Search Results for "logic" - Page 4
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Showing 221 open source projects for "logic"
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Griptape
Python framework for AI workflows and pipelines with chain of thought
The Griptape framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition... -
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Feast
Feature Store for Machine Learning
...Avoid data leakage by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset joining logic. This ensure that future feature values do not leak to models during training. Decouple ML from data infrastructure by providing a single data access layer that abstracts feature storage from feature retrieval, ensuring models remain portable as you move from training models to serving models, from batch model -
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Agent Stack
Deploy and share agents with open infrastructure
...It also offers multi-model, multi-provider support (OpenAI, Anthropic, Gemini, IBM WatsonX, Ollama etc.), letting users compare performance and cost across models. For developers and organizations building AI-agent products or automations, Agent Stack gives a scaffold that handles the “plumbing”, so they can focus on logic and domain. -
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OpenQASM
Quantum assembly language for extended quantum circuits
...Quantum programs are described using the measurement-based quantum circuit model with support for classical feed-forward flow control based on measurement outcomes. OpenQASM presents a parameterized set of physical logic gates and concurrent real-time classical computations. Its main goal is to serve as an intermediate representation for higher-level compilers to communicate with quantum hardware. Allowances have been made for human usability. In particular, the language admits different representations of the same program as it is transformed from a high-level description to a pulse representation. -
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ChatterBot
Machine learning, conversational dialog engine for creating chat bots
ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram. The language independent design of ChatterBot allows it to be trained to speak any language. Additionally,... -
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AnyTool
AnyTool: Universal Tool-Use Layer for AI Agents
AnyTool is an open-source universal tool-use layer for AI agents that addresses the critical problem of how autonomous agents reliably interact with external tools and environments. Rather than having each agent handle tool invocation logic on its own, AnyTool provides a standardized interface and orchestrator that intelligently selects and manages tools, reduces context overhead, and improves execution reliability across diverse capabilities like web APIs, local commands, and GUI automation. It uses progressive filtering and adaptive orchestration to ensure the right tools are retrieved efficiently and work cohesively with agents of varying complexity, scaling to thousands of tools with self-optimizing behavior. ... -
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QuantDinger
AI-driven, local-first quantitative trading platform for research
...Traders and researchers can develop custom strategies in Python, run historical backtests, analyze performance, and connect to supported exchanges for live trading, making it suitable for equities, crypto, forex, and futures markets in a local environment. QuantDinger also incorporates optional AI features via external APIs, assisting in tasks like strategy ideation or interpreting market indicators, but strategy logic remains inspectable and transparent in code. -
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AutoCoder
A long-running autonomous coding agent powered by the Claude Agent
...The core idea is to accelerate software production while preserving correctness and readability, minimizing the cognitive overhead that comes from switching between concept and implementation. Its architecture typically integrates language models with static analysis and template logic so that generated code is not only syntactically valid but also idiomatic and testable. -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid... -
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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Dynamiq
An orchestration framework for agentic AI and LLM applications
Dynamiq is an open-source orchestration framework designed to streamline the development of generative AI applications that rely on large language models and autonomous agents. The framework focuses on simplifying the creation of complex AI workflows that involve multiple agents, retrieval systems, and reasoning steps. Instead of building each component manually, developers can use Dynamiq’s structured APIs and modular architecture to connect language models, vector databases, and external... -
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Agent SOP
Natural language workflows for AI agents
Agent SOP is a framework that implements structured operational procedures (SOPs) for autonomous agents so that they can carry out complex multi-step tasks reliably and in a defined order. Instead of relying solely on broad language model reasoning, this project enforces explicit step sequences with checkpoints, conditional transitions, and rollback logic, making agent workflows more predictable and auditable. It defines reusable SOP templates that agents can instantiate with context-specific parameters, allowing organizations to codify best practices for customer support, data processing, document workflows, or incident response. The framework supports monitoring and state tracking, so external systems can observe progress, intervene if necessary, and log outcomes for compliance or auditing. ... -
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mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for... -
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OpenPlanter
Language-model investigation agent with a terminal UI
