Search Results for "decision"
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Showing 602 open source projects for "decision"
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Markdown Any Decision Records
Markdown Architectural Decision Records
...For some decisions, these bits of information could not be enough. Therfore, it is possible to “unpack” the decision. This is done in the presented longer version of the ADR. -
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IBM-ODM-Docker
This repository allows to deploy an IBM Operational Decision Manager
...IBM ODM is a decisioning platform to automate your business policies. Business rules are used at the heart of the platform to implement decision logic on a business vocabulary and run it as web decision services. -
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dtreeviz
Python library for decision tree visualization & model interpretation
A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. -
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POMDPs
Interface for defining, solving, simulating Markov decision processes
A Julia interface for defining, solving and simulating partially observable Markov decision processes and their fully observable counterparts. The POMDPs.jl package contains only the interface used for expressing and solving Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). The POMDPTools package acts as a "standard library" for the POMDPs.jl interface, providing implementations of commonly-used components such as policies, belief updaters, distributions, and simulators. -
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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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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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MiroFish
A Simple and Universal Swarm Intelligence Engine
...Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. The engine includes both backend and frontend components, with configuration and deployment instructions for local and containerized setups, and is designed to produce detailed predictive reports based on interactions and emergent patterns within the simulated world. -
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ORY Oathkeeper
A cloud native Identity & Access Proxy / API (IAP)
ORY Oathkeeper is an Identity & Access Proxy (IAP) and Access Control Decision API that authorizes HTTP requests based on sets of Access Rules. The BeyondCorp Model is designed by Google and secures applications in Zero-Trust networks. An Identity & Access Proxy is typically deployed in front of (think API Gateway) web-facing applications and is capable of authenticating and optionally authorizing access requests. -
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AutoHedge
Build your autonomous hedge fund in minutes
...It also emphasizes modularity, enabling developers to customize strategies, risk parameters, and decision logic. AutoHedge is particularly useful for experimentation and research in algorithmic trading and financial automation. Overall, it represents an attempt to bring agent-based intelligence into portfolio management and risk mitigation workflows. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Semantic Router
Superfast AI decision making and processing of multi-modal data
...Semantic Router enables lightning-fast and cheap tool usage that can scale to many thousands of tools. LLMs are slow, yet we use them for every decision in agentic use-cases. Semantic Router swaps slow LLM calls for superfast route decisions. -
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Loomio
Make better decisions together
...Loomio adapts to various discussion formats and styles, and offers effective polling and decision-making tools so your group can always decide your next steps fast, firmly and fairly. -
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FLEXible
Federated Learning (FL) experiment simulation in Python
FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees. -
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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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TradingAgents
Chinese Financial Trading Framework Based on Multi-Agent LLM
TradingAgents-CN is a Chinese-enhanced, multi-agent LLM framework aimed at building financial analysis and trading-oriented workflows, with an emphasis on collaboration between specialized agents rather than a single monolithic prompt. It organizes market-related tasks into roles and stages so different agents can contribute research, reasoning, aggregation, and decision support in a structured pipeline. The project is oriented toward practical usage, including a stack that can be run in a modern development environment and commonly paired with containerized backends, configuration files, and service components. It also pays attention to distribution and misuse risks, clearly warning users about unauthorized commercial repackaging and stating that commercial use requires explicit authorization while personal use is open. -
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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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Intelligent stock analysis system
LLM-driven A/H/US stock intelligent analyzer
Intelligent stock analysis system is a Python-based smart stock analysis system that leverages large language models to automatically analyze selected equities across A-shares, Hong Kong stocks, and U.S. markets. It’s designed to produce a daily “decision dashboard” summarizing key insights such as core conclusions, precise entry/exit points, and checklists for potential trades, combining multi-dimensional technical analysis, market sentiment, chip distribution, and real-time price data. The system supports scheduled execution using GitHub Actions, enabling fully automated daily analysis and multi-channel notifications via platforms like Telegram, Enterprise WeChat, Feishu, email, and push services. ... -
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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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CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. ... -
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Endless Sky
Endless Sky is a sandbox-style space exploration game
Endless Sky starts off the player with a decision to pick their own ship based on their style of gameplay. Will you choose to be the merchant and enjoy quiet and steady work among the stars? Or will you become a bounty hunter and fight off pirates to strike it rich? You may even decide to become the bad guy and choose to be the pirate yourself. -
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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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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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Log4brains
Log and publish your architecture decisions (ADR)
Log4brains is a docs-as-code knowledge base for your development and infrastructure projects. It enables you to log Architecture Decision Records (ADR) right from your IDE and to publish them automatically as a static website. -
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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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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.
