Search Results for "path-movement"
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Showing 13 open source projects for "path-movement"
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TencentDB Agent Memory
TencentDB Agent Memory delivers fully local long-term memory for AI
...For personalization, it organizes memory from raw conversations into atoms, scenarios, and persona-level knowledge. The design keeps high-level memory inspectable while preserving a drill-down path back to raw evidence. It is built for OpenClaw and Hermes-style agent workflows that need lower token usage, better continuity, and no external API dependency. -
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Codex with ChatGPT
ChatGPT thinks. Codex works. Use ChatGPT as the planning brain
...After Codex makes changes, ChatGPT can independently inspect the actual diff and test evidence before continuing the loop. The bridge uses OAuth, one-time pairing, workspace boundaries, path containment, and sensitive-file blocking to limit access. Setup and updates are designed to be largely automated through a Codex skill, with Cloudflare tunneling used to expose the local read-only bridge when needed. -
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OpenClaw Installer
ClawdBot one-click deployment tool
OpenClaw Installer is an open-source one-click deployment and configuration tool for installing OpenClaw — a personal AI assistant — onto systems with minimal manual setup, giving users a streamlined path to get their own AI assistant running quickly. The project provides shell scripts and configuration menus that detect the host environment, install dependencies, download OpenClaw, configure core settings like AI models and identity channels, and start the server automatically. It supports multiple platforms, including macOS, Linux distributions (Ubuntu, Debian, CentOS), and Windows environments via compatible shells, and simplifies otherwise complex installation steps into a guided, terminal-based experience. ... -
4
Sol Advisor
Codex-native architect orchestration with Luna and Terra
...The workflow can monitor implementation, inspect diffs, request corrections, coordinate dependent work, and control final approval. It is distributed as a Codex plugin with companion custom-agent definitions for the native execution path. -
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5
DeepSeek Engineer v2
A powerful coding assistant application
...Version 2.0 focuses on native function calling instead of rigid structured JSON responses. The assistant can read files, read multiple files, create files, create multiple files, and edit specific snippets when needed. It includes safeguards such as path validation, directory traversal protection, file size limits, and binary file exclusion. Overall, it is designed for developers who want a conversational coding tool that can inspect, modify, and reason about project files from the command line. -
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eve
The Framework for Building Agents
eve is Vercel’s open-source framework for building, running, and scaling durable backend AI agents. It uses a filesystem-first model where an agent is defined as a directory of files, making the project structure itself the agent contract. The framework is designed to include production concerns from the start instead of leaving developers to assemble durability, sandboxing, approvals, and deployment separately. Eve supports durable execution, sandboxed compute, human-in-the-loop approvals,... -
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Hello-Agents
Building an Intelligent Agent from Scratch
...It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. Its goal is to demystify agent engineering and help developers move from simple prompt scripts to robust autonomous systems. -
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Keep Codex Fast
A backup-first Codex skill for keeping local Codex state fast
...When applied manually, it backs up first, archives old sessions, rotates large logs, moves stale worktrees, and prunes dead references instead of deleting important state. It also includes an optional repair path for oversized thread title and preview metadata. Its main value is helping users preserve continuity through handoff documents while reducing local drag in Codex. -
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RWARE
MuA multi-agent reinforcement learning environment
robotic-warehouse is a simulation environment and framework for robotic warehouse automation, enabling research and development of AI and robotic agents to manage warehouse logistics, such as item picking and transport. -
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Multi-Agent path planning in Python
Python implementation of a bunch of multi-robot path-planning
multi_agent_path_planning is a Python-based implementation of multi-agent pathfinding algorithms for coordinating multiple agents in shared environments without collisions. It is useful in robotics, warehouse automation, and gaming AI. -
11
Game of Giants - 2 player AI
2 Player (Bot or Human) AI Gaming
...After moving, the giant shoots an arrow from its landing square to another square, using another rook-like move. This arrow may travel in any orthogonal direction (even backwards along the same path the giant just traveled, into or across the starting square if desired). An arrow, like a giant, cannot cross or enter a square where another arrow has landed or a giant stands. The square where the arrow lands is marked to show that it can no longer be used. The last player to be able to make a move wins. How to play: 1. Select players from the panel 2. ... -
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This project aim to provide a crowd movement simulation based on primitive human behaviours and emerging behaviours for research and educational purposes. The application (front-end) is generic and can be reused.
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13
videonetwork
A simulator for self-organizing video replication and more
...We tackle these problems by introducing a bio-inspired algorithm, which lets the content place itself where it is needed. It is robust, scalable and adaptive. The transport path of content is exploited to replicate content and limited storage space is handled by clean-up mechanisms. We implemented a simulator to show the algorithm's performance and to test its behavior under real circumstances. It is easily extendable to support several new scenarios or changes to the algorithm (see replication and client models).
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