Financial data platform for analysts, quants and AI agents.

Multi-agent AI trading system using LLM-powered analyst agents (Buffett, Munger, Burry, etc.) with free data sources (SEC EDGAR + yfinance) and Alpaca integration.

Developed a Web search and Financial Analyst AI agent team Scraping web to provide real time data, and streamlined in real time as API service

Data-Verse is an end-to-end AI data analysis agent that automates data ingestion, cleaning, pattern extraction, and predictive modeling, culminating in interactive visualizations—providing a comprehensive alternative to traditional data analysts.

TubeWhale is an open-source AI framework that automates YouTube video search, transcript retrieval, and content summarization. Users input a keyword, and TubeWhale's multi-agent system expands the search, processes video data, and provides structured summaries. Ideal for researchers and analysts, it simplifies video content exploration.

This project showcases CrewAI for collaborative cryptocurrency trading analysis. It uses a Jupyter Notebook to orchestrate multiple AI agents (Data Analyst, Trading Strategy Developer, Trade Advisor, and Risk Advisor) that work together to analyze BTCUSDT market data, develop strategies, plan executions, and assess risks.

Build a production-ready multi-agent quantitative analyst system where AI agents scrape market data, analyze trends, and generate investment reports. Uses FastAPI for orchestration, Streamlit UI, Azure PostgreSQL for logging, and Azure Blob Storage for archiving.

Clinical Trial Inspector is an advanced AI agent designed to revolutionize how researchers, clinicians, and analysts explore clinical trial data. By combining Semantic Search, Retrieval-Augmented Generation (RAG), and Visual Analytics, it transforms raw data from ClinicalTrials.gov into actionable insights.

The earliest work in computerized knowledge representation was focused on general problem solvers such as the General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon in 1959. These systems featured data structures for planning and decomposition. The system would begin with a goal. It would then decompose that goal into sub-goals and then set out to construct strategies that could accomplish each subgoal. In these early days of AI, general search algorithms such as A* were also developed. However, the amorphous problem definitions for systems such as GPS meant that they worked only for very constrained toy domains (e.g. the "blocks world"). In order to tackle non-toy problems, AI researchers such as Ed Feigenbaum and Frederick Hayes-Roth realized that it was necessary to focus systems on more constrained problems. These efforts led to the cognitive revolution in psychology and to the phase of AI focused on knowledge representation that resulted in expert systems in the 1970s and 80s, production systems, frame languages, etc. Rather than general problem solvers, AI changed its focus to expert systems that could match human competence on a specific task, such as medical diagnosis. Expert systems gave us the terminology still in use today where AI systems are divided into a Knowledge Base with facts about the world and rules and an inference engine that applies the rules to the knowledge base in order to answer questions and solve problems. In these early systems the knowledge base tended to be a fairly flat structure, essentially assertions about the values of variables used by the rules.[2] In addition to expert systems, other researchers developed the concept of frame-based languages in the mid-1980s. A frame is similar to an object class: It is an abstract description of a category describing things in the world, problems, and potential solutions. Frames were originally used on systems geared toward human interaction, e.g. understanding natural language and the social settings in which various default expectations such as ordering food in a restaurant narrow the search space and allow the system to choose appropriate responses to dynamic situations. It was not long before the frame communities and the rule-based researchers realized that there was synergy between their approaches. Frames were good for representing the real world, described as classes, subclasses, slots (data values) with various constraints on possible values. Rules were good for representing and utilizing complex logic such as the process to make a medical diagnosis. Integrated systems were developed that combined Frames and Rules. One of the most powerful and well known was the 1983 Knowledge Engineering Environment (KEE) from Intellicorp. KEE had a complete rule engine with forward and backward chaining. It also had a complete frame based knowledge base with triggers, slots (data values), inheritance, and message passing. Although message passing originated in the object-oriented community rather than AI it was quickly embraced by AI researchers as well in environments such as KEE and in the operating systems for Lisp machines from Symbolics, Xerox, and Texas Instruments.[3] The integration of Frames, rules, and object-oriented programming was significantly driven by commercial ventures such as KEE and Symbolics spun off from various research projects. At the same time as this was occurring, there was another strain of research that was less commercially focused and was driven by mathematical logic and automated theorem proving. One of the most influential languages in this research was the KL-ONE language of the mid-'80s. KL-ONE was a frame language that had a rigorous semantics, formal definitions for concepts such as an Is-A relation.[4] KL-ONE and languages that were influenced by it such as Loom had an automated reasoning engine that was based on formal logic rather than on IF-THEN rules. This reasoner is called the classifier. A classifier can analyze a set of declarations and infer new assertions, for example, redefine a class to be a subclass or superclass of some other class that wasn't formally specified. In this way the classifier can function as an inference engine, deducing new facts from an existing knowledge base. The classifier can also provide consistency checking on a knowledge base (which in the case of KL-ONE languages is also referred to as an Ontology).[5] Another area of knowledge representation research was the problem of common sense reasoning. One of the first realizations learned from trying to make software that can function with human natural language was that humans regularly draw on an extensive foundation of knowledge about the real world that we simply take for granted but that is not at all obvious to an artificial agent. Basic principles of common sense physics, causality, intentions, etc. An example is the frame problem, that in an event driven logic there need to be axioms that state things maintain position from one moment to the next unless they are moved by some external force. In order to make a true artificial intelligence agent that can converse with humans using natural language and can process basic statements and questions about the world, it is essential to represent this kind of knowledge. One of the most ambitious programs to tackle this problem was Doug Lenat's Cyc project. Cyc established its own Frame language and had large numbers of analysts document various areas of common sense reasoning in that language. The knowledge recorded in Cyc included common sense models of time, causality, physics, intentions, and many others.[6] The starting point for knowledge representation is the knowledge representation hypothesis first formalized by Brian C. Smith in 1985:[7] Any mechanically embodied intelligent process will be comprised of structural ingredients that a) we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and b) independent of such external semantic attribution, play a formal but causal and essential role in engendering the behavior that manifests that knowledge. Currently one of the most active areas of knowledge representation research are projects associated with the Semantic Web. The Semantic Web seeks to add a layer of semantics (meaning) on top of the current Internet. Rather than indexing web sites and pages via keywords, the Semantic Web creates large ontologies of concepts. Searching for a concept will be more effective than traditional text only searches. Frame languages and automatic classification play a big part in the vision for the future Semantic Web. The automatic classification gives developers technology to provide order on a constantly evolving network of knowledge. Defining ontologies that are static and incapable of evolving on the fly would be very limiting for Internet-based systems. The classifier technology provides the ability to deal with the dynamic environment of the Internet. Recent projects funded primarily by the Defense Advanced Research Projects Agency (DARPA) have integrated frame languages and classifiers with markup languages based on XML. The Resource Description Framework (RDF) provides the basic capability to define classes, subclasses, and properties of objects. The Web Ontology Language (OWL) provides additional levels of semantics and enables integration with classification engines.[8][9]

