Search Results for "decision" - Page 5
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Showing 488 open source projects for "decision"
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Excalibur
Excalibur is a highly opinionated agent harness
Excalibur is an experimental or utility-oriented project that appears to focus on enabling structured execution, control, or enhancement of workflows within AI or development environments. The system likely provides tools for managing tasks, orchestrating processes, or enhancing decision-making capabilities in automated systems. Its design suggests an emphasis on control and precision, allowing users to define how tasks are executed and monitored. It may include abstractions for handling inputs, outputs, and intermediate steps, enabling more predictable behavior in complex workflows. The architecture is likely modular, supporting customization and extension for different use cases. ... -
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Dynamiq
An orchestration framework for agentic AI and LLM applications
...The framework supports the creation of multi-agent systems where different AI agents collaborate to solve tasks such as information retrieval, document analysis, or automated decision making. Dynamiq also includes built-in support for retrieval-augmented generation pipelines that allow models to access external documents and knowledge bases during inference. -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
...Unlike single-agent systems, it treats task completion as a collaborative workforce where agents take on specialized roles (planning, execution, analysis) and coordinate via a modular multi-agent architecture that supports flexible teamwork across domains. OWL delivers state-of-the-art performance on benchmarks like GAIA and emphasizes real-time decision-making, web automation, rich search integration, document parsing, and multi-tool workflows, making it suitable for tasks ranging from information retrieval to interactive automation. -
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FinGPT
Open-Source Financial Large Language Models
FinGPT is an open-source, finance-specialized large language model framework that blends the capabilities of general LLMs with real-time financial data feeds, domain-specific knowledge bases, and task-oriented agents to support market analysis, research automation, and decision support. It extends traditional GPT-style models by connecting them to live or historical financial datasets, news APIs, and economic indicators so that outputs are grounded in relevant and recent market conditions rather than generic knowledge alone. The platform typically includes tools for fine-tuning, context engineering, and prompt templating, enabling users to build specialized assistants for tasks like sentiment analysis, earnings summary generation, risk profiling, trading signal interpretation, and document extraction from financial reports. -
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UI-TARS
UI-TARS-desktop version that can operate on your local personal device
UI-TARS is an open-source multimodal “GUI agent” created by ByteDance: a model designed to perceive raw screenshots (or rendered UI frames), reason about what needs to be done, and then perform real interactions with graphical user interfaces (GUIs) — like clicking, typing, navigating menus — across desktop, browser, mobile, or game environments. Rather than relying on rigid, manually scripted UI automation, UI-TARS uses a unified vision-language model (VLM) that integrates perception,... -
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Blue Whale Configuration Platform
Blue Whale smart cloud configuration platform
...From configuration management to job execution, task scheduling and monitoring self-healing, and then through operation and maintenance big data analysis to assist operational decision-making, it covers the full-cycle assurance management of business operations in a comprehensive manner. The open PaaS has a powerful development framework and scheduling engine, as well as a complete operation and maintenance development training system, which helps the rapid transformation and upgrading of operation and maintenance. ... -
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autoMate
AI tool for automating desktop tasks via natural language input
autoMate is an AI-powered local automation tool designed to enable users to control and automate their computers using natural language instructions instead of traditional scripting or rule-based systems. It combines large language models with computer vision techniques to interpret user intent and understand on-screen content, allowing it to interact with graphical interfaces similarly to a human user. autoMate follows an observe-decide-act workflow, where it analyzes the screen, plans... -
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HASH
The best way to use and work with blocks
This is HASH's public monorepo which contains our public code, docs, and other key resources. HASH is a platform for decision-making, which helps you integrate, understand and use data in a variety of different ways. HASH does this by combining various different powerful tools together into one simple interface. These range from data pipelines and a graph database, through to an all-in-one workspace, no-code tool builder, and agent-based simulation engine. -
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OPAL
Policy and data administration, distribution, and real-time updates
...The project aggregates policy and data from external sources, watches for changes, and distributes updates to connected policy agents. It is built for cloud-native and microservice environments where authorization rules change frequently and need to propagate quickly. OPAL separates policy decision-making from policy distribution, which helps teams build more maintainable and consistent permission systems. It is especially useful for engineering teams implementing fine-grained authorization, relationship-based access control, feature access rules, and multi-service policy governance. -
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Alpamayo 1
Bridging Reasoning and Action Prediction
...It incorporates vision-language-action modeling, enabling it to process sensor data and contextual information simultaneously. Alpamayo supports tasks such as trajectory prediction, auto-labeling, and reasoning-based decision making. The system is optimized for high-performance GPU environments and is intended primarily for experimentation and benchmarking. Overall, it represents an advanced step toward integrating reasoning into autonomous driving pipelines. -
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NFH Self-Improvement Loop
Minimal adversarial framework for AI agent self-modification
...The project emphasizes iterative learning, allowing systems to evolve over time through repeated evaluation and adjustment. It can be applied to areas such as content generation, decision-making, and personal productivity systems. The framework encourages structured reflection and optimization, ensuring that each iteration builds upon previous results. It is particularly useful for experimenting with autonomous improvement processes in AI workflows. Overall, it provides a practical model for implementing continuous optimization in AI-driven systems. -
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Cloud Native Landscape
The Cloud Native Interactive Landscape filters
