Search Results for "decision making"
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Showing 232 open source projects for "decision making"
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Quint Code
Structured reasoning framework for Claude Code, Gemini, and Cursor
Quint Code is a structured reasoning and decision-support framework aimed at making AI-assisted software engineering and decision workflows more rigorous and auditable. It implements the First Principles Framework (FPF) to guide users and AI tools through hypothesis generation, logical verification, evidence gathering, and documented decision making, reducing reliance on ad hoc or “vibe” coding. -
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Loomio
Make better decisions together
Making important decisions is critical for any business or organization to succeed. However, making wise decisions as a group can be quite the challenge, especially if these decision makers aren't in the same location. Loomio is the solution for such businesses and organizations. It is a collaborative decision-making tool that makes it easy for anyone to participate in decisions, and helps groups make the best decisions together. -
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zhangxuefeng-skill
Zhang Xuefeng's cognitive operating system
zhangxuefeng-skill is an AI agent skill package that distills the decision-making frameworks and practical reasoning style of Chinese educator Zhang Xuefeng into a reusable, executable cognitive system. Rather than functioning as a simple quote collection, it encodes structured heuristics, mental models, and decision logic derived from books, interviews, and real-life case analysis. The skill is designed to be used within AI coding agents such as Claude Code, where it can be invoked to provide guidance on topics like college major selection, career planning, and long-term life decisions. ... -
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highway-env
A minimalist environment for decision-making in autonomous driving
HighwayEnv is an OpenAI Gym-compatible environment focused on autonomous driving scenarios. It provides flexible simulations for testing decision-making algorithms in highway, intersection, and merging traffic situations. -
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AutoHedge
Build your autonomous hedge fund in minutes
AutoHedge is an AI-driven financial automation system designed to simulate and manage hedging strategies using intelligent agents. The project focuses on applying autonomous decision-making to financial risk management, allowing agents to analyze market conditions and adjust positions dynamically. It is built around the concept of algorithmic hedging, where strategies are executed programmatically rather than manually, enabling faster and more consistent responses to market changes. The system supports integration with financial data sources, allowing it to process real-time or historical data for analysis and strategy execution. ... -
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Clay Foundation Model
The Clay Foundation Model - An open source AI model and interface
The Clay Foundation Model is an open-source AI model and interface designed to provide comprehensive data and insights about Earth. It aims to serve as a foundational tool for environmental monitoring, research, and decision-making by integrating various data sources and offering an accessible platform for analysis. -
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BehaviorTree.CPP
C++ behavior tree library for robotics and AI decision systems
BehaviorTree.CPP is a C++ library designed to create, manage, and execute behavior trees, a widely used model for decision-making in robotics and artificial intelligence systems. It provides a flexible and modular framework that allows developers to define complex behaviors as reusable tree structures composed of nodes. BehaviorTree.CPP emphasizes performance and real-time execution, making it particularly suitable for robotics applications where responsiveness is critical. ... -
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MetaClaw
Just talk to your agent
...The project likely emphasizes meta-level reasoning, where agents are not only executing tasks but also adapting their strategies based on feedback and performance signals. It may incorporate mechanisms for learning from interactions, improving decision-making over time, and generalizing across different domains. The architecture suggests scalability, allowing the system to handle multiple agents or complex workflows simultaneously. It is likely designed for experimentation with next-generation agent systems that combine planning, learning, and execution. Overall, MetaClaw represents a research-driven effort to push the boundaries of intelligent agent coordination and adaptability. -
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Semantic Router
Superfast AI decision making and processing of multi-modal data
Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning. Combining LLMs with deterministic rules means we can be confident that our AI systems behave as intended. Cramming agent tools into the limited context window is expensive, slow, and fundamentally limited. -
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Matter AI
Matter AI is open-source AI Code Reviewer Agent
Matter AI is an AI-powered platform designed to enhance productivity through automated content generation, data analysis, and decision support. It leverages machine learning models to process text, analyze patterns, and generate insights, making it suitable for businesses looking to optimize data-driven decision-making. Matter AI integrates with various data sources and provides customizable AI workflows tailored to different industries. -
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julep
A new DSL and server for AI agents and multi-step tasks
Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It offers long-term memory and manages multi-step processes. Julep enables the creation of multi-step tasks incorporating decision-making, loops, parallel processing, and integration with numerous external tools and APIs. While many AI applications are limited to simple, linear chains of prompts and API calls with minimal branching, Julep is built to handle more complex scenarios. -
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SwarmZero
SwarmZero's SDK for building AI agents, swarms of agents and much more
SwarmZero is an open-source platform designed for deploying and managing autonomous robot swarms. It enables collective coordination, decentralized decision-making, and real-time collaboration among large groups of autonomous agents, focusing on multi-robot systems and research in swarm robotics. -
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Agno
Lightweight framework for building Agents with memory, knowledge, etc.