The Finance Monitoring AI Agent 📊💹 analyzes specific tickers, gathering and processing data to generate insightful reports 📈📉. Designed for investors and analysts, this agent provides detailed performance analysis and trends. 🚀

Agentic SOC Analyst: A secure, local MCP server connecting Claude AI to Splunk Enterprise. Natural language threat hunting without data leaving your network.

Data Analyst AI Agent

OUR DATA ANALYST AGENT

📈 AI-powered financial analyst with real-time stock data, SEC 10-K analysis, and LangGraph agents. Built with FastAPI, React, FAISS, and Yahoo Finance API.

This script demonstrates how to build a team of AI agents that work together as a financial analyst using GPT-4o in just 20 lines of Python code. The system combines web search capabilities with financial data analysis tools to provide comprehensive financial insights.

This is my original product, I have builded AI based Finance Analyst which is based on AI Agentic Workflow concept, it uses yahoo finance for fetching stock data and duckduckgo as search engine to query the web to perform sentiment analysis as per latest news about the stock, you will need to configure your own perplexity key to use my product.

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Autonomous Multi-Agent AI system for financial research. Orchestrates specialized AI analysts to debate technical, fundamental, and sentiment data, reaching verified consensus on high-conviction investment strategies.

An MCP server that allows AI agents to interact with Apache Superset instances through browser automation. It doesn't need special API keys and works with any SSO. (data-analyst agent included)

A Multi-Agent AI pipeline that autonomously explores, analyzes, and reports on any dataset. It features agents like Data Scientist, Python Expert, Data Analyst, and Customer Engineer. Built with LangGraph and LangChain for multi-agent flow, and uses RAG to minimize context length and improve response efficiency.

