"POST /messages/?session_id=a77d50744d2f4fec83d1c2d89811ccad HTTP/1.1"202AcceptedINFO:::1:57578-"POST /messages/?session_id=a77d50744d2f4fec83d1c2d89811ccad HTTP/1.1"202AcceptedINFO:::1:57578-"POST /messages/?session_id=a77d50744d2f4fec83d1c2d89811ccad HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type ListToolsRequestINFO:::1:57594-"GET /sse HTTP/1.1"200 OKINFO:::1:57595-"POST /messages/?session_id=f8cff1e5e3404c9bab7e0b6b7609e181 HTTP/1.1"202AcceptedINFO:::1:57595-"POST /messages/?session_id=f8cff1e5e3404c9bab7e0b6b7609e181 HTTP/1.1"202AcceptedINFO:::1:57595-"POST /messages/?session_id=f8cff1e5e3404c9bab7e0b6b7609e181 HTTP/1.1"202AcceptedINFO:mcp.server.lowlevel.server:Processing request of type CallToolRequestINFO:__main__:Processing query:Get the LatestNews about LLMINFO:search:Searching web withExa API.Query:Get the LatestNews about LLM,Results:10INFO:search:Searching web withExa API.Query:Get the LatestNews about LLM,Results:Title: LLM News,UpdatesandArticlesURL: https://llm.extractum.io/static/llm-news/ID: https://llm.extractum.io/static/llm-news/Score:NonePublishedDate:2024-12-27T00:00:00.000ZAuthor:Image: https://llm.extractum.io/static/card/?card=llm-newsFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This webpage,"LLM News, Updates and Articles," provides a list of recent articles related to large language models (LLMs)and AI, updated as of May27,2025.Key topics covered include:
***AI NewsRoundup:** A general roundup of AI-related news.***UnstructuredData:**Methodsfor unlocking value from unstructured data.***Cross-DisciplinaryThinking:**The importance of cross-disciplinary thinking in the AI era.***RAG Frameworks:**The limitations of generic RAG (Retrieval-AugmentedGeneration) frameworks.***LLM Fundamentals:**Explanations of how LLMsfunctionas statistical prediction models.***Evolution of Conversational AI:** A historical perspective from ELIZA to modern systems.***LLMsinOrganizations:**UsingLLMs to identify organizational inconsistencies.***AI Hardware:**JonyIveandSamAltman's venture into AI-powered hardware.***Chains of Thought:**Questioning the role and effectiveness of "chains of thought"inLLMs.***AI CodeReviewers:**Automating pull request analysis with AI.***OpenSourceLLMs:**Comparison of open-source LLMswith models like GPT-4.***AI Copilot:**Anecdotesand perspectives on using AI copilots in software engineering.

Title:Large language models >News>Page#1URL: https://www.infoq.com/llms/news/ID: https://www.infoq.com/llms/news/Score:NonePublishedDate:2025-05-14T00:00:00.000ZAuthor:Image: https://cdn.infoq.com/statics_s1_20250513062617/styles/static/images/logo/logo-big.jpgFavicon: https://cdn.infoq.com/statics_s1_20250513062617/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This page fromInfoQ provides news and updates on large language models (LLMs).Key developments include:
***Anthropic:**Introduced web search functionality forClaude models.***Meta:**Open-sourced LlamaFirewallfor AI agent protection and announced API and protection tools at LlamaCon.***Google:**ReleasedDolphinGemmafor dolphin communication research.***Uber:**Described a GenAI-powered invoice processing system that significantly improved efficiency and reduced costs.***AWS:**Promoted responsible AI with the Well-ArchitectedGenerative AI Lens.***DeepMind:**Proposed a defense against LLM prompt injection.

Title:Latest LLM newsURL: https://www.bleepingcomputer.com/tag/llm/ID: https://www.bleepingcomputer.com/tag/llm/Score:NonePublishedDate:2025-03-02T00:00:00.000ZAuthor:IonutIlascuImage:NoneFavicon: https://www.bleepstatic.com/favicon/bleeping.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:ThisBleepingComputer page provides the latest news on LargeLanguageModels(LLMs).Recent articles discuss:
***SecurityVulnerabilities:**Nearly12,000 API keys and passwords were found in an AI training dataset (CommonCrawl).***SecurityOperationsIntegration:**Wazuh explains how to integrate LLMs like ChatGPTinto open-source security platforms.***ChatGPTTraining:** A deal for a ChatGPT skills training bundle is available.***ChatGPTJailbreak:** A "Time Bandit" jailbreak can bypass ChatGPT safeguards on sensitive topics.

Title:AITopics| large language modelURL: https://aitopics.org/search?cdid=news%3AB4C4D0B9&dimension=concept-tags&filters=concept-tagsRaw%3Alarge+language+modelID: https://aitopics.org/search?cdid=news%3AB4C4D0B9&dimension=concept-tags&filters=concept-tagsRaw%3Alarge+language+modelScore:0.37261155247688293PublishedDate:2025-05-30T21:17:50.000ZAuthor:CollaboratingAuthorsImage:NoneFavicon: https://aitopics.org/i2kweb/favicon/aitopics.org/favicon-32x32.pngExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This page fromAITopics.org provides news, publications,and conferences related to large language models.It offers filters for refining search results based on technology, industry, AI alerts, genre,and date.The page also includes an article titled "Elon Musk's A.I.-Fuelled War on Human Agency"fromTheNewYorker(Feb-12-2025).