Agno is a modular, open-source artificial general intelligence (AGI) research platform that allows developers to build, evaluate, and experiment with cognitive architectures in a composable way. It provides a flexible framework for modeling reasoning, memory, decision-making, and planning, aimed at long-term AI research beyond narrow learning. Agno embraces multi-agent environments and symbolic reasoning as part of its core design, enabling experiments with structured knowledge, goal-oriented behaviors, and meta-learning. It’s designed for researchers seeking an extensible platform to explore AGI components without being tied to black-box models. -
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Android Use
Automate native Android apps with AI using accessibility APIs
...The project works by using Android’s accessibility API to extract structured UI state (as XML) from the device, which is then fed to a large language model (LLM) like OpenAI’s models for decision-making, and actions are executed via the Android Debug Bridge (ADB). This approach bypasses expensive vision-based models and provides faster, cheaper automation with fine-grained interaction capabilities (for example, tapping buttons, typing text, navigating screens). -
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Sapiens
High-resolution models for human tasks
...The project emphasizes long-horizon reasoning and cross-modal grounding—connecting language, perception, and action into a single agentic model capable of following abstract goals. It includes simulation environments, datasets, and benchmarks for testing grounded understanding, imitation learning, and decision-making. The system’s modular pipeline supports both imitation-based and reinforcement-based training strategies, allowing flexible experimentation with different embodiments and tasks. -
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AgentForge
Extensible AGI Framework
AgentForge is a framework for creating and deploying AI agents that can perform autonomous decision-making and task execution. It enables developers to define agent behaviors, train models, and integrate AI-powered automation into various applications. -
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AGI
The first distributed AGI system
...It aims to provide a foundation for creating agents that can reason, plan, and execute tasks across diverse domains by integrating multiple AI capabilities into a unified system. The project typically explores concepts such as agent orchestration, memory systems, task decomposition, and decision-making loops, enabling the development of more generalized and adaptive AI behaviors. It is designed to be extensible, allowing developers to plug in different models, tools, and data sources to enhance agent performance. The framework encourages experimentation with AGI-like architectures, making it useful for researchers and developers interested in advancing beyond narrow AI applications. -
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The Minimalist Entrepreneur
Claude Code skills based on The Minimalist Entrepreneur
The Minimalist Entrepreneur repository is a curated collection of structured “skills” designed to enhance the capabilities of AI systems by providing reusable patterns, workflows, and domain-specific knowledge. It acts as a modular layer that can be integrated into AI agents to improve how they perform tasks such as reasoning, coding, communication, and decision-making. The project emphasizes composability, allowing individual skills to be combined into larger workflows that solve more complex problems. It is designed to be human-readable and easy to extend, making it accessible for developers who want to customize or build their own AI skill sets. The repository reflects a broader shift toward treating AI behavior as programmable and modular rather than monolithic. ... -
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DreamerV3
Mastering Diverse Domains through World Models
...The system works by building an internal model of the environment and then using that model to simulate possible future outcomes of actions, allowing the agent to learn from imagined experiences rather than only from real interactions. This approach enables the algorithm to efficiently learn policies for decision-making tasks that would otherwise require enormous amounts of data or computational resources. DreamerV3 was designed as a general reinforcement learning framework that can solve diverse tasks using the same configuration of hyperparameters across many environments. In research demonstrations, the algorithm has been shown to perform strongly across more than one hundred control tasks and complex simulated environments. -
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verl-agent
Designed for training LLM/VLM agents via RL
...Built as an extension of the veRL reinforcement learning infrastructure, the project focuses on enabling scalable training for agents that perform multi-step reasoning and decision-making tasks. The framework supports multi-turn interactions between agents and their environments, allowing the system to receive feedback after each step and adjust its strategy accordingly. This step-wise interaction model makes it possible to train agents to operate in long-horizon scenarios where decisions depend on cumulative context and previous outcomes. ... -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
...The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making abilities in real time. Instead of relying purely on reward signals as in reinforcement learning agents, the models analyze contextual information and generate strategic actions based on the game environment. Performance is evaluated using a competitive ranking system that assigns models an ELO rating based on their results across matches against other models. -
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AI Agents From Scratch
Demystify AI agents by building them yourself. Local LLMs
...The repository provides example implementations that demonstrate how language models can interact with external systems, perform reasoning tasks, and execute structured workflows. It focuses on explaining the architecture of agent systems rather than simply providing finished code, making it useful for developers who want to understand how AI agents actually work internally. By building agents incrementally, the project helps learners grasp concepts such as decision loops, task decomposition, and environment interaction. -
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WorkflowEngine.NET
WorkflowEngine.NET - component that adds workflow in your application
...It enables developers to model complex workflows with conditions, branching, and state transitions. The engine is highly customizable and can be embedded into any .NET project to automate tasks and decision-making. -
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FingerprintPro.ServerSdk
C#/Dotnet SDK for Fingerprint Pro Server API
Fingerprint Pro Server API allows you to get information about visitors and about individual events in a server environment. It can be used for data exports, decision-making, and data analysis scenarios. Server API is intended for server-side usage, it's not intended to be used from the client side, whether it's a browser or a mobile device. -
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MemPalace
The highest-scoring AI memory system ever benchmarked
...MemPalace emphasizes fidelity over compression, preserving full conversational history to maintain reasoning, nuance, and decision-making context that is typically lost in other systems.
