Search Results for "core"

OpenManus is an open‑agent AI framework focused on building versatile general-purpose agents capable of autonomously executing complex workflows — such as planning, browsing, tool invocation — all via a pluggable prompts and tools interface. It's being extended with reinforcement learning‑based tuning modules and designed for researchers and developers building custom AI agents.

...It provides high-level asynchronous agents (like AsyncDefaultAgent and AsyncActor) that encapsulate the loop of capturing screenshots, sending them to Lux, interpreting responses, and executing UI actions with PyAutoGUI. Multiple installation flavors let you choose between a minimal oagi-core package or variants that bundle desktop automation and FastAPI/Socket.IO server capabilities.

Kagent is a Kubernetes-native framework for building, deploying, and operating AI agents as first-class cloud-native workloads. It models core agent concepts declaratively using Kubernetes custom resources, so teams can manage agents similarly to other platform components via YAML, controllers, and standard cluster workflows. In kagent’s design, an “Agent” represents a system prompt plus a set of tools and other agents, along with an LLM configuration, making the agent definition portable and repeatable across environments. ...

...It integrates cleanly with React, Next.js, and Node-based backends, but can also run as a standalone server, giving teams flexibility in how they deploy their AI logic. At its core, Mastra provides abstractions for agents, workflows, tools, memory, retrieval, and model routing, so developers can focus on specifying behavior rather than wiring infrastructure from scratch. Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. ...

...The repository follows a clear pattern for defining agents and their behaviors, making it easier to extend with new skills or tailor existing ones to your use case. Because it’s framework-agnostic at its core, the Antigravity Kit can integrate with IDEs and other development tooling for smoother AI-augmented coding.

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/

The project has moved to github https://github.com/EnFlexIT/AgentWorkbench Agent.GUI is a simulation framework and toolkit based on the JADE framework. It provides functionalities for time aspects, agent-environment interaction, visualization and load balancing, Furthermore, the included application focuses the usability for end users.

...The world has an amount of energy that the user may use to create Actors and components while Actors may also create other Actors from their own energy. The code is structured in several layers: - april-core simply defines the library and the objects - april-gui builds on the concepts in april-core, giving a visual interpretation to the concepts - AprilDream is a GUI program that uses april-gui objects to display a World in evolution. The code base also contains a test suite based on [Google test](http://code.google.com/p/googletest/) and a set of ever-growing commented examples.

...Ellipsis uses the standard application server JBoss (version 4.2.3 GA) without any modifications. In contrast to existing mobile agent toolkits, Ellipsis has an agent- and message-oriented architecture. Ellipsis is built on top of the JMX architecture. Core Ellipsis components are implemented as MBeans which provides for basic ser-vices to execute agents and control their life-cycle. One of the core components is a special MBean (AgentModul) that makes it possible to load an agent powered by agent engines. Agent engines can execute one type of agent. Based on this mechanism, agents can be added dynamically to a running system, can be stopped (for example in case of an error) and restarted later. ...

nu-BDI is an extension of the Jason agent interpreter aimed at processing norms using constraints on the agent's instantiated plans. The core agent reasoning cycle now includes a component for processing norms and enforcing norm constraints.

Java JNI API for morphological analysis of Russian. Note: C++ JNI code and the core code is at the project http://sourceforge.net/projects/seman/ .

PIQLE is a Platform Implementing Q-LEarning (and other Reinforcement Learning) algorithms in JAVA. Version 2 is a major refactoring. The core data structures and algorithms are in piqle-coreVersion2. Examples are in piqle-examplesVersion2. A complete doc

This project intends to create an indexing search engine, for knowledge management. The primary object is to apply an information retrieval core. And implement a knowledge data discovery theory such as data mining algorithm, text mining.

SB-MASE is aimed at students and researchers in AI, for simulation and control of agents and robots. It uses task based decomposition and the subsumption architecture as its core control mechanisms and includes numerous possibilities for adding plug-ins.