Deeper Seeker is an simpler OSS version of OpenAI's latest Deep Research feature in ChatGPT.It is an agentic research tool to reason , create multi step tasks , synthesize data from multiple online resources and create neat reports

A simple agent framework that's capable of browser use + mcp + auto instrument + plan + deep research + more

A Simple, Distributed and Asynchronous Multi-Agent Reinforcement Learning Framework for Google Research Football AI.

This is a simple implementation for crypto research agent inspired by Claude Skills repo

simple multi-agent pathfinding (MAPF) visualizer for research usage

一个为 Claude Code 设计的简化版多智能体研究系统，通过并行子代理执行基于 Web 的综合性研究。 概述 本系统使用三种专门的代理类型： 研究主导代理（Research Lead Agent）：统筹研究、规划任务、委派给子代理、综合发现 研究子代理（Research Subagents）：使用 Web 工具并行执行专注的研究任务 引用代理（Citations Agent）：为研究报告添加适当的引用

A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives). According to Forrester (2015), AI will replace 16 percent of American jobs by the end of the decade.Chatbots have been used in applications such as customer service, sales and product education. However, a study conducted by Narrative Science in 2015 found that 80 percent of their respondents believe AI improves worker performance and creates jobs.[citation needed] is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities. Background

Simbad is a Java 3d robot simulator for scientific and educationnal purposes. It is mainly dedicated to researchers/programmers who want a simple basis for studying Situated Artificial Intelligence, Machine Learning, and more generally AI algorithms, in the context of Autonomous Robotics and Autonomous Agents. It is not intented to provide a real world simulation and is kept voluntarily readable and simple. Simbad enables programmers to write their own robot controller, modify the environment and use the available sensors. Don't think of it as a finite product but merely as an opened framework to test your own ideas.

An Ai agent that takes a simple prompt, then uses a series of agents to conduct research and and can produce blog posts, linkedin posts or technical guides using a friendly web UI

A simple human interface for human-in-the-loop machine learning research, which allows: 1. annote image on webpage, 2. collect human feedback through keyboard input, 3. direct interaction with agents on remote server

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system utility, decrease the overall cost, and increase mission success probability. Deep Reinforcement Learning (DRL) helps organize agents' behaviors and actions based on their state and represents complex strategies (composition of actions). This research introduces a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN).

A simple GPT-powered agent to help you research

YouTube Content Creation Agent is a cutting-edge, Streamlit-based application designed to streamline the research and content creation process for video creators. By combining real-time search capabilities with advanced large language models (LLMs), it allows users to go from a simple topic query to a polished short-form video script in seconds.

A simple graph library with single-agent pathfinding algorithms for research use

A simple, free research stack for OpenClaw agents. Layered tool hierarchy with community signal, web scraping, and JS rendering.

Originally, ATOM (Automated Theory of Mind) was intended to be a rule-based simulation of how people form intentions from goals, beliefs, and other states. It also aimed to model how people monitor their progress on intentions. It was to be implemented in Prolog (for the agent reasoning parts) and in Java (for simulated physical environments for the agents, and for generating graphical views). The Prolog portion would require infrastructure that isn't built-in to Prolog in order for unit-testing to be possible, and we need automated testing for the ever-larger chunks of reasoning that each agent will be designed to do. Such tests require tight control of initial conditions, such as the environment, and the plan was that, eventually, the Prolog unit-tester would be integrated with a parent TestNG tester in Java. At this point, ATOM is an inference engine capable of both backward and forward-chaining, and of maintaining a trace of its reasoning. Such traces are used to verify the reasoning, and this aspect of the original design was achieved. However, the objective to simulate attributions of mental states, particularly now when we have chosen to use simple animations like Heider and Simmel's (1944), appears to require an ability to propagate constraints before committing to var bindings. For constraint propagation, we are moving away from Amzi Prolog (which nonetheless has the best Prolog debugger I've seen) to ECLiPSe which is another flavor of Prolog whose primary use is constraint propagation and which has an active user base that includes AI researchers. We plan to continue using ATOM as the core reasoning engine of the Wayang project, which is ECLiPSe-based and has already been embedded in Java (allowing for integrations such as Adobe SWF-parsing, visualizations, and the TestNG testing framework). --David Pautler 2009-12-04

A simple langgraph project that synergizes research for content info on the Web, generation of content, and critiquing of written content in one single multi-agent system

A simple yet effective research agent that is fully under your control. It comes packed with a Web UI, and offers full insight into what it's doing and which sources it's consulting (using OpenTelemetry traces). The agent can browse the web, analyze GitHub repositories and review GitHub PRs, and access local files.

An end‑to‑end multi‑agent pipeline that turns a simple topic into a 30‑second video by researching the niche, writing a 6‑segment script, generating AI voiceover and images (OpenAI, Hugging Face, or Pollinations), animating clips with MoviePy, and assembling the final MP4.

LegalBot is an AI-powered multi-agent chatbot built with CrewAI and Grok that makes legal information simple and accessible. It can classify queries, research laws, draft documents, and give bilingual (English & Hindi) explanations — all through a clean Streamlit interface.

A simple Deep research Agent

A simple data model to represent contributions made by agents to research artifacts

A simple, reliable research digest agent that automatically fetches recent AI research papers and delivers clean email summaries to your inbox.

This project implements a sophisticated **Deep Research Agent** using Pydantic AI, Gradio, and OpenAI's latest models. Unlike simple chatbots, this agent functions as an autonomous research team. It orchestrates multiple specialized AI agents to plan, research, and synthesize detailed reports on any stock ticker or general topic.

BioAgent is a curated repository dedicated to tracking, analyzing, and reviewing cutting-edge AI Agents and tools that are revolutionizing biomedical research. We focus on tools that move beyond simple data processing to become autonomous "AI Research Assistants" or "AI Scientists."

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an example of 1 week vibe coding for pocketflow simple academic research agent (Thaijo and Arxiv)

This is a simple project showcasing the usecase of LangChain with AutoGPT agent as AI research assistant

A multi-agent workflow where Research, Copywriter, Art Director, and Manager AI agents collaborate to generate a full marketing campaign brief from a simple product description.

Simple UI, strong filters, and clear sorting for Ollama models and there tags.It ships APIs for easy integration, powering agents, apps, and research.