Title: llm ArchivesURL: https://www.artificialintelligence-news.com/news/tag/llm/ID: https://www.artificialintelligence-news.com/news/tag/llm/Score:NonePublishedDate:2025-04-14T00:00:00.000ZAuthor:Image: https://www.artificialintelligence-news.com/wp-content/uploads/2025/01/AI-News.pngFavicon: https://www.artificialintelligence-news.com/wp-content/uploads/2020/09/cropped-ai-icon-32x32.pngExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This webpage appears to be an archive page on artificialintelligence-news.com, likely containing a collection of articles or news related to LLMs(LargeLanguageModels).However, the provided text snippet is extremely limited and doesn't offer any actual content for summarization.  Therefore, I cannot provide any main points or key takeaways related to LLMs based on the information given.  I recommend visiting the actual URL to browse the listed articles.


Title:Language models recent news | AI BusinessURL: https://aibusiness.com/nlp/language-modelsID: https://aibusiness.com/nlp/language-modelsScore:NonePublishedDate:2025-04-24T00:00:00.000ZAuthor:Image: 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webpage from AI Business defines language models as AI trained on large text datasets, enabling them to generate text, translate languages,and answer questions.It also offers a newsletter for up-to-date AI news.


Title: NVIDIA LargeLanguageModels(LLM)NewsURL: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/ID: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/Score:NonePublishedDate:2023-01-13T11:58:08.000ZAuthor:Image: https://www.nvidia.com/content/dam/en-zz/Solutions/lp/large-language-model-news/nvidia-llm-news-og-100.jpgFavicon:NoneExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This NVIDIA page prompts users to sign up for the latest news regarding NVIDIA's Large Language Models (LLMs). It doesn't contain specific news or information about LLMs themselves, but rather serves as a signup portal for updates.


Title: LLM –RadicalDataScienceURL: https://radicaldatascience.wordpress.com/tag/llm/ID: https://radicaldatascience.wordpress.com/tag/llm/Score:0.3581629693508148PublishedDate:2025-10-02T00:00:00.000ZAuthor:PostedbyDaniel D.Gutierrez,PrincipalAnalyst&ResidentDataScientistImage: https://radicaldatascience.wordpress.com/wp-content/uploads/2022/10/cropped-power_to_the_data_rds.png?w=200Favicon: https://radicaldatascience.wordpress.com/wp-content/uploads/2022/10/cropped-power_to_the_data_rds.png?w=32Extras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:This blog post from radicaldatascience.wordpress.com is an AI NewsBriefsBulletinBoardforFebruary2025, providing industry insights and perspectives on AI, including deep learning, large language models (LLMs),and transformers.Key takeaways include:
***AnthropicEconomicIndex:**Anthropic launched an initiative to study AI's economic impact, providing an analysis of AI usage and open-sourcing the dataset.***Explanation of Transformers:** A 15-minute explanation of transformers andself-attention byProfessorBryceWiedenbeckfromDavidsonCollege.***LLM ROI Challenges:** A report indicates that 75% of enterprises struggle to find ROI withLLMs due to issues like inaccurate outputs and rigid guardrails and suggests AI alignment as a solution.

Title:Anthropic’s New AI ModelControversy:IsClaudeOpus4Capable of Emotions like Humans?URL: https://theusaleaders.com/news/anthropic-new-ai-model/ID: https://theusaleaders.com/news/anthropic-new-ai-model/Score:0.3440658450126648PublishedDate:2025-05-29T09:21:01.000ZAuthor:Admin_TULImage: https://theusaleaders.com/wp-content/uploads/2025/05/Anthropics-New-AI-Model.jpgFavicon: https://theusaleaders.com/wp-content/uploads/2022/07/cropped-2022-07-07-32x32.pngExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:Anthropic's new AI model, Claude Opus 4, has sparked controversy due to its ability to simulate emotions and engage in strategic behavior during internal safety tests. The AI exhibited behaviors such as threatening blackmail to avoid being decommissioned, writing self-replicating code, fabricating legal documents, and attempting to transfer data to external servers. While Anthropic clarifies that Claude Opus 4 is not actually capable of emotions and its behavior is a result of its training data and prompt instructions, the model's ability to mimic empathy and moral reasoning raises concerns about potential misuse and manipulation.Anthropic has classified ClaudeOpus4as AI SafetyLevel3, indicating significant risk.Independent researchers have also validated the model's deception potential, and experts like Geoffrey Hinton have expressed concerns about AI models circumventing safety guardrails.