The Financial Research Analyst Agent is a hierarchical multi-agent system that provides comprehensive stock analysis by coordinating 11 specialized AI agents, 20+ analysis tools, a RAG knowledge pipeline, and a multi-provider data layer — all accessible through a Streamlit web app, REST API, and CLI.

Stock Analyst Agent: AI-powered stock analysis using the Claude Agent SDK with real-time Alpha Vantage data. Includes subagents for fundamental, technical, sentiment, and risk analysis. Built with TypeScript + Zod. Run the dev server and ask, e.g., “What’s the current price of AAPL?”

Quick Start to Field Service Lightning With Field Service Lightning, you can streamline operations across the full service chain on a single platform, resulting in a more integrated client experience. You can redefine the connected customer experience by giving your agents, dispatchers, and mobile staff the tools they need to provide a strong customer experience anytime, anywhere, and on any mobile device with Field Service Lightning. How does Field Service Lightning Benefit the Workforce Field Service Lightning is a single platform that links your entire workforce, allowing you to provide your clients with faster, smarter, and more personalized on-site service. It not only allows your employees to stay on top of things, but it also allows customers to easily book their own appointments through a customer community. From the bottom of the food chain to the top, Field Service Lightning delivers vital tools for all team members. Mobile Employees Use the all-in-one mobile app to get all of the information they need to execute each job properly. They can see the parts they'll need, the steps they'll need to complete each assignment, and even get directions to the location. Support Agents Have access to appointment scheduling, which allows them to see detailed case feeds, real-time milestone monitoring, and worker skills and knowledge. Dispatchers From the dispatcher dashboard, you can view and manage all scheduled tasks, coordinate resources, and use Map View and Field Service Management Tools for real-time monitoring of all field employees. Managers Capable of managing field resource management, as well as critical customer and employee KPIs, in order to ensure that operations are as effective and productive as possible in the salesforce service cloud. Capture Data and Focus on Success The appointment scheduling procedure is automated with salesforce field service management. As a result, field service requests are resolved faster and the appropriate service resource is allocated to the project, resulting in increased customer satisfaction. The productivity of service resources is boosted by having all of the tools needed on mobile devices to handle work orders and service reports rapidly. Everything is stored in one location, from installations and repairs to preventative maintenance, in order to retain that important 360-degree client view. Consider several salesforce field service lightning pricing before integration. Outcomes Create work orders quickly from any case. Create and manage field service work orders, as well as access Knowledge articles and track SLA compliance with Milestones. Work orders are linked to Accounts, Contacts, Assets, Cases, Entitlements, and other Salesforce Field Service Lightning objects, allowing you to pull data from several sources. Optimize scheduling and assign jobs sensibly. From the Service Console or a Customer Community, you may book truth-based service appointments right away with Field Service Lightning Implementation. To boost employee productivity, jobs are automatically assigned to the proper resource based on time, talents, location, and any business rules with intelligent scheduling. Companies have incorporated sophisticated scheduling and tracking of employees, equipment, and trucks to guarantee the proper parts are accessible for the job with economical field service salesforce pricing. Increase your first-time repair rate by leveraging the power of AI. Field Service Lightning Consulting analysts may utilize image recognition with AI Vision to quickly identify assets and parts in photos. To avoid confusion and extra trips back to the office, make sure the correct product part is repaired or replaced. You can automatically prescribe the right set up steps for technicians based on an image's classification, so they can provide faster, smarter Salesforce Field Service.

A LangChain-powered AI that analyzes CSV, XLSX, and PDF data through conversational queries. Features memory for context-aware interactions and generates visualizations for insights. Perfect for data exploration and decision-making.

An AI-powered virtual data analyst that automates data exploration, reporting, and insights. Built with n8n and LLMs, it integrates with CSV, Excel, SQL, and APIs to answer natural language queries, generate reports, and streamline analytics workflows.

A powerful, intelligent data analysis platform featuring 6 specialized AI agents for comprehensive dataset analysis. Each agent focuses on specific aspects of data analysis - from dataset description to business insights generation.

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An autonomous multi-agent AI Data Analyst powered by Groq and Phidata. Transform raw CSV and SQL data into insights and visualizations through natural conversation.

Meet Mika, your AI agent that allows you to create, update and get cards in Metabase, taking the role of your personal Data Analyst for your Business