Search Results for "dynamicreports-examples" - Page 9
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Showing 659 open source projects for "dynamicreports-examples"
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OpenLIT
OpenLIT is an open-source LLM Observability tool
OpenLIT is an OpenTelemetry-native tool designed to help developers gain insights into the performance of their LLM applications in production. It automatically collects LLM input and output metadata and monitors GPU performance for self-hosted LLMs. OpenLIT makes integrating observability into GenAI projects effortless with just a single line of code. Whether you're working with popular LLM providers such as OpenAI and HuggingFace, or leveraging vector databases like ChromaDB, OpenLIT... -
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The Web MCP
A powerful Model Context Protocol (MCP) server
...Bright Data also advertises a getting-started tier with a free monthly allotment, plus options for remote or self-hosted operation depending on governance needs. Ecosystem materials and examples show how it plugs into MCP-capable runtimes and agent frameworks. Overall, the project is aimed at making web intelligence a reliable building block for agent workflows. -
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GPT Researcher
LLM based autonomous agent that does online comprehensive research
Say Hello to GPT Researcher, your AI agent for rapid insights and comprehensive research. GPT Researcher is the leading autonomous agent that takes care of everything from accurate source gathering to organization of research results. -
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The Hundred-Page Machine Learning Book
The Python code to reproduce illustrations from Machine Learning Book
The Hundred-Page Machine Learning Book is the official companion repository for The Hundred-Page Machine Learning Book written by machine learning researcher Andriy Burkov. The repository contains Python code used to generate the figures, visualizations, and illustrative examples presented in the book. Its purpose is to help readers better understand the concepts explained in the text by allowing them to run and experiment with the underlying code themselves. The book itself provides a concise overview of machine learning theory and practice, covering topics such as supervised learning, unsupervised learning, neural networks, and optimization algorithms. ... -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
...The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. Advanced sections explore modern AI topics including transformers, BERT-based natural language processing systems, and practical competition-style machine learning workflows. -
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Prompt Poet
Streamlines and simplifies prompt design for both developers
...By separating prompt structure from program logic, Prompt Poet encourages iterative prompt design and experimentation without requiring constant changes to application code. The framework supports dynamic prompts that adapt to runtime data, allowing developers to inject variables, context, and examples directly into templates. This approach is particularly useful in production environments where prompt consistency, maintainability, and versioning are important. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
...Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding the internal mechanisms of modern language models, including tokenization, attention mechanisms, transformer architecture, and training workflows. Through a collection of notebooks, code examples, and translated learning materials, users can explore how to implement components such as multi-head attention, data loaders, and training pipelines using Python and PyTorch. -
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Cody
Type less, code more: Cody is an AI code assistant
Cody is an AI coding assistant that uses advanced search and codebase context to help you understand, write, and fix code faster. Generate code on demand using AI. Cody also unblocks you when you’re jumping into new projects or trying to understand legacy code. Run Cody's one-click prompts or create your own custom prompts to execute AI workflows. Cody generates single lines, or whole functions, in any programming language, configuration file, or documentation. Cody uses your code graph plus... -
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SageMaker Python SDK
Training and deploying machine learning models on Amazon SageMaker
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker-compatible Docker... -
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DoWhy
DoWhy is a Python library for causal inference
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal... -
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OpenLLMetry
Open-source observability for your LLM application
The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly. -
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AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. -
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imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in ML
Imbalanced-learn (imported as imblearn) is an open source, MIT-licensed library relying on scikit-learn (imported as sklearn) and provides tools when dealing with classification with imbalanced classes. -
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Telegram Notifications Channel Laravel
Telegram Notifications Channel for Laravel
This package makes it easy to send Telegram notifications using Telegram Bot API with Laravel. -
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pg-aiguide
MCP server and Claude plugin for Postgres skills and documentation
...The system integrates with Claude Code through an MCP server, allowing agents to access curated PostgreSQL knowledge in real time. It focuses on improving developer productivity by reducing errors and providing context-aware suggestions. The project also includes documentation and examples that help users understand best practices in database design and querying. It is particularly useful for teams working heavily with PostgreSQL who want to leverage AI tools effectively. Overall, pg-aiguide enhances database-related AI workflows by providing structured, domain-specific knowledge. -
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Grounded Docs
Open-Source Alternative to Context7, Nia, and Ref.Tools
...By acting as an intermediary layer between documentation sources and AI tools, the server enables models to access structured documentation in a consistent and machine-readable format. This makes it easier for AI systems to answer technical questions, generate code examples, or retrieve reference material without requiring developers to manually integrate documentation into prompts. The architecture follows the MCP specification, which allows AI assistants and agent frameworks to connect to external tools through standardized protocols. -
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OpenMLDB
OpenMLDB is an open-source machine learning database
OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference. OpenMLDB is an open-source machine learning database that is committed to solving the data and feature challenges. OpenMLDB has been deployed in hundreds of real-world enterprise applications. It prioritizes the capability of feature engineering using SQL for open-source, which offers a feature platform enabling consistent features for training and... -
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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. Semantic Router enables... -
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handy-ollama
Implement CPU from scratch and play with large model deployments
...The project includes step-by-step guides that walk learners through tasks such as installing Ollama, managing local models, calling model APIs, and building simple AI applications on top of locally hosted models. Through hands-on exercises and practical examples, the tutorial demonstrates how developers can create applications like chat assistants or retrieval systems using locally deployed models. -
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Agents Towards Production
Code-first tutorials covering every layer of GenAI agents
Agents Towards Production is an opinionated, code-first playbook for taking AI agents from prototype to production-ready systems. Instead of focusing only on toy examples, it dives into every layer of an agent stack: orchestration, memory, RAG, tool and API integration, security, observability, deployment, evaluation, and UI. The repository is built around runnable tutorials, each in its own folder, often sponsored by or built in collaboration with infrastructure providers like LangChain, Redis, Bright Data, Contextual AI, Tavily, Runpod, Portia, and others. ... -
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Simd Library
C++ image processing and machine learning library with using of SIMD
The Simd Library is a free open-source image processing and machine learning library, designed for C and C++ programmers. It provides many useful high-performance algorithms for image processing such as pixel format conversion, image scaling and filtration, extraction of statistical information from images, motion detection, object detection and classification, neural networks. The algorithms are optimized with using of different SIMD CPU extensions. In particular, the library supports the... -
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OpenRLHF
An Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures. -
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AniList MCP
AniList MCP server for accessing anime and manga data
An MCP server that interfaces with the AniList API, allowing AI clients to access and interact with anime, manga, character, staff, and user data from AniList. -
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Empirical
Test and evaluate LLMs and model configurations
Empirical is the fastest way to test different LLMs and model configurations, across all the scenarios that matter for your application. -
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OpenAI-DotNet
A Non-Official OpenAI RESTful API Client for DotNet
A simple C# .NET client library for OpenAI to use though their RESTful API. Independently developed, this is not an official library and I am not affiliated with OpenAI. An OpenAI API account is required.
