In this blog, we will introduce a comprehensive list of AI Agents Marketplace Store and Search Portals to watch in 2025. With the rapid development of LLM based AI systems, AI agents is projected to grow tremendously and faster than ever before. There is need for users or enterprise owners to find and navigate the best AI Agents to satisfy their needs, in various industries, for different jobs etc.

In this blog, we will give you a brief introduction to AgentBoard, an open source AI agent visualization toolkit which can be used the same way as Tensorboard, which helps visaulize Tensors data during model training. AgentBoard aims to visualize key elements and information of AI Agents running loops in development and production stages of real-world scenarios. It provides easy logging APIs in python and can help log multiple data types such as text (prompt), tools use (function calling of LLM API), image, audio and video. With organized logging schemas, agentboard will visualize the agent-loop (PLAN -> ACT -> REFLECT -> REACT etc ), RAG (Retrieval Augumented Generation), Autonomous Agents running, Multi-Agents orchestration, etc. The rest of the blog will cover the basic data types usage, visualization of Agent Loop, Chat History, Tool Use, Functions Calling, RAG, etc.

大模型LLM 应用+AI Agents框架，为我们提供了非常便利的自动化执行任务的能力。微信公众号(订阅号)则是非常适合落地各种AI Agents的场景，我们可以利用微信公众号提供的文本、图像、语音的输入，在自己服务器上部署一套API框架，把自己感兴趣的一些对话、图文、语音等能力的API封装为Agents。这里给大家介绍一个拆箱即用的微信公众号服务端框架 Flask+tencent代码库来实现，并且会利用一个简单的金融智能助理(Finance Agent)的例子来实现一个根据用户输入来查询实时股价，并且返回给微信公众号用户的功能，支持更加复杂定制的AI Agents业务逻辑。

AI agent research have attracted a lot of attention with the rapid development of Large Language Model (LLM) and Reasoning (RL or CoT Chain of Thought) technology. Generative AI Agents use LLM to plan, act and reflect on the memories from the environment as well as the interactions from other agents, just like the environment in Stanford's Generative Agents paper "Generative Agents: Interactive Simulacra of Human Behavior". The simulation environment usually consists of a environment (Such as A Small Town) and multiple AI Agents taking actions to make plans and achieve goals. Before making plans and taking actions, agents will retrieve related information from memories, which is a list of descriptions of the timestamp and exact events, such as "2023-02-03 Someone is watching a movie...". During multi-agents simulation, each agent will have its own timeline and their actions are usually asynchronous. In this blog, we will give a review of the tools of AI Agents Visualization, especially in the situation where there are multiple agents with asynchronous actions during the simulation.

This blog summarizes the latest research development of dialogue and large language models (LLM) papers published in ACL2023 conferences. This year there are total 79 papers related to dialogue in ACL2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).

In this blog, we will introduce how to use ChatGPT stock analysis chatbot to quickly generate professional charts of financial data. A few AI techniques such as RAG (Retrieval Augmented Generation), Text2SQL, and Larget Language Models (LLM) are used to help users find financial data, draw chart to compare key metrics, and adjust the color and shape of the chart using simple natural language prompts. A typical scenrio for professional financial analysts is to draw chart to compare the market capitalization, PE ratio of stocks from global market in converted currencies (US Dollars for example). This is a frequent scenario of daily workflow. The snapshots of AI generated charts is shown using DeepNLP Financial Assistant. You can try by clicking the button below the page, copy and paste the query in the chatbox and ask AI to generate charts or tables in few seconds, copy the charts to your reports or send to your manager. From the chart, we can see that on January 15th, Microsoft (MSFT) market cap reaches 2,887 Billon US dollars, and Apple (AAPL) reaches 2,874.68 Billion US dollars. Microsoft's market cap surpassed Apple and became the company of largest market capitalization in United States. The real-time stock data is from the web APIs, and converted to SQL using Text2SQL and LLM techniques.

In this blog, we will introduce how to use ChatGPT stock analysis chatbot to quickly generate professional charts of financial data. A few AI techniques such as RAG (Retrieval Augmented Generation), Text2SQL, and Larget Language Models (LLM) are used to help users find financial data, draw chart to compare key metrics, and adjust the color and shape of the chart using simple natural language prompts. A typical scenrio for professional financial analysts is to draw chart to compare the market capitalization, PE ratio of stocks from global market in converted currencies (US Dollars for example). This is a frequent scenario of daily workflow. The snapshots of AI generated charts is shown using DeepNLP Financial Assistant. You can try by clicking the button below the page, copy and paste the query in the chatbox and ask AI to generate charts or tables in few seconds, copy the charts to your reports or send to your manager. From the chart, we can see that on January 15th, Microsoft (MSFT) market cap reaches 2,887 Billon US dollars, and Apple (AAPL) reaches 2,874.68 Billion US dollars. Microsoft's market cap surpassed Apple and became the company of largest market capitalization in United States. The real-time stock data is from the web APIs, and converted to SQL using Text2SQL and LLM techniques.

We are witnessing great success in recent development of generative Artificial Intelligence in many fields, such as AI assistant, Chatbot, AI Writer. Among all the AI native products, AI Search Engine such as Perplexity, Gemini and SearchGPT are most attrative to website owners, bloggers and web content publishers. AI Search Engine is a new tool to provide answers directly to users' questions (queries). In this blog, we will give some brief introduction to basic concepts of AI Search Engine, including Large Language Models (LLM), Retrieval-Augmented Generation(RAG), Citations and Sources. Then we will highlight some majors differences between traditional Search Engine Optimization (SEO) and Generative Engine Optimization(GEO). And then we will cover some latest research and strategies to help website owners or content publishers to better optimize their content in Generative AI Search Engines.

In this blog, we will summarize the latex code of most fundamental equations of reinforcement learning (RL). This blog will cover many topics, including Bellman Equation, Markov Decision Process(MDP), Partial Observable Markov Decision Process(POMDP), DQN, A3C, etc.

In this blog, we will show you 3 easy steps to create your personal financial chatbot assistant powered by ChatGPT. ChatGPT is Large Language Model (LLM) based Artificial Intelligent service created by OpenAI company, who just released their latest Assistant API for chatbot creation. These AI assistants are very useful in financial industries. Common use cases include: Generating realtime stock price quotes, Analyzing financial data, Providing investment advices, generating summary of financial reports, etc. Keywords: Financial Chatbot, Financial ChatGPT, Chatbot Stock Price, Chatbot NYSE, Chatbot.