...It includes strict contribution guidelines to maintain consistency and ensure that listed projects meet certain criteria, such as community adoption and relevance. The landscape is widely used as a reference by engineers, architects, and decision-makers when evaluating cloud-native technologies. -
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Beehave
Behavior tree AI for Godot Engine
...It uses a node-based system that integrates directly into the Godot scene tree, allowing developers to visually design and organize complex AI logic in a structured and intuitive way. Behavior trees provide a modular approach to decision-making, making it easier to manage large and adaptive AI systems that respond to changing game conditions. Beehave includes built-in debugging tools that allow developers to inspect and analyze AI behavior in real time, which is crucial for refining gameplay mechanics and ensuring reliability. The framework also incorporates performance monitoring features to help maintain optimal frame rates and identify bottlenecks in AI execution. -
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Finance
150+ quantitative finance Python programs
...The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical frameworks used in financial decision-making. It organizes concepts into structured documents that explain both theoretical principles and practical calculations used in finance. The materials often include definitions, formulas, conceptual explanations, and examples to help readers understand how financial models and instruments function in real markets. -
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NitroGen
A Foundation Model for Generalist Gaming Agents
...The project draws on MineDojo’s broader ecosystem for embodied AI, where multi-modal inputs and richly diverse benchmarks help push toward generalist AI capable of interactive decision making. -
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CallMe
Minimal plugin that lets Claude Code call you on the phone
CallMe is a minimalist plugin for Claude Code that bridges computational tasks with real-world alerts by letting the AI call your phone, smartwatch, or even a landline when an operation finishes, gets stuck, or needs a user decision. It is designed to let users start a long-running task, leave their device, and then be notified in a natural way with a phone call instead of second-guessing progress in the console. The plugin uses a local MCP server alongside a webhook tunnel (typically via ngrok) to connect with voice providers like Telnyx or Twilio for outbound calls, with prompts and responses flowing between Claude Code and the user’s voice device. ... -
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bu-agent-sdk
An agent is just a for-loop
...At its core, the agent loop repeatedly queries a large language model, interprets its output, and executes defined “tools” — functions annotated with task names — to perform actions, allowing the agent to complete tasks like arithmetic, decision-making, or domain-specific work. The SDK emphasizes simplicity and control, avoiding heavy orchestration frameworks and instead letting developers specify exactly what tools an agent can employ and how it should signal task completion. This lightweight design means developers can rapidly prototype AI agent behavior, test custom tools, and integrate models like Anthropic’s Claude with minimal overhead. -
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ValueCell
Community-driven, multi-agent platform for financial applications
ValueCell is a community-driven multi-agent AI platform focused on financial research, analysis, and decision-making that lets users leverage multiple specialized AI agents for tasks like data retrieval, investment research, strategy execution, and market tracking. The system brings together a suite of collaborative agents—such as research agents that gather and interpret fundamentals, strategy agents that implement trading logic, and news agents that deliver personalized updates—to help users make more informed financial decisions across stocks, crypto, and other markets. ... -
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AI-Job-Notes
AI algorithm position job search strategy
...The repository’s structure encourages progressive preparation—from fundamentals to mock interviews and post-interview retrospectives. It’s designed to reduce uncertainty and decision fatigue during the often lengthy job-hunt cycle. -
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AI Hedge Fund
An AI Hedge Fund Team
This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions. Its structure is educational: intended more as a proof-of-concept than a ready-to-use financial product, giving learners insight into the mechanics of quantitative finance automation. ... -
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TruLens
Evaluation and Tracking for LLM Experiments
...Fine-grained, stack-agnostic instrumentation and comprehensive evaluations help identify failure modes and systematically iterate to improve applications. An easy-to-use interface that allows developers to compare different versions of their applications, facilitating informed decision-making and optimization. TruLens supports various use cases, including question-answering, summarization, retrieval-augmented generation, and agent-based applications. -
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Claude for Financial Services
Reference agents, skills, and data for the financial-services
...Its architecture emphasizes modularity, enabling firms to customize workflows and extend functionality for proprietary use cases. Overall, the project serves as a foundation for building AI-enhanced financial research and decision-support systems. -
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OpenClaw-RL
Train any agents simply by 'talking'
...One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process. The system incorporates reinforcement learning techniques to refine the agent’s policies for tool use, decision making, and task completion over time. It also explores approaches such as online policy distillation and hindsight feedback signals to strengthen training signals from real interactions. The framework operates asynchronously and does not require external API keys, making it easier to experiment with local agent training workflows. -
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ClawSweeper
ClawSweeper scans all issues and PRs
...The project runs across issues and PRs on a scheduled basis, reviewing each item and producing a reasoned recommendation instead of simply closing content automatically. Its goal is to reduce maintainer workload while keeping the decision process visible and reviewable. ClawSweeper is especially useful for repositories where old issue queues make it difficult to identify what still needs attention. It functions as a focused repository hygiene tool rather than a general-purpose project management platform. -
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SEO Machine
A specialized Claude Code workspace for creating long-form
...The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It incorporates real data sources like Google Analytics and Search Console to guide decision-making and improve content effectiveness. The architecture emphasizes context-awareness, using brand voice, style guides, and keyword strategies to maintain consistency across outputs. It also includes performance evaluation tools that score content and suggest improvements before publishing.