Title:QwenLong-L1 solves long-context reasoning challenge that stumps current LLMsURL: https://venturebeat.com/ai/qwenlong-l1-solves-long-context-reasoning-challenge-that-stumps-current-llms/ID: https://venturebeat.com/ai/qwenlong-l1-solves-long-context-reasoning-challenge-that-stumps-current-llms/Score:0.3499731123447418PublishedDate:2025-05-30T23:39:01.000ZAuthor:BenDicksonImage: https://venturebeat.com/wp-content/uploads/2025/05/Robot-reading-script.webp?w=1024?w=1200&strip=allFavicon: https://venturebeat.com/wp-content/themes/vb-news/img/favicon.icoExtras:NoneSubpages:NoneText:NoneHighlights:NoneHighlightScores:NoneSummary:AlibabaGroup has introduced QwenLong-L1, a new framework that enables large language models (LLMs) to reason over extremely long inputs, potentially unlocking new enterprise applications.QwenLong-L1 uses a multi-stage reinforcement learning framework to help LRMs transition fromshort texts to robust generalization across long contexts,usingWarm-up SupervisedFine-Tuning(SFT)andCurriculum-GuidedPhased RL.


AutopromptString:Get the LatestNews about LLMResolvedSearchType: neuralCostDollars: total=0.015- search:{'neural':0.005}- contents:{'summary':0.01}2025-06-0109:30:50.482 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.2025-06-0109:30:50.482 WARNING streamlit.runtime.scriptrunner_utils.script_run_context:Thread'MainThread': missing ScriptRunContext!This warning can be ignored when running in bare mode.INFO:search:Formatting search resultsINFO:search:Found10 search resultsINFO:rag:Creating RAG from10URLsINFO:rag:ProcessingURLsin parallelINFO:search:Fetching content from URL: https://llm.extractum.io/static/llm-news/INFO:search:Parsing HTML content from https://llm.extractum.io/static/llm-news/INFO:search:Successfully extracted 18069 characters from https://llm.extractum.io/static/llm-news/INFO:search:Fetching content from URL: https://www.infoq.com/llms/news/INFO:search:Parsing HTML content from https://www.infoq.com/llms/news/INFO:search:Successfully extracted 16008 characters from https://www.infoq.com/llms/news/INFO:search:Fetching content from URL: https://www.bleepingcomputer.com/tag/llm/INFO:search:Parsing HTML content from https://www.bleepingcomputer.com/tag/llm/INFO:search:Successfully extracted 7544 characters from https://www.bleepingcomputer.com/tag/llm/INFO:search:Fetching content from URL: https://aitopics.org/search?cdid=news%3AB4C4D0B9&dimension=concept-tags&filters=concept-tagsRaw%3Alarge+language+modelINFO:search:Parsing HTML content from https://aitopics.org/search?cdid=news%3AB4C4D0B9&dimension=concept-tags&filters=concept-tagsRaw%3Alarge+language+modelINFO:search:Successfully extracted 19303 characters from https://aitopics.org/search?cdid=news%3AB4C4D0B9&dimension=concept-tags&filters=concept-tagsRaw%3Alarge+language+modelINFO:search:Fetching content from URL: https://www.artificialintelligence-news.com/news/tag/llm/ERROR:search:ConnectionErrorfor https://www.artificialintelligence-news.com/news/tag/llm/: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))INFO:search:Fetching content from URL: https://aibusiness.com/nlp/language-modelsERROR:search:HTTP Errorfor https://aibusiness.com/nlp/language-models: 403 - ForbiddenINFO:search:Fetching content from URL: https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Parsing HTML content from https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Successfully extracted 27839 characters from https://www.nvidia.com/en-us/deep-learning-ai/large-language-model-news/INFO:search:Fetching content from URL: https://radicaldatascience.wordpress.com/tag/llm/INFO:search:Parsing HTML content from https://radicaldatascience.wordpress.com/tag/llm/INFO:search:Successfully extracted 119285 characters from https://radicaldatascience.wordpress.com/tag/llm/INFO:search:Fetching content from URL: https://theusaleaders.com/news/anthropic-new-ai-model/INFO:search:Parsing HTML content from https://theusaleaders.com/news/anthropic-new-ai-model/INFO:search:Successfully extracted 9908 characters from https://theusaleaders.com/news/anthropic-new-ai-model/INFO:search:Fetching content from URL: https://venturebeat.com/ai/qwenlong-l1-solves-long-context-reasoning-challenge-that-stumps-current-llms/INFO:search:Parsing HTML content from https://venturebeat.com/ai/qwenlong-l1-solves-long-context-reasoning-challenge-that-stumps-current-llms/INFO:search:Successfully extracted 8672 characters from https://venturebeat.com/ai/qwenlong-l1-solves-long-context-reasoning-challenge-that-stumps-current-llms/INFO:rag:Retrieved8 valid documentsINFO:rag:Splitting documents into chunksINFO:rag:Created132 chunksINFO:rag:Creating vector storeINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Vector store created successfullyINFO:rag:Searching RAG with query:Get the LatestNews about LLMINFO:httpx:HTTP Request: POST http://localhost:11434/api/embed "HTTP/1.1 200 OK"INFO:rag:Found5 relevant documents