...The repository is designed to help developers and hobbyists create programmable plant management workflows that can monitor, schedule, and optimize growing conditions. It emphasizes automation and extensibility, allowing integration with sensors, environmental data, and control logic for smart cultivation setups. The system is structured to support experimentation and customization, making it suitable for both research and DIY agriculture projects. With its modular Python codebase, users can adapt the platform for different plant types, hardware setups, or automation strategies. Overall, OpenPlanter aims to simplify the creation of programmable, data-driven plant care systems. -
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Manim Skills Repository
Agent skills for Manim to create 3Blue1Brown style animations
...It supplies templates, best-practice patterns, and example scenes that help users quickly get started with both Manim Community Edition and the older ManimGL framework, allowing for expressive mathematical and conceptual animations without reinventing the wheel. The project aims to wrap common animation logic into structured skills that can be invoked programmatically, reducing boilerplate and allowing creators to focus on creative content rather than low-level animation setup. With clear installation instructions and a collection of test cases, it’s approachable for developers comfortable with Python and interested in educational or technical visualization. -
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authentik
The authentication glue you need
authentik is an open-source Identity Provider that emphasizes flexibility and versatility. It can be seamlessly integrated into existing environments to support new protocols. authentik is also a great solution for implementing sign-up, recovery, and other similar features in your application, saving you the hassle of dealing with them. authentik is an open-source Identity Provider focused on flexibility and versatility. You can use authentik in an existing environment to add support for new... -
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ZAPI
ZAPI by Adopt AI is an open-source Python library
...It emphasizes a declarative router and schema model that uses types to define request and response formats, providing clear contracts for frontend and backend teams while automatically generating documentation. Zapi abstracts many repetitive tasks such as validation, authentication flows, and error handling so developers can focus on business logic instead of infrastructure plumbing. It integrates smoothly into modern development stacks, supports hot reloading for rapid iteration, and includes a command-line toolchain for scaffolding new endpoints or services with sensible defaults. The framework also supports plugin extensions that add things like rate limiting, caching layers, and telemetry without cluttering core code. -
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MiniRAG
Making RAG Simpler with Small and Open-Sourced Language Models
...Its minimal footprint makes it suitable for local research assistants, chatbots, help desks, or knowledge bases embedded in applications with limited resources. Despite its simplicity, it includes features such as chunking logic, configurable embedding models, and optional caching to balance performance and accuracy. -
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Tally
Let agents classify your bank transactions
Tally is an open-source, AI-assisted tool designed to automate the classification of personal financial transactions, helping users turn raw bank data into meaningful categories without manual tagging. At its core, Tally pairs a local rule engine with large language models so that an AI assistant (like Claude Code, Copilot, or any CLI agent) interprets, suggests, and categorizes expenses, savings, subscriptions, and income events based on your own rules and behavior. It generates... -
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EPLB
Expert Parallelism Load Balancer
...EPLB solves this by duplicating heavily used experts (redundancy) and then placing those duplicates across GPUs to even out computational load. It uses policies like hierarchical load balancing (grouped experts placed at node and then GPU level) and global load balancing depending on configuration. The logic is implemented in eplb.py and supports predicting placements given estimated expert usage weights. EPLB aims to reduce hot-spotting and ensure more uniform usage of compute resources in large MoE deployments. -
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DualPipe
A bidirectional pipeline parallelism algorithm
DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap... -
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. ... -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live... -
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Atheris
A Coverage-Guided, Native Python Fuzzer
Atheris is a coverage-guided fuzzer for CPython that treats Python as a first-class fuzzing target, enabling rapid discovery of crashes and logic errors in pure-Python code and native extensions. It hooks into Python’s interpreter to collect fine-grained coverage and uses that signal to evolve inputs, pushing programs into previously unexplored code paths. Because many Python libraries are thin wrappers over C/C++ code, Atheris is equally adept at surfacing memory safety issues in extension modules compiled with sanitizers. ... -
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MetaCLIP
ICLR2024 Spotlight: curation/training code, metadata, distribution
...The goal is to preserve CLIP’s strong zero-shot transfer capability while enabling fast adaptation to domain shifts or novel class sets with minimal data and without catastrophic forgetting. The repository provides training logic, adaptation strategies (e.g. prompt tuning, adapter modules), and evaluation across base and target domains to measure how well the model retains its general knowledge while specializing as needed. It includes utilities to fine-tune vision-language embeddings, compute prompt or adapter updates, and benchmark across transfer and retention metrics. ... -
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DeepEP
DeepEP: an efficient expert-parallel communication library
...Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